Keywords: Robotics, Control Systems & Theory
Abstract: Crude oil is extremely flammable & can cause eye, skin, gastrointestinal, and respiratory irritation. Breathing the fumes from crude oil are known to cause chemical pneumonia, irritation of the nose, throat, & lungs, etc.. In the shipyard, workers are exposed to the fumes of crude oil while doing maintenance on ships. Their safety & health can be at risk while doing their job. This research aims to develop a device that will detect & assess specific types of gases in order to determine the permissible and impermissible time of work for employees' safety precaution. Unmanned ground vehicle is the machine that operates while in contact with the ground and without an on-board human presence. By gathering the data from the sensors who has a specific sensitivity on the substances in crude oil fumes, the data will then convert to a gas concentration in a unit of ppm where distinct values can be analyzed. Using data sets and Gradient Boost Algorithm, the system can train data based on the output of the acquired signal which eventually will be more accurate in determining the permissible exposure limit of a worker to the crude oil fume. Results shows Gradient Boost Algorithm has an average accuracy of 93.75% with a failure rate of 0.0823. The time response of the design is 2.98 seconds. It is recommended that the device used for getting the input gas sensor should be accurate to the gas decomposition of the oil / petroleum you are trying to test.

Keywords: Robotics, Control Systems & Theory
Abstract: In recent years, the need for controlling an unknown plant arises in various fields like aircraft, automotive, and manufacturing industries in order to provide quicker and more accurate response in the presence of parametric uncertainties. Classical adaptive control may not as yet be able to give the desired results when the plant is unknown with time-varying parameters. In such a scenario, it is quite likely that the response of the plant grow unbounded. Moreover, adaptive control leads to better transient response only when the estimates of the plant parameters are reasonably close to that of the actual values. In this paper, we propose a two-step adaptive strategy that helps to quickly converge into that compact region wherein the plant parameters reside. Further, we demonstrate that our proposed method provides partial answers to the adaptive control of linear time-varying plants.

Keywords: Robotics, Control Systems & Theory
Abstract: When cleaning the dirt pile around the conveyor with a vacuum car, there are problems that workers collide with falling iron ore and suffer health problems by breathing in dust. Therefore, it is necessary to develop a robot that performs the cleaning with a vacuum car instead of workers. Many studies on robots that perform cleaning work have discussed about automation and cleaning efficiency such as automatic mapping and coverage path planning. However, there is no discussion about cleaning efficiency and automation of robots that can perform the cleaning with a vacuum car. To perform the cleaning efficiently, it is necessary to avoid cleaning the same place several times and leaving uncleaned areas because it increases the cleaning time. Therefore, in this study, we first formulated the area where the hose sucks the dirt pile. Then, we proposed a method to derive the optimal path that minimizes the cleaning time. Furthermore, we developed a control system that realizes the optimal path. Finally, we conducted a verification experiment assuming an aisle next to the conveyor. In the experiment, it was confirmed that the coincidence rate with the optimal path was about 70% by the suction-port position control system.

Keywords: Robotics, Control Systems & Theory
Abstract: This research proposes a controller that solves the problems of divergence of behaviors during a collision and steady-state deviation during hovering. An attitude controller that sets the target attitude angle to 0 is used to stabilize the behaviour during a collision. And a designed controller that treat a quadrotor with a safety harness as a double pendulum solves steady-state errors during hover by calculating inputs that reduce the energy of the system. We tested the proposed controller by using MATLAB and virtual robot experimentation platform (V-REP). The results of the simulation showed that a quadrotor with a safety harness prevents post-collision divergence and allows for hovering directly below the rod.

Keywords: Robotics, Control Systems & Theory
Abstract: This work presents a new type of Reconfigurable Spherical Robot for confined environment. The robot is a spherical shape with three legs kept inside spherical shell. Each leg has four degrees of freedom. All three legs can be extended for two types of locomotion such as legged locomotion and rolling sphere. The simulation results performed in MATLAB Simulink have shown that the robot can move in a confined environment by rolling its spherical shape and can traverse over obstacles.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Shrimp farming is a major industry covering 23% of Philippine annual aquaculture production, which requires performing better management practices (BMPs) including growth monitoring and feed management. Traditionally, growth is monitored manually using analog weighing scale and caliper; but the manual measurement is a tedious task for large-scale farming. Feed management entails providing the most suitable feed type based on the shrimp痴 current growth stage; furthermore, it addresses issues of underfeeding and overfeeding. The limitations of manual method led to the implementation of computer vision applications for growth measurement. However, existing vision-based measurement studies are not yet applied for feed management. This paper presented a fuzzy-logic based shrimp feed type classification system utilizing Mamdani痴 methodology. The output classes are Starter, Grower, and Finisher based on the three inputs: pixel area, length, and weight. The system was developed using the FIS feature of the MATLAB Fuzzy Logic toolbox. The classification system was evaluated and resulted to 93.33% correct classification accuracy. Based on these results, it can be concluded that fuzzy logic can be utilized to determine the suitable shrimp feed type corresponding to the input features.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: The Aspergillus genus is deemed relevant for distinction and classification in the field of food, agriculture and medicine. As there are harmful and useful ones, it adds to the necessity of correct classification. Categorization of this conidial fungi is usually done through manual microscopical procedures which apparently has a degree of subjectiveness. In order to classify Aspergillus samples faster and more accurately, technology, specifically image processing and machine learning are incorporated in this study. Pre-trained deep learning models are employed in classifying 9 kinds of Aspergillus. The methodology is generally comprised of pre-processing, deep-learning (training) and performance evaluation. Performance evaluation pertains to the validation accuracy and running times of the system after training through visual display of graphs and tabulation of acquired data. This study achieved a 93.3333% testing accuracy proving that the transferred knowledge is accurate, compatible and reliable.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Lettuce is one of the most popular crops for urban farming because it is easy to grow and it has high nutritional value. Moreover, it is adaptable and can be combined with other food options, or it can be eaten alone without too much preparation. Predicting lettuce growth can be crucial to find the optimum maturity and harvest time. This paper proposed to use a model of a hybrid tree-fuzzy logic approach, the classification tree was used to select the most significant features from the texture features then the fuzzy inference system was utilized in predicting the lettuce growth stage classification. The hybrid system produced accurate results with low percentage error and correct classifications. Based on these results, the most accurate prediction can be observed in the head development growth stage; the harvest growth stage has a slight variance, while the vegetative stage has the most variance. Overall, the trained hybrid system is reliable in predicting and identifying lettuce growth stage classification.

Keywords: Signal and Image Processing, Multimedia Engineering, Disasters and Humanitarian Technology
Abstract: This paper presents an evaluation of acoustic feature aggregation and acoustic-linguistic features combination for valence and arousal prediction within a speech. First, acoustic features were aggregated from chunk-based processing for story- based processing. We evaluated mean and maximum aggregation methods for those acoustic features and compared the results with the baseline, which used majority voting aggregation. Second, the extracted acoustic features are combined with linguistic features for predicting valence and arousal categories: low, medium, or high. The unimodal result using acoustic features aggregation showed an improvement over the baseline majority voting on development partition for the same acoustic feature set. The bimodal results (by combining acoustic and linguistic information at the feature level) improved both development and test scores over the official baseline. This combination of acoustic-linguistic information targeted speech-based applications where acoustic and linguistic features can be extracted from the sole speech modality.

Keywords: Signal and Image Processing, Power & Energy
Abstract: This paper discusses a method of open circuit voltage (OCV) estimation system for lithium-ion batteries based on the join method of Kalman filter and RLS method. We set mean filter to improve the stability of RC parameter estimation. As result, the propose method performs stable and accurate estimation in each test pattern.

