Computer Engineering Commons^™

Human Activity Recognition Based On Wearable Flex Sensor And Pulse Sensor, Xiaozhu Jin

Electronic Theses and Dissertations

In order to fulfill the needs of everyday monitoring for healthcare and emergency advice, many HAR systems have been designed [1]. Based on the healthcare purpose, these systems can be implanted into an astronaut’s spacesuit to provide necessary life movement monitoring and healthcare suggestions. Most of these systems use acceleration data-based data record as human activity representation [2,3]. But this data attribute approach has a limitation that makes it impossible to be used as an activity monitoring system for astronavigation. Because an accelerometer senses acceleration by distinguishing acceleration data based on the earth’s gravity offset [4], the accelerometer cannot read …

Speed-Sensorless Predictive Torque Controlled Induction Motor Drive Withfeed-Forward Control Of Load Torque For Electric Vehicle Applications, Emrah Zerdali̇, Ridvan Demi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Nowadays, the global trend is towards reducing CO2 emissions and one solution is to replace internal combustion vehicles with electric vehicles. To this end, electric drive system, the most crucial part of an electric vehicle, has gained importance and has become a major research field. The induction motor (IM) is one of the best candidates for electric vehicle applications due to its advantages such as having simple and robust design, its low cost maintenance requirements and the ability to operate in harsh environments. However, it has a highly nonlinear model with timevarying electrical and mechanical parameters making them difficult to …

Optimal Coordination Of Directional Overcurrent Relay Based On Combination Ofimproved Particle Swarm Optimization And Linear Programming Consideringmultiple Characteristics Curve, Suzana Pil Ramli, Hazlie Mokhlis, Wei Ru Wong, Munir Azam Muhammad, Nurulafiqah Nadzirah Mansor, Muhamad Hatta Hussain

Turkish Journal of Electrical Engineering and Computer Sciences

Optimal coordination of directional over-current relays (DOCRs) is a crucial task in ensuring the security and reliability of power system network. In this paper, a hybridization of an improved particle swarm optimization and linear programming (IPSO-LP) is proposed to solve DOCRs coordination problem. The considered decision variables in the optimization are plug setting current, time multiplier setting, type of relay, and type of curve. By considering these parameters in the optimization, the best relay operating time can be determined. Furthermore, the proposed technique also considered the continuous values of pick-up current setting (PSC) and time setting multiplier (TMS). Test on …

Adaptation Of Metaheuristic Algorithms To Improve Training Performance Of Aneszsl Model, Şi̇fa Özsari, Mehmet Serdar Güzel, Gazi̇ Erkan Bostanci, Ayhan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

Zero-shot learning (ZSL) is a recent promising learning approach that is similar to human vision systems. ZSL essentially allows machines to categorize objects without requiring labeled training data. In principle, ZSL proposes a novel recognition model by specifying merely the attributes of the category. Recently, several sophisticated approaches have been introduced to address the challenges regarding this problem. Embarrassingly simple approach to zeroshot learning (ESZSL) is one of the critical of those approaches that basically proposes a simple but efficient linear code solution. However, the performance of the ESZSL model mainly depends on parameter selection. Metaheuristic algorithms are considered as …

Fpga Implementation Of Lsd-Omp For Real-Time Ecg Signal Reconstruction, Önder Polat, Sema Kayhan

Turkish Journal of Electrical Engineering and Computer Sciences

Compressed sensing is widely used to compress electrocardiogram (ECG) signals, but the major challenges of the compressed sensing algorithms are their highly complex signal reconstruction processes. In this paper, a reconfigurable high-speed and low-power field-programmable gate array (FPGA) implementation of the least support denoising-orthogonal matching pursuit (LSD-OMP) algorithm for the real-time reconstruction of the ECG signals is presented. The contribution of this study is two-fold: Firstly, LSD-OMP can pick more than one element at each iteration and reconstruct the sparse signal using less number of iterations as compared to the standard OMP algorithms. Latency of the proposed design is therefore …

A Novel Hybrid Decision-Based Filter And Universal Edge-Based Logical Smoothingadd-On To Remove Impulsive Noise, Rajanbir Singh Ghumaan, Prateek Jeet Singh Sohi, Nikhil Sharma, Bharat Garg

