Keywords: Circuits and Systems, Engineering Education, Power & Energy
Abstract: This research studies the effect of a starving resistor in various pre-driver schemes for shoot-through loss reduction in the buffer of an integrated DC-DC converter and explores how the efficiency is affected. The starving resistor (Bi-directional delay element) reduces the short circuit current of an inverter by developing time skewed gate driving signals for the driven stage NMOS and PMOS inside a buffer. The starving resistor scheme enhances the efficiency if it is inside the buffer of a switch, but is not as efficient if it is inside the buffer of an active diode. The efficiency of the buffer can be further enhanced by adding delay generator schemes within a buffer. Results are validated via extensive SPICE simulations

Keywords: Circuits and Systems, Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: A combined approach of low-complexity light field depth filtering and deep learning is proposed for object classification in the presence of partial occlusions. The proposed approach exploits depth information embedded in multi-perspective four-dimensional (4-D) light fields via low-complexity 4-D sparse depth filtering and deep-learning. The proposed 4-D depth filter, designed using numerical optimization techniques by formulating as a minimization problem, is shown to outperform typical light field refocusing based on 4-D shift-sum averaging filters. Experiments conducted using a light field dataset acquired by a Lytro camera verify 45% and 27% better performance in terms of object classification accuracy compared to the cases when no depth filtering is employed and standard shift-sum refocusing is employed, respectively.

Keywords: Circuits and Systems, Robotics, Control Systems & Theory
Abstract: This paper shows the design, construction, and implementation of an Automated Aquaponics system. The majority of the commercial aquaponics system in the Philippines is grid-dependent thus making it cost-ineffective. The design of the system has three-part; hydroponics, aquaculture, and microcontrollers. The design consists of a hybrid power supply that allows operation dependent on the grid or PV installations. Aside from integrating it to be partially dependent on renewable energy, the system was designed to adapt and adjust the monitored environment to be more habitable for the aquaculture. Through the use of relays, dump energy is also observed. Through the real-time monitoring system, the design allows users to record and system’s condition, power, energy consumption, and power supply dependency simultaneously.

Keywords: Circuits and Systems, Signal and Image Processing
Abstract: This paper presents an Arduino-based monitoring system that measures four physicochemical parameters of water: pH, temperature, turbidity, and electrical conductivity to identify possible water contamination. The system is designed with two nodes: the sensor node and the sink node. The sensor node performs the collection, pre-processing of data, relay of sensed data wirelessly, data storage systems, data display in an LCD and ThingSpeak channel, and SMS notifications. While the sink node performs data reception from the sensor node, data display in an LCD, and alert systems utilizing a buzzer whenever water is determined to be unsafe for drinking.

Keywords: Circuits and Systems, Signal and Image Processing, Wireless Communications & Networks
Abstract: This paper presents a framework for modelling Serdes system, SymbaPy, based on Python scripting language along with efficient numerical computation libraries. This paper discusses the building blocks of Serdes to create a framework in Python for system models. The paper also discusses the features of Python language which makes it an adequate tool for modelling the Serdes. We also demonstrate application of this framework by creating a model for MIPI DPHY and CPHY which generates the eye-diagrams at different points in the communication chain.

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: Several papers and articles regarding the measurement of UX (user experience) as the satisfaction have been published. However, in the most approaches, UX was measured by questionnaire or survey collection method, which may lead to bias and a lack of exact feeling data of the target users. On the other hand, soft biometric data such as gender, age and facial expression can be used as the essential data for the user satisfaction analysis. In this research, we assume that the facial expression is essential in physical expressions and can be used as the accurate satisfaction data. It may be possible to capture the user’s facial expression during the particular use of products or services without users’ consciousness. However, in general cases, it is difficult to get the final user satisfaction. This study aimed to propose a framework to classify the final user satisfaction of products or services by the facial expression recognition and machine learning. The proposed framework consists of the three main steps. First, the data of facial expression, gender, age and the final user satisfaction are experimentally collected. Second, classification models are built by machine learning algorithms using the data. Finally, the model evaluation is employed to verify the accuracy of the model. After making the classification model, it is possible to classify the final user satisfaction only from the data of facial expression, gender and age.

Keywords: Signal and Image Processing
Abstract: The aim of this study is to authenticate individuals using an electroencephalogram (EEG) evoked by a stimulus. EEGs are highly confidential and enable continuous authentication during the use of or access to the given information or service. However, perceivable stimulation distracts the users from the activity they are carrying out while using the service. Therefore, ultrasound stimuli were chosen for EEG evocation. In our previous study, an Equal Error Rate (EER) of 0 % was achieved; however, there were some features which had not been evaluated. In this paper, we introduce a new type of feature, namely fractal dimension, as a nonlinear feature, and evaluate its verification performance on its own and in combination with other conventional features. As a result, an EER of 0 % was achieved when using five features and 14 electrodes, which accounted for 70 support vector machine (SVM) models. However, the construction of the 70 SVM models required extensive calculations. Thus, we reduced the number of SVM models to 24 while maintaining an EER = 0 %.

Keywords: Signal and Image Processing
Abstract: We have proposed to authenticate individuals using evoked electroencephalogram (EEG) by invisible visual stimulation. In the previous study, we introduced a wavelet transform, which is a time-frequency analysis method, and extracted the features including time information to discriminate individuals more accurately. By using the Euclidean distance matching, Equal Error Rate (EER) was 9.4%. In this paper, to further improve the verification performance, we introduce machine learning. EER of 8.1% is achieved when neural networks trained by ensemble learning using 30 networks.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Mental stress is a universal condition experienced by all humans alike at least once in their lifespan. Stress can vary from person to person depending on their age, gender, socioeconomic background and lifestyle. Although some amount of stress act as a beneficial factor, accumulated stress levels over a long period could lead to many other health problems. Hence, early detection and diagnosis is the pre-eminent method in which this damaging phenomenon can be managed. Vocal indices and facial expressions of an individual disclose surfeit amounts of information including emotions, and in turn stress. In this research two noninvasive and dynamic mechanisms, in the form of speech emotion analysis and facial expression analysis, are used in detecting stress, through emotion analysis, of an individual in a mobile and real-life environment as opposed to utilizing only one mechanism to detect stress in a controlled environment. This study proposes a holistic approach in detecting mental stress, through the categorization and identification of fear/anxiety, sadness, anger and disgust as stress emotions via extracted vocal and facial features. A finalized product is proposed to recognize stress, averaged biased on the prediction probabilities of the two detection mechanisms which then can be used to individually and independently monitor stress in order to maintain it without relying on physical medical checkups.

Keywords: Disasters and Humanitarian Technology, Biomedical Engineering, Machine Learning, Cloud and Data Analytics
Abstract: Radiology examination of chest radiography or chest X-ray (CXR), is currently performed manually by radiologists. With the onset of the COVID-19 pandemic, there is now a need to automate this process which is currently one of the key methods of primary detection of the SARS-Cov-2 virus. This will lead to shorter diagnosis time and less human error. In this study, we try to perform three-class image classification on a dataset of chest X-rays of confirmed COVID-19 patients(408 images), confirmed pneumonia patients(4273 images), and chest X-rays of healthy people(1590 images). In total the dataset consists of 6271 people. We aim to use a Convolutional Neural Network(CNN) and transfer learning to perform this image classification task. Our model is based on a pre-trained InceptionV3 network with weights trained on the ImageNet dataset. We fine-tune the layers of the Inception network to train it to our specific task. We try fine-tuning the network to different extents by freezing a different number of layers and then comparing accuracy for each variation of the network. To evaluate the performance of our network we use several metrics which include Classification accuracy, Precision, Sensitivity, and Specificity. Our proposed method achieves an accuracy of 96.33% on a 3-class classification task (Normal, COVID-19, Pneumonia) and an accuracy of 99.39% on a 2-class (COVID and Non-COVID) classification task.

Keywords: Disasters and Humanitarian Technology, Biomedical Engineering, Machine Learning, Cloud and Data Analytics
Abstract: Since the onset of COVID-19, radiographic image analysis coupled with artificial intelligence (AI) has become popular due to insufficient RT-PCR test kits. In this paper, an automated AI-assisted COVID-19 diagnosis scheme is proposed utilizing the ensembling approach of multiple convolutional neural networks (CNNs). Two different strategies have been carried out for ensembling: A feature level fusion-based ensembling method and a decision level ensembling method. Several traditional CNN architectures are tested and finally in the ensembling operation, MobileNet, InceptionV3, DenseNet201, DenseNet121 and Xception are used. To handle the computational complexity of multiple networks, transfer learning strategy is incorporated through ImageNet pre-trained weight initialization. For feature-level ensembling scheme, global averages of the convolutional feature maps generated from multiple networks are aggregated and undergo through fully connected layers for combined optimization. Additionally, for decision level ensembling scheme, final prediction generated from multiple networks are converged into a single prediction by utilizing the maximum voting criterion. Both strategies perform better than any individual network. Outstanding performances have been achieved through extensive experimentation on a public database with 96% accuracy on 3-class (COVID-19/normal/pneumonia) diagnosis and 89.21% on 4- class (COVID-19/normal/viral pneumonia/bacterial pneumonia) diagnosis.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Mapping tree species is essential for monitoring, planning, and better managing industrial tree plantations (ITP). Due to the intensive procedure of field sampling and multi-class manual training data collection for image classification, an approach that allows fewer data would be efficient. This study evaluated the performance of a one-class classifier called Maximum Entropy (MaxEnt) for mapping Falcata (Paraserianthes falcataria) in Sentinel-2 imagery. Two MaxEnt parameters were tested, namely sample size and binary threshold. Using a default threshold of 0.5, MaxEnt can provide classification accuracies ranging from 89.41-92.84% using sample sizes as small as 30 and as high as 500. A 0.3 binary threshold applied to MaxEnt logistic output with 500 samples were the best parameter values for classifying Falcata using Sentinel-2 imagery.