Keywords: Biomedical Engineering, Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: Femur bone marrow MRI images may provide more information for better characterizing hematologic malignancies and understanding their prognostics compared with blind bone marrow biopsies and aspirates. However, the interpretation of femur bone marrow MRI images is time-consuming and intervariable among different physicians. To develop a computer-aided diagnosis system for hematologic malignancies using femur bone marrow MRI images, we propose a fully automatic method for femur bone segmentation using deep learning. Five classical pretrained networks such as U-Net and Segnet based on a backbone of Resnet50 network are used for femoral bone segmentation. We conducted an experimental study on 200 cases with hematologic malignancies. Through training using 149 patientsT1-enhanced MRI images, we obtained an average Dice coefficient of 0.907 using Segnet based on a validation using 38 patients' T1-enhanced MRI images.

Keywords: Biomedical Engineering, Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: This paper presents a classification method for a Computer-Aided Diagnosis (CAD) system in a colorectal magnified Narrow Band Imaging (NBI) endoscopy. For the classification of a histologic findings, we consider an output result of a lesion endoscopic image from a pre-learned Convolutional Neural Network (CNN) as a feature vector and construct a set of Support Vector Machines (SVMs) by learning a set of the CNN feature vectors. In the video images, each frame has appearance changes such as blur, color shift, reflection of light and so on and it affects classification results. To improve the robustness of CAD system, we construct the SVM learned by multiple image sizes data sets so as to adapt to the noise peculiar to the video image. We confirmed that the proposed method achieves higher robustness, stable, and high classification accuracy in the endoscopic video image. The proposed method also can cope with differences in resolution by old and new endoscopes and perform stably with respect to the input endoscopic video image. We evaluated the proposed method on a customizable embedded DSP core implemented into a FPGA based prototyping system.

Keywords: Biomedical Engineering, Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: Recently, convolutional neural networks (CNNs)-based automated Gleason grading system for prostate cancer has been widely researched. However, these systems still need further improvement to achieve pathologist-level performance. To this end, this paper introduces a novel self-attentive normalization (SAN) which is the first work to employ the attention mechanism for the automated Gleason grading system. Unlike conventional normalization techniques, e.g. batch normalization and instance normalization, which learn a single affine transformation, the proposed method can learn the element-wise affine transformation to focus on more informative regions of the feature map. Since SAN requires a small number of extra learning parameters, it can be integrated into existing automated Gleason grading systems image classification seamlessly with negligible overheads. Extensive quantitative evaluations show that, by applying SAN to various CNN architectures, the diagnostic accuracy can be significantly improved. For instance, we raise VGG-16's diagnostic accuracy from 73.99% to 79.16% on the Harvard Dataverse.

Keywords: Signal and Image Processing, Biomedical Engineering, Machine Learning, Cloud and Data Analytics
Abstract: Automated anomaly detection in panoramic dental x-rays is a crucial step in streamlining post diagnosis treatment. It can reduce clinical time for a patient and also aid in giving them faster access to medical care. In this paper, we propose a hybrid deep learning and machine learning based approach to detect evident dental caries/periapical infection, altered periodontal bone height, and third molar impactions using panoramic dental radiographs. We use a Convolutional Neural Network as a feature extractor for an input image and use a Support Vector Machine to classify the image as either "Normal" or "Anomalous" based on the extracted features. We compare the performance of this model with the performance of a Convolutional Neural Network and a Support Vector Machine for the same classification task. We also compare our best model with other existing models trained to detect carries and periodontal bone loss. The results obtained with the hybrid deep learning and machine learning approach outperformed the existing methods in the literature.

Keywords: Engineering Education, Multimedia Engineering
Abstract: The rise of E-learning systems offers a large number of open online educational videos through portals like YouTube, MOOCs, which result in an enormous volume of data for every inquisitive e-learner. This work proposes a novel approach for recommending educational video clips (VCs) for children's joyful learning. It caters to the need for hard-pressed parents, who are unable to tutor their children efficaciously. As children are unable to choose the recommended VCs on their own, the proposed system offers a precise summary of the recommended VCs for parents to effectively select the learning content for their children. For joyful learning experience, the selected VCs are then style transferred using our deep style transfer network. The extensive evaluation methods demonstrate the effectiveness and practicability of the proposed approach.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing, Multimedia Engineering
Abstract: Music separation aims to extract the signals of individual sources from a given audio mixture. Recent studies explored the use of deep learning algorithms for this problem. Although these algorithms have proven to have good performance, they are inefficient as they need to learn an independent model for each sound source. In this study, we demonstrate a multi-task learning system for music separation, detection, and recovery. The proposed system separates polyphonic music into four sound sources using a single model. It also detects the presence of a source in the given mixture. Lastly, it reconstructs the input mixture to help the network further learn the audio representation. Our novel approach exploits the shared information in each task, thus, improving the separation performance of the system. It was determined that the best configuration for the multi-task learning is to separate the sources first, followed by parallel modules for classification and recovery. Quantitative and qualitative results show that the performance of our system is comparable to baselines for separation and classification.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing, Multimedia Engineering
Abstract: The proliferation of convolutional neural networks (CNN) has resulted in increased interest in the field of visual speech recognition (VSR). However, while VSR for word-level and sentence-level classification has received much of this attention, recognition of visemes has remained relatively unexplored. This paper focuses on the visemic approach for VSR as it can be used to build language-independent models. Our method employs generative adversarial networks (GANs) to create synthetic images that are used for data augmentation. VGG16 is used for classification both before and after augmentation. The results obtained prove that data augmentation using GANs is a viable technique for improving the performance of VSR models. Augmenting the dataset with images generated using the Progressive Growing Generative Adversarial Network (PGGAN) model led to an average increase in test accuracy of 3.695% across speakers. An average increase in test accuracy of 2.59% was achieved by augmenting the dataset using images generated by the conditional Deep Convolutional Generative Adversarial Network (DCGAN) model.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics, Multimedia Engineering
Abstract: Various recent advancements in deep learning models have greatly boosted the performance of semantic pattern recognition using images. Various state estimation of an individual like emotional state and other certain character features or traits can be estimated from the facial images. With this motivation, in this work we are attempting to infer criminal tendency or (crime prediction/detection) from facial images by using the learning capabilities of various deep learning architectures. More precisely two type of deep learning models we have used in this study: standard convolutional neural network(CNN) architecture and pre-trained CNN architectures, namely VGG-16, VGG-19, and InceptionV3. We have done a performance comparative analysis among these models for efficiently capturing criminal traits from a human face. The efficacy of the above deep learning models was evaluated on a public database, National Institute of Standards and Technology (NIST). To avoid any discrepancies, we have only used male images in this work. It was found that VGG CNN models are best performing models, especially in a limited data scenario producing the classification accuracy of 99.5% in identifying criminal faces.

Keywords: Machine Learning, Cloud and Data Analytics, Devices, Materials & Processing, Social Implications of Technology
Abstract: In various information systems, application learning algorithms have to act in a dynamic environment where the acquired data is in data streams. In contrast to static data mining, processing streams introduce an array of computational and algorithmic stipulations. With the continuous input of data in data streams, one would like a mechanism that automatically identifies unusual events in the time series. The topic has been in the limelight as it has huge potential for real-time activities. To show the algorithm's robustness, we have trained the classifier to multiple activities and its success in identifying each activity. The paper explores the possibility of using the One-Class Support Vector Machine (OCSVM) for novelty detection in data streams.

Keywords: Machine Learning, Cloud and Data Analytics, Disasters and Humanitarian Technology, Signal and Image Processing
Abstract: Forest fires have become a serious threat to mankind. Besides providing shelter and protection to a large number of living beings, they have been a major source of food, wood, and a great supply of other products. Since ancient times forests have played an important role in social, economic, and religious activities and have enriched human life in a variety of ways both material and psychological. To protect our nature from these rapidly rising forest fires, we need to be cautious enough of every decision we take which could lead to a disastrous end, once and for all. So for the early detection of forest fires, we propose an image recognition method based on Convolutional Neural Networks (CNN). We have fine-tuned the Resnet50 architecture and added a few convolutional layers with ReLu as the activation functions, and a binary classification output layer which showed a huge impact on the training and test results when compared to the other SOTA methods like VGG16 AND DenseNet121. We achieved a training set accuracy of 92.27% and 89.57% test accuracy with a stochastic gradient descent optimizer and we have avoided the underfit/overfitting on the model with the help of the Stochastic Gradient Descent (SGD) algorithm.

Keywords: Machine Learning, Cloud and Data Analytics, Power & Energy
Abstract: Due to climate change effects, the demand for renewable energy is growing immensely around the world. Photovoltaic (PV) panels are widely popular as a vital source of renewable energy all over the world as well as in Bangladesh. However, besides solar irradiance, the panel output is greatly affected by some of the weather parameters like temperature, humidity, wind, etc. Reliable forecasting of PV panel output is essential for capacity planning in advance to efficiently manage the energy distribution. This paper presents a method to forecast the PV panel output energy using a machine learning model, known as the Prophet Model used for a univariate time series forecasting. For this study, the PV panel generated data are collected from an outdoor experimental set-up throughout the full winter season in Bangladesh. Based on the data, forecasting of one-day-ahead PV panel short circuit current is done, and then the estimation of PV panel output energy is made. The results show the proposed forecasting method to be quite encouraging and reliable one while providing a higher coefficient of determination value with an average 0.9772 for one-day-ahead PV panel output energy forecasting.

Keywords: Machine Learning, Cloud and Data Analytics, Power & Energy
Abstract: In this paper, we propose an internal impedance estimation for lithium-ion battery using LSTM neural network method. Compare to the conventional RLS method, the LSTM performs stable estimation with less variance.

Keywords: Machine Learning, Cloud and Data Analytics, Power & Energy, Marine and Offshore Engineering
Abstract: Offshore fish farms remain a vibrant and important production sector for seafood in the aquaculture industry. Being offshore with no grid-based power supply, the continuity and quality of the power supply to the fish farm is crucial. This paper presents an implementation of an Internet of Things (IoT)-based dc nanogrid in an offshore fish farm. The standalone self-sustainable dc nanogrid primarily consists of photovoltaic panels as the power supply and a battery bank as the energy storage. An added feature of the system is a variety of dc voltages with differentiated sockets to cater to different dc-powered loads and appliances. There are various sensor points in the system to acquire readings of temperature, voltage and current for monitoring of the performance of the system. The system is enhanced with IoT features, which give the user the option to access the local computer remotely to monitor and manage the system or to view real-time data on a cloud platform. Smart features are also added in the system to detect abnormal readings of data and send warning emails to the user.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Dehazing through Dark Channel Prior (DCP), originally developed for land-based images, has translated its potential for improving the quality of underwater images. However, the DCP default parameters, which are just adapted from land-based applications, may not be applicable for underwater images. Such constraint limits the capability of this restoration algorithm to improve the quality of an underwater image; the values of these parameters must be searched for each underwater image. A proposed approach on the parameter values assignment problem is to conduct an optimized search based on Genetic Algorithm. The presentation of this proposed approach focuses on the Genetic Algorithm processes: chromosome encoding, fitness function development, and selection, mutation, and crossover, to perform an effective search of the best solution out of a pool of possible solutions. Qualitative and quantitative evaluations show that utilization of optimized combination of DCP parameters, achieves images of higher quality in comparison to the utilization of established default DCP parameters.

Keywords: Robotics, Control Systems & Theory, Aerospace Technology
Abstract: This paper introduces a model predictive control approach of an unmanned aerial vehicle (UAV) with the aid of a feedback linearization.As is well known,the feedback linearization is one of the effective techniques to cope with the nonlinearity of dynamical systems.Since the UAV is an underactuated nonlinear system, it is impossible to exactly linearize the dynamics of the UAV.Therefore, we take an approach to linearize only the translational motion, and then apply the linear optimal control to it.However, the UAV is easily affected by wind disturbances in an actual environment. A model predictive control is proposed to cope with the disturbances. We apply this approach to a landing control of the UAV to a moving ground vehicle.The effectiveness of the proposed method is verified by numerical simulations.

Keywords: Robotics, Control Systems & Theory, Biomedical Engineering
Abstract: Manipulating mechanical energy intelligently, passive exoskeletons can improve the energy efficiency of cyclic human motions. Aiming to reduce the energy cost of knee moment during stair ascent, this paper presents a concept of brake-torque support activated when the knee moment is required in the opposite direction to the angular velocity of the knee joint. Integrating an electromagnetic brake to a crossing four-bar knee joint with a compact design, the passive knee exoskeleton enables the polycentric knee center of rotation covering the wide range of knee angle during stair ascent. For preliminary validation, the surface electromyography (EMG) of rectus femoris (RF) and biceps femoris (BF) were studied on a healthy male volunteer wearing the exoskeleton on his right leg. The reduction of peak muscle activity is observed as the brake torque is applied during knee extension while the knee flexion moment is required.

Keywords: Robotics, Control Systems & Theory, Circuits and Systems
Abstract: Soil nutrients and season have direct impact on the growth and yield of a crop. Deficiency on the nutrient level of the soil may result to plant disease while applying excessive amount of soil fertilizer on the other hand, may also cause negative results to the development of the crop. Nutrients on the soil also changes as the season changes from wet season to dry season. This study aims to develop a fuzzy logic-based program that will provide an appropriate amount of fertilizer to soil. The parameters such as season, nitrogen, phosphorus and potassium level are the inputs used on the fuzzy logic system. The researchers proposed four kinds of fertilizer to use in this paper such as Complete, Urea, Solophos and Muriate of Potash. Combination and amount of these fertilizers will be based on the input parameters and fuzzy rules. These soil fertilizer recommendations can be used for rice in an inbred light soil.

Keywords: Robotics, Control Systems & Theory, Computer Architecture & Systems, Circuits and Systems
Abstract: Fire incidents often result to associated deaths, injuries, and losses occurring structures and properties, particularly in homes every year. In this study, the researchers proposed a 4-wheeled fire extinguishing robot with the ability to detect human presence in the area even when there is fire. Multiple sensors are utilized in this study to detect nearby flame, smoke, temperature and humidity, and obstacles through integration with Arduino and Raspberry Pi. The proposed robot is remotely controlled by the user over Wi-Fi through the graphical user interface created by the researchers in Python for easy monitoring of data and control. A camera is also mounted to the robot for surveillance purposes. The human detection system of the robot is implemented through using ultra-wide band radar (UWB) by utilizing the X4M300 presence sensor, which could detect human presence based on their respiration movement. Initial testing and four experiments were conducted to test the radar sensor's capabilities compared to the existing methods of human detection. The researchers yielded an accuracy of 97.29% in the testing of human detection system, proving that the implementation of UWB radar sensor is successful.

Keywords: Robotics, Control Systems & Theory, Disasters and Humanitarian Technology
Abstract: This paper shows the composition and evaluation of a suspension to ensure that an EPM-equipped inspection Unmanned Aerial Vehicle (UAV) is adsorbed to the bridge. Because an Electro-Permanent Magnet (EPM) adsorbs by magnetic force, if the contact area is small, or if a gap exists between it and magnetic material as the target, the adsorption force decreases when EPM is magnetized and sticks. As a result, inspection work cannot be performed safely, and there is a risk of crashing during inspection work. Therefore, we developed a suspension that allows the EPM to be horizontally attracted to the EPM even if there is a step on the object to be attracted. The effect of the suspensions was confirmed by comparing the adsorption of EPMs on steps with and without suspensions.

Keywords: Software & Database Systems, Machine Learning, Cloud and Data Analytics
Abstract: With the increasing ubiquity of e-commerce also arises the need for faster and more efficient methods to deliver goods from one place to another. Such systems rely heavily on vehicle routing systems, which couriers usually follow to determine their route for the day. In some countries, vehicle routing systems are managed manually, which either depends on the couriers themselves or on another person called a dispatcher, which is prone to errors and may not be very efficient. Hence, this study proposes a two-phase heuristic algorithm that will serve as the "dispatcher" for couriers to efficiently service all the customers within their area. A pairing of clustering and routing algorithms that solve the travelling salesman problem the best was found. Results show that using k-means as the clustering algorithm and a genetic algorithm with some modifications yield the best route recommendations in terms of total cost and processing time. It also is more efficient than the manual method with a savings of 16.5%

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: This paper presents FITS, or Feature-value / Instance Transposition Selection, a method for unsupervised clustering. FITS is a tractable, explicable clustering method, which leverages the unsupervised feature value selection algorithm known as UFVS in the literature. FITS combines repeated rounds of UFVS with alternating steps of matrix transposition to produce a set of homogenous clusters that describe data well.By repeatedly swapping the role of feature and instance and applying the same selection process to them, FITS leverages UFVS's speed and can perform clustering in our experiments in tens milliseconds for datasets of thousands of features and thousands of instances.We performed feature selection-based clustering on two real-world data sets. One is aimed at topic extraction from Twitter data, and the other is aimed at gaining awareness of energy conservation from time-series power consumption data. This study also proposes a novel method based on iterative feature extraction and transposition. The effectiveness of this method is shown in an application of Twitter data analysis. On the other hand, a more straightforward use of feature selection is adopted in the application of time series power consumption data analysis.

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: Host-based intrusion detection system (HIDS) is a necessary component for network security, especially when more and more data are encrypted which makes network-based intrusion detection system lose its functionality of packet content inspection. After many years of research, it is widely acknowledged that system calls are the preferred data source for HIDS. In a recent paper, a novel semantic analysis approach was proposed and shown to achieve the best performance, as compared with various previous syntactic analysis schemes. The performance difference is profound for modern attacks. However, the semantic analysis approach requires considerable computational complexity. In this paper, we present a deep learning architecture which requires no data pre-processing and is easy to train. Experimental results show that our design has a better performance than the semantic analysis approach.

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: Deep learning has been a successful model which can effectively represent several features of input space and remarkably improve image recognition performance on the deep architectures. In our research, an adaptive structural learning method of Restricted Boltzmann Machine (Adaptive RBM) and Deep Belief Network (Adaptive DBN) have been developed as a deep learning model. The models have a self-organize function which can discover an optimal number of hidden neurons for given input data in a RBM by neuron generation-annihilation algorithm, and can obtain an appropriate number of RBM as hidden layers in the trained DBN. The proposed method was applied to a concrete image benchmark data set SDNET 2018 for crack detection. The dataset contains about 56,000 crack images for three types of concrete structures: bridge decks, walls, and paved roads. The fine-tuning method of the Adaptive DBN can show 99.7%, 99.7%, and 99.4% classification accuracy for test dataset of three types of structures. In this paper, our developed Adaptive DBN was embedded to a tiny PC with GPU for real-time inference on a drone. For fast inference, the fine tuning algorithm also removed some inactivated hidden neurons to make a small model and then the model was able to improve not only classification accuracy but also inference speed simultaneously. The inference speed and running time of portable battery charger were evaluated on three kinds of Nvidia embedded systems.

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: This research presents a new approach to Heart Arrhythmia classification with the help of neural networks. A victim diagnosed with heart arrhythmia has an irregular beat pattern. Heart arrhythmia is harmless in most situations however, beyond a certain limit of irregularity, it causes severe and critical complications. Heart arrhythmia can be detected at the early stages. Doctors can determine symptoms of the patient and prevent further damage. Tests have been done to check the validity of neural networks for this job, and the attempted tool used in this paper is the same. The purpose of this research is to increase the accuracy, precision classifying ability while conserving the generalized pre-processing method. This research attempts to find a correlation-based feature selection. Our proposed algorithm exceeds a range of acceptance, in detecting an array of heart arrhythmias from an electrocardiogram recording used with a single lead wearable monitor. The data set used is the MIT-BIH corpus which contains ECG recordings of unique patients. On this data set, the 5-layered neural network which was trained maps a sequence of ECG samples to a sequence of rhythm classes. We have created a hybrid neural network. Through the stacking of the neural networks, we were able to achieve an accuracy of about ninety-eight percent which was the best-case scenario.

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: In this paper, we predict the stock prices of five companies listed on India痴 National Stock Exchange (NSE) using two models- the Long Short Term Memory (LSTM) model and the Generative Adversarial Network (GAN) model with Long Short-Term Memory (LSTM) as the generator and a simple dense neural network as the discriminant. Both models take the online published historical stock-price data as input and produce the prediction of the closing price for the next trading day. To emulate the thought process of a real trader, our implementation applies the technique of rolling segmentation for the partition of training and testing dataset to examine the effect of different interval partitions on the prediction performance.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: In weakly-supervised temporal action detection, only the category labels of actions could be used for training. Without any temporal boundary annotations in an untrimmed video, it's a challenge to propose the starter and the end of an action (named as the proposal). In this paper, we focus on the temporal differences of visual contents which may change significantly when motion occurs and carry the information of temporal boundaries. First, a motion-propagating feature vector (MPV) is designed to describe different kinds of temporal difference in a video. Second, we propose a temporal difference perceptron (TDP). According to the MPV, TDP computes an attention to each video snippet to infer action areas. However, the TDP only concentrates on the most discriminate action areas and the attentions fail in slow actions. To solve these, an unbalanced action-background self-supervised loss (USSL) is custom to tune the distribution of activated areas. With an unbalanced constraint, all forward change and some backward change action regions are activated. And the self-supervised attention labels force the model to widen the gap between background and action in feature space, contributing to boundary inferring. Our method achieves comparable performance with the state-of-arts, that outperforms STPN, an algorithm without temporal difference message, more than 2% mAP on THUMOS14, and is 20.8%(mAP@tIou=0.5) higher than UntrimmedNet on ActivityNet1.2 that used temporal difference roughly.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: In this paper, we explore the effects various pre-processing techniques have on the performance of a text-independent speaker recognition system. The methods used in these analysis are Spectral Subtraction Method (SSM), Spectral Subtraction Method using Oversubtraction (SSMO), Wiener Filtering and Minimum Mean-Square-Error Short-Time Spectral Amplitude Estimator (MMSE-STSA), followed by a recently proposed voice-activity-detection (VAD) algorithm. For this analysis, we extract the Mel-frequency cepstral coefficients (MFCCs) features to distinguish and verify the identity of various individuals using two different modelling methods, namely, (i) Gaussian Mixture Model (GMM) [using Expectation-Maximization (EM) algorithm] and (ii) Vector Quantization (VQ) [using Linde-Buzo-Gray algorithm], by mapping and classifying the MFCC features. The focus of this work is not to explore the latest or best performance algorithm for a speaker recognition system, but rather a robust and stable, hardware friendly, real-time system, specifically for internet-of-things (IoT) applications. For this purpose, we have manually collected data recorded by different individuals, using different devices, in different environments, from various locations in India. The results indicate that among the eight models trained using pre-processing techniques with 47 users enrolled, six of them gave a recognition accuracy of more than 95%, with the best accuracy of 97.8%.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: This research aims to develop smart defect detection and sortation for corn with the use of image processing. This project helps to speed up manual inspection to improve productivity and reduce the time consumed by farmers in sorting. The defect detection process is done through image processing using an open computer vision library and Python. The corn is manually placed in the roller conveyor, passing under a camera acquiring the real-time image. The sorting process eliminates the damaged corn from those that were in good condition. The system acquires real-time image data from a camera feed to a computer for analyzing purposes. Images were scanned as the corn ear was traveling through the conveyor. A program that effectively analyzes acquired corn images' required features was developed in Python using the Open Computer Vision (OCV) library. A conveyor that comes with a built-in corn sortation mechanism was controlled by Raspberry Pi that directs the corn to its desired group. On the evaluation of the accuracy of the system, a series of trials were conducted. The result of the evaluation yielded a 92% success rate in terms of defect detection and sortation. Revisions were made after the initial testing of the project. Problems and its causes were identified to improve the performance of the whole system.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics, Aerospace Technology
Abstract: The goal of Hyper-Spectral Unmixing (HSU) is to decompose HSC imagery into a group of pure material spectral signatures also known as endmember signatures and fractional proportions weights that characterize the contribution of each endmember in forming a sample. In 1999, Winter痴 proposed an idea to address HSU that considers vertices of a simplex whose volume is maximum as pure pixel vectors. In hyperspectral remote sensing, this belief has much influence on HSU, especially on endmember extraction methods. Moreover, this belief has inspired much attention, resulting in various endmember extraction frameworks such as Simplex Growing Algorithm (SGA), N-point FINDeR (NFINDR), Alternating Volume Maximization (AVMAX), Successive Volume Maximization (SVMAX). In this paper, we propose an ensemble of these frameworks intending to utilize the best part of the result of each framework. The proposed ensemble framework uses a majority voting approach. Our experiments, applied on four hyperspectral datasets (Cuprite, Urban, Samson and Jasper), expose that the ensemble framework by majority voting can provide efficient and competitive performance compared to individual winter's belief-based endmember extraction frameworks.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics, Social Implications of Technology
Abstract: Visual inspection of plant health status and disease severity may yield subjective assessments due to error-prone sphere of colors and textures as affected by angular photosynthetic light source and the complexity of chlorosis. Quantification of damages on leaves due to destructive diseases is paramount for plant and pathogen interactions. To address this challenge, the proposed solution is the integration of computer vision and computational intelligence for tomato Septoria leaf spot necrotic and chlorotic region computational assessment. Dataset contains healthy and diseased tomato leaves that were captured individually. Non-vegetation pixels removal was done using CIELab color space. RGB color components and five Haralick texture features were extracted from the segmented leaf. Hybrid neighborhood component analysis and ReliefF algorithm were employed to select the important predictors resulting to RGB-entropy vector. A new tomato leaf disease index (tomLDI) optimized using artificial bee colony (ABC) was developed by normalizing visible red reflectance, and introducing red-green and red-blue reflectance ratios to enhance Septoria leaf spots pixels and reducing sensitivity to healthy green pixels. KNN bested classification tree, linear discriminant analysis and Na阮e Bayes in detecting Septoria leaf disease with accuracy of 97.46%. Deep transfer image regression was tested using raw infected leaf images and the tomLDI transformed colored channels through MobileNetV2, ResNe

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics, Social Implications of Technology
Abstract: This study presents a technique to generate face age progression by adopting a conditional generative adversarial network based approach. The best model resulting from a five-fold cross validation has an accuracy of 91.93%, False Omission Rate of 0.45% and Negative Prediction Value of 99.55%. Building on prior work, this paper has three contributions. First, the use of uneven age clusters is presented to account for more rapid and drastic ageing in babies and toddlers than older individuals. Second, perceptual losses rather than per-pixel losses are considered to enable identity preservation. Third, a facial recognition system is applied to verify the identity of individuals upon ageing. Identity preservation was achieved and confirmed, with a facial recognition accuracy of 92.4%. Visual fidelity was also confirmed, with 95.2% subjects identifying aging in the conducted survey.

Keywords: Multimedia Engineering, Engineering Education
Abstract: Nowadays, it is convenient to gain new knowledge and skills by learning from the internet. However, some contents may be hard to understand just by reading texts. Photography knowledge is one of those, in which learner may need a large amount of time and cost to practice it. Therefore, this paper provides an alternative way of a game-based learning tool. It simulates a camera into a game combining with a story and some challenges of engagement. It is designed such that a photography knowledge can be delivered to learners through this developed game. To validate this assumption, 25 participants are asked to join our experiment. They are asked to do a pre-test, play our game, and do a post-test. Then, they are asked to answer questionnaires regarding effectiveness and benefits of the proposed game. It is shown that the participants could improve their scores from the pre-test of 44% to the post-test of 89%, regarding the understanding of photography knowledge. Also, the questionnairesresults show that our game could help the participants to gain the knowledge.

Keywords: Multimedia Engineering, Machine Learning, Cloud and Data Analytics
Abstract: In recent years, human action recognition has received a considerable amount of research attention because of it's potential in a variety of applications, such as video surveillance, human-computer interaction, and virtual reality (VR). However, many researches on human action recognition performed in single-camera or double-camera system, which achieve reduced performance due to vulnerability to partial occlusion and missrecognition of back. Some works on human action recognition use multiple cameras but are too complex for practical application. In this paper, we propose a new human action recognition system using triple Kinect sensors for VR application. Particularly, we design a mark detection method to determine the front of user and fusion skeleton data in real time. Features are extracted from three-dimensional (3D) skeleton data sequences, and divided into five parts according to body parts. A classification model based on the part-aware long short-term memory networks is proposed to recognize human motion. Finally, we demonstrate the system with a virtual reality basketball application and the results of experiment validate the feasibility of the proposed system.

Keywords: Multimedia Engineering, Robotics, Control Systems & Theory
Abstract: In this paper, we investigate influences of the network delay on haptic cooperative work in a networked virtual environment by experiment. In the work, two users carry an object cooperatively in a networked maze system by manipulating haptic interface devices. We handle two carrying methods in our experiment. In one method, the two users hold the object. In the other method, one user holds the object and the other user pushed it. We measure the operation time and reaction force, and we compare them between the two methods. Experimental results demonstrate that the former method has smaller average operation time than the latter method, but the average reaction force is larger in the former.

Keywords湧etworked virtual environment, Haptics, Cooperative work, Maze system, Network delay, Experiment

Keywords: Multimedia Engineering, Signal and Image Processing, Circuits and Systems
Abstract: Video processing usually requires one to read in an entire image into a framebuffer, usually taking the form of random access memory (RAM). For kernel-based image and video processing, square-sized kernels are then extracted from this framebuffer, typically in 3ラ3-pixel sizes, though other sizes are also common. To meet the demand for ever higher image resolutions, larger and larger framebuffer RAM memories are required.

While it is not feasible for software to read and process parts of an image quickly and efficiently enough due to the high speed of incoming video, a hardware-based video processing solution poses no such limitations. RAM-based framebuffers can also be found on hardware-based video processing systems, however such designs are not leveraging the full power and potential of processing image kernels with digital hardware. This paper introduces hardware techniques to read and process kernels without the need to store the entire image frame. This reduces the memory requirements significantly without loss of quality to the processed images.

Keywords: Devices, Materials & Processing, Signal and Image Processing
Abstract: Protecting and increasing worldwide green space have been an international effort. Individuals and organizations are encouraged to plant urban trees and to get involved in many reforestation and restoration projects. Offsetting these much needed plans to save the forests is illegal logging. Trees that have grown for many years, some are protected resources inside restricted areas, are felled and the wood is smuggled. Watching for these illegal activities is very difficult and also very dangerous. It is quite impossible for rangers to patrol every entry and exit point of forests that cover thousands of squared kilometers. Applying Internet of Things technology to ecological forestry, we are proposing integrating sound acquisition networks and acoustic signal analyzers to enhance the robustness of an already successful camera-based surveillance solution that is also equipped with a global positioning system tracker. Our listener devices record sounds of the forest and periodically send it to a cloud storage over cellular networks. The device is affordable, the system is small and portable, and the network is flexibly extensible. From the data, acoustic features are extracted and visualized. The Mel-frequency cepstral coefficients of the signals have exhibited promising distinctiveness for detection of illegal chainsaw activities in the wild.

Keywords: Disasters and Humanitarian Technology, Signal and Image Processing, Machine Learning, Cloud and Data Analytics
Abstract: Natural disasters are a global problem that causes widespread losses and damage. A system to provide timely information is required in order to help reduce losses. Flooding is one of the major natural disasters that requires a monitoring and detection system. The traditional flood detection systems use remote sensors such as river water levels and rainfall to provide information to both disaster management professionals and the general public. There is an attempt to use visual information such as CCTV cameras to detect extreme flooding events; however, it requires human experts and consistent attention to monitor any changes. In this paper, we introduce an approach to the automatic river water level detection using deep learning to determine the water level from surveillance cameras. The model achieves 93% accuracy using a single camera location and 83% accuracy using multiple camera locations.

Keywords: Power & Energy, Circuits and Systems
Abstract: An Energy Harvesting (E.H.) has been paid attention for the field of Internet of Things (IoT) in recent years. The wireless sensor as a key component of IoT can be realized battery less operation by using E.H technology. The oscillating circuit with a step-up transformer is used as a voltage booster for E.H. The voltage booster has not been fully analyzed the influences of the circuit parameters yet. In this paper, the influence of JFET parameters such as parasitic capacitance and threshold voltage are analyzed, and the self-oscillating voltage booster is designed including the JFET parameters. As a result, it is clarified that the large additional capacitance connected into gate-source terminal of JFET can be remarkably reduced the influence of input capacitance of JFET. Moreover, the relationship between threshold voltage and amplitude condition are clarified. Finally, the validity of the analysis and design method are experimentally confirmed.

Keywords: Power & Energy, Circuits and Systems
Abstract: A power flow reversal control strategy is proposed for bidirectional power transmission of LCC and FBMMC hybrid HVDC transmission system. The power flow reversal sequence is designed with integrating DC voltage control and active current control firstly. The proposed dual-loop control strategy for FBMMC effectively eases the FBMMC AC-side power and submodule capacitor voltage fluctuations during the power flow reversal process. The inner current control loop of FBMMC model based on the sliding mode variable structure control theory is established to suppress the influence of the reference value mutation on the system. Finally, a simulation model of hybrid HVDC transmission system with 12-pulse LCC and 51-level FBMMC is built in MATLAB/Simulink, and the improvement of the system dynamic performance is demonstrated.

Keywords: Power & Energy, Circuits and Systems
Abstract: For the operational safety and stability of electrical system, effective power flow control in meshed high voltage direct current (HVDC) grids makes sense. In order to realize the objective of controlling several lines simultaneously and independently in a convenient way, interline dc power flow controller (IDCPFC) has been introduced. To cover some shortages of present IDCPFC topologies, an improved triple interline dc power flow controller (TI-DCPFC), capable of controlling power flow of two lines actively, has been proposed in this paper. To explain it thoroughly and explicitly, its operational principle is illustrated combined with the mathematic equations firstly. Then, to validate its capacity of dealing with occasions of bidirectional flows and the dual-freedom control function, the theoretical control strategy and simulation results in a typical working mode are referred to comprehensively

Keywords: Power & Energy, Circuits and Systems
Abstract: Efficiency is a requirement when it comes to utilizing a wireless sensor network (WSN) where hydrokinetic energy harvesting through turbine is involved. Thus, not only WSN needs an efficient supply but also the sensors and the storage unit which are powered up by an energy harvesting module, a turbine DC generator. Turbine DC generator produces a low voltage and low voltage means low power. To produce high efficiency output despite the low power it produces, a DC-DC converter is one of the preliminaries. DC-DC converter regulates its input coming from the turbine DC generator and produces a more stable power supply. However, blocks that the DC-DC converter supplies have different voltage requirement. Therefore, the researchers will develop two DC-DC converter topology which are the Buck Converter and the Boost Converter. On the contrary, turbine DC generator produces varying DC supply to the boost converter inducing noises and reducing the efficiency needed. Therefore, to achieve a highly efficient output the device needs to be low noise. To prevent noise from affecting the efficiency of the device, the researchers will use a technique called Hysteretic Control (HC) of DC-DC converter. This research intended to design a high efficiency Direct Current-to-Direct Current Converter for hydroelectric energy harvester in wireless sensor network. The researchers develop a DC-DC converter with an efficiency of 80% - 95% by reducing the noise by using a switching DC-DC converter.

Keywords: Power & Energy, Circuits and Systems, Computer Architecture & Systems
Abstract: Abstract湧owadays, in energy-efficient design of digital systems, approximate computing (AC) has an increasingly important role. Due to human perceptual limitations, redundancy in input data and so on, there is a huge amount of applications that can tolerate errors. In this paper, an accuracy-configurable approximate floating-point (FP) multiplier is proposed to improve hardware consumption for such applications. Mantissa is divided into a short exactly processed part and a remaining approximately processed part. A new addition and shifting method is applied to the approximate part to replace multiplication to improve hardware performance. Experimental results show the 4-bit exact part configuration of the proposed work ensures the accuracy of 99.17% (MRED is 0.83%) with the reduction 67.65% of area, 16.64% of delay and 75.62% of power. The proposed work also shows good performance in image processing and neural networks.

Keywords: Power & Energy, Circuits and Systems, Signal and Image Processing
Abstract: One of the basic sources of life in earth is water and as the population of humans living in it increases, water consumption and contamination also increases. Thus, the researchers aim to develop a sensor platform that monitors the quality and contamination of water bodies. There are commercially available sensor platforms that has sensor nodes which transmit data it gathered wirelessly to a base station and are battery-driven. However, due to lack of recharging capability of batteries, the life of sensor platforms is shortened, and the replacement of these batteries became a burden. Hence, this research aims to develop and implement an energy management unit of a harvesting system in a sensor platform through a 90nm process technology in order to eliminate the burden of replacing the batteries and extend the sensor platform痴 life by efficiently managing the energy sources given by the environment and the battery.

Keywords: Engineering Education
Abstract: This paper emerged during the process of continuous quality improvement of an introductory course on occupational safety and health. Did the students correctly associate an assessment activity to the right course outcome? The paper compared the evaluation of the students and the facilitator on the different activities used to assess the demonstration of abilities/outcomes attributed with the course. There were two activities per quarter of the semester. Each activity had been scheduled to demonstrate specific outcome indicators by the facilitator and is described in detail. An evaluation questionnaire was provided at the end of the semester to ascertain whether the students assessed the same outcome indicator as what course the set for each activity. The contention was to harmonize what outcomes the students believed they are exhibiting and what outcomes the course had intended for such activity. The gathered data from the evaluation questionnaire were tabulated and then compared with the original intended outcomes by the facilitator. Interpretation of the data was also forwarded and the path for continuous quality improvement was drawn.

Keywords: Engineering Education
Abstract: The KOSEN-KMITL, the first Japanese style KOSEN in Thailand, was established on May 2019 in King Mongkut痴 Institute of Technology Ladkrabang (KMITL). KOSEN-KMITL is an Engineering, Technology and Innovation workforce development project, to support, raise the investment and increase the industrial capacity of the Thai industry region. The establishment of the KOSEN-KMITL , which adopts the high-quality teaching and learning program courses under the guidance of the Japan KOSEN system, will build and develop the skilled human resources in engineering who can apply knowledge and skill, not only to create and develop innovations that help solving problems, but also to make 砺alue addedin the industrial and social sector in Thailand. The KOSEN-KMITL course is different from the typical Engineering program in the Universities, with its 5-year course focusing on the practical engineering along with the theory and building knowledge based in several areas such as social sciences and economics that will students to understand human, public mind, social responsibility and the ability to communicate in both English and Japanese. In the first year in KOSEN-KMITL, students will learn 的ntroduction to Engineering Approach 的ntroduction to Engineering Design and 鏑ab workas an early Engineering introductory education stage. These are the main subjects to learn the concept and methodology of Engineering Approach, Engineering Design, and various measurement technique and theoretical

Keywords: Engineering Education, Engineering Management, Devices, Materials & Processing
Abstract: The modern world is concerned about the coal reserves, hiking prices of petroleum products, air pollution, and global warming. These all create a great demand for Electric Vehicles (EV). The government authorities have already initiated several projects which targeted on sales of millions of EVs by 2025. Electric three wheelers (E-rickshaw) have a high demand in a country like India since it痴 a common mode of transportation. Different types of AC and DC motors are used in EV, among them Permanent Magnet Synchronous Motor (PMSM) posses high efficiency due to its high power density, small size, great torque inertia ratio, better overload capacity, high output torque, and fewer losses. The torque, efficiency and power density of PMSM is dependent upon its rotor configuration. The paper discusses detailed design of PMSM with different rotor configurations, especially concentrating on the design and calculation of air gap length and permanent magnet dimensions. The designed values are further used for the virtual development of PMSM on ANSYS MAXWELL platform, considering seven different rotor topologies like surface- mounted, de-centred PM, spoke type, interior PM, U-shaped, V-shaped, modified V-shaped. A detailed comparative study based on performance characteristics like weight, cogging torque, power, efficiency, air gap flux density, magnetic flux density, etc. obtained from the Maxwell platform was done. The paper concludes by the selection of modified Vshaped rotor topology.

Keywords: Engineering Management
Abstract: Six Sigma is an engineering systematic methodology modeled within the DMAIC course (Define, Measure, Analyze, Improve, and Control). It is used for improving high-risk areas that can affect the profitability of the organization. This paper aims to prove the flexibility of Six Sigma by increasing the efficiency rate of the internal audit team in an existing real-estate company by targeting standard man-days or further reducing it to less than the set timeline. The efficiency rate of the audit team is 41.67% with average man-days of 26.9. Timeliness of audit report has been an integral contributor of on-time resolution of non-conformities and appropriate mitigation of high risks incurred on those findings. Through cause-effect analysis guided by the Affinity method, probable causes were listed down, and for the perceived causes with no measurable data further investigation and walkthrough was done using GEMBA walk. Data were collected and analyzed to validate these causes. Hypothesis tests and control charts like ANOVA test, Ishikawa Diagram, and Boxplot were used through the use of Engineering simulation programs, to analyze the gathered data and results revealed the root causes of the problem such as manual extraction of audit evidence, difficulty searching and retrieving records, a variation on auditorsperformance, and unawareness of risk management procedures among auditees. Upon implementation of improvement solutions, the average man-days reduced from 26.9 days to 23.8

Keywords: Engineering Management
Abstract: Abstract Butuan City is a highly urbanized city and the regional center of the Caraga Region. However, improper land use is a significant problem that leads to the promulgation and implementation of the Comprehensive Land Use Plan in 2002 to ensure sustainable urban development and proper utilization of land resources. This study aimed to map the urban built-up of Butuan City for the years 2003, 2011, and 2019 using conventional machine learning classification techniques, and to quantify its conformity to the CLUP. The researcher used Landsat Imagery to generate the land cover of Butuan City. In this study, the Support Vector Machine (SVM) Classification overall accuracy is 95.62%, 95.64%, and 93.60% for 2003, 2011, and 2019, respectively. The urban built-up conformity is 88.68%, 83.42% and 78.08% for 2003, 2011 and 2019, respectively. This study concludes that the remote sensing technique can help monitor urban development, and the results can help establish strategies to promote sustainable development.

Keywords: Engineering Management, Biomedical Engineering
Abstract: Plastic revolutionized the industry during its invention. It helps us not only to have strong and flexible tools but also to have cheaper materials for almost everything we make. Despite of its usefulness, it also has diverse effects in our ecosystem. Hazardous chemicals leaking from landfilled plastics runs through rivers and lakes which are primary sources of water, aquatic animal suspect plastics as foods which make them feel full all the time without nutrients coming from their body and causes them to die in hunger and the birth of 溺icroplasticpollution in the environment. Those are some of negative effects of plastics in our environment which is critical for our future generations. The researchers introduces a biodegradable packaging bag that focuses on creating a product to be used as alternative to plastics and solve the negative effects of plastics that affect environment through the use of safe and bio-degradable materials such as natural starch from cassava, glycerol, and many more. By mixing the right proportions of the main materials and perfect amount of heat, the researchers achieved the best features of the bio-degradable polymer that will be discussed later in this paper.

Keywords: Robotics, Control Systems & Theory, Machine Learning, Cloud and Data Analytics
Abstract: Irrigation is essential for growing crops and leads to gradual growth in the economy. This research proposal aims to resolve the issue of scarcity and proper water management in the tank system through the Fuzzy Irrigation System. Fuzzy logic improves the irrigation system that includes three input parameters, such as soil moisture, soil temperature, and the water level. The combinations of these parameters will produce the time duration to have an efficient flow of water to the crop fields. Likewise, the Rain Detection Model (RDM) and the Fertilizer Control Model (FCM) are other features that support, strengthen, and innovate the system. The pilot test conducted by the researcher through MATLAB simulations were performed to check the effectiveness of the proposed system before its actual implementation.

Keywords: Robotics, Control Systems & Theory, Machine Learning, Cloud and Data Analytics, Circuits and Systems
Abstract: The tuning process for a magnetic levitation to control the object's gap from the electromagnet is laborious and demands ample effort to obtain an adaptive PID compensator. Hence, this study has schemed an unexplored adaptive feedforward compensator for a 1-DOF maglev system using equation search based on a symbolic regression through an evolutionary algorithm. Results have shown an exceptional accuracy with an r2 of 0.9997, almost zero root mean square error (RMSE) and mean absolute error (MAE). The approach has paved the way for an adaptive nonlinear system requiring a highly accurate model with a baseline dataset containing few modifiable parameters.

Keywords: Robotics, Control Systems & Theory, Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Greenhouses plays a major role in today痴 agriculture since farmers can grow plants under controlled climatic conditions and can optimize production. The greenhouses are usually built in areas where the climatic conditions for the growth of plants are not optimal so requires some artificial setups to bring about productivity. Automating process of a greenhouse requires monitoring and controlling of the climatic parameters.This paper is an attempt to minimize the cost of maintaining greenhouse environments using new technologies. The end goal of this research an automated system to optimally monitor and control the environmental factors inside greenhouse by monitoring temperature, soil moisture, humidity and pH through a cloud connected mobile robot which can detect unhealthy plants using image processing and machine learning. The mobile robot navigates through a predefined map of greenhouse. Database server has created to store gathered real-time data. And the necessary accurate data represent by using proper application for analyzing.

Keywords: Robotics, Control Systems & Theory, Wireless Communications & Networks
Abstract: In order to navigate Unmanned Aerial Vehicles (UAVs) in GNSS-denied environments localized indoor positioning systems is essential. One of the most common methods for such positioning systems uses high-bandwidth radio frequency called Ultra-wideband (UWB). The UWB radio has several characteristics suited for indoor communication such as low susceptibility to multipath and high immunity against wireless network interference. Although UWB-based positioning is widely used, most of the existing solutions require accurate coordinates of UWB nodes to be known in advance. This put limitation on deployment of positioning systems. In this study, we designed a method to automatically calibrate UWB nodes positions in order to make deployment of indoor positioning systems easier. The proposed method for auto-calibration are tested in real indoor environments to validate effectiveness of the strategy. The effect of node arrangement on accuracy of position estimation was examined as well. As the result, we could improve performance of auto-calibration by changing geometry of UWB nodes.

Keywords: Power & Energy, Machine Learning, Cloud and Data Analytics
Abstract: Deployment of reconfigurable hardware or field programmable gate arrays (FPGAs) in cloud platforms is the modern trend. Practical scenarios include Amazon's EC2 F1 cloud services, Microsoft's Project Catapult and many others. Efficient task scheduling algorithms exist that can ensure green computing, i.e. order the operation of user tasks in the available FPGAs in such a manner that the power dissipated is optimum. But recent literature has exhibited eradication of the hardware root of trust, which is not taken into account by the existing task scheduling algorithms that can facilitate green computing. In this work, we analyze how vulnerability in hardware like hardware trojan horses (HTH) can increment power dissipation suddenly at runtime, without affecting the basic security primitives like integrity, confidentiality or availability of the system. Thus, are difficult to detect but may hamper the system due to unnecessary high power dissipation. We also develop a suitable runtime task scheduling algorithm which schedules the tasks at runtime based on the dynamic status of the resources, such that the power dissipation incurred at runtime is optimum. Finally, we also propose a mechanism via which we can detect affected cloud resources based on the runtime operations. We validate our proposed methodology via simulation based experiments.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: In most developing countries, a lag in maintaining the existing road infrastructure often leads to development of potholes, cracks, etc. Such irregularities in maintenance are one of the main causes of road accidents as well as wear and tear of vehicles. This necessitates development of automated techniques for detecting and segmenting the potholes on roads. In this paper, a solution is proposed to detect and segment dry and wet potholes (potholes filled with water) using smartphone sensors and camera images. For the first set of experiments using smartphone sensor records, various machine learning techniques (RF, XGBoost and ANN) with balanced and imbalanced classifiers were used to detect the potholes. A new pothole detection approach with the sensor data is proposed using GMM (Gaussian mixture model) for a two-class (pothole and normal) hypothesis testing problem that rendered 70% accuracy with precision and recall values ranging from 50% to 60%. For the second set of experiments on camera images and videos captured from a moving vehicle, popular semantic segmentation techniques like Mask-RCNN and U-Net algorithms were applied. The results showed that Mask RCNN rendered an overall accuracy of 80% with precision, recall and F1 score of 0.85. The proposed framework is not only a cost effective mechanism for detecting potholes on existing roads, its usage can also be extended for real time pothole detection in autonomous vehicles.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Executive function and Academic stress is a psychological factor that affects the mental process of human beings such as their cognitive-behavioral control: the identification and effective monitoring of activities that promote the achievement of chosen objectives. Left unattended, it impairs someone痴 ability to control their behavior and negatively affects their mental development. The developed system makes use of a wireless body sensor network that obtains ECG signals from the student in real-time. Which the system condition are analysed or evaluated. The testing results which are demonstrated tested that from the CNN and ECG Analysis, 61.11% accuracy of emotion recognition was still achieved. Games were used to determine the WM, Flexibility, and Self-Control levels and were similar to the neuropsychological test results.

Keywords: Signal and Image Processing, Multimedia Engineering
Abstract: Histogram equalization (HE) is the one of the simplest and most effective methods for contrast enhancement. It can automatically define the gray-level transformation function based on the distribution of gray-level included in the image. However, HE fails to produce satisfactory results for broad range of low-contrast images because the HE does not use a spatial feature included in the input image. The differential gray-level histogram (DH) which is contained edge information of the input image, were defined and the differential gray-level histogram equalization (DHE) has been proposed. The DHE shows better enhancement results compared to the HE result for many kinds of images. In this paper, we propose a generalized histogram equalization (GHE) method including the HE and the DHE. In GHE, the histograms is created using power of gradient including a special feature. In the HE, the mean brightness of the enhancement image cannot be controlled. On the other hand, the GHE can control the mean brightness of the enhancement image by changing the power, thus, the mean brightness of the input image is perfectly preserved.

Keywords: Social Implications of Technology, Signal and Image Processing, Biomedical Engineering
Abstract: This paper presents a non-invasive method to detect Anemia (a low level of Hemoglobin) easily. The Hemoglobin concentration in human blood is an important substance to health condition determination. With the results which are obtained from Hemoglobin test, a condition which is called as Anemia can be revealed. Traditionally the Hemoglobin test is done using blood samples which are taken using needles. The non-invasive Hemoglobin measurement system, discussed in this paper, describes a better idea about the hemoglobin concentration in the human blood. The images of the finger- tip of the different hemoglobin level patients which are taken using a camera is used to develop the neural network-based algorithm. The pre-mentioned algorithm is used in the developed non-invasive device to display the Hemoglobin level. Before doing the above procedure, an account is created in the mobile app and a questionnaire is given to answer by the patient. Finally, both the results which are obtained from the mobile app and the device are run through a machine learning algorithm to get the final output. According to the result patient would be able to detect anemia at an early stage.

Keywords: Wireless Communications & Networks, Multimedia Engineering, Robotics, Control Systems & Theory
Abstract: This paper proposes a control method for the transmission rate of haptic media to avoid network congestion in audiovisual and haptic interactive communications using TCP. The previous study has proposed a media-adaptive buffering control for QoE enhancement of audiovisual and haptic interactive communications over UDP. Most of the communications on the Internet use TCP. When we communicate audiovisual and haptic using TCP because of some restrictions, communication delay occurs by such as retransmission control, and media output quality degrades. Then, we control the media transmission rate according to network conditions and enhance QoE. We employ an application-level QoS parameter in the judgment of rate control of haptic media. We show that the interactive communication of audiovisual and haptic using TCP is feasible as using UDP through a subjective experiment.