Keywords: Circuits and Systems
Abstract: The reversible logic circuit is a prominent research area for its low-power design, and also quantum computing. The development of synthesis and optimization is a well-known problem in the reversible circuits. For ensuring the high reliability and integrity performance of these circuits, the proper testing technique will be required to detect and locate the faults. In this paper, we consider the problem of reversible circuit testing, specifically targeting the fault detection for the missing-gate fault model in the k-CNOT based reversible circuit. It has been shown that n number of test vectors is sufficient for the detection of all single missing-gate faults (SMGFs), repeated-gate faults (RGFs), and partial missing-gate faults (PMGFs) of the proposed fault detection scheme in a reversible circuit with n inputs. Finally, we provide our experimental results based on several benchmark circuits and also show the comparative analysis with existing methods.

Keywords: Circuits and Systems
Abstract: An application-based memristor prototype by employing only a single Voltage Difference Transconductance Amplifier (VDTA) as an active device and one grounded MOS-capacitor is reported in this research article. The transient analysis, pinched hysteresis loop for various frequencies along with serial and parallel combinations of the memristor are also examined. This grounded memristor design can suitably operate up-to MHz ranges. As an application of the stated memristor prototype, demodulation of grounded memristor-based Binary Frequency Shift Keying (BFSK), Amplitude Demodulation and a realization of Schmitt trigger configurations are shown. Moreover, the layout of this memristor is performed which occupies an effective area of 1432μm2. The simulation and layout of the entire design are implemented using Analog Design Environment (ADE) tool of the Cadence Virtuoso.

Keywords: Circuits and Systems
Abstract: This article describes a realization of higher-order Inverse Band Pass Filter (SO-IBPF) configuration for low-power applications based on Current Differencing Buffered Amplifier (CDBA) as active elements and a few passive components. The proposed structure is proficient to operate with 0.6V supply voltage, 0.45V bias voltage and dissipates 0.918mW power. To justify the practicability of the proposed design, frequency and phase response outputs are produced through PSPICE simulation using 0.18牛 TSMC CMOS process parameters. The other performance investigations such as sensitivity, non-ideality, input-output noise analysis, temperature variations, Monte-Carlo analysis and percentage of total harmonic distortion (%THD) are also examined. Moreover, the proposed configuration is experimentally tested using commercially available IC AD844AN to verify the theoretical propositions and simulation results. The layout of the proposed structure is also realized with Analog Design Environment tool (ADE) of Cadence/ spectre environment.

Keywords: Circuits and Systems
Abstract: It is known that graphene nanoribbon (GNR) based devices and interconnects can be treated to be better alternative in nano-scale designs. In this paper, given a source pin and a set of target pins inside a GNR routing plane with a set of rectangular obstacles, based on the selection of the possible obstacle-avoiding delay-driven routing paths on the target pins, an efficient routing algorithm can be proposed to construct an obstacle-avoiding delay-driven GNR routing tree with minimizing the total wirelength for the target pins. Compared with Das痴 algorithm with obstacle avoidance on total wirelength and maximum source-to-target delay, the experimental results show that our proposed routing algorithm only uses 7.87% of the extra wirelength to reduce 23.42% of the maximum delay in the construction of an obstacle-avoiding delay-driven GNR routing tree for 6 tested examples on the average.

Keywords: Circuits and Systems, Devices, Materials & Processing, Wireless Communications & Networks
Abstract: In this paper, we have analyzed the analog single event transient (ASET) performance of MOSFET based LNA circuits at two different frequencies (one in X band and another in Ka-band) to study the frequency dependency. The ASET impact is analyzed in the time and frequency domains under two scenarios, (i) absence of the RF input signal and (ii) the Presence of the RF input signal. In the first case, the collected charge (Qc) is taken as metric in the time domain and the second case, the spectrogram is used to analyze the disturbing presence in the fundamental frequency due to radiation strike. Based on the QC, the LNA used in X-band is more vulnerable compared to LNA used in Ka-band. The spectrogram results show that the LNA output spectrum in the X-band is disturbed more and stronger to the radiation strike compared to the LNA used in Ka-band

Keywords: Biomedical Engineering
Abstract: Fetal head circumference (HC) is one of the fetal biometrics that is often used to determine gestational age and monitor the fetal growth in the womb. Nowadays, head circumference measurement from ultrasound images is performed manually by a doctor or sonographer by drawing a line or forming an ellipse to surround the fetal head. However, manual annotations are prone to human error and intra-observer as well as inter-observer variabilities. In this research, an automatic fetal head candidate localization was implemented using Haar Cascade Classifier (HCC) and further optimized by Enhanced Localization Algorithm (ELA). The combination of HCC and ELA was evaluated on 703 ultrasound images of the second trimester and 141 ultrasound images of the third trimester using the Jaccard Index (JI), Dice Similarity Coefficient (DSC), and Overlapped Area Ratio (OAR). The localization results showed that the HCC + ELA produced an average JI of 90.5%, DSC of 94.58%, OAR of 97.77% for the second trimester and an average JI of 88.17%, DSC of 93.33%, OAR of 96.97% for the third trimester. Based on the three evaluation parameters, we analyzed the factors affecting the accuracy of the localization algorithm and the correspondence of the localization results with the ellipse fitting outcome as the final process to determine the fetal head circumference.

Keywords: Biomedical Engineering
Abstract: Diabetes is a chronic disease in which the blood glucose level stays high over a prolonged period. Uncontrolled diabetes put patients into critical conditions when the blood glucose level moves outside the clinically accepted range. Knowing the trend of the blood glucose level changes in advance will give the ability to take corrective actions to maintain it within the recommended levels. Continuous glucose monitoring (CGM) devices with an associated prediction model integrated into insulin pumps implanted in type 1 diabetic patients are able to predict the future glucose level in real time. A few different techniques for short-time glucose prediction have been reported incorporating past CGM readings, insulin dosage and meals. However, future glucose level predictions for patients without implanted insulin pumps are not available. This paper presents a machine learning algorithm to implement a short-term prediction model using non-invasive data; a past glucose level (measured or previously predicted), drugs dosage, food consumption, and life styles of diabetes patients. A deep learning time series forecasting based Long short-term memory (LSTM) model is used to predict the blood glucose level 30 minutes in the future. The model used data from diabetic patients to predict the short-term blood glucose levels with an accuracy level of 79.97%. The proposed model was able to predict blood glucose levels 30-min in advance with an average daily glucose levels error of 22.2 mg/dl.

Keywords: Biomedical Engineering
Abstract: Gestational age (GA) monitoring from fetal ultrasound imaging is one method to observe pre-birth risk factors and to prepare early treatment for neonatal problems. There are several parameters in an ultrasound image that can be used to estimate GA, one of which is the fetal head circumference (HC). However, fetal HC measurement is prone to error since it relies on manual annotation by sonographer or obstetrician. This research aims to design an algorithm to automatically calculate the fetal HC based on optimized ellipse fitting on a localized region of interest (RoI) previously defined as fetal head candidate area. Our optimization method consists of pre-processing steps to exclude noise within the RoI and to select the optimum representation of fetal head pixels to be processed by the ellipse fitting algorithm. We managed to perform ellipse fitting on 699 and 141 ultrasound images representing respectively the second and third trimester pregnancies; with the average dice similarity coefficient (DSC) of 95.27%ア6.25%, hausdorff distance (HD) of 3.51 mmア5.54 mm, a difference in fetal HC (DF) of -3.42 mmア13.66 mm, and an absolute difference in fetal HC (ADF) of 6.53 mmア12.5 mm. The results demonstrated that the presented method performed comparably to other systems published in the literature. Moreover, our results represent an evaluation of a significantly larger number of data compared to most of the previous works.

Keywords: Biomedical Engineering
Abstract: Breast Cancer has very low survival rate when detected at later stages. Hence, Computer Aided Diagnostic Models can assist the medical practitioners by diagnosing reports without human intervention. In this paper the sample size considered is 401(200 benign and 201 malignant images) and has been acquired from the Digital Database for Screening Mammography. This paper propose a novel method to detect breast malignancies using texton based analysis. The filter bank that has been used here to obtain the texton based response is the Maximum Response 8 Filter Bank. Further Haralick's features from the Gray Level Co-occurence Matrix , histogram based features from Local Binary Pattern and statistical features namely skewness and kurtosis are extracted from each filter response. Genetic Algorithm and Linear Discriminant Analysis has been used for feature selection and feature reduction respectively. Classification is performed using three classifiers namely Naive bayes, Logistic Regression and Linear SVM. The proposed algorthm exhibit an Accuracy of 87.5% and Area under Curve of 0.95 using Logistic Regression classifier.

Keywords: Biomedical Engineering, Devices, Materials & Processing
Abstract: Radiofrequency cathode ablation is an effective tool for the treatment of cardiac arrhythmia. Bipolar ablation may provide a more localized lesion and therefore prevent side effects and complications caused by cathode ablation. However, myocardial impedance and heart fat痴 thickness may have significant influence on the performance of bipolar ablation. In this study, we perform computer simulation to study the influence of myocardial impedance and heart fat痴 thickness on bipolar and unipolar cathode ablation. Through computer simulation, we obtain and compare the treatment effects under different configurations of cathode ablation. The simulation results are generally consistent with experiment results. Through our simulation, when the fat layer痴 thickness increases, the bipolar ablation痴 heating effect decreases, but the final ablation effect is still better than that of unipolar ablation. The ablation performance is improved when myocardial impedance decreases. However, the heating effect of the bipolar configuration is more sensitive to the variation of myocardial impedance and fat layersthickness compared with that of the unipolar configuration.

Keywords: Biomedical Engineering, Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Tumor buddings (TB), a special formation of cancerous cells that bud from the tumor front, are fast becoming the key indicator in modern clinical applications where they play a significant role in prognostic and evaluation of colorectal cancers in histopathological images. Recently, computational methods have been rapidly evolving in the domain of digital pathology, yet the literature lacks computerized approaches to automate the localization and segmentation of TBs in hematoxylin and eosin (H&E) stained images. This research addresses this very challenging task of tumor budding detection in H&E images by presenting different deep learning architectures designed for semantic segmentation. The proposed design for a new Convolutional Neural Network (CNN) incorporates convolution filters with different factors of dilations. Multiple experiments based on a newly constructed colorectal cancer histopathological image collection provided promising performances. The best average intersection over union (IOU) for TB of 0.11, IOU for non-TB of 0.86, mean IOU of 0.49 and weighted IOU of 0.83 were observed.

Keywords: Biomedical Engineering, Disasters and Humanitarian Technology, Machine Learning, Cloud and Data Analytics
Abstract: Automatic disease detection using machine learning-based techniques from X-ray and computed tomography (CT) can play a major role in the frontline to assist medical professionals during the current outbreak of COVID-19. Fast diagnosis of the disease is the key to reduce the uncontrollable spread of this life-threatening disease, where machine learning-based applications can contribute greatly by predicting the situation of patients so that professionals can decide accordingly. The major drawbacks of detecting COVID-19 are its similarities with different types of pneumonia, and the absence of properly labeled data. Considering the ResNet152V2 as a backbone network, an efficient architecture, namely ResCovNet is proposed to detect COVID-19 accurately from chest CT scan images by separating it from three types of pneumonia and normal cases. Otsu's thresholding is applied in the pre-processing step to strengthen the features for the classification network. With the use of proposed architecture, a very satisfactory classification accuracy of 88.1% is achieved to separate COVID-19 from all other four classes. Evaluating the performance of this study by 3-fold cross-validation, and comparison with related works prove that this adroit algorithm provides an effective way to be implemented as a diagnostic tool in the COVID-19 screening.

Keywords: Biomedical Engineering, Machine Learning, Cloud and Data Analytics
Abstract: This paper focuses on Acute Lymphocytic Leukemia (ALL) as this is the most common type of Leukemia in Bangladesh. It is common knowledge among oncologists, that cancer is much easier to treat if it is detected in the early stages. Thus the treatment needs to begin as early as possible. We propose a hands-on approach in detecting the irregular blood components (e.g., Neutrophils, Eosinophils, Basophils, Lymphocytes and Monocytes) that are typically found in a cancer patient. In this work, we first identify 14 attributes to prepare the dataset and determine 4 major attributes that play a significant role in determining a Leukemia patient. We have also collected 256 primary data from Leukemia patient. The data is then processed using microscope to obtain images and fetch into Faster-RCNN machine learning algorithm to predict the odds of cancer cells forming. Here we have applied two loss functions to both the RPN (Region Convolutional Neural Network) model and the classifier model to detect the similar blood object. After identifying the object, we have calculated the corresponding object and based on the count of the corresponding object we finally detect Leukemia. The mean average precision observed are 0.10, 0.16 and 0, where the epochs are 40, 60 and 120, respectively.

Keywords: Biomedical Engineering, Machine Learning, Cloud and Data Analytics, Circuits and Systems
Abstract: Wake-up stroke refers to a kind of ischemic stroke where a person wakes up with symptoms of stroke that are not present before going to sleep. These symptoms may include muscle weakness, drowsiness, walking difficulties, face drooping among others. Risk factors for ischemic strokes are Diabetes, Hypertension, Obesity, age, tobacco use, etc. From the Statistical Findings, 8-28 percent of all brain ischemic strokes consist of Wake-up strokes. The main method of treating an ischemic stroke is tissue Plasminogen Activator (tPA), the use of which is approved for 3-4.5 hours from the onset time of stroke. As the onset time in wake-up strokes is difficult to determine, the person may not be eligible for tPA treatment. This paper presents the development of a prototype to detect the wake-up stroke using the appropriate sensors to obtain physiological data and Internet of things technology to issue an alert signal to the concerned people of the patient. In India, various factors causes the delay of the patient痴 arrival to the hospital. The proposed device helps the people to take the patient to the hospital within time to reduce the risk of permanent disabilities like paralysis and memory loss.

Keywords: Biomedical Engineering, Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: In this paper, we present our recent work on cells detection and segmentation in estrogen receptor immuno-histochemistry (ER-IHC)-stained breast carcinoma images. The proposed cell detection and segmentation is very useful in the predictive scoring of hormone receptor status in ER-IHC stained whole-slide images, which helps pathologists to decide whether a patient should be offered hormonal therapy or other treatments. The proposed method is based on deep convolutional neural network, followed by watershed-based post-processing. The cell detection results are compared and evaluated objectively against the ground truth provided by our collaborating pathologists. The cell segmentation results, on the other hand, are evaluated visually by overlaying the computer segmented boundaries on the ER-IHC images for comparison. The automated cell detection algorithm recorded precision and recall rates of 95% and 91% respectively. The very promising performances for both the detection and segmentation paves the way for an automated system for hormone receptor scoring in ER-IHC stained whole-slide breast carcinoma images.

Keywords: Devices, Materials & Processing
Abstract: In this article, the edge transport of Zigzag Graphene NanoRibbon (ZGNR) in the presence of an abrupt structure change due to missing atoms, which we define as 'cut' is studied through Non-Equilibrium Green's Function formalism. Interesting results are found that are notably different for difference in the width of the 'cut'. For ZGNR, depending on the width of the 'cut', the electrons can be fully transmitted (T=1) or fully blocked (T=0) in the device scattering region.

Keywords: Devices, Materials & Processing
Abstract: In this paper, vertically aligned MnO2 nanowire (NW) array were fabricated on silicon (Si) by using glancing angle deposition (GLAD). Successful formation of the NW can be confirmed from the field emission scanning electron microscope (FESEM). Optical absorption shows the absorption in visible region which corresponds with the bandgap of MnO2. From Tauc plot, the optical bandgap was found to be 2.7 eV and 1.8 eV. Under light illumination, there is a dramatic increase of photocurrent due to the electric field formed between Gold (Au) and MnO2 NW. The photoresponse measurement shows that the rise time(tr) and fall time (tf) of the NW are 0.18 s and 0.16 s respectively. MnO2 NW proves to be a good candidate for optoelectronics application.

Keywords: Devices, Materials & Processing, Photonics
Abstract: In this paper, we report the electrical discharge by crystalline and amorphous Ge2Sb2Te5 (GST) in lateral metal-semiconductor-metal form (MSM) on different substrates through design and simulation. This is an extension to a previous work where optical analysis with same structure and platforms were performed. Silicon, silicon dioxide and metal (Au) are used as semiconducting, insulating and conducting substrates, respectively, beneath the GST material. The electron, hole and total current behaviors are analyzed and optimized for incident light wavelength and dimension for every substrate platform. The analyses are performed for the optical spectrum ranging from 650 nm to 2500 nm (the NIR range and a portion of visible radiation) under a moderate range of external biasing from -5 V to +5 V at the electrode. Crystalline-GST (CGST) on Si is found suitable for 1550 nm wavelength range, which is optical fiber communication wavelength, with 130 nm optimized thickness and <0.1 A of current. CGST is found to have maximum current at lower thicknesses for all substrate types whereas amorphous-GST (AGST) exhibits higher current with SiO2 platform at comparatively lower wavelength, making both AGST/CGST suitable for other Si/SOI based NIR range optoelectronic devices. The photo-current to the dark-current ratio is almost 1000 for all platforms which inferred to the low noise/intensity detection systems.

Keywords: Photonics, Devices, Materials & Processing
Abstract: This paper presents a refractive index sensor that includes two horizontal MIM (Metal-Insulator-Metal) waveguides coupled with a rectangular ohmic cavity and a Concentric Double-Ring Resonator (CDRR). The cavities contain the Material Under Sensing (MUS), while the surrounding area is filled with silver. The designed model is simulated using the Finite Element Method (FEM). Results from simulation show a linear shift of the resonant peaks for variations in the MUS refractive index. Therefore, unknown MUS is identified through peak shifts. The sensor achieves a maximum sensitivity of 1070 nm/RIU with the most sensitive geometry obtained from the simulations. Moreover, the ability to identify glucose concentration levels in the human blood samples makes the proposed sensor an attractive choice for bio-sensing applications

Keywords: Wireless Communications & Networks
Abstract: The mmWave band with its broad unused spectrum finds its way into the 5th generation (5G) of communication networks to tackle the traffic demand and need for higher data rates. However, the mmWaves are subjected to high levels of attenuation and this loss can be compensated by making use of highly directional transmissions, this leads to delays that are incurred in searching for the appropriate narrow beam transmission which involves scanning the cell area with narrow directional beams. This paper deals with a beam search algorithm that makes use of user location information in reducing the time taken for obtaining the optimum beam configuration. We investigate the performance of the proposed algorithm in a scenario with location inaccuracies. We also evaluate how the algorithm fare in scenarios involving obstructions in the transmission path.

Keywords: Wireless Communications & Networks
Abstract: The 5G ultra-dense small cell network plays a key role in the future generation of mobile networks. It provides high data rate, seamless coverage and reliable services for wireless communication in an ultra-dense network. The dense deployment of small cells is needed to control forthcoming traffic demands which leads to enhance the operational cost and reduces the energy efficiency. One way to improve the energy efficiency is by using the sleeping strategy of small cell by transferring the traffic load of a small cell to other small cells. This work proposes an Initial Connection algorithm for establishing an initial association between the UEs and small base stations (s-BSs) while considering the UE preference. The Initial Connection algorithm creates a connected network. Moreover, the proposed Load Sharing Based Sleep Approach (LSBSA) algorithm performs small cells sleeping on the connected network which results in the deployment of s-BSs. The proposed Initial Connection and LSBSA algorithms are implemented and evaluated in MATLAB for different mobile data traffic. Also, various UEs distribution scenarios are are considered. The results are demonstrated that the proposed approaches improve network performance in terms of energy efficiency of the small cell network by deploying the optimal number of active s-BSs.

Keywords: Wireless Communications & Networks
Abstract: Employing intelligent reflecting surfaces (IRSs) is emerging as a green alternative to improve the signal quality and suppress interference for massive antenna systems. Specifically, IRS is a planar surface consisting of a large number of low-cost and passive elements each being able to reflect the incident signal independently with an adjustable phase shift. In this paper, we study the power control problem at the user for an IRS-aided uplink system under the quality of service (QoS) constraints. Our goal is to minimize the total transmit power at the user by jointly optimizing the phase shifts of passive elements at the IRS and the receiving beamforming at the BS, subject to the signal-to-noise ratio (SNR) constraint at the user. To solve the resulting non-convex optimization problem, we develop an efficient algorithm, called the manifold-based alternating optimization (M-AO). Simulation results show that the proposed algorithm significantly saved the transmit power.

Keywords: Wireless Communications & Networks, Antenna & Microwave
Abstract: The commercial service of fifth generation mobile communication system started in the spring of 2020 in Japan. This paper discusses a highly precise prediction of 28 GHz millimeter wave indoor propagation characteristics in an office environment by using a large-scale electro-magnetic field simulation based on the finite difference time domain technique. The computer simulations are carried out using the high-performance computer system operated in Information Initiative Center, Hokkaido University. They give us a detail of electromagnetic field distributions in an FDTD problem space including targets at once although they require a lot of computer resources and a long running time in general. The paper compares calculated path loss model parameters such as path loss exponents, shadow factors and cross-polarization discriminations in the LOS environment with the measured ones demonstrated by the other research groups to confirm the effectiveness of numerical results and the accurate prediction of path loss model parameters.

Keywords: Wireless Communications & Networks
Abstract: The increasing demand for higher data rates pushes the boundaries of the currently used radio spectrum. The terahertz (THz) frequency band (0.1-10 THz) is widely considered by scientific community to address spectrum scarcity. In this paper we developed a theoretical model for device-to-device (D2D) communication operating in THz band. We also derived a close form formula of data rates, outage probability, and energy efficiency. Our simulation results show an improvement of data rates and energy efficiency while decreasing the outage probability of D2D communication in THz. For instance, there is 86% of increase in energy efficiency when the transmission power is 19dBm. Finally, the improvement of energy efficiency is 87% using optimal transmission power due to 50 resource blocks.

Keywords: Power & Energy, Social Implications of Technology
Abstract: Cooking system has been evolving for a long time. From the parabolic system to solar cooking. Every method had its unique concepts. Attempts towards solar cooking have always been proven beneficent and successful. Solar cooking is a system where energy comes from the photovoltaic solar panel which is powered by sunlight. We have designed a system where solar energy will be stored in a pack of battery which can be used in the absence of the sun. Combination of PV panels and batteries will provide the required power during sun time. A full-time main grid back- up is also present in the system

Keywords: Power & Energy, Software & Database Systems
Abstract: Along the transmission corridor, overhead transmission lines require a portion of land which is to be reserved as right-of-ways (ROW). Main criteria to obtain suitable value of ROW is effects produced by static electric field, magnetic fields and effects due to corona phenomena. A tough need therefore exists to determine the distribution of these fields, especially electric fields throughout the corridor along the length of transmission lines. Determination of the maximum value of the electric and magnetic fields produced by transmission lines close to ground surface has thus become significant. The present research work deals with estimation of electric and magnetic field distribution along with corona effects all the way through the length of an ultra-high voltage transmission line corridor. Analysis is carried out by making use of newly developed computer software that uses MATLAB as programming platform. Charge simulation method (CSM) has been used for computation whereas Maxwell痴 potential coefficient theory forms the fundamental base of computations. Results for 1200 kV Ultra High Voltage AC line are presented here. Single circuit horizontal line configuration has been considered for analysis. Profiles of all the fields are plotted in 3D environment to find maximum possible value of the fields considering effects of sag through catenary model. Optimization is carried out through a recently developed objective function with the help of Genetic Algorithm.

Keywords: Devices, Materials & Processing, Power & Energy
Abstract: This paper investigate instability in device characteristics related to the hot carrier effect, Negative Bias Temperature Instability (NBTI) and Positive Bias Temperature (PBTI) under DC stress for n- and p-channel thin-film Silicon on Insulator (SOI) power MOSFET at high temperature. The threshold voltage shift increases as the temperature rises due to PBTI for n-MOSFET and NBTI for p-MOSFET. Drain Avalanche Hot Carrier (DAHC) occurs when the gate stress voltage is near the threshold voltage and Channel Hot Carrier (CHC) occurs when the gate voltage is high. The threshold voltage shift and the degradation rate of on-resistance of the n-MOSFET is larger than that of the p-MOSFET due to the difference in the impact ionization coefficient between electrons and holes.

Keywords: Devices, Materials & Processing, Power & Energy
Abstract: The environmental friendliness and excellent thermal stability proves Cesium Tin Iodide (CsSnI₃) as one of the promising materials for the commercialization of the perovskite solar cells. However, CsSnI₃ solar cells suffer from poor efficiency due to having low open circuit voltage, V_OC attributed to poor absorber film quality as well as energy level mismatch at the interfaces between different layers like transparent front contact (typically used Indium Tin Oxide, ITO) carrier transport layer interface. In order to improve the energy matching between ITO hole transport layer (HTL) interface, a typical inverted planar perovskite solar cell (ITO/HTL/CsSnI₃/ETL) is numerically simulated (Finite difference time domain, FDTD analysis followed by electrical simulation) here, with various types of doped ITO layers having higher workfuctions (≥ 5eV), replacing the typical undoped ITO material having lower workfunction (≤4.7eV). The simulation results illustrate that, utilizing the higher workfunction and optical transparency of available doped ITO materials the V_OC of a typical CsSnI₃ cell can be increased from 0.5V up to 1.1V which will make it possible to achieve power conversion efficiency as high as 11% (which is 3.3% with undoped ITO), without adopting complex modification of the absorber layer.

Keywords: Marine and Offshore Engineering, Power & Energy
Abstract: The Capital Expenditure (CapEx) of offshore floating wind turbine generation (WTG) and battery energy storage system (BESS) are declining over the years. This can mitigate gas turbine power generation in offshore oil and gas (O&G) platforms with lower cost impact. This paper proposes integrated systems consisting a WTG and O&G production platforms with BESS onboard to meet the load demand. Cost analysis shows that this integrated system paradigm with BESS can lower overall cost in CapEx and Operational Expenditure (OpEx) compared with typical system. Moreover, transient stability in simulation shows that the system 2 has a significant reduction in term of both voltage and frequency transient deviation, with transient recovery time that could meet the IEC standards 61892-1 for O&G platforms.

Keywords: Biomedical Engineering, Machine Learning, Cloud and Data Analytics
Abstract: Initially, we developed KNApSAcK as a species-metabolite relational database and subsequently, inspired by its popularity we extended it to KNApSAcK family databases by adding different types of omics data together with data regarding edible plants and traditional medicines mainly focusing human health care and ecology. Previously we also developed graph clustering algorithms DPClus and DPClusO, which we and many other researchers applied to analysis of versatile omics data. In the present talk, first, I will briefly focus on the KNApSAcK database and the DPClus algorithm. Then I will discuss a new method to predict the relation between plant and disease using network analysis and supervised clustering based on Jamu formulas. Jamu is the common name of Indonesian traditional medicines. Next, I will extend the talk on the analysis for predicting Jamu efficacy based on metabolite composition and identifying important metabolites by applying various machine learning techniques and algorithms including support vector machine (SVM) and random forest. I will then focus on prediction of target proteins by Jamu metabolites. Finally, I will discuss application of DPClusO algorithm in finding inflammatory bowel disease related genes.

Keywords: Circuits and Systems
Abstract: In this paper, we propose design and implementation of novel line adder architecture capable of adding multiple addends in a single step. The architecture is derived based on addition algorithm from Shuddha system of vedic mathematics. The adder architecture is particularly suitable for a class of applications, such as FIR filter, where multiple operands are required to be added. The implementation is particularly suited for portable systems where area and power consumption are highly constrained. We then quantitatively compare proposed adder architecture against popular Parallel-prefix adder architectures for different wordlengths (N = 4, 8, 16 and 32 bits). All the architectures are implemented on Genesys2 board(Xilinx part number xc7k325t-2ffg900c) using VHDL coding and Xilinx Vivado 2016.2 platform. Standard performance metrics such as FPGA device utilisation (number of slices and logic LUTs), Power Consumption and Performance are considered for comparison. Results indicate that proposed vedic adder occupies 35.12 % lesser logic LUTs and 28.71 % lesser slices than any other adder architectures, under consideration, while at the same time, there is an improvement of 9.1 % in on-chip FPGA power consumption than all other adder architectures. The performance of vedic adder architecture draws parallel with that of fastest prefix adder being considered.

Keywords: Circuits and Systems, Biomedical Engineering, Power & Energy
Abstract: A class-D power amplifier (PA) driving a pair of resonant coils is studied. Time-domain analysis of the inductor currents of the primary and the secondary sections with series-series (SS) and series-parallel (SP) resonance are derived, with either resistor or rectifier loads. Both the ripple and rectified output voltage of the secondary section are analyzed. Results are validated through extensive SPICE simulations. Analytical and simulated results are matched with better than 90% accuracy.

Keywords: Circuits and Systems, Computer Architecture & Systems
Abstract: In this paper, we have proposed a new fault tolerant design technique for majority voter that is used in selection of final output for fault-tolerant methods like N-tuple modular redundancy (NMR) and N-tuple interwoven redundancy (NIR). The common assumption that majority voters are robust and hence does not affect the final reliability of a system, is false for most of the practical applications. We have used redundancy at transistor level combined with redundancy at gate level to design a defect tolerant majority voter that provides notable improvement in reliability over conventional triple modular redundancy technique using traditional non-reliable voters and other existing methods.

Keywords: Circuits and Systems, Computer Architecture & Systems
Abstract: In modern-day deep sub-micron technology various cores and components are integrated on a single chip called System on Chip (SOC). To assure performance of SOC, intracore communication must be efficient and accurate. Network on Chip (NOC) plays a vital role in communication among cores, memory, input-output, and other components. Increased density of components on-chip enhances the probability of failure. Failure can be due to faulty components, link failure, deadlock, livelock, starvation, congestion, etc. To avoid failure in communication, e need an efficient algorithm that must have properties like deadlock-free, livelock free, highly adaptive, fault-tolerant, minimal etc. In this paper, we propose a novel fault-tolerant, deadlock-free, livelock free, fully adaptive, and minimal breadthfirst search (BFS) based routing algorithm which routes a packet from source to destination efficiently. The simulation results depict that our algorithm can route packets in the presence of faulty links or faulty components. It also gives the alternate routes while facing faults.

Keywords: Circuits and Systems, Computer Architecture & Systems
Abstract: In most multimedia applications, human beings can gather useful information from approximate outputs. The approximation concept is dominating in the field of digital signal processing and multimedia applications as it allows the reduction of area and power significantly with some loss of accuracy. Little inaccuracy does not affect the visual quality as it seems unnoticeable in the perception of a human eye. As an advantage, this technique is to reduce the device utilization for such applications, which will eventually lead to a reduction in terms of power as well as transmission speed. Finally, the redundant bit, as well as the storage required to store the data, will be reduced. The approximation of arithmetic circuits is useful for reducing its computational complexity without a significant impact on its coding performance. In the end, the proposed approximate almost full adder based dada multiplier utilizes less power, delay, and device utilization. In this paper, analysis of various multipliers using approximate almost full adder is evaluated.

Keywords: Circuits and Systems, Computer Architecture & Systems
Abstract: The maximizing yield concept ensures a semiconductor manufacturing structure towards recognizing, diminishing, and avoiding yield-related defects and contamination. According to the current scenario of technology scaling, manufacturing yield is measured in terms of the number of perfect chips produced. However, in this paper, an imperfect chip producing a good-enough result like Approximate Circuit (AxIC) can also be considered and helps enhance the yield. Hence, this paper's primary objective is to identify those acceptable circuits (AcICs) through retesting, which indirectly increases the effective yield. The basic idea is to divide the testing process into two phases. In the first phase, we follow a conventional test flow architecture and collect the rejected circuits. In the second phase, all the rejected circuits that are tested imperfect in the first phase are retested by applying the test patterns. During this phase, the circuit may produce wrong results for some test patterns, but we should ignore and continue the test until all the test patterns are applied. The test patterns for which the circuit produces a wrong result (error) are quantified and checked against the golden output for deviation. If the amount of deviation is nominal and does not affect the circuit's overall performance, then the circuit is accepted as an AxIC. Though the circuit does not precisely follow the definitions of AxICs, it can significantly contribute to the yield enhancement and termed as

Keywords: Robotics, Control Systems & Theory, Power & Energy
Abstract: Permanent magnet synchronous motor (PMSM) is typically used to drive the mobile robotic chassis system. In this paper, a modified model reference adaptive system (MRAS) based algorithm is proposed to estimate the moment of inertia of the PMSM drive system. First, an extended state observer (ESO) technique is introduced to reconstruct the adjustable model so that the adverse effects of nonlinear dynamics on its accuracy are removed. Thus, the estimation precision of the moment of inertia can be enhanced. Then, a novel sliding-mode adaptive law is designed to replace the PI adaptive law, which avoids complicated PI parameters adjustment and improves the dynamic estimation performance. The existence and the reachability of the sliding mode are proved with aiding from the Lyapunov function. Experimental results verify the effectiveness of the proposed method.

Keywords: Robotics, Control Systems & Theory
Abstract: With mounted robot arm, the compound trackless automatic guided vehicle (AGV) has strong flexibility and adaptability in industrial environments. However, due to the space constraints of the robot arm, the path planning in complex large scenes is hard to achieve high efficiency, and it is easy for falling into local minimum and stagnation. In this paper, a novel AGV path planning algorithm is proposed on the basis of map preprocessing to improve the planning efficiency and guarantee the operating safety. First, by integrating the multiple constraints including the obstacles and robotic position/posture, the preprocessing method of the environmental map is proposed utilizing obstacle expansion and Delaunay triangulation. Then, to achieve better convergence and global optimization capacity, the global path searching is modified by (1) constructing the OpenList with priority queues; (2) exploring the adaptive rules for step size and dynamic weighted heuristic function. Finally, combining linearization and cubic hermite interpolation method, the planned path is smoothed to enhance the movement stability and energy consumption rate. Simulation analysis and experimental results verify the feasibility, efficiency and superiority of the proposed path planning method.

Keywords: Robotics, Control Systems & Theory
Abstract: Four-wheel-steering mobile robots (FMR) are widely-adopted in industrial manufacturing applications. However, the robust trajectory tracking of FMR is hard to achieve due to the unknown disturbances and uncertainties. To address this challenge, this paper presents an asymmetric barrier function-based adaptive control (ABFAC) method for obtaining a robust direct yaw moment control scheme for the FMR. The praiseworthy features of the proposed ABFAC method are twofold: (a) It does not need the information concerning the upper bound of the disturbances; (b) By introducing the barrier function, the output signals are guaranteed to be within the desired neighborhood since the sliding mode gains will grow once the disturbance derivatives increase. In this context, the proposed ABFAC method makes the control signal follows the absolute value of the disturbance, ensuring robust closed-loop responses. The stability of the resultant system is ensured by the Lyapunov theory. Finally, experimental results of real-time FMR are given to show the effectiveness and practicability of the proposed ABFAC method

Keywords: Robotics, Control Systems & Theory, Circuits and Systems
Abstract: With a modified form of the rapidly- exploring random tree (RRT), RRT* algorithm is an important and effective tool for sampling-based path planning. However, the partial extension and low efficiency of the traditional RRT* make it very difficult to satisfy specific constraints or real-time requirements of mobile robotic scenarios. Based on the double tree structure expansion, a double-tree RRT* (D-RRT*) algorithm is proposed in this paper with the ability to improve space collision detection and search the feasible connection area using constrained nodes. The proposed method can effectively utilize the fast preprocessing ability of a double-tree structure and reduce the implementation of iterations. Meanwhile, node filtering and regression are designed to reduce the node numbers and prevent over space searching. Through the validation examples of mobile robots, it is shown that the proposed D-RRT* method can search for a global safe path with enhanced efficiency and convergence as compared with conventional methods.

Keywords: Machine Learning, Cloud and Data Analytics, Biomedical Engineering
Abstract: The advancements in Computer Vision with Deep Learning are playing an essential role in aiding Healthcare Organizations to offer better patient care while reducing costs and improving efficiencies. The medical industry has seen a sharp growth since the past few decades and it has benefited thousands of living beings. Skin being the largest part of human beings,it has never been given the proper care nor importance, hence widely resulting in the ignorance of skin infections or cancers. Despite the diagnosis of several such diseases being detected at the later or critical stages, there is a little to no scope of detection where the affected people can identify the symptoms immediately and could reach a physician. In this paper, we present the development of a model using deep learning approach to identify the specific dermatitis and a mobile application that functions in a way to detect the disease when a picture of the symptomatic part is taken and uploaded. This scheme assists the medical professionals and patients during pandemic situations like COVID 19. This application is user friendly and very much useful for the people living in rural areas and hilly area where it takes long time and expenditure to reach hospital

Keywords: Machine Learning, Cloud and Data Analytics, Biomedical Engineering
Abstract: The study aims to design an intelligent total body water measuring device which will help to determine the total body water level or percentage of an individual using ultrasonic sensor, load cell and bioelectric impedance analysis (BIA). The system utilized the Scaled Conjugate Gradient Artificial Neural Network (ANN) as the machine learning algorithm. The system used the dataset splitting of 70%-15%-15% for training, validation and testing. Different hidden neurons were used and compared during neural network training and found out that using 10 neurons will provide the lowest mean square error (MSE) with best value of Pearson痴 correlation (R). Based on the results, using 10 neurons, Scaled Conjugate Gradient algorithm has better performance as compared to Levenberg-Marquardt algorithm with MSE equal to 0.180033, 0.118954, 0.529157 while the R value is equal to 0.997887, 0.997488, 0.99644 for training, validation and testing.

Keywords: Machine Learning, Cloud and Data Analytics, Biomedical Engineering, Engineering Management
Abstract: When the human population is experiencing a decline but the turnover rate of pharmacists in general hospitals is gradually increasing, department of pharmacy starts to import more modern technologies including automation and artificial intelligence to aid in the workflow. One of the lengthy and routine work is to count the number of remaining medications of each ward, which requires many pharmacists and technicians depends on the size of hospital. This study thereby introduces a design of a medication assist device with an integration of the machine vision and multiple object recognition algorithm. The work can be divided into hardware design, data collection, training and validation, respectively. The recognition algorithm is based on deep learning Faster RCNN, which can successfully identify 7 classes of the anesthetics often used with an accuracy of 99.03%. This pilot study presents the capability of medication recognition, and the potential to expand numbers of medication.

Keywords: Machine Learning, Cloud and Data Analytics, Biomedical Engineering, Signal and Image Processing
Abstract: In the Western Pacific, the Philippines ranks fifth in the number of diagnosed diabetics. According to Philippine Statistics Authority痴 2016 report, Diabetes Mellitus ranks sixth as the cause of death in the country comprising of 5.7% of the total deaths. With the increasing urbanization, the number of diabetics in the country is expected to grow, but with the recent technological advancements, artificial intelligence could be used to facilitate and diagnose the people who could potentially suffer from the said disease. This study focuses on the implementation of Multilayer Perceptron with Histogram of Oriented Gradient to classify different fingerprint patterns, namely arch, loops, and whorls and lastly Logistic Regression to predict whether a person has a high or low chance of having a diabetes. The system is implemented using a 64-bit Windows operating system through Python, java programming languages, and U.are.U fingerprint scanner for the input images.

Keywords: Machine Learning, Cloud and Data Analytics, Biomedical Engineering, Signal and Image Processing
Abstract: Deep learning has been proposed as one of the automated solutions for diabetic retinopathy (DR) severity classification problem. However, most of the successful deep learning models are based on large convolutional neural network (CNN) architectures, requiring a vast volume of training data as well as dedicated computational resources. In this study, we used MobileNetV2 architecture, which was considered a small-scale architecture (4.2 million trainable parameters), to perform DR classification task in APTOS 2019 dataset (3662 color retinal images). We used the generic MobileNetV2 pre-trained weights from ImageNet as initialization and implemented data augmentation and resampling during training. We further optimized our model by combining it with an SVM classifier, resulting a hybrid and computationally efficient deep learning model, MobileNetV2-SVM. This model obtained quadratic weighted kappa score of 0.925, 85% accuracy, and area under receiving operating characteristic (AUROC) of 1.00, 0.82, 0.94, 0.94, 0.93 for normal, mild, moderate, severe, and proliferative DR classes, respectively; which is better or at least comparable with the larger architecture performance on the same dataset. Our result shows that with proper optimization strategy, a relatively small and generic CNN architecture, can achieve promising DR classification performance, and even outperform the performance of CNN model with larger architecture.

Keywords: Machine Learning, Cloud and Data Analytics, Biomedical Engineering, Signal and Image Processing
Abstract: Malaria, a life-threatening disease, develops due to the bite of female Anopheles mosquito. It spreads the plasmodium parasites in human blood, killing hundreds of millions of people every year. Modern scientific advancements play a pivotal role to combat the disease, along with biomedical research by the medical experts to possibly eradicate this disease from all parts of the world. With the significant development in deep learning research, faultless identification of medical imaging has become an important factor in medical diagnosis and decision-making. To this end, we present a deep learning based approach using a convolutional neural network for detecting malaria from microscopic cell images using image classification. The proposed CNN model implemented using 5-fold cross validation approach outperforms all the existing methods in terms of accuracy and other evaluation metrics, thus achieving the best results till date in malaria detection using deep learning.

Keywords: Signal and Image Processing
Abstract: In this paper, rational graph filters are designed by using conventional IIR digital filters and spectral transformation. First, the specification of the rational graph filter design is converted to that of IIR digital filter by using a spectral transformation. Then, conventional IIR digital filter design methods are directly employed to design converted digital filter without needing to develop new design algorithms. Next, the coefficients of rational graph filter are obtained from those of the designed digital filter by using Chebyshev polynomials and spectral transformation. Finally, one design example is demonstrated to show the effectiveness of the proposed method, and the application to temperature data denoising illustrates the usefulness of graph filter to practical problem.

Keywords: Signal and Image Processing
Abstract: In this article, a novel edge preserving image fusion method is proposed by merging multiple images captured from different imaging sensors. The objective of this paper is to highlight the informative contents of multiple images into a single fused image. Fusion of data from multiple sensors is a difficult task as the imaging modality are different and sensors capturing the data may be affected by sensors noise. As the images captured from multiple sensors possess uncertainty within a pixel due to the multi-valued level of brightness. It is obvious that a deterministic method of fusion may not give a better results. Hence, it is required to explore the use of fuzzy sets theoretic approaches in this regard. The proposed scheme follow three stages. In the first stage of the algorithm, a resultant image is obtained by setting the maximum value between the pixel intensity of visible and infrared sub-images considered within a small spatial neighborhood. The edges of the visible image are preserved in the second stage of the algorithm using a Fuzzy edge technique. Finally the fused image is obtained by combining the obtained resultant image and the edges of the visible image. In order to evaluate the performance of the proposed method quantitatively, and qualitatively experiments were carried out on publicly available benchmark database, 典NO-database The proposed method is compared with those of eight state-of-the-arts techniques. The experimental results of the proposed method

Keywords: Signal and Image Processing
Abstract: Mangrove forest plays an important role in ecosystem and the need to conserve this is essential in our time as global warming was realized. One way to monitor this forest is to estimate its aboveground biomass (AGB). This study aims to estimate the AGB of the largest single mangrove forest in the Philippines located in the municipality of Del Carmen, Siargao Islands using remotely sensed Interferometric Synthetic Aperture Radar (IfSAR) datasets and Sentinel-1 image. There were three predictor variables derived from the Sentinel -1 image used for modeling the AGB: the backscatter value from VV polarization, backscatter value from VH polarization, and the combination of the backscatter values from VV and VH polarizations. Also, the predictor variable from IfSAR dataset that was used is the canopy height. Linear regression between the field-measured AGB and the predictor variables was used to generate AGB model, and the coefficient of determination (r2) and root mean square error (RMSE) were determined. The results show that the model derived from IfSAR data obtained a coefficient of determination of 0.42 and RMSE of 19.39 Mg/ha. However, the combination of the two polarizations derived from Sentinel-1 image was more accurate in estimating AGB since it obtained the highest r2 of 0.43 and lowest RMSE of 12.65 Mg/ha. Based on the best model the estimated above ground biomass of whole mangrove forest ranged from 73.37 Mg/ha to 225.51 Mg/ha with an average of 140.2 Mg/ha.

Keywords: Signal and Image Processing, Circuits and Systems, Devices, Materials & Processing
Abstract: 輸 method for extracting lettuce phenotypic features using an ARTY A7-35T FPGA platform is proposed. Image acquisition is done by interfacing an OV7670 CMOS camera with FPGA and saving image data on DDR3 memory. The image processing techniques firstly involve color model conversion for saturation enhancement. Then, binarization is done as an initial step in background discrimination, using a threshold value on the green channel of image. To construct a solid figure for foreground, morphological transformations are implemented. Then, pixel count of foreground white pixels are used as an argument in the computation of lettuce canopy area. The FPGA implementation consumes 85.89% of total LUTs resources of the chosen FPGA board with errors as low as 1.28% on the computed canopy area value compared with a MATLAB benchmark. Power consumption reached up to 1.73W, with a total calculated latency of 596.51 ms from image acquisition to canopy area value.

Keywords: Signal and Image Processing, Devices, Materials & Processing, Biomedical Engineering
Abstract: The systematic investigation of the vocal foldsphysical phenomenon and corresponding kymographic vibratory patterns can establish clinically important relationships which are vital in the diagnosis of laryngeal disorders. The routine investigation is often done clinically through the in-vivo examination of human vocal folds using laryngoscopes. Physical modelling of human vocal folds overcomes the accessibility limitations of the laryngoscopes in imaging the medial and coronal parts of the vocal folds in-vivo, thus allowing investigation of mucosal wave propagation for different geometrical configurations of the vocal folds. Here, the modelled vocal folds are fabricated using flexible silicone compounds, which can closely reproduce the vibratory characteristics of human vocal folds. The kymogram, generated by recording the vibration pattern of the vocal folds, is useful in the analysis of its functional characteristics. The systematic investigation involves quantification of the vibratory parameters from the kymogram of the modelled vocal folds and establishing its relationship with the physical phenomenon. From the quantification, the important parameters: amplitude, open quotient, closed quotient, speed quotient and skewness are analysed. The quantified parameters reflect the influence of physical parameters on the vibratory characteristics of the vocal folds.

Keywords: Wireless Communications & Networks
Abstract: In this work, we investigate the performance of a power domain based downlink decode and forward coordinated-non-orthogonal multiple access (NOMA) system with two users and one relay node. The system is considered to operate in half-duplex mode over a Rayleigh faded channel, where the weak user is heavily shadowed with no direct link. Framework for average symbol rate error (ASER) and asymptotic ASER of generalized M-ary QAM for both weak and strong user is developed. Further, the impact of various power levels over different QAM schemes for users is analyzed. Furthermore, the impact of symmetric and asymmetric constellations for the users is also considered and their impact over the system is detailed and useful insights drawn. The derived analytical expressions are verified through Monte-Carlo simulations and compared with non-coordinated NOMA.

Keywords: Wireless Communications & Networks
Abstract: The hybridization of MIMO-OFDM systems became one of the most used wireless communication model for broadband, mobile, and multimedia applications because of its high bandwidth efficiency, bandwidth capacity, and robustness to fading. However, it suffers from the underlying disadvantage of OFDM system which is having a high peak-to-average-power ratio (PAPR) due to large envelope variations. These variations cause non-linear distortion when the OFDM signal is amplified for transmission. Hence, in order to eliminate the non-linear distortion effects of the high power amplifier in MIMO-OFDM systems, the input signal power must have an appropriate power level to satisfy an optimal input back off (IBO) value that also contributes to an amplifier痴 maximum efficiency. A Fuzzy Logic Controller is used to control the IBO of the system as well as the signal power level. Results shows that using the proposed Single-Input Single-Output (SISO) Fuzzy Power Controller reduces the bit error rate (BER) significantly compared to the traditional scheme.

Keywords: Wireless Communications & Networks
Abstract: In this paper, mixed Gibbs sampling multiple-input multiple-output (MIMO) detection with forcible search is proposed. In conventional Gibbs sampling MIMO detection, the problem of stalling occurs under high signal-to-noise ratios (SNRs) which degrades the detection performance. Mixed GS (MGS) is one solution to this problem. In the MGS, random sampling is carried out with a constant probability without judging if a current search is at a local minimum. In the proposed scheme, combined with MGS, multiple candidate symbols are forcibly changed when the search is captured by a local minimum. The search restarts away from the local minimum which effectively enlarges the search area in a solution space. Numerical results obtained through computer simulation show that the proposed scheme achieves better performance in a large scale MIMO system with QPSK signals.

Keywords: Wireless Communications & Networks
Abstract: Terahertz (THz) band communication is considered as a promising technology for 6G wireless technology due to availability of large bandwidth at THz bands. Most receivers for symbol detection need channel state information (CSI), however estimating CSI is complex for THz bands. Hence, in this paper, we design and analyze low complexity THz receiver without considering the CSI for symbol detection. The rate adaptation for THz communication systems is also analysed for the distance attenuation. Since, at THz the performance is sensitive to distance. We consider modified transmitted-reference (MTR) pulsed and optimal lth-order energy detector (OED) based THz communication system. Simulations are carried out to show effectiveness of MTR and OED for pulse-based THz band wireless systems by considering the effect of distance, data rate, and absorption by molecular gases.

Keywords: Wireless Communications & Networks
Abstract: Sensing-based semi-persistent scheduling algorithm was developed by 3GPP as the standard for distributed resource selection in V2X sidelink communications. Sensing before resource selection largely reduces collision probability. However, we found that considerable collisions can happen if user equipments use different resource reservation intervals. An enhancement is proposed in this paper to prevent collisions. The proposed enhancement requires only simple computation and does not need additional information to be transmitted in sidelink control information. Simulations are conducted and results show that the improvement is significant. For our simulation scenario, the collision probability can be reduced by more than 77% under perfect channel condition. When channel condition is taken into consideration, the packet reception ratio can be improved by more than 6.41%.

Keywords: Wireless Communications & Networks
Abstract: With the recent outbreak of COVID-19 and other pandemics, improving the public safety communication is essential for efficient communication in the 5G and beyond wireless communication networks. The key requirements shall be lower delays, improved coordination and efficient resource utilization, to achieve higher efficiency in the network performance in an emergency/pandemic situation. Since deployment costs and scarce resource availability are major constraints in the network functioning, looking forward to a new network solution, a heterogenous network (HetNet) architecture has been proposed in this paper, for an efficient broadcast network set up during emergency situations. This paper proposes a two-tier heterogenous network (HetNet) architecture, with the macro base station (MBS) tier being Tier 1 and the small cell tier (SCT) being Tier 2. Here the SCT is mostly involved in setting up of a public warning communication system. The HetNets also intend to promote device-to-device (D2D) communication links, in case of absence of connectivity to the user via the MBT or the SCT. Use of small cells and D2D links shall improve the overall system performance. Certain research challenges however persist, and are stated in the paper.

Keywords: Power & Energy
Abstract: Large-scale concentrated solar power (CSP) with thermal energy storage (TES) is the most viable option in a centralized generation for a sustainable energy system. To have a successful CSP-TES plant operation, selecting the best plant configuration is deemed necessary. Thus, this study presented a flexible method to determine the optimal plant parameters while considering the electricity demand curve of the location. Modeling and performance simulation were done in the System Advisor Model. Elitist Nondominated Sorting Genetic Algorithm of MATLAB was used to solve multi-objective optimization problem. Results showed the optimal performance of the plant in terms of its energy, economic, and environmental aspects. Also, it was established that the parabolic trough CSP-TES plant can provide high electrical output to meet peak demands during nighttime.

Keywords: Power & Energy
Abstract: This study proposes the problem of optimizing the annual operation, equipment configuration and capacity in a smart city with renewable energy and Combined Cooling Heating and Power (CCHP). The effectiveness of this study is demonstrated by comparing the case where renewable energy and CCHP are introduced and the case where only electricity purchased from the electric utility is used.

Keywords: Power & Energy
Abstract: With the increasing penetration of electric vehicles (EVs) into the automotive market, the electricity peak demand would increase significantly due to residential EV charging. This paper tackles this problem by defining an 'ideal' EV consumption profile, from which a day-ahead pricing scheme is derived. Based on historical residential EV-use data ranging over a year, we demonstrate that the proposed optimization process results in a pricing profile that achieves a dual objective of minimizing the total electricity cost as well as the peak aggregate system demand. Importantly, the proposed formulation is simple, and accounts for the tradeoff between consumer convenience in terms of the number of available charging slots during a day and the reduction in the total electricity cost. This technique is demonstrated to be scalable with respect to the size of the community whose EV charging demands are being optimized.

Keywords: Power & Energy
Abstract: The grow of electrical energy utilization and day by day increment of nonlinear loads in power system forced for high electrical power and better stability. Flexible AC Transmission System (FACTS) is a key solution for power system performance enhancement. Based on that, a particle swarm optimization (PSO) algorithm is applied to design the coordinated parameters of static VAR compensator (SVC) and Thyristor Controlled Series Capacitor (TCSC). A self-sufficient model of IEEE fourteen bus system has been taken and voltage stability analysis is done by considering load changing at a bus and change of power factor at load. PSO applied here is built on searching the values of L and C of SVC. PSO is available with few modification like change in PSO range & number, difference in selection criteria technique, control technique etc, with due respect to normalize PSO. From the results, it is found that coordination of FACTS devices with each other promises the efficiency of the suggested method for enhancement of voltage stability.

Keywords: Power & Energy
Abstract: AC breakdown strength is a critical parameter in the performance of an insulating liquid. Developing a method to predict the breakdown strength of dielectric liquid makes it possible to predetermine the voltage withstand capability of materials. Prediction of breakdown voltage of nanofluids (NFs) has great significance to the optimization of filler loading concentration of NFs. Based on the electric field distribution inside the dielectric liquid samples, this paper proposes a prediction method for AC breakdown voltage (BDV) using curve fitting technique. By analyzing various statistical parameters of electric field distribution such as average, standard deviation, variance, skewness, and kurtosis along with AC breakdown voltage values of NFs, a relationship connecting these parameters and breakdown voltage is formed. Using this relation and statistical parameters of electric field distribution obtained from simulation model, breakdown voltage is predicted for another set of NFs. TiO2 NFs are used for framing the equations and breakdown voltage is predicted for Al2O3 NFs. It is observed that AC breakdown strength is predicted with least error by using kurtosis as the statistical parameter. Test results show that the relative errors between predicted values and actual values are all less than 8%, which indicates the accuracy and reliability of this method.

Keywords: Power & Energy
Abstract: In the present trend, the more importance has been given for the power generation using renewable energy sources (RES). Worldwide research is going on for the power generation near the load side with fewer losses and better efficiency using RES. In this regard, distributed generation (DG) and micro-grids are the two major areas of research. Distributed generation is nothing but the small-to-medium power generating plants located at or near the loads with or without connection with the main Grid. In this paper, reliability evaluation of RBTS bus-2 system has been carried out with and without considering distributed generation in Grid connected mode. The reliability has been evaluated feeder wise, each feeder is considered as separate micro-Grid with grid connected mode. Also the sizing and siting of Distributed Generation in considered in the reliability evaluation. The Analytical technique has been used to obtain the load point indices and total system reliability indices. The results obtained with and without distributed generation have been compared.

Keywords: Circuits and Systems, Wireless Communications & Networks, Marine and Offshore Engineering
Abstract: Municipal fishermen experience collisions and being rammed by larger marine vessels. Automatic Identification System (AIS) can be a solution for them, if not for its high cost. Likewise, in emergency situations, due to limited cellular coverage at sea, fishermen do not have the means to communicate with each other and request for assistance. Therefore, this design addresses these concerns with a low-cost AIS receiver embedded with intercommunication using microcomputer and software-defined radio.

Keywords: Circuits and Systems, Wireless Communications & Networks, Power & Energy
Abstract: A Duty Cycle Corrector (DCC) with wide operation frequency band, wide duty-cycle correction range, high duty accuracy and low power consumption performance is proposed in this paper. A dual feedback loop with differential input clock is added to reduce the impact of charge pump imbalance on circuit performance. The chopping technique is also introduced to improve the loop gain meanwhile suppress the DC offset in the feedback loop. Furthermore, a novel duty-cycle adjuster (DCA) with configurable load capacitance is presented to maintain the duty-cycle correction range in a wide frequency range. The proposed DCC is implemented in 65-nm CMOS process with 1V supply voltage. Post-simulation results indicate that the DCC corrects the input duty cycle with a range from 20% to 80% to 50ア0.7% in the 0.5-5 GHz frequency range. The maximum power consumption of the DCC is 0.32 mW when the input clock frequency is set to 5 GHz.

Keywords: Biomedical Engineering, Circuits and Systems, Engineering Management
Abstract: In an industrial environment, there is always a surging demand for a wearable device to cope up with the increasing degree of automation and reduced manual intervention. The requirement of the wearable is related to the employee's health and well-being. Safety remains a prime concern despite other large-scale availability of personal protective equipment (PPE) and the adoption of strict safety measures. The wearable can be an essential component of the worker's toolkit, as its application is increasingly demanding. We have developed an integrated cardiorespiratory & sweat monitoring wearable and performed extensive experiments in an operational underground mine in different conditions. The appearance of a wearable platform with biochemical analysis, physiological parameters monitoring along with ambient environment sensors has enabled the observation of critical indicators of performance as well as the stress of mineworkers.

Keywords: Biomedical Engineering, Circuits and Systems, Robotics, Control Systems & Theory
Abstract: The conventional rehabilitation process has benefited in aiding a patient痴 recovery significantly. However, for a typical traditional rehabilitation session to occur, a medical professional is required to present alongside the patient. With the advancement of technology, robotics is now being integrated into the rehabilitation process to provide significant benefits to the rehabilitation environment. The objective of this project is to develop a Rehabilitative Robotics System that assists stroke patients with the recovery of their hand movements. A hand exoskeleton is integrated with an Electromyography (EMG) sensor, a linear actuator, a microcontroller, and a screen. The EMG input is used to detect a patient痴 intention to open or close his/her arm with the assistance of the linear actuator. The screen will display the options which allow the patient to select the mode of the rehabilitation process. The results of the session are then recorded in the computer for further analysis by the medical professionals. The system is aimed to design a portable, lightweight device that can be mobile with the patient without a secondary assistant. This paper presents the literature review alongside the development of the hand exoskeleton design, hardware components and design, software implementation, and system implementations.

Keywords: Devices, Materials & Processing, Circuits and Systems, Robotics, Control Systems & Theory
Abstract: This paper introduces the development of monitoring and maintaining optimal water quality in an aquaponics system. The design is based on the hydroponics system痴 Nutrient Film Technique (NFT) in which plant roots are being exposed to a thin layer of nutrient water in a long narrow channel. The design used a developmental and experimental type with the use of microprocessors and sensors for enhanced monitoring and error-correcting within the aquaponics system where standard data was obtained from different reliable sources. Various sensors are calibrated for different measurements to provide accurate and reliable readings of water temperature, pH level, dissolved oxygen, total dissolved solids, water flow, and temperature and humidity. The Arduino Mega reads and analyzes data collected by various sensors, and instructs actuators such as aquarium heater, cooling fan, aerator, grow light, and water pump. The data gathered appears on the built-in LCD screen and will be sent to the owner's mobile phone regarding the condition of the system. Also, the system has a fish feeder that automatically dispenses food at a given time. The device can be controlled wirelessly using a mobile phone and manually using a 4x4 keypad. Also, the owner can monitor the way each actuator was controlled.

Keywords: Software & Database Systems, Devices, Materials & Processing, Circuits and Systems
Abstract: Over the years, the manual checking of attendance has been carried across most of the educational institutions. Manual attendance monitoring results in a lot of time consumed. To overcome the problem for manual attendance, the researchers proposed a Quick Response (QR) Code Attendance System with SMS Location Tracker that can provide information about the student痴 arrival and departure time in school. The main purpose of the study is to design a QR Code Attendance System to improve the manual/traditional attendance and to provide a Global Positioning System (GPS) that can track the location of the students. The researchers used an Incremental Methodology to approach the study. It is a method in which the product is incrementally designed, implemented, and evaluated until the product is complete. The design project was tested and evaluated by 50 users and 10 experts. Based on the series of testing the system can provide an easier and more convenient recording and checking of attendance using the QR Code Scanner, it is also capable of providing the information about attendance by sending a text message and can provide location by requesting on the Android Application.

Keywords: Robotics, Control Systems & Theory, Signal and Image Processing
Abstract: This paper proposes an efficient and robust localization recovery method considering time variation for Monte Carlo localization (MCL), which can handle the kidnapped robot problem (KRP) with improved recovery speed and success rate. The presence of particles near the real robot痴 pose is necessary for the localization recovery of MCL. Therefore, the generation number and position of random particles are the vital factors to solve KRP. It is generally assumed that the robot cannot move instantaneously, so the size of the search space where robot may appear should be time-dependent after the KRP occurs. Given these considerations, a restricted search space is firstly constructed as a set subject to a time-varying normal distribution, which can effectively narrow the search space and estimate the probability that the robot may appear. In addition, a short-long term random particle generation strategy considering time variation is designed to availably determine the number of random particles according to the change of the likelihood and time. And then, the random particles are spread into the restricted search space. Finally, the above particle set is integrated into the MCL for localization recovery. The effectiveness of the proposed method is verified through real scene experiments.

Keywords: Robotics, Control Systems & Theory, Devices, Materials & Processing
Abstract: RV reducers are widely recognized in mobile manipulators due to their advantages, such as high efficiency, high reduction ratio, and high precision. However, for different types of reducers under different working conditions, there is no fast quantitative method for determining the dynamic characteristics of new materials and structures yet, so, this paper deals with the study of system stability by combining contact mechanics, kinematics, and Lyapunov exponents. First, kinematics analysis of one-stage cycloid reducer is performed, and the kinematics equations have been solved using the fourth-order Runge-Kutta numerical method, then, Lyapunov exponent is used to analyzing the motion smoothness characteristics of the system with various parameters. Also, the numerical experiment for the single stage cycloid pin gear system is given and the diagrams are obtained, showing the resulting translation, corresponding velocity, dynamic force, and Lyapunov exponent in terms of time. Research shows that a quantitative analysis index of RV reducer is of great significance for its nonlinear dynamic performance evaluation.

Keywords: Robotics, Control Systems & Theory
Abstract: Abstract揚lobal localization is essential for pose initialization and pose recovery. However, for the lack of prior information, global localization is always unreliable and time consuming, especially in featureless and dynamic industry environment. To alleviate the negative influence of such environment, this paper uses reflector as landmarks. Then, several maps including labeled occupancy grid map and multi-resolution likelihood field are proposed to model the positions of landmarks as well as ordinary obstacles. Furthermore, a branch and bound method is employed to achieve fast global search based on those proposed maps. Through experiments in a real industry application, the reliability and efficiency of our proposed global localization method is verified.

Keywords: Machine Learning, Cloud and Data Analytics, Biomedical Engineering, Software & Database Systems
Abstract: The lack of health professionals, quality health care, and accessible health centers in rural and isolated communities have resulted in high maternal and fetal mortality rates in the Philippines, especially since quality maternal and child healthcare services are concentrated in more developed urban areas. This study proposes applying the Telemedicine framework as a helping tool for doctors and health professionals. The implemented Telemedicine approach on prenatal care followed a set-up that included patient information input, a mobile application used for both data input and visualization, a cloud-based server for the database, and a machine learning system which analyzed data from the patient profile. With a dataset of 97 samples, four algorithms were implemented for the development of the machine learning system Decision Tree, Random Forest Decision Tree, K-Nearest Neighbor, and Support Vector Machine. The performance of each algorithm was tested in terms of accuracy, precision, recall, and the F1 score or the weighted average of precision and recall. Based on these parameters, the most effective algorithm was the Random Forest Decision Tree with the highest train score (0.987) and test score (0.900). The results of this algorithm were visualized in an Android mobile application that displayed whether the patient was a positive or negative case with respect to the possibility of having a high-risk pregnancy.

Keywords: Machine Learning, Cloud and Data Analytics, Circuits and Systems, Robotics, Control Systems & Theory
Abstract: This study presents the design and development of a nutrient film technique hydroponics system for lettuce. Hydroponics is a method of cultivating crops with the use of water with nutrient solutions as medium. This nutrient film technique hydroponics system was built as an alternative to traditional farming that requires a lot of space. This system can produce a good number of crops without consuming large land area. The system also features monitoring of the key parameters needed for by the crop to survive. A fuzzy logic control will also be used to maintain the level of the parameters. Data from the sensors for measuring electrical conductivity, pH, and as well as the water level of the mixing tank will be the input of the fuzzy logic and will control the pumps of fresh water and nutrient concentrate reservoir, and the drain of the mixing tank. The optimum values for electrical conductivity, pH, water flow rate, and temperature were all based on the existing studies that also cultivate lettuce as their primary crop.

Keywords: Machine Learning, Cloud and Data Analytics, Computer Architecture & Systems
Abstract: The proliferation of IoT devices in recent years has resulted in an exponential increase in data being transmitted over the internet. The traffic is slated for further increase in the coming years and will result in excessive network congestion and high latency. To alleviate this problem, an alternate approach needs to be considered. A prominent option would be to move the computing domain to the edge device. This option is constrained due to reduced computing, storage and power available on the edge. A novel approach combining both software and hardware solutions is required to perform analytics at the edge. This paper proposes an architecture for analysing data on the edge, combining hardware and software solutions. The proposed methodology explores machine learning algorithms for edge computing combined with the use of hardware accelerators to achieve truly intelligent edge devices. A qualitative and quantitative comparison of performance of various algorithms on CPU, GPU, FPGA platforms is carried out. A machine learning model for predicting Remaining Useful Life (RUL) for a multivariate time series dataset is developed and its deployment on the edge is discussed. The results of the experiments carried out are promising and hold potential for further research.

Keywords: Machine Learning, Cloud and Data Analytics, Computer Architecture & Systems, Signal and Image Processing
Abstract: Recurrent deep learning language models like the LSTM are often used to provide advanced cyber-defense for high-value assets. The underlying assumption for using LSTM networks for malware-detection is that the op-code sequence of a malware could be treated as a language representation. There are differences between any spoken-language and the machine-language. In this paper we demonstrate that due to these inherent differences, an LSTM model with its default configuration as tuned for a spoken-language, may not work well to detect malware unless the network's essential hyper-parameters are tuned appropriately. In the process, we also determine the relative importance of all the different hyper-parameters of an LSTM network as applied to malware detection using their op-code sequence representations. We experimented with different configurations of LSTM networks, and altered hyper-parameters like the embedding-size, number of hidden-layers, number of LSTM-units in a hidden layers, pruning/padding-length of the input-vector, activation-function, and batch-size. We discovered that owing to the enhanced complexity of the malware/machine-language, the performance of an LSTM network configured for an Intrusion Detection System, is very sensitive towards the number-of-hidden-layers, input sequence-length and the choice of the activation-function. We also assess how sequential DL architectures compares against their non-sequential counterparts for the purpose of malware-detection.

Keywords: Machine Learning, Cloud and Data Analytics, Computer Architecture & Systems
Abstract: Traffic-Sign Classification is a major task in self-driving cars as well as modern driving assisting systems can be deployed as an inference engine on the Field Programmable Gate Array (FPGA) combined with host processor-based edge device like Zynq Platform considering the property of dynamic reconfigurability of FPGA adoptable to architectural innovation. Hence this paper proposes an optimized Convolutional Neural Network (CNN) architecture based on VGGNet combined with image-preprocessing techniques. The proposed methodology utilizes pruning combined with post-training quantization-based optimization and obtains an accuracy loss of less than 1%. The architecture is trained, tested and validated using German Traffic Sign Detection Benchmark (GTSDB) with Google痴 TensorFlow framework obtaining a validation accuracy of 99.2% and test accuracy of 100% for novel input inference. The experimental results show the reduction in memory footprint of CNN model readily implementable on FPGA.

Keywords: Machine Learning, Cloud and Data Analytics, Computer Architecture & Systems
Abstract: Performance prediction is an important and active research area. In particular, several research efforts have built empirical models using machine learning algorithms for performance prediction. These models enable us to understand the dependence on hardware components for algorithm execution, system痴 scaling capabilities, cross-platform prediction in multi-core systems, and many others. The user community can use this knowledge to select hardware configurations best suited for executing a given software. In recent times, neural network-based models are widely used to build empirical models that understand complex relations between independent and dependent variables of an unknown data set. This paper has studied one-layer and multi-layer neural network models for performance prediction of three algorithms with different computations and memory-access patterns. We have shown that the multi-layer model outperforms the one-layer model, especially for computationally intensive algorithms. We have also shown that computationally intensive algorithms having a higher variance in runtime due to manufacturer variability require a higher number of neurons for convergence than memory-intensive algorithms. Our multi-layer neural network with optimal configuration has a prediction accuracy of about 88% for computationally intensive algorithms and about 95% for the memory-intensive algorithm.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: Segmentation is a challenging task for the complex and low-quality underwater images, as this is prerequisite to advanced tasks in fish monitoring such as fish detection and classification. A demand exists for underwater image segmentation algorithms that can robustly segment fish from its background. A competitive approach is the integration of states-of-the-art image segmentation algorithms: SLIC superpixels merging by KAZE Keypoints clustering and Fast Marching Method (FMM) to a single framework. The combination of these established methods offers robustness towards underwater images of different visual qualities. First, a locally acquired underwater image is represented as superpixels. Then, the KAZE features of an underwater image is extracted. Such features are utilized by the k-means clustering to group superpixels which contains fish pixels into a region. Lastly, the merged region is further segmented with Fast Marching Method and corresponding morphological processes. The study presents the viability of the integration of different image segmentation techniques for localized application. The number of superpixels, KAZE Keypoint score threshold and FMM threshold are identified to affect the performance of the proposed algorithm. Qualitative observations and quantitative measures validate the robustness of this generated algorithm to address this difficult and persistent task.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: Food shortage is a serious problem facing the world and is prevalent in urban areas. The scarcity of food is mainly caused by crop failure. Environmental factors offered by the rural areas determine the condition of crops to be produced. This scenario pomps, the explication of urban farming. However, urban farming requires all-out monitoring and control. This study specifically solves the predicament of identifying the developmental growth of plants from seed leaf to amend the techniques of plant science and cultivation management. With a view to this, the paper shows coupled color-based superpixels and multifold watershed transformation in segmenting the lettuce image from the background. To fathom it out, a comparative analysis of three unsupervised machine learning algorithms: Self Organizing Map (SOM), Hierarchical, and K - means algorithms were conducted. These were done by modeling each algorithm from the features extracted from morphological computations of the lettuce images raised in a smart aquaponics setup. Each of the models was optimized to increase cross and hold-out validations. The results showed that K means algorithm having the parameters of algorithm = 疎uto copyx= 禅rue init = 銭-means++ maxiter = 000 nclusters = ninit = 5 n_jobs = precompute_distance = 疎uto random_state = 0 tol = .000001 verbose = leaf_size = 0was the most effective model for the given dataset, yielding a high precision and recall unsupervis

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: The guided image filter (GIF) technique is used for haze removal. It reduces the gradient reversal artifact as well as preserves the edge information precisely in smooth (flat) region only. However, it fails to avoid halo artifact and edge-smoothing effect in sharp regions. So, to mitigate this problem, we propose an adaptive haze removal algorithm using a steering kernel-based guided image filter (SKGIF). Steering kernel determines the edge-direction in guidance image more adequately. The edge-direction is an essential feature of guidance image, and it helps to determine more edge-preserving information in flat as well as sharp regions. Experimental outcomes on different hazy images prove the effectiveness of the proposed algorithm.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: In non-stationary environments, data might change over time, leading to variations in the underlying data distributions. This phenomenon is called concept drift and it negatively impacts the performance of scene detection models due to them being trained and evaluated on data with different distributions. This paper presents a new algorithm for detecting and adapting to concept drifts based on combining the existing and new components Gaussian mixture model then merging it. The algorithm is equipped with a drift detector based on kernel density estimation enabling the algorithm to adapt to new data and generalize over old and new concepts well.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: Facial landmark points that are precisely extracted from the face images improve the performance of many applications in the domains of computer vision and graphics. Face swapping is one of such applications. With the availability of sophisticated image editing tools and the use of deep learning models, it is easy to create swapped face images or face swap attacks in images or videos even for non-professionals. Face swapping transfers a face from a source to a destination image, while preserving photo realism. It has potential applications in computer games, privacy protection, etc. However, it could also be used for fraudulent purposes. In this paper, we propose an approach to create face swap attacks and detect them from the original images. The augmented 81-facial landmark points are extracted for creating the face swap attacks. The feature descriptors Weighted Local Magnitude Patterns (WLMP) and Support Vector Machines (SVM) are utilized for the swapped face images detection. The performance of the proposed approach is demonstrated by different types of SVM classifiers on a real-world dataset. Experimental results show that the proposed system effectively does face swapping and detection with an accuracy of 95%.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: The research work on user authentication using biometric parameters like face, iris, fingerprint, and gait, have been vastly carried out over the last few decades. Also, the researchers are putting great efforts in the field of gender recognition of a person. Predicting the gender of a person by considering the biometric parameters described above has been the topic of interest in many areas like security, forensic, e-commerce applications, investigation, and some smart automation systems. In this paper, we have presented an analytical approach for gender recognition based on the information of human gait obtained from the inertial sensors of a smartphone, and the comparative analysis of various machine learning classification and regression algorithms on the recognition performance. The overall aim of our research is to provide an insight and to improve the preciseness of the gender recognition system. Moreover, the proposed system can enhance the processing speed of the biometric recognition systems by determining the gender of the person at the initial stage to reduce the search area. We have achieved the state of the art performance and obtained an accuracy of 96.3% using the proposed method.