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a novel hybrid filter along with a universal extension to remove salt and pepper noise even at a very high noise density. The proposed filter initially specifies a threshold and then denoises the image using a combination of linear, nonlinear, and probabilistic techniques. Furthermore, to improve the quality, a universal add-on is presented which uses edge detection and smoothening techniques to brush out fine details from the restored image. To evaluate the efficacy, the proposed and existing filtering techniques are implemented in MATLAB and simulated with benchmark images. The simulation results show that the proposed filter is …

An Enhanced Bandwidth Disturbance Observer Based Control- S-Filter Approach, Mehmet Önder Efe, Coşku Kasnakoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

A continuous time enhanced bandwidth disturbance observer based control (DOBC) scheme is proposed in this paper. The classical Q -filter is implemented in feedback form and a signum function is inserted into the loop. The loop with this modification becomes capable of detecting small magnitude matched disturbances and we present an in depth discussion of the stability and performance issues comparatively. The proposed approach is called S-filter approach and the results outperform the classical approach under certain conditions. The contribution of the current paper is to advance the subject area to nonlinear filters for DOBC loops with guaranteed stability and …

A Linear Programming Approach To Multiple Instance Learning, Emel Şeyma Küçükaşci, Mustafa Gökçe Baydoğan, Zeki̇ Caner Taşkin

Turkish Journal of Electrical Engineering and Computer Sciences

Multiple instance learning (MIL) aims to classify objects with complex structures and covers a wide range of real-world data mining applications. In MIL, objects are represented by a bag of instances instead of a single instance, and class labels are provided only for the bags. Some of the earlier MIL methods focus on solving MIL problem under the standard MIL assumption, which requires at least one positive instance in positive bags and all remaining instances are negative. This study proposes a linear programming framework to learn instance level contributions to bag label without emposing the standart assumption. Each instance of …

Towards An Ontology-Based Approach To The "New Normality" After Covid-19:The Spanish Case During Pandemic First Wave, Evelio Gonzalez

Turkish Journal of Electrical Engineering and Computer Sciences

The impact of the pandemic caused by COVID-19 has been immense in all fields of human activity. In most of the affected countries, the authorities have decreed a series of legal measures to try to stop the growth of the disease and the number of people affected by it. These legal measures involved, in most cases, restrictions on the free movement of people and on work and trade activities, new hygiene procedures, and social distancing. In the particular case of Spain, the rapid evolution of the pandemic led to the declaration of a so-called state of alarm and a period …

A 1-Kw Wireless Power Transfer System For Electric Vehicle Charging Withhexagonal Flat Spiral Coil, Emrullah Aydin, Mehmet Ti̇mur Aydemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Wireless power transfer (WPT) technology is getting more attention in these days as a clean, safe, and easy alternative to charging batteries in several power levels. Different coil types and system structures have been proposed in the literature. Hexagonal coils, which have a common usage for low power applications, have not been well studied for high and mid power applications such as in electric vehicle (EV) battery charging. In order to fill this knowledge gap, the self and mutual inductance equations of a hexagonal coil are obtained, and these equations have been used to design a 1 kW WPT system …

A New Classification Method For Encrypted Internet Traffic Using Machine Learning, Mesut Uğurlu, İbrahi̇m Alper Doğru, Recep Si̇nan Arslan

Turkish Journal of Electrical Engineering and Computer Sciences

The rate of internet usage in the world is over 62% and this rate is increasing day by day. With this increase, it becomes important to ensure the confidentiality of the information in the traffic flowing over the internet. Encryption algorithms and protocols are used for this purpose. This situation, which is beneficial for normal users, is also used by attackers to hide. Cyber attackers or hackers gain the ability to bypass security precautions such as IDS/IPS and antivirus systems with using encrypted traffic. Since payload analysis cannot be performed without deciphering the encrypted traffic, existing commercial security solutions fall …

Sleep Staging With Deep Structured Neural Net Using Gabor Layer And Dataaugmentation, Ali Erfani Sholeyan, Fereidoun Nowshiravan Rahatabad, Kamal Setaredan

Turkish Journal of Electrical Engineering and Computer Sciences

Slow wave sleep (SWS) and rapid eye movement (REM) are two of the most important sleep stages that are considered in many studies. Detection of these two sleep stages will help researchers in many applications to detect sleeprelated diseases and disorders and also in many fields of neuroscience studies such as cognitive impairment and memory consolidation. Since manual sleep staging is time-consuming, subjective, and expensive; designing an efficient automatic sleep scoring system will overcome some of these difficulties. Many studies have proposed automatic sleep staging systems with different methods. In recent years, deep learning methods show their potential in different …