Keywords: Disasters and Humanitarian Technology, Software & Database Systems, Machine Learning, Cloud and Data Analytics
Abstract: River overflowing is a common problem in the Philippines. And knowing the fact that we lack proper devices and systems to monitor the water flow makes it even more disturbing. Therefore, this project is to develop a prototype that will measure, monitor, and forecast the water level and the volume of water flowing through the riverbanks. The proponents used Agile development method includes systematic process of designing, developing the prototype, evaluating instructional programs, and system that must meet the criteria of the effectiveness of the project. The prototype consists of algorithms that is performed to have a reliable data. In measuring the river parameters, Wireless Sensor Network (WSN) Algorithm is used to interconnect the sensor nodes in different area of the riverbank. In forecasting, the device performed a machine learning by gathering raw data to run in Voting Algorithm. In monitoring, the prototype provides Cabuyao River Monitoring System (CRMS) a web-based application that uses a Decision Tree Algorithm that will give information and warning to the community. This device and system aim to give early warnings to the communities being both in a timely and systematic way, providing advanced information on its behavior to better prepare the disaster team and government.

Keywords: Software & Database Systems
Abstract: The Taguibo River Watershed Forest Reserve TRWFR is its only major source of water in Butuan City. The City of Butuan has experienced rapid growth and becomes more progressive and urbanized community which leads to the increase of water demand, thus, intermittent water supply was experienced by the consumers, and the need to look for alternative water source is inevitable. This study is focused on the identification of possible alternative water sources for the city using remote sensing and GIS-based approaches. Specifically, this study aims to locate and delineate candidate watersheds within the proximity of the city using GIS-based approaches, conduct hydrological modeling in each candidate to determine physical characteristics and the quantity of water produced, determine the water quality of the candidates, and to assess the number of people in the city each candidate can supply. After the HEC-GeoHMS pre-processing, five potential watersheds other than Taguibo watershed that is within the proximity of Butuan City were identified. Considering the different variables such as watershed area, river slope, soil type, curve number, and water quantity, results showed that the most suitable alternative watershed was Simbalan watershed.

Keywords: Software & Database Systems, Aerospace Technology, Engineering Education
Abstract: With the rise in popularity of small satellites, there has been an increasing demand for a software tool that covers different stages of satellite development. In this paper, we extend a small satellite simulation software originally developed for earth-observation satellites Diwata-1 and Diwata-2 to support other satellite missions. This support covers various stages: from ideation, development, and up to post-launch assessment. This paper focuses on the Mission, Attitude, and Telemetry Analysis (MATA) software, which can simulate orbit, attitude, and camera views from planned earth-observation missions. Satellite engineers can also use MATA in a hardware-in-the-loop configuration, serving as one of the last functionality checks before launching the satellite. MATA can also read telemetry files from an orbiting satellite and re-project it in a virtual environment for a more intuitive assessment. This paper also covers the implemented framework for the simulator. This framework would help future developers to extend the simulator to other applications like star tracking simulations, mixed reality satellite training, and space educational software.

Keywords: Disasters and Humanitarian Technology, Engineering Management
Abstract: Abstract— Remote Sensing has been used nowadays for environmental monitoring as it offers a faster and less expensive way of monitoring the environment. With various activities conducted around the Tubay catchment (e.g., mining, agriculture), monitoring the quality of its soil by determining the heavy metal concentration (HMC) in soil, mainly its Lead (Pb) content, became the main objective of this study. Remote sensing technologies, together with field data, are used in this study to create a model that would predict the lead content of the soil in Tubay catchment through statistical analysis. The model created in this study is used in an ArcGIS software. It resulted to a model-predicted value of -263.993 ppm of Lead in minimum, and a model-predicted value of 308.482 ppm of Lead in maximum. Due to the soil test result, which yields a majority of Not Detected values, the model created in this study is not validated.

Keywords: Disasters and Humanitarian Technology, Engineering Management
Abstract: Drought is characterized by a deficiency or lack of rain in a specific or extended period of time resulting to water shortage affecting animals, plants, and people. In the occurrence of this phenomenon, agriculture is the most affected industry. Agriculture plays a significant role in the Philippines, considering that 32% of the country's total area is agricultural lands; of these, 44% are permanent croplands. In the absence of precipitation, irrigation systems are constructed to supply water in agricultural areas. In Butuan City, the agriculture industry is an essential contributor to the city's economy. Drought occurred despite the presence of a vast network of irrigation systems. There is a necessity to quantify the effectiveness of these irrigation systems. In this study, irrigation systems were evaluated using an integrated approach of geographic information system (GIS), remote sensing data (RS), and field surveys at the farm level in Butuan City. The hydraulic model simulation provides a map on the extent of water delivered by the irrigation canal, including the discharge of water that was delivered. Such maps were used to evaluate the effectiveness of the irrigation system.

Keywords: Wireless Communications & Networks, Signal and Image Processing, Aerospace Technology
Abstract: In this paper, the author investigates the positioning outage probability performance of navigation satellite link. For the analysis, the author considers the satellite downlink wherein the user has a mobile navigation receiver. The author presents an insightful analysis which comprises of analytical results for positioning outage probability (POP). Specifically, the author derives tractable expressions for POP as functions of link parameters in various fading scenarios, namely, small scale Rayleigh fading, Rician fading, Shadow fading, and combined Rician and shadow fading. To numerically evaluate the POP performance of the derived analytical results, the author plots the POP for various simulation parameters. From the numerical plots, the author observes that the POP improves as the average signal power to interference plus noise power ratio (SINR) and bandwidth. However, this dependence on model parameters is not the same for all the fading scenarios. Finally, the author suggests multi-antenna receive diversity and combining technique to improve POP performance.

Keywords: Wireless Communications & Networks, Signal and Image Processing, Power & Energy
Abstract: For the design of energy-efficient or green communication systems and networks, power adaptation has been studied extensively in the literature. A vital aspect of the same is to minimize the usage of resources wherever possible. This research implements a Source Transmit Power Adaptation Policy (STPAP) for a two hop collaborative wireless communication system. Our objective is to optimize end-to-end fading averaged-energy efficiency (FA-EE) when the source is subject to an average source power constraint. Specifically, we derive an analytical expression for FA-EE in terms of source power, which is adapted as a function of its local channel state information (CSI). We compare optimal FA-EE results with CSI-independent, fixed source power FA-EE and verify through Monte-Carlo (MC) simulations and analytical results. We find that the proposed STPAP delivers superior performance in terms of FA-EE. We further extend our benchmarking to a scenario where a direct link between source and destination is absent. We also compare optimal FA-EE with fixed power FA-EE and quantify the performance gains achieved by the proposed policy.

Keywords: Disasters and Humanitarian Technology, Software & Database Systems, Wireless Communications & Networks
Abstract: The design, development and results from the deployment of a hand hygiene monitoring and reminder system is presented. The system is based on a dispenser equipped with low-energy Bluetooth technology which when coupled with a mobile application provides the basic location-awareness necessary to track the proximity of health-care worker to the dispenser. The system architecture provides the unique feature of selectively enabling the dispenser action in the smart dispenser only when the intended user is in its proximity. This feature is critical for deployment of hand sanitizer dispensers in hospital wards where there is apprehension in patients ingesting the sanitizer persists. The mobile application maintains an online database of the hand hygiene event records. The records are presented to administrators through a web-based dashboard for assessment and improvement of organizational hand-hygiene compliance levels. Preliminary quantitative results as well as qualitative results are presented and demonstrate the effectiveness of the system to serve as form of motivation for health care workers to improve their hand hygiene compliance.

Keywords: Wireless Communications & Networks
Abstract: The performance of the quasi-cyclic low-density parity-check (QC-LDPC) coded partial-access interleave division multiple access (IDMA) systems is evaluated with the SNR (signal-to noise ratio) evolution algorithm. The partial access IDMA system is the IDMA system in which the 0s, i.e., nonenergy transmission, are inserted into the chip sequence. The SNR evolution algorithm is developed and employed to evaluate the systems. Numerical and simulation results show that the partial access has better BER (bit error rate) performance than that of the conventional full access in a range of low Eb/N0, and the proposed IDMA system with the 3GPP NR QC-LDPC codes has a good error-floor performance.

Keywords: Social Implications of Technology
Abstract: Airline industry is a major global leader in aviation. It even provides service to almost every other sector, however, it does not receive significant attention, despite its importance. Moreover, its service quality is an aggregate of different interactions between the customers and airline companies. This drove them to implement new business model to conform to the new competitive atmosphere in the industry whose operation is taking place in a completely globalized environment. Thus, the use of chatbots is one of the avenues to meet this end. However, there is a dearth of standardized and validated instrument that best fit to evaluate the use of chatbots in the airline industry based on the combined set of constructs as identified in the conceptual framework. Thus, this study aims to develop and validate an instrument that will evaluate the adoption of chatbots in a service industry, like the airline industry, as well as its non-adoption. Based on the reliability and internal consistency evaluation, only one item was deleted (PIIT construct) since it has the lowest item-test correlation that caused the reliability coefficient to be less than the suggested value of 0.70. All statements in each of the constructs had positive signs, thus, were not reversely worded. Lastly, all the scale reliability coefficient or the overall alpha values are way higher than the suggested value, which means that the internal consistency is either acceptable or highly acceptable.

Keywords: Social Implications of Technology
Abstract: Autism is a developmental disorder that impairs the ability of affected to communicate and interact. This disease impacts the nervous system, resulting in poor emotional, social, cognitive and physical health. Affected ones are however capable of excelling in some or other field of their interest. To identify their interest, they need to be exposed to wide range of activities on a daily basis. Manual interpretations can go wrong as a person can complete a task with interest, fear, etc. Brain Computer Interface (BCI), helps read and analyze the human brain activity using brain waves. Attention values and brain waves from samples are analyzed while performing activities as part of experiment. So in this study using BCI, manually interpreted sample's interest to a task is verified experimentally. It is learnt that, samples show an improved percentage attention during sessions of their areas of interest.

Keywords: Social Implications of Technology, Disasters and Humanitarian Technology
Abstract: It is widely recognized that a nation with minimum problems relating to areas such as health, environment, infrastructure, and technology is a developed country [1]. However, the developing/ lower-middle income countries need much improvements in the above-mentioned areas, as they are still lacking in those areas [1]. Apart from the risk associated with these problems, the main challenge faced by developing countries is, making the public aware of these problems. In this paper, we are proposing a mobile game-based learning platform: “AwareME” which focuses on following problems: (1) health awareness (dengue fever), (2) environmental awareness (garbage disposal), (3) cyber security awareness (social media) and (4) safety awareness (road safety). The “AwareME” platform includes quizzes, 2D/3D puzzle games, and 3D action games with activities to improve the cognitive skills and awareness of the public. We have provided the results of an initial performance evaluation of “AwareME” platform.

Keywords: Social Implications of Technology, Engineering Education
Abstract: Online examinations have become the norm, owing to the global Covid-19 pandemic in various academic settings such as schools, colleges etc... and even for job selection. The existing applications in this space, do not assure against fraudulent practices, though some of them are highly restrictive and constrain the candidates severely. We propose our system e-Parakh, an online examination system that can be used by the candidate even from his mobile phone app, which significantly reduces the resource requirements and therefore the cost involved for the candidate. The application also facilitates both supervised and unsupervised remote monitoring of the examination, through a variety of techniques including live video and audio streaming of not only the candidate, but also the candidate's surrounding environment, liveliness check of the candidate, facial comparison of the candidate with his/her photograph etc.. This application provides the evaluator to cross-check the candidate activity at any time during the examination as well as after the examination by recording the whole video and audio.

Keywords: Social Implications of Technology, Engineering Management, Biomedical Engineering
Abstract: The Indian pharmaceutical industry today is No. 3 in the world based on volume. Due to the World Trade Organization’s Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS), enforced in 1995, India revised its patent law in 2005 and re-introduced product patents. Taking advantage of TRIPS enforcement in 1995 and the re-introduction of product patents in India in 2005, large foreign capital pharmaceutical firms successively re-entered the Indian market; they began to engage in R&D activities and produce formulations and active pharmaceutical ingredients (APIs). Such foreign capital companies started to submit patent applications to the Indian Patent Office (IPO), which began in 2005 to examine product patent applications under the new, revised patent law. However, both IPO and IQVIA data show that the number of patent applications in pharmaceuticals has been declining in past years.

Keywords: Computer Architecture & Systems, Machine Learning, Cloud and Data Analytics
Abstract: Supported by extensive experimental efforts for the realization of quantum computing devices, a quantum computer of a hundred qubits is now within reach. This level of a quantum computer is not enough for fully-fledged fault-tolerant quantum computing, which is inevitable for large-scale quantum computing supporting theoretically proven exponential computational speedup. However, state-of-the-art quantum computers still are thought to have a computational advantage against classical computers for certain tasks and called noisy intermediate-scale quantum (NISQ) devices. In this talk, I will provide an overview of the NISQ devices and their applications for quantum simulation and machine learning. In addition, I will show the prospects and challenges for the realization of a fault-tolerant quantum computer in the long term.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing, Software & Database Systems
Abstract: Lightness signal and color reflectance constitute the reflected luminance spectra from camera captured image to camera lenses. The intensity of lightness and visible RGB signals deviates as the camera distance to object varies. The presence of uneven distribution of photosynthetic light causes angular light effect of shadowing on the focal object and light emitting objects placed on the visually noisy background added a challenge in materializing an efficient greenness index for crop phenotyping. The proposed method in this study compensates excessive relative brightness on the image by introducing lightness rectification coefficient and employing genetic algorithm to derive a novel visible tetrahedron greenness index (gvTeGI) based on normalized green waveband. Hybrid neighborhood component analysis and Pearson’s correlation coefficient approach for feature selection resulted to retaining photosynthetic canopy area, and correlation and homogeneity texture features as highly important descriptors for biophysical signatures considered in this study which are lettuce fresh weight, height and number of spanning leaves. The selection, crossover and mutation rates used to optimize the genetic algorithm model are 0.2, 0.8 and 0.01 respectively. Indoor and outdoor aquaponic system was deployed for 6-week full crop life cycle cultivation. Regression machine learning models were used to estimate biophysical signatures from extracted gvTeGI channels. Optimized Gaussian processing regr

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing, Software & Database Systems
Abstract: We present HelipadCat, a dataset of aerial images of helipads, together with a method to identify and locate such helipads from the air. Based on the FAA’s database of US airports, we create the first dataset of helipads, including a classification by visual helipad shape and features, which we make available to the research community. The dataset includes nearly 6,000 images with 12 different categories. We then train several Mask-RCNN models based on ResNet101 using our dataset. Image augmentation is applied according to learned augmentation policies. We characterize the performance of the models on HelipadCat and pick the best-performing configuration. We further evaluate that model on the metropolitan area of Manila and show that it is able to detect helipads successfully, with their exact geographical coordinates, in another country. To reduce false positives, the bounding boxes are filtered by confidence score, size, and the presence of shadows. Dataset and code are available for download.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing, Software & Database Systems
Abstract: Sri Lanka, which is known as "the pearl of the Indian ocean" provides great survival and civilization history dating back to the 3rd century. Most of the archaeological sites are attracted by not only Sri Lankans but also by tourists. When searching for the information about the archaeological sites, there are lack of trusted information sources and smart online platforms. Even though some information is available, no convenient and efficient ways to retrieve them. When trusted information is provided in a user-friendly manner, the value will be added to the Sri Lankan economy. Since the world is driving towards the “E-Era”, everything is involved with Information Technology. The proposed system contributes to solve the above problems with Artificial Intelligence & Machine Learning concepts. The system is assisted using four major components namely, image identification, community platform, conversational bot, and image visualization. The image Identification component identifies the archaeological sites using image processing techniques. The community platform gathers trusted information from archaeologists and deep learning techniques are used to deliver that content to the users. The artificial intelligence conversational bot is established to communicate and retrieve available information in a convenient manner. The image visualization component is used to provide reality visualization on archaeological sites using the augmented reality techniques.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing, Software & Database Systems
Abstract: The technological capabilities of computers in today's time continues to improve in ways that seemed impossible before. It is common knowledge that most people use computers to make everyday lives easier. Therefore, it is vital to bridge the gap between humans and computers to provide more suitable aid to the user. One way to do this is to use emotion recognition as a tool to make the computer understand and analyze how it can help its user on a much deeper level. This paper proposes a way to use both face and speech emotion recognition as a basis for selecting an appropriate music that can improve or relieve one's emotion or stress. To accomplish this, Support Vector Machine with different kernels are used to create the models for validation and testing on both the face and speech emotion recognition. The final integrated system yielded an accuracy rate of 78.5%.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing, Software & Database Systems
Abstract: In the Philippines, the number of earthquakes occurring has risen to an alarming rate. ’The Big One’ is one of the biggest expected catastrophes that is undoubtedly going to occur in the next decade as said by various experts. Buildings that were able to withstand the upcoming earthquakes, are to be inspected by engineers without knowing if the safety of the building is compromised. Thus, there is a need for a system that can inspect the cracks on the wall for faster and safer inspection. The objective of this study is to develop a crack detecting system capable of analyzing physical characteristics of cracks and mapping the surface of the wall. The model to be used for classifying and determining what cracks are, was trained with the use of Faster R-CNN machine learning architecture. Trained using the SDNET2018 combined with actual data generated by the proponents, the resulting system can detect cracks with an accuracy of 90% and classify the cracks according to the shape The system also calculates its physical properties, and has a recommender system that provides remarks on what necessary actions can be done.

Keywords: Machine Learning, Cloud and Data Analytics, Social Implications of Technology, Signal and Image Processing
Abstract: With machine learning-based innovations becoming a trend, practical resolutions of its implementation to large-scale data and computing problems must be able to cope up as well. Currently, Graphic Processing Units (GPUs) are being chosen over other available physical devices due to its powerful computing capability and easier handling. Several cloud service providers also made it possible for these to be accessible online allowing higher serviceability and lower cost upfront for businesses. With this said, the proponent would implement a common machine learning-based application, automated annotation through Mask RCNN Object Detection Model in CVAT, using AWS instance. The key purpose is to showcase the viability of deploying data and computing intensive system on the cloud.

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: The rise of artificial intelligence (AI) technologies and the increasing demand for defect-free wafers encourage semiconductor manufacturers to pursue automatic defect classification (ADC). The current ADC system classifies wafer surface defects using optical and Scanning Electron Microscope (SEM) images however manual classification is still a major part of the process and it is not only labour-intensive and slow but also highly prone to human error. This paper explores an ADC system based on deep learning that automatically classifies wafer surface defects, particularly from the metal layers, which brings consistency and speed, allowing for better determination of wafer lifecycle as well as defect root cause analysis in yield management. The proposed method adopts a deep convolutional neural network (CNN) architecture for defect classification using SEM images which can sub-classify defects into respective sizing groups whereby defect size serves as an important indicator of the origin of machine failure. This research observes that the proposed ADC method achieves industrially pragmatic defect classification performance based on experimentations with real semiconductor datasets. This paper investigates the promise of transfer learning for reducing computational cost and improving testing accuracy.

Keywords: Photonics
Abstract: Satellite-based Internet access for the whole globe, a newly emerging market, has recently received much attention from both academia and industry. In this paper, we present an analytical investigation of transmission control protocol (TCP), which is the most popular protocol for various Internet applications, in free-space optical (FSO) communications based low earth orbit (LEO) satellite systems. Specifically, the throughput performance of the potential deployed TCP variants for highspeed and long-distance of FSO-based satellite networks, namely TCP Hybla and TCP Cubic, are analyzed. Additionally, the incremental redundancy hybrid automatic repeat request (IRHARQ) protocol is employed to enhance the system performance over satellite-to-vehicles FSO links. The numerical results quantitatively demonstrate the impact of atmospheric turbulence on the TCP throughput and show that TCP Cubic outperforms Hybla in the low error-rate conditions while Hybla provides the better performance when the transmission errors happen more frequently. Monte Carlo simulations are also performed to validate the accuracy of theoretical derivations.

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: Nowadays, there is a rapidly increasing number of conferences and journals in computer science that make a lot of challenges for researchers to find an appropriate venue to submit their scientific work. There is a need for a recommendation system that can support researchers for a better process of paper submission. In this paper, we present an efficient approach for building such a recommendation model by using embedding methods, Global Vector (GloVe) 1 created by Pennington et al. [1] and FastText 2 proposed by Facebook [2], Convolutional Neural Network (CNN) [3], and LSTM. The experimental results show that the combination of CNNs and FastText, CNN + FastText, can achieve the best performance in terms of the Top 1 Accuracy compared with other techniques, including the S2RSCS model, as presented in [4]. Moreover, the performance by using GloVe or FastText is much better, faster, and more stable than S2RSCS in most cases.

Keywords: Wireless Communications & Networks
Abstract: In wireless sensor networks, sensor nodes send environmental data to the sink or base station via a single hop or multiple hops. Due to limited resources, constructing a good routing path to prolong the network lifetime is critical to its effectiveness. One approach is using data aggregation techniques, where sensors aggregate data through multiple levels before reaching the base station, to reduce energy consumption. A data aggregation tree describes the flow of data from each sensor node to the sink or base station. In this paper, we study the problem of constructing a data aggregation tree optimizing four objectives: total energy consumption, network lifetime, latency and interference. This problem was proven to be NP-hard. We propose a tree-based evolutionary approach with edge-set representation, named KGASL, to solve the problem. Experimental validation on our benchmarks have been carried out to demonstrate proposed algorithm’s performance.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Rice is the staple food of the Filipinos. According to the Bureau of Agricultural and Fisheries Product Standards, an average Filipino consumes 4-5 servings of rice per day. But because there is no accurate way of detecting rice spoilage before consumption, Filipinos only rely on their senses to know whether the rice is spoiled or not. This makes them at risk of foodborne illness due to rice spoilage. But with the latest technology advancements, machine learning could be used to help lessen the risk and cases of food illness caused by rice spoilage. This study focuses on the implementation of Azure Custom Vision API to detect rice spoilage. Gas sensor readings and images captured during data gathering were correlated with a resulting value of 1 which corresponds to a very strong correlation. The system was tested by the researchers using 20 different rice samples that includes 10 samples of spoiled rice and 10 samples of not spoiled rice which resulted in a detection accuracy of 85%. The system is implemented with its own container using a Raspberry Pi 3B with a camera module through Python programming language.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Monitoring the dynamics of fish behavior is impactful both in the research for fisheries and aquaculture production. One of the most explored approaches to monitor the fish is tracking-by-detection along with computer vision. Presently, there are several challenges in this field, including underwater environment conditions and fish movement complexity. This study presents an initial investigation towards tracking the fish by predicting the trajectory 2D coordinates of fish from the sequential sampled frames in underwater videos. Here, the authors explored the Genetic Algorithm based on natural evolution selection and the Long Short-Term Memory (LSTM) algorithm. Results have shown tolerable trajectory prediction inaccuracies using the GA and LSTM. Specifically, it obtained the Mean Absolute Percentage Error at 2.8% to 30.5% and 3.33% to 17.74% for GA and LSTM, respectively. These results have allowed the authors and researchers to extend its study towards tracking the fish using these approaches.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing, Biomedical Engineering
Abstract: Schistosomiasis, trichuriasis, and ascariasis are few of the many neglected tropical diseases that still affect populations in poor countries. These diseases cause a variety of symptoms such as abdominal pain, may lead to complications, and may even result in death in severe schistosomiasis cases. To complement the efforts of governments and health organizations in mitigating the morbidity and transmission of neglected tropical diseases, several applications utilizing machine learning techniques have been developed in recent years to automate the detection of parasites in microscopy samples. In this paper, we explore the use of YOLO, a convolutional neural network framework, in the detection of helminth eggs in stool samples. We collected and labelled a dataset with varying imaging conditions due to different staining conditions and acquisition by smartphone cameras with different parameters. We demonstrate that the approach works well despite this variance in imaging conditions in the dataset, achieving high sensitivity in the detection of helminth eggs and high accuracy in the identification of egg species. The trained model operates in real-time, making it suitable for automated diagnosis and real-time annotation.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing, Biomedical Engineering
Abstract: Using the recent known EfficientNet architecture of deep convolutional neural network (CNN), we present an automatic detection of diabetic retinopathy (DR) from given retinal images. We experiment with subsets of the Kaggle diabetic retinopathy dataset consisting of retinal images with varied diagnostic quality. To address the quality variation, we incorporate two preprocessing steps, i.e. contrast limited adaptive histogram equalization (CLAHE) and image central cropping. We trained EfficientNet-B4 and EfficientNet-B5 model on two Kaggle subsets with different class proportions. In this paper, we propose an automatic early diagnosis of diabetic retinopathy which gained 0.7922 / 83.87% and 0.7931 / 83.89% of quadratic weight kappa and accuracy score on EfficientNet-B4 and EfficientNet-B5 respectively.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing, Social Implications of Technology
Abstract: Due to the rise of autonomous vehicles like drones and cars anomaly detection for better and robust surveillance becomes prominent for real-time recognition of normal and abnormal states. But the whole system fails if the unmanned device is unable to detect own device’s anomaly in real time. Considering the scenario, we can make use of various data of autonomous vehicles like images, video streams and other digital or analog sensor data to detect device anomaly. In this paper, we have demonstrated a heterogeneous system that estimates the degree of an anomaly in unmanned surveillance drone by inspecting IMU (Inertial Measurement Unit) sensor data and real time image in an unsupervised approach. We’ve used AngleNet for detecting images taken in abnormal state. On top of that, an autoencoder fed by the IMU data has been ensembled with AngleNet for evaluating the final degree of the anomaly. This proposed method is based on the result of the IEEE SP Cup 2020 which achieved 97.3 percent accuracy on provided dataset. Besides, this approach has been evaluated on an in-house setup for substantiating its robustness.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing, Social Implications of Technology
Abstract: Identifying variant diseases in leaves is a significant method for optimizing food production. As the global population continues to arise and agricultural space continues to decline, every possible way of increasing the supply of food in any given condition and limited resources will address the above-mentioned problems. This study proposes a way for detecting three different diseases from grape leaves apart from the healthy leaves and considers the confidence value of the system in correctly identifying the classes. The diseases are namely: Black Rot, Black Measles, and Isariopsis. The system conducted a comparative analysis to determine which among the three pre-trained networks, AlexNet, GoogLeNet, and ResNet-18 will be the most suitable network to be integrated with Regions with Convolutional Neural Networks (RCNN) in performing multiple object detection in a given image. The data used in training the models comprised of annotated image data represented as a ground truth table with image files and their corresponding bounding boxes coordinates. The models evaluated resulted to AlexNet being the best pre-trained network to be working on the RCNN with an accuracy of 95.65%. The other two models from GoogLeNet and ResNet-18 only obtained accuracies of 92.29% and 89.49% respectively.

Keywords: Signal and Image Processing, Social Implications of Technology, Machine Learning, Cloud and Data Analytics
Abstract: Sign language is the primary medium of communication for the majority of the world’s population suffering from disabling hearing loss that creates a barrier between the hearing and the hearing-impaired people. In this paper, sign language translation is undertaken for German Sign Language (GSL) characters from a single image by leveraging the technique of 3D object detection. We make use of a three-network architecture that performs segmentation, keypoint localization, and elevation from a two-dimensional plane to the three-dimensional space, from a single RGB image containing the signed gesture. Thirty gestures have been used and the best results were obtained using a combination of pose representation coordinates, joint angles, and pool layer features of AlexNet for classification. The system gives a character error rate of 0.29, a reduction of error rate by 12.12% when compared to the state-of-the-art approach.

Keywords: Biomedical Engineering, Signal and Image Processing
Abstract: Conventionally, bowel sound is considered an important index for understanding a patient’s intestinal condition. At present, bowel sound is determined by a doctor’s auscultation with a stethoscope. The auscultation is based on experience and intuition, and is short-term. Therefore, research has been conducted to enable a long-term and quantitative measurement of bowel sound. However, these studies measured bowel sound for bedridden patients only, and no study has yet measured them for moving patients. To measure bowel sound even for a moving patient, a portable bowel sound detection device needs to be developed. For this purpose, a new sensor that is smaller than the conventional stationary prototype, with the addition of noise resistance, is developed in this study. The noise resistance is added through signal processing conducted on a computer. This study focuses on the rubbing sound of clothes, which is the most influential noise generated when moving. In addition, a notch filter, wavelet filter, and low-pass filter are used to extract only the cloth rubbing and bowel sounds. In addition, the difference in the number of peaks and the enhancement of the bowel sound spectrum characteristics are used to distinguish between the two sounds. As a result, automatically, only the bowel sound is detected.

Keywords: Biomedical Engineering, Signal and Image Processing
Abstract: Unpredictable disequilibrium syndrome and blood pressure fluctuations can occur during hemodialysis therapy. We have proposed a method for analyzing shunt sounds using EMD to predict these symptoms. The shunt sound is the sound of turbulent blood flow generated in the shunt, which can be measured from the puncture needle of the dialyzer. This shunt sound may contain elements that identify these symptoms. To identify these elements, it is necessary to decompose the shunt sound into a minimum number of components, assuming that the shunt sound is composed of a plurality of basic components. Therefore, in this report, how to decompose the shunt sound into the minimum components by dividing the shunt sound into beats and by empirical mode decomposition is explained.

Keywords: Biomedical Engineering, Signal and Image Processing
Abstract: In ultrasound imaging, a non-linear beamforming algorithm called filtered delay multiply and sum (F-DMAS) has been demonstrated to provide improved contrast and resolution compared to the commonly employed delay and sum (DAS) algorithm. In F-DMAS, the delay compensated radio-frequency narrowband signals from the transducer channels is pairwise multiplied to generate baseband and higher-order harmonic components in the output spectrum. The final image is reconstructed by filtering the second harmonic component to provide better contrast and resolution. However, other generated harmonic components, which are not utilized in the standard F-DMAS could be employed for improved contrast and resolution. In this work, a modification to standard F-DMAS is proposed where the different frequency bands generated through pairwise multiplications are selectively combined through additive or difference compounding techniques to form the final image. The results show that the proposed approach outperforms the standard F-DMAS in terms of contrast and resolution.

Keywords: Biomedical Engineering, Signal and Image Processing
Abstract: Musculoskeletal (MSK) ultrasound imaging aims to provide pictures of tissues and bones such as muscles, tendons, ligaments, joints and soft tissues throughout the body. One of the major landmarks in MSK ultrasound are the bones, and segmentation of bone surface has numerous applications in computer-aided orthopedic diagnosis. In this work, a novel method of bone aware image enhancement of MSK ultrasound images is presented. A combination of fundamental and harmonic US images is used for bone segmentation. The method for bone segmentation takes into account the acoustic characteristics of the intensity of bones used for computing their acoustic shadows, local phase-based features such as local energy, local phase, and feature symmetry based on a reported work in literature. It is combined with integrated backscattering of the bone to provide a probability map of the bone. Bone location in probability map was found based on the centroid of the intensity distribution. Further, image enhancement of the extracted region of interest based on the bone for distinctive visualization of the muscular and tendon region above the bone structure is presented. The image enhancement techniques employed are gamma correction, histogram equalization, adaptive histogram equalization and an improved frequency based superresolution of ultrasound images.

Keywords: Biomedical Engineering, Signal and Image Processing
Abstract: Functional near-infrared spectroscopy (fNIRS) has gained interest in the development of brain-computer interface (BCI) for working memory (WM) training. Amplitude averaging of oxygenated hemoglobin (oxy-Hb) signal over the predefined region of interest (ROI) is typically used to compute WM load. It is unclear to what extent extracerebral artifacts can affect WM assessment. To examine this, a technique adopting multi-distance probe configuration and independent component analysis (MD-ICA) was applied to split the original fNIRS signals into hemodynamic signals originating from the deep (cerebral) and shallow (extracerebral) tissue layers. Twenty-five healthy participants performed letter 1- and 2-back tasks, symbolizing low and high WM load respectively. In the bilateral dorsolateral prefrontal cortex (DLPFC), increasing WM load evoked significant changes in both original and deep oxy-Hb activation, but not in the shallow oxy-Hb. Under low WM load, the bilateral DLFPC activation was significantly higher than that of the middle prefrontal cortex (mPFC), only seen through the deep signal. Conversely, under high WM load, the significant difference in brain activation between the bilateral DLPFC and mPFC were observed in both original and deep oxy-Hb. This highlights extracerebral artifacts correction might be useful when searching for activation regions. However, when activation areas are known, the signal intensities in original oxy-Hb are sufficiently profound to quantify and

Keywords: Computer Architecture & Systems
Abstract: With the rise of IoT and its many applications, the capabilities of sensor nodes in wireless sensor networks have increased due to the large amounts of sensed data that incur a significant amount of workload at the network core. As such, edge computing applications, which take computing away from the network core into the network edge, become more widely used. This paper presents a pipelined RISC-V RV32IMC processor with interrupt support as a solution to this challenge. For communication with peripherals, the processor supports the protocols I^2C, SPI, and UART. Design optimizations, delay balancing and clock gating, resulted in a 13.3% maximum operating frequency increase and a 23.3% reduction in the dynamic power consumption of the core processor. The implemented processor utilizes an average core power of 30.752 mW while operating at a frequency of 50 MHz on a Digilent Arty A7 Board with a Xilinx Artix-7 FPGA.

Keywords: Computer Architecture & Systems
Abstract: Cloud computing is a computing platform that allows users to access various kinds of computing services over the internet. Cloud provides on-demand, scalable and highly available resources on pay-per-usage subscriptions. Cloud is an optimum solution for executing a large number of different size tasks as for the computing capability it offers. Task scheduling is one of the major open challenges that need to be addressed. The Task scheduling problem in the cloud is known to be an NP-complete problem. Hence heuristics can be used to get an optimal solution. There have been many heuristics proposed for the task scheduling problem in the cloud. None of them has considered the total execution time of the virtual machine as a factor for finding a better schedule. In this paper, we proposed a new task scheduling algorithm named Total Resource Execution Time Aware Algorithm (TRETA) which takes into account the total execution time of computing resources in obtaining an optimal schedule. The algorithm is compared with Min-Min, Min-Max, FCFS, and MCT heuristics for Makespan, Degree of Imbalance and System Throughput. The proposed algorithm shows a significant amount of improvement in Makespan compared to other heuristics. The algorithm also outperforms other heuristics with respect to System Throughput and Degree of Imbalance which results in better workload distribution among the cloud resources.

Keywords: Computer Architecture & Systems, Circuits and Systems, Machine Learning, Cloud and Data Analytics
Abstract: Hyperdimensional computing (HDC) is a brain-inspired computing framework that provides simple and convenient methods to perform cognitive tasks like classification. Its foundation lies in the properties of very high dimensional vectors called hypervectors (HV). HDC is a promising alternative to the conventional von-Neumann architectures, but its high-dimensional processes still contain massive bit-wise operations. Current optimizations often sacrifice accuracy for better energy-efficiency. This work finds redundant bits in the associative memory that do not contribute any information during classification. A proposed bit-selection control trims these redundant bits leading to improved throughput and energy-savings without sacrificing accuracy. For the handwritten digits recognition problem, this simple control results in a 44.62% energy reduction at the cost of 8.34% increase in area.

Keywords: Computer Architecture & Systems, Machine Learning, Cloud and Data Analytics, Robotics, Control Systems & Theory
Abstract: Supervisory Control and Data Acquisition (SCADA) system which monitors and controls physical processes/operations within a rail infrastructure is critical. SCADA system’s accessing to key components and infrastructure information make it a promising attack target. This paper explores building machine learning models to detect anomalies in a rail SCADA system through the usage of network traffic data. The attack scenarios designed based on domain expertise are epoch time attack and TCP payload length attack in this paper. Data pre-processing is done before passing into machine learning approaches for training. The anomaly detection machine learning models are evaluated using several metrics such as true positive rate and precision. Results show that supervised learning approaches (K-Nearest Neighbours (KNN), Linear Support Vector Classification (LinearSVC), Random Forest, Gaussian Bayes) outperform unsupervised learning approach (K-Means). Exploration into the use of the full original network traffic versus a subset of network traffic for model training has shown that the latter performed better in precision due to the presence of overfitting to specific alarm network traffic. Finally, our experiment results show that supervised learning approach KNN is effective to detect the attacks with high precision.

Keywords: Computer Architecture & Systems, Software & Database Systems, Wireless Communications & Networks
Abstract: Currently, Internet of Things (IoT) becomes pervasive and widely deployed. However, the lack of developer and user cyber security awareness leaves IoT devices become the new target of cyber attacks. Therefore, we design and develop "A System for Preventing IoT Device Attacks on Home Wi-Fi Router" (SPIDAR) in order to protect home Wi-Fi networks. This system consists of SPIDAR home Wi-Fi router, SPIDAR Raspberry Pi, and SPIDAR web application to prevent attacks and display the attack statistics to home users. It also helps saving costs from purchasing expensive intrusion prevention software and hardware to install at home. For the prevention method, we provide both the signature-based method using Snort software and the behavior-based method which learns and analyzes IoT devices’ behavior by using either the baseline or the machine learning in order to increase the system performance. SPIDAR can prevent five major attack types specified in the OWASP IoT Top 10 vulnerabilities 2018.

Keywords: Power & Energy, Devices, Materials & Processing
Abstract: Industrial plants utilize sensitive equipment for the past years to increase their efficiency to yield the required needs of their customers. Power quality disturbances such as voltage sag causes this equipment to operate poorly and even malfunction due to its nonlinear characteristic and thus, affecting the performance of the whole process to produce an output product. To address this, manufacturers sought to find means to determine its equivalent response through its voltage tolerance curve to improve the equipment immunity under this disturbance. The objective of this research study is to determine the possibility of adding optimized boundaries to the voltage tolerance curve for the protection of sensitive equipment and to improve their ride through capability to voltage sags by using Cuckoo search algorithm. The voltage tolerance curve of each equipment is plotted against voltage sag events to determine its possible response and optimized using its parameters and sag events as constraints and boundaries using MatLAB. The simulation shows that an equipment can have a significant improved performance against voltage sags by setting up optimized boundaries to the voltage tolerance curve in addition to the conventional upper and lower boundaries.

Keywords: Power & Energy, Engineering Education, Devices, Materials & Processing
Abstract: This paper gives the design of IMC and IMC derived biphasic Interleaved Boost Converter (IBC) in voltage mode control technique. Small-signal based modelling of IBC in continuous conduction mode (CCM) of operation shows a right half plane zero (RHPZ) in control to output plant transfer function (TF) of the IBC. IBC produces substantially low output voltage ripple compared to boost converter due to the emergence of multiple power switches in the parallel path. Subsequently, the size and output filter losses of IBC can be substantially reduced in comparison to the Boost Converter (BC). Execution of the converter-controller system is validated by SISO tool-based step response analysis. Developed IMC and IMC dependent PID controllers are performing better than highly cited Type III controller-based IBC in line, load regulation, and reference tracking study. They have comparable performance in parametric uncertainty (variation) study, validated using MATLAB/SIMULINK. IMC derived PID is performing better than the IMC controller in line regulation, load regulation, and parametric variations, but in step response checking, they have nearly equal settling time.

Keywords: Power & Energy, Robotics, Control Systems & Theory
Abstract: A decentralised fault tolerant control is presented in this paper for a class of interconnected system. The bounded control technique is applied to address the interconnection effects and the dynamic changes due to the faults. Multiple local controllers are designed as part of decentralised algorithm to guarantee the system stability during the fault period. In this case, the system can deal with the fault with large magnitude. To illustrate the effectiveness the proposed scheme, the system is tested for the case that multiple fault occurred in different local subsystems.

Keywords: Power & Energy, Robotics, Control Systems & Theory, Devices, Materials & Processing
Abstract: Batteries are very important in many different applications. In the solar energy system, the batteries are used as power storage when solar energy is not available especially during night time. Batteries need to be maintained and closely monitor their condition. Battery management systems are normally used for this application but many of them are not monitoring the battery’s temperature. This study will use a fuzzy logic-controlled system to manage the operation of the battery. This system will maintain the operation of the battery in the allowed operating temperature to prevent it from damaged caused by excessive internal temperature.

Keywords: Power & Energy, Engineering Management
Abstract: The aim of the study is to know the efficiency of Cabuyao Water District (CABWAD) in their service of distributing water to their customers and to know what will be their plans for the next 5 to 10 years regarding water projection and water availability. After conducting surveys and interviews, the researchers found out that the service efficiency of CABWAD is moderately efficient. In terms of water pressure efficiency, it is also rated moderately efficient with mean of 2.2 with the highest rating of 1. Their plans for the next 5 and 10 years are; first, to increase the number of pumping stations from other sources of water like surface water, underground water and rainwater. Second, to put additional water sources located strategically within the districts jurisdiction. And last, to invest applicable water quality equipment required in the PNSDW parameters. It was concluded that the water availability may be affected by the location of the customers from the water source and the number of member in every household may also affect by a high demand on water consumption. Regardless of demographic profile, the respondents opt to stand that CABWAD needs improvement specially to the barangays suffering water shortage.

Keywords: Software & Database Systems, Machine Learning, Cloud and Data Analytics
Abstract: Whenever any internet user visits a website, a scripting language runs in the background known as JavaScript. The embedding of malicious activities within the script poses a great threat to the cyber world. Attackers take advantage of dynamic nature of the JavaScript and embed malicious code within the website to download malware and damage the host. JavaScript developers obfuscate the script to keep it shielded from getting detected by the malware detectors. In this paper, we propose a novel technique for analysing and detecting JavaScript using sandbox assisted ensemble model. We extract the payload using malware-jail sandbox to get the real script. Upon getting the extracted script, we analyse it to define the features that is needed for creating the dataset. We compute Pearson's r between every features for feature extraction. An ensemble model consisting of Sequential Minimal Optimization (SMO), Voted Perceptron and AdaBoost algorithm is used with voting technique to detect malicious JavaScript. Experimental results show that our proposed model can detect obfuscated and de-obfuscated malicious JavaScript with an accuracy of 99.6% and 0.03s detection time. Our model performs better than other state-of-the-art models in terms of accuracy and least training and detection time.

Keywords: Software & Database Systems, Machine Learning, Cloud and Data Analytics
Abstract: Ride sharing is one of the several transportation alternatives used to ease and skip traffic problems worldwide. A platform of interest is GrabShare, where its ride sharing algorithm was empirically found to be simple. However, the algorithm has several limitations, such as it being not truly optimal due to catering to user experiences, and only able to handle up to two bookings. Hence, there is a need to develop a ride sharing system that is scalable, fast, and efficient especially in terms of finding matches and recommending routes. A Modified Search-based Ride Sharing algorithm, which uses an expansion-based method, was developed as a response to these requirements. Results showed that the Modified Search-based Ride Sharing algorithm generally outperforms the empirically-derived GrabShare algorithm in terms of route length, shared route percentage, and processing time. However, GrabShare performs better when there are few passengers in the area while the Modified Search-based Ride Sharing algorithm runs relatively slower when the sources and destinations are far from each other.

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: Automated Essay Scoring (AES) is a task that deals with grading written essays automatically without human intervention. This study compares the performance of three AES models which utilize different text embedding methods, namely Global Vectors for Word Representation (GloVe), Embeddings from Language Models (ELMo), and Bidirectional Encoder Representations from Transformers (BERT). We used two evaluation metrics: Quadratic Weighted Kappa (QWK) and a novel "robustness", which quantifies the models' ability to detect adversarial essays created by modifying normal essays to cause them to be less coherent. We found that: (1) the BERT-based model achieved the greatest robustness, followed by the GloVe-based and ELMo-based models, respectively, and (2) fine-tuning the embeddings improves QWK but lowers robustness. These findings could be informative on how to choose, and whether to fine-tune, an appropriate model based on how much the AES program places emphasis on proper grading of adversarial essays.

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: Food plays a vital role in our day-to-day life to get all the required nutrients for a healthy lifestyle. In recent years, obesity has become one of the major concerns among humans. Therefore, it is necessary for each individual to keep track of the nutrition intake in order to have a balanced diet. This has scaled up the implementation of automatic food analysis and semantic food detection using different image classification approaches, among which Deep Learning has brought a series of breakthroughs in this field. We have proposed the Food Classifier and Nutrition Interpreter (FCNI), a user-friendly tool that classifies various food types with a different graphical representation of food nutrients values in terms of calorie estimation along with a multimedia audio response. FCNI improves state-of-the-art food detection by a considerable margin on achieving about 96.81% accuracy.

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: Multiple object detection using deep neural networks can lead to transportation vehicles estimate, a necessary requirement for prediction and management of road traffic and parking lot. Highly overlapped objects that look similar and objects that are there at far distances have lesser probability of detection by state-of-art techniques. We propose techniques to estimate the traffic at regions of poor detection probability in the image based on (i) density based clustering and (ii) exclusive object detection in the regions of poor detection. The proposed techniques lead to better estimation in comparison to state-of-art by approximately 12 %. We have utilized RetinaNet and YOLOv3 networks for object detection.

Keywords: Machine Learning, Cloud and Data Analytics
Abstract: Recent advancements in Artificial Intelligence namely in Deep Learning has heightened its adoption in many applications. Some are playing important roles to the extent that we are heavily dependent on them for our livelihood. However, as with all technologies, there are vulnerabilities that malicious actors could exploit. A form of exploitation is to turn these technologies, intended for good, to become dual-purposed instruments to support deviant acts like malicious software trojans. As part of proactive defense, researchers are proactively identifying such vulnerabilities so that protective measures could be developed subsequently. This research explores a novel blackbox trojanising approach using a simple network structure modification to any deep learning image classification model that would transform a benign model into a deviant one with a simple manipulation of the weights to induce specific types of errors. Propositions to protect the occurrence of such simple exploits are discussed in this research. This research highlights the importance of providing sufficient safeguards to these models so that the intended good of AI innovation and adoption may be protected.

Keywords: Antenna & Microwave
Abstract: Fabrication of PLA-based horn antenna has a drawback that produces imperfect prototype such as an air gap between parts, surface roughness and impure conductivity. Characterization of PLA-based quad-ridged horn antenna is presented in this paper to observe the effect of imperfect fabrication to the horn antenna performance. The research is carried out by investigating the antenna parameter in terms of reflection coefficient and antenna gain of QRHA with an imperfect fabrication. The investigation is performed by using 3D simulation software. The result shows that an air gap and surface roughness influence the antenna bandwidth and impure conductivity decrease the antenna gain.

Keywords: Antenna & Microwave
Abstract: This paper presents details of the effects of shorting along the width of the air-suspended rectangular microstrip antenna (RMSA). The entire width of the patch is shorted using a number of shorting pins and as the number of shorting pins increases, the gain, bandwidth, and resonance frequency of the antenna increase. By decreasing the shorting width, the antenna can be made more compact. For the shorted compact air-suspended RMSA the effects of the finite ground plane on its performance have been analyzed. When the geometric parameters of the ground plane are changed, it affects the gain, bandwidth, resonance frequency, and back lobe radiation. Various graphs have been presented to choose the optimum performance depending on the size constraints.

Keywords: Antenna & Microwave
Abstract: A novel, wide band circular patch antenna with symmetrical coplanar waveguide (S-CPW) fed is proposed for wireless access point operating in NR-79/Wi-Fi-5/Wi-Fi-6 frequency bands. To attain multiband operation, a circular patch is loaded with an intersecting of two slots namely vertical slot and comb shaped slot. The complete structure resonates at 5.3 GHz to produce wide band with fractional bandwidth of 55.77% in the frequency range (4.37-7.75GHz). The nearly omnidirectional 3D patterns followed by stable gain of 2.2 dBi throughout the functioning bands confirm the appropriateness of the proposed antenna for wireless access point operating in NR-79/Wi-Fi-5/Wi-Fi-6 frequency bands.

Keywords: Antenna & Microwave
Abstract: The antenna proposed in this study is a wearable microstrip patch antenna that utilizes jeans (permittivity: 1.77) as its substrate for GPS application. Tests shows that the antenna has frequency range of 1.57 – 1.61 GHz. Resonant frequency of the antenna is 1.595 GHz, with return loss value of -14.18 dB. The antenna achieved its desired specification with truncated edge, quarter wave transformator, and slot utilization. The antenna is safe to be used on thigh, chest, and arm as simulation shows that SAR value of the antenna is under the maximum standard allowed. However, when the antenna is moved further away from the phantom, the axial ratio value decreases and goes > 3 dB when antenna is placed over the distance recommendation.

Keywords: Antenna & Microwave
Abstract: Wearable antennas made from textile materials for GPS (Global Positioning System) applications has been widely developed. However, textile materials have properties that can absorb water and be bent which will affect the performance of the antenna. Therefore, the testing of GPS antennas made from textile materials with five different substrates has been carried out to determine the effect of textile use on the antenna. Five substrate materials were felt, spun bond, cotton, drill, and denim. All antennas were designed to work on the GPS L1 frequency of 1,575 GHz with a value of S11 <- 10 dB, bandwidth > 10 MHz, and axial ratio <3 dB to achieve circular polarization. The study shows when affected by water absorption, there are 4 antennas that remain successfully work at GPS L1 frequency. When the antenna is tested in bending conditions, there are several antennas that can still work at GPS L1 frequency but are not stable for every bend condition.

Keywords: Biomedical Engineering, Antenna & Microwave
Abstract: Determination of material properties is one of essential stages to more understanding the characteristics of biological sample especially in biomedical research. In this paper, a method of transmission phase-shift is proposed to determine complex permittivity of biological sample which is performed using a WR90 type X-band rectangular waveguide. Some samples of chicken meat, liver, and skin are applied as biological materials for the experimentation. The irregular shape of biological sample is examined by placing it into a thin container to obtain a flat surface and putting the container in inside of the rectangular waveguide. The complex relative permittivity of each sample is extracted from measured S-parameters and then determined using the method. The results show that the method could successfully determine the complex permittivity of biological sample. In addition, the water content in the material has become a critical issue to be considered in the examination especially for the biological sample with high permittivity.

Keywords: Machine Learning, Cloud and Data Analytics, Software & Database Systems
Abstract: Major tools to implement any Artificial Intelligence and Machine Learning systems are Symbolic AI and Artificial Neural Network (ANN) AI. ANN has made a dramatic improvement in the versatile area of Machine Learning (ML). ANN is a gathering of vast number of weighted interconnected artificial neurons, initially invented with the inspiration of biological neurons. These models are much better than previous models implemented with Symbolic AI so far as their performance is concerned. One revolutionary change in ANN is Convolution Neural Network (CNN). These structures are mainly suitable for complex pattern recognition tasks within images. Here we would discuss basics of ANN as a tool for complex pattern recognition and image processing task. Also as some applications of the CNN tool, we will present OCR based text translation and biometric based uni modal and multimodal person identification systems.

Keywords: Machine Learning, Cloud and Data Analytics, Wireless Communications & Networks
Abstract: Urban air pollution has significant effects in living beings and nature. Automobile exhaust emissions are the main cause of air pollution. Moreover, the major contribution of Air pollution is by static vehicle traffic over a long period when vehicles stop at a traffic crossing This article purposed a framework for managing traffic police duty hours based on the recommended time exposure to the pollutants. Air Pollution Monitoring System measures concentration value of harmful gases like CO, CO2, NO2, SO2 and particulate matters in real-time and send these value wirelessly to ThingSpeak IoT cloud through ESP8266 Wi-Fi module. An analysis of personal exposure to pollution of traffic police individually and total Air Quality Index (AQI) calculated in MATLAB environment. An alert email has been sent to traffic police control room about apprise duty hour of traffic police to aware less affected exposure time of urban air pollution for that particular crossing.

Keywords: Machine Learning, Cloud and Data Analytics, Wireless Communications & Networks, Computer Architecture & Systems
Abstract: Edge computing offers real-time applications because the edge device closes with the data source such as the end device. This condition gives the challenge to implement deep learning in the edge device. Unfortunately, deep learning requires high computing resources, but often edge-side devices have limitations. In this study, we built an image search system based on CNN (Convolutional Neural Network)’s feature vectors to address the challenges by enlarging the implementation of CNN in the edge device such as Raspberry Pi 3. The image search system applied these informative features vector to get similar images in the image searching task by using cosine similarity. We used a 102-flower categories dataset and we prepared a light database to run the system as an off-line system in the edge device. The MobileNetV2 as CNN’s model reached 70.02% of the top 1 accuracy and 92.84 % for the top 5 accuracy. As a result, the image search system showed five images result with the most similar image from the same image category. Image resolution, model complexity, and hardware capability give the significant time in this image search system. The framework of this system can be simply used for other deep learning models and applications by updating the model, dataset, database, and hardware.

Keywords: Marine and Offshore Engineering, Disasters and Humanitarian Technology, Machine Learning, Cloud and Data Analytics
Abstract: Last May 2019, fish farms in Taal Lake suffer from fish kill resulting in an estimated loss of 405 tons of fish. It was reported that the measured water sample from the lake shows significant loss of dissolved-oxygen due to over-crowding of fish farm. With the crisis mentioned, recent studies utilize satellite remote sensors to map and monitor the aquaculture inside the lake. The maps are being used as reference material for progress monitoring, as decision-support and lake management tool by the local government and regulatory agencies. Aerial maps were captured using Unmanned Aerial Vehicle (UAV) as it has better resolution than satellite imagery. This study implements image processing and Mask Regional Convolutional Neural Network (Mask RCNN) on high resolution images to create an object detection and segmentation model for aquaculture structures and coastal settlement. To create the detection model, the image dataset undergoes preprocessing before feeding into the training process. Finally, an analytical software was developed to utilize segmented maps for zone management plan implementation, lake resource usage calculation, and gauge the population of settlers along the coastline. This provides meaningful visual and statistical data regarding aquaculture population, lake resource usage, local settlement population and zone development plan status.

Keywords: Power & Energy, Machine Learning, Cloud and Data Analytics
Abstract: Energy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns. In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM. Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R2 score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R2, MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.

Keywords: Machine Learning, Cloud and Data Analytics, Signal and Image Processing
Abstract: Wearing of improper uniform has been one of the problems being faced by Pamantasan ng Cabuyao due to a massive number of students entering the university. The security guards do not have the ability to monitor the student’s attire all the time. There are also some students who do not wear Identification Cards (ID) upon entering the school premise which is also important for the student’s or staff’s identification as well as the school’s security and integrity. This paper aims to plan and built a device whose main function is to monitor student’s attire for most of the time. Uniform recognition-activated gate for dress code implementation of Pamantasan ng Cabuyao focused on improving the security system upon entering the gate of the university. This device used biometrics, barcode scanner of the Identification (ID) card and image recognition for uniform to open the gate. The mechanism to open the gate uses a servo motor which is connected to the gate structure. Based on the evaluation done by the professionals and preferred users, the device has been considered very good for each criteria provided of its scores. The device will be available for further improvement to develop more functions necessary to the workplace of its application.

Keywords: Biomedical Engineering, Signal and Image Processing
Abstract: Melanoma is a type of skin cancer that is mainly caused by intense UV exposure. If melanoma is identified at an early stage, then it is generally remediable. However, if it is not diagnosed properly, cancer can grow to rest of the body which then makes it difficult to cure and can be lethal. Conventionally, melanoma is diagnosed through visual methods and biopsies but their accuracy may not be reliable for all the cases. Hence, the risks involved for such a diagnosis have emerged identification and classification of melanoma as benign or malignant a very important research problem in medical imaging. This paper employs various feature descriptors like local binary pattern (LBP), complete LBP (CLBP) and their variants, which are based on histogram mapping such as uniform, rotation invariant and rotation invariant uniform patterns. The extracted features are then used to train different classifiers such as decision tree, random forest (RF), support vector machine (SVM) and k nearest neighbour (kNN). A comparative study of the various feature descriptors and classifiers are analyzed for accurate identification and classification of melanoma as benign or malignant. An image dataset which has been used in our work has been downloaded from ISIC-Archive, which consists of 947 dermoscopic images and the dataset is made freely available online by realizing the importance of the research. The best accuracy has been obtained by using RF in CLBP with an accuracy of 80.3%.

Keywords: Biomedical Engineering, Signal and Image Processing, Antenna & Microwave
Abstract: In recent years, the interest in utilizing non-contact Doppler radar for vital sign detection is growing. In case that there is another person around a subject, the influence due to the obstructive person is treated as a fundamental problem for the estimation of heart rate (HR) in non-contact heart rate (HR) monitoring 25-GHz Doppler radar. This paper investigates the suitability of bispectrum estimation in extinguishing the influence in the received signal. The bispectrum represents the dependency between two different frequency spectra. Assuming that the heart-beat component from the subject has a strong phase coupling, the bispectrum estimation of a received signal enhances the heart-beat component, and then the influence can be reduced. The experimental results showed the bispectrum estimation improves the estimation accuracy of heart rate.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics, Social Implications of Technology
Abstract: In this paper, we argue that singing voice (song) is more emotional than speech. We evaluate different features sets, feature types, and classifiers on both song and speech emotion recognition. Three feature sets: GeMAPS, pyAudioAnalysis, and LibROSA; two feature types, low-level descriptors and high-level statistical functions; and four classifiers: multilayer perceptron, LSTM, GRU, and convolution neural networks; are examined on both songand speech data with the same parameter values. The results show no remarkable difference between song and speech data on using the same method. Comparisons of two results reveal that song is more emotional than speech. In addition, high-level statistical functions of acoustic features gained higher performance than low-level descriptors in this classification task. This result strengthens the previous finding on the regression task which reported the advantage use of high-level features.

Keywords: Signal and Image Processing, Machine Learning, Cloud and Data Analytics, Software & Database Systems
Abstract: This study centers on the design and development of a microcontroller based hardware interface that connects the serial camera, the processor, the WiFi module, and the LCD screen and identification software for corn plant diseases through web-server using image processing and artificial neural network. This is done by capturing and displaying the image of the leaf inside the box and transmits it to the web server as an input image; process, analyze and interpret the data through image processing. The result of the processed image will be sent to the displaying microcontroller based hardware interface through the web-server and display the Pest Management Recommendations.

Keywords: Signal and Image Processing, Marine and Offshore Engineering
Abstract: Behavior analysis of animals involves the observation of intraspecific and interspecific interactions among various organisms in the environment. Collective behavior such as herding in farm animals, flocking of birds, and shoaling and schooling of fish provide information on its benefits on collective survival, fitness, reproductive patterns, group decision-making, and effects in animal epidemiology. In marine ethology, the investigation of behavioral patterns in schooling species can provide supplemental information in the planning and management of marine resources. Currently, damselfish species, although prevalent in tropical waters, have no adequate established base behavior information. This limits reef managers in efficiently planning for stress and disaster responses in protecting the reef. Visual marine data captured in the wild are scarce and prone to multiple scene variations, primarily caused by motion and changes in the natural environment. The gathered videos of damselfish by this research exhibit several scene distortions caused by erratic camera motions during acquisition. To effectively analyze shoaling behavior given the issues posed by capturing data in the wild, we propose a pre-processing system that utilizes color correction and image stitching techniques and extracts behavior features for manual analysis.

Keywords: Signal and Image Processing, Wireless Communications & Networks
Abstract: Aquaponics is an integration of aquaculture and hydroponics and the naked eye cannot predict and distinguish abiotic factors specifically in the water. The study used a Costus Igneus (insulin plant) to discover if it will grow successfully in the Aquaponics setup rather than traditional soil setup and Nile tilapia for cultivating fish to promote urban farming. With the objectives of the study are to develop an array of sensors using a fuzzy logic algorithm to know the aqueous state ideal value of the system such as water temperature, electrical conductivity, pH content, dissolved oxygen and water level of the tank and sending notification and updates through Global System Communication (GSM). In the result of weekly data gathering plant height between two environments, the statistical value result to 3.728 which exceeds t-critical values of 2.228 proved monitoring of abiotic factors of Aquaponics is significantly greater than traditional soil farming.

Keywords: Computer Architecture & Systems, Wireless Communications & Networks, Software & Database Systems
Abstract: The evolution of Software Defined Networks (SDN) and Network Function Virtualization (NFV) introduced a revolutionary development in network architecture. SDN together with NFV provides users a platform to design flexible virtual networks (VNs) on a shared computer infrastructure. However, network administrators have specific requirements to secure this network. There are new security demands for VN such as flexible network function migrations and user-focused security system, which may not be supported by traditional firewalls. In our work we have implemented SDN and NFV based firewalls on an open source platform mininet. POX module and Click Modular Router are used to develop our firewall modules. Then we evaluated the performance of both firewalls with packet loss and throughput measurement.

Keywords: Engineering Education, Computer Architecture & Systems
Abstract: Programming is an essential skill that an electrical and electronics engineering student should possess. With the advances made in technology such as IoT, low cost embedded systems, even the simplest electrical devices become intelligent and connected. In electrical engineering curriculum, introduction to programming is traditionally taught though without rigor. Computer programming is a challenging task since it requires abstract thinking, logic and mathematics skills. Novice students rapidly develop a disliking and avoid tasks/projects that involves programming in future. In order to overcome these issues and get students interested in programming, we took an evidence based approach for teaching computer programming. The method involves combining a number of proven best practices in teaching, together with a reflective tool for the instructor when planning and delivering the course. We experimented with novice students who are taking the basic computer programming for the first time and found out its highly effective. This paper describes the evidence based practices used and our observations on the outcome.

Keywords: Engineering Education, Computer Architecture & Systems, Software & Database Systems
Abstract: Online learning is becoming increasingly popular and used in many academic disciplines due to its advantages, where learners can access courses from anywhere and at any time. Besides benefits, online learning may have limitations, such as slow response times when bandwidth is limited, or inflexible one-size-fits-all content without regard for the learner’s background or knowledge state. This paper presents an approach to more flexible online learning, where recommended learning paths are derived from the results of learning activities and assessment tasks. The proposed paths comprise multiple intended learning outcome (ILO) nodes based upon and sequenced according to Bloom’s taxonomies and Biggs’ principles of constructive alignment (PCA).

Keywords: Power & Energy
Abstract: The intent of this paper is to analyze the power consumption or heat dissipation of defective bypass diode (BPD) at open circuit (OC) and maximum power point (MPP) load conditions when the BPD turns to have resistive behavior in a photovoltaic (PV) string and array. We also analyze the thermal impact of defective BPD in a PV string under different irradiances and cell temperatures. It was observed that the power consumption of defective BPD largely depends on load condition and the thermal impact is more severe in OC condition. It was also observed that the thermal impact of defective BPD is larger under either high irradiance level for the same cell temperature or lower cell temperature on the same irradiance. This study indicates that it is necessary to monitor the heat dissipation of BPDs and to evaluate the electrical properties of the BPDs to realize the reliable PV systems.

Keywords: Power & Energy
Abstract: This paper describes optimal voltage control and optimal placement of the three-phase individual step voltage regulator 3ΦSVR considering voltage unbalance improvement. As a result of active efforts to promote renewable energy, there is a concern that voltage unbalance will increase due to an increase in distributed power sources. Therefore, this paper proposes the optimal control and placement method for 3ΦSVR for voltage unbalance improvement and loss minimization. Simulations verified that all the voltage unbalanced indices satisfied the constraint value and the objective function improved. These results confirmed that the effectiveness of the optimal control and placement method for 3ΦSVR.

Keywords: Power & Energy
Abstract: This study proposes an optimal installed capacity of Distributed Energy Resources (DERs) for a small remote island (Aguni-Island) is belongs to Okinawa Prefecture in Japan. Photovoltaic (PV), Wind Generator (WG), Battery Energy Storage System (BESS), and Fuel Cell (FC) are considered to be installed for optimal sizing. The simulation conducted in this study has been simplified to account seasonal load and weather variations. This simulations aims to minimize the fuel and total cost of the system as it resulted in a total cost, which is lower than it was before DERs to be implemented. In addition, carbon dioxide emissions from diesel generators have been reduced.

Keywords: Power & Energy
Abstract: In recent years, there is a tendency to reduce greenhouse gas emissions internationally in consideration of environmental problems. In the field of electric power systems, the introduction of renewable energy power generation facilities centered on photovoltaic power generation is being promoted in order to reduce greenhouse gas emissions. However, if these power generation facilities are introduced excessively, a duck curve phenomenon occurs in which the operating efficiency of the thermal power generator is poor. Currently, we are reducing the output of photovoltaic power generation equipment to avoid the duck curve phenomenon. In order to suppress the output, it is not possible to further install renewable energy power generation equipment, and the limit of introduction comes with a fixed amount. Therefore, this paper proposes a new load demand control method that improves the duck curve phenomenon without hindering the introduction of renewable energy power generation equipment. In addition, in order to make the proposed method more active, the usual unit commitment has a time interval of 1 hour, but at the same time, we also devised a time interval of 30 minutes. We show that by performing a short-term unit commitment using the proposed method, the optimal operation can be performed even when a large amount of renewable energy power generation is introduced.

Keywords: Power & Energy
Abstract: An emerging subsynchronous oscillation (SSO) associated with wind generators and voltage-sourced converter (VSC) based HVDC has been observed around the world. It is revealed that this new type of SSO is caused by the subsynchronous control interaction among power electronic converters and the grid. In this paper, the stability criterion of SSO is examined using frequency-dependent impedance models. Then a shunt VSC based subsynchronous damping control (SVSDC) has been proposed to reshape the impedance characteristic of the whole system and thus mitigate the SSO. The stability criterion and the control has been applied to a typical system with DFIG-based wind farms connected to a modular multilevel converter (MMC) based HVDC transmission. Impedance model based analyses as well as electromagnetic simulations have verified the effectiveness of the criterion and the control scheme. For its flexibility of capacity and deployment, the proposed SVSDC offers a promising option to address the SSO issues associated with wind generators and/or VSC-HVDCs.