Keywords: Wireless Communications & Networks, Antenna & Microwave, Signal and Image Processing
Abstract: Millimeter Wave (mmWave) communication with the advantage of high spectrum availability is a key technology to offer several gigabits per second data rates for future 5G and beyond applications. Limited power availability and smaller coverage area are some critical issues in it due to its high attenuation and atmospheric absorption. Beamforming has become a skillful technique to keep track of the angle of arrival (AoA) and angle of departure (AoD) by directing the steering vector along with these phases with the least beam-width. Providing an optimal power allocation towards a user's location through beamforming has become an efficient technique to direct beam along the angle of departure (AoD) of the transmitted wave with the smallest beam-width. In a multi-input and multi-output (MIMO) channel, the channel state information (CSI) of a large dimension array leads to a considerable feedback overhead. The sparse nature of the MIMO channel has been exploited by researchers using compressed sensing, where, the resulting precoder indices can be sent back to the transmitter in order to reduce the feedback overhead. The precoder and combiner are updated with their estimated AoA and AoD during the training period. However, the training time goes enormously high as the number of antennas become too large. To overcome such limitation, an improved codebook training procedure (CTP) incorporated with hybrid beamforming (HBF) has been suggested in this research. The proposed scheme will e

Keywords: Wireless Communications & Networks, Circuits and Systems
Abstract: Security is a concern in wireless sensor networks, which are inherently prone to third party attacks. As such, cryptography is used to make a secure mode of communication among nodes and/or between nodes and base stations. However, conventional algorithms are resource-hungry and therefore not fit for small devices, hence the creation of a new category of cryptography known as Lightweight Cryptographic Algorithms. These algorithms are still continuously being improved to fit in the decreasing sizes of small scale devices like FPGA-based wireless sensor networks. In this study, we quantify the effects of Data Width Reduction, and Round Unrolling on area, and power consumption. These are tested on three lightweight ciphers: LiCi, ANUII, and QTL. Data Width Reduction reduces area by using only a fraction of the original datapath. It was however found to be ineffective for the chosen ciphers, increasing area by 5.64%, 6%, and 9.2% in LiCi, ANUII, and QTL, respectively. Round Unrolling reduces latency which lessens the contribution of static power for each computation. Results show that round unrolling improves power efficiency by 25.97%, 3%, and 14% in LiCi, ANUII, and QTL, respectively. This comes at 299%, 414%, and 52% increase in area. The results allow newer and better lightweight ciphers to be further improved for small scale devices.

Keywords: Wireless Communications & Networks, Machine Learning, Cloud and Data Analytics, Photonics
Abstract: Neural networks are extensively used for determining different characteristics of optical burst switching networks. The main disadvantage of optical burst switching network is burst drop and burst contention, which occurs because of burst getting blocked. Using neural network approaches, blocking probability can be pre-determined for the upcoming traffic. In this paper, Log-incremental modified extreme learning machine trained generalized radial basis function neural network (MELM-GRBFNN) model is used for training and predicting burst contention or burst blocking probability. From the obtained results, it is inferred that the prediction accuracy of our proposed model is more accurate and faster than the contemporary approaches. It is observed that our proposed method is competent in predicting the burst blocking probability with higher accuracy and indicates a reduction in the burst loss. Thus, it will help network designers to have a preliminary idea about the performance of the network model under specific configurations.

Keywords: Wireless Communications & Networks, Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Indoor Positioning Systems (IPSs) are able to provide information on the location of personnel and assets in various indoor environments such as offices, warehouses and factories. Using the position data from the IPS, Location Based Services (LBS) can be offered to personnel in offices. Also, various productivity improvement measures such as bottleneck identification, cutting wastage in searching for assets and floor layout optimization can be carried out in industry spaces like factories and warehouses. The widespread adoption of IPS is however currently hindered by insufficient positioning accuracy and the high infrastructure cost of installing and maintaining several reference nodes. This paper therefore proposes using just a single reference node and map matching for positioning along pre-determined routes. Furthermore, by employing Machine Learning, it is shown through simulation that the solution can be less complex and yet more accurate than traditional map matching, even in challenging indoor environments.

Keywords: Wireless Communications & Networks, Photonics, Signal and Image Processing
Abstract: Visible light communication (VLC) is eco-friendly and low-cost supplement to the existing radio frequency (RF) based communication system. However, the performance of VLC based system is limited by dispersive characteristics of the VLC channel which leads to inter-symbol-interference (ISI) and inter-carrier-interference (ICI). To alleviate the ISI and ICI, orthogonal time frequency space (OTFS) modulation has been proposed in the literature for both RF and millimeter wave communication systems. However, the performance analysis of OTFS over mobile multipath VLC channels is still an open area to investigate. This paper investigates the performance of OTFS over mobile multipath channels for the VLC system. Simulations performed over the realistic mobile multipath VLC channel indicate that OTFS with message passing detector exhibit better bit error rate over the conventional orthogonal frequency division multiplexing scheme with minimum mean square error based detector.

Keywords: Wireless Communications & Networks, Signal and Image Processing
Abstract: In this paper, block parallel Gibbs sampling (BPGS) multiple-input multiple-output (MIMO) detection is proposed. In a conventional Gibbs sampling scheme, MIMO detection is carried out sequentially symbol-by-symbol. The proposed scheme divides a symbol vector to blocks and updates candidate transmit symbols in parallel in a block so that the total number of iterations in a unit period increases. Furthermore, maximum ratio combining (MRC) is adopted to BPGS to improve accuracy in this paper. Numerical results obtained through computer simulations show that bit error rate performance under high bit-energy-to-noise-spectrum-density conditions improves with the proposed scheme. It is also shown that the block size of three achieves the best performance when number of antennas is 16times16 and the number of iterations is 50.

Keywords: Power & Energy
Abstract: Agent-based models(ABM)are a kind of micro scale model that imitate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex process. Netlogo is a real time simulation software tool to design this model with the help of programming and coding. This paper identifies decision variables based on electric vehicles (EVs) charging statistics and the heuristic decisions in EVs charging at public charging stations, commercial place and offices are converted into constraints of (ABM). This unique model is the version of real time charging scenario at the charging stations. With the help of programmed model in Netlogo, the behavior of EVs user under different real life scenarios are observed and recorded. The proposed system is implemented and designed in Netlogo to test the results.

Keywords: Power & Energy
Abstract: For an autonomous microgrid operated at constant frequency, the main power quality issue is the voltage profile. In this paper, a fine-tuning of a control coefficient for real power reference generation is proposed. Matlab/Simulink real-time fixed-time-step modelling has been conducted to carry out validation of such proposal. It is found that by taking proper tuning, the terminal voltage at each DG can be kept within voltage limit. Furthermore different conventions for denoting reactive power in inverter have been summarized in this paper.

Keywords: Power & Energy
Abstract: This paper proposes a novel technique for unknown parameter estimation of a detailed solar photovoltaic (PV) cell model based on the artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithm. This combination allows to balance between the exploration and exploitation abilities of each algorithm for achieving a good optimization performance. The detailed solar PV cell model is the double-diode model with seven unknown parameters which are estimated by using the hybrid ABC-PSO algorithm. The numerical results of the parameter estimation using the hybrid ABC-PSO algorithm are compared with those using the PSO, advanced PSO and ABC algorithms showing that the convergence speed and value of the hybrid ABC-PSO algorithm are always better than the PSO, advanced PSO and ABC algorithms.

Keywords: Power & Energy
Abstract: Cooking is an essential activity in the households in Sri Lanka. Sri Lankan households utilize several types of fuel to produce heat for their daily cooking needs. The most commonly used energy sources are Liquid Petroleum Gas (LPG) and Electricity. Therefore, it is essential to investigate the economic aspects of using each of these energy sources. This research aims to use existing data on several cooking appliances along with LPG and Electricity charges in Sri Lanka to calculate the costs of using each of the energy sources. With the total monthly cost of LPG at Rs.560/= and Electricity at Rs.1150/= as of March 2020, this study suggests that LPG is the most suitable energy source for cooking in Sri Lankan households.

Keywords: Power & Energy
Abstract: This paper proposes a high voltage gain flyback DC-DC converter for PV applications. The flyback converter is composed of a voltage-doubler circuitry that is designed to reduce the turn ratio of the flyback converter. This enhance the gain of the converter and hence reduce the voltage and current stresses of the power semiconductor devices. Moreover, a resonant active-clamp circuit is employed in the primary that limits the voltage stress and provides zero voltage switching (ZVS) turn-on of the power switches. The resonant operation of the switches reduces the conduction loss and switching turn-off loss as well. The soft switching operation and low stresses in the switches make the overall efficiency high. The simulation result of the proposed converter has been analysed and the experiment of the prototype is done to verify the result.

Keywords: Power & Energy, Aerospace Technology
Abstract: The Philippines, specifically Agusan, is rich in water resources but lacks the reliable information in terms of its potential. Therefore, this study estimates the theoretical ROR hydro potential of Agusan River Basin which passes from Davao de Oro and throughout Agusan del Norte. A 30-m spatial resolution elevation of SRTM-DEM, Climate Forecast System Reanalysis, Global Land Cover Characteristics Database v 2, and Harmonized World Soil Database v 1.2 have been gathered to make the assessment possible using GIS-based spatial tool and SWAT (Soil and Water Assessment Tool) hydrological model Interface. Simulated head drop and flow rate of 111 delineated stream networks were generated considering the meteorological data of 2001 to 2014. The estimated total theoretical run-of-river hydro potential of the basin is 74.49 MW at annual mean flow. Issues like polluting energy sources and pivoting power outages within the territories and provinces of Mindanao can be aided by this hydropower potential associated with geographic and hydrographic modelling.