Design Development And Performance Analysis Of Distributed Least Square Twinsupport Vector Machine For Binary Classification, Bakshi Rohit Prasad, Sonali Agarwal

Turkish Journal of Electrical Engineering and Computer Sciences

Machine learning (ML) on Big Data has gone beyond the capacity of traditional machines and technologies. ML for large scale datasets is the current focus of researchers. Most of the ML algorithms primarily suffer from memory constraints, complex computation, and scalability issues.The least square twin support vector machine (LSTSVM) technique is an extended version of support vector machine (SVM). It is much faster as compared to SVM and is widely used for classification tasks. However, when applied to large scale datasets having millions or billions of samples and/or large number of classes, it causes computational and storage bottlenecks. This paper …

Robust And Efficient Ebg-Backed Wearable Antenna For Ism Applications, Ayesha Saeed, Asma Ejaz, Humayun Shahid, Yasar Amin, Hannu Tenhunen

Turkish Journal of Electrical Engineering and Computer Sciences

A structurally compact, semiflexible wearable antenna composed of a distinctively miniaturized electromagnetic band gap (EBG) structure is presented in this work. Designed for body-centric applications in the 5.8 GHz band, the design draws heavily from a novel planar geometry realized on Rogers RT/duroid 5880 laminate with a compact physical footprint spanning lateral dimensions of $0.6$$\lambda$$_0$$\times$$0.06$$\lambda$$_0$. Incorporating a 2$\times$2 EBG structure at the rear of the proposed design ensures sufficient isolation between the body and the antenna, doing away with the performance degradation associated with high permittivity of the tissue layer. The peculiar antenna geometry allows for reduced backward radiation and …

Attention Augmented Residual Network For Tomato Disease Detection Andclassification, Getinet Yilma Abawatew, Seid Belay, Kumie Gedamu, Maregu Assefa, Melese Ayalew, Ariyo Oluwasanmi, Zhiguang Qin

Turkish Journal of Electrical Engineering and Computer Sciences

Deep learning techniques help agronomists efficiently identify, analyze, and monitor tomato health. CNN (convolutional neural network) locality constraint and existing small train sample adversely influenced disease recognition performance. To alleviate these challenges, we proposed a discriminative feature learning attention augmented residual (AAR) network. The AAR network contains a stacked pre-activated residual block that learns deep coarse level features with locality context, whereas the attention block captures salient feature sets while maintaining the global relationship in data points, attention features augment the learning of the residual block. We used conditional variational generative adversarial network (CVGAN) image reconstruction network and augmentation techniques …

Deep Learning-Based Covid-19 Detection System Using Pulmonary Ct Scans, Rajit Nair, Adi Alhudhaif, Deepika Koundal, Rumi Iqbal Doewes, Preeti Sharma

Turkish Journal of Electrical Engineering and Computer Sciences

One of the most significant pandemics has been raised in the form of Coronavirus disease 2019 (COVID19). Many researchers have faced various types of challenges for finding the accurate model, which can automatically detect the COVID-19 using computed pulmonary tomography (CT) scans of the chest. This paper has also focused on the same area, and a fully automatic model has been developed, which can predict the COVID-19 using the chest CT scans. The performance of the proposed method has been evaluated by classifying the CT scans of community-acquired pneumonia (CAP) and other non-pneumonia. The proposed deep learning model is based …

A Novel Design Of Current Differencing Transconductance Amplifier With Hightransconductance Gain And Enhanced Bandwidth, Shireesh Kumar Rai, Rishikesh Pandey, Bharat Garg, Sujit Patel

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, transconductance gain of current differencing transconductance amplifier (CDTA) has been boosted by using a novel approach. Transconductance is generally varied by two well-known techniques. In the first technique, bias current of differential pair MOSFETs is varied whereas in the second technique, aspect ratios of differential pair MOSFETs are changed. The drawbacks of first technique are limited range of transconductance and higher power dissipation whereas second technique restricts dynamic range, output swing and bandwidth of CDTA. To overcome these drawbacks, 2 new structures of CDTA, namely high transconductance gain CDTAs (HTG-CDTA-I & HTG-CDTA-II) have been proposed. HTG-CDTA-I utilizes …

Mismatch Error Shaping Of Dac Unit Elements In Multibit $\Delta$$\Sigma$ Modulators Using A Novel Unified Adc/Dac, Leila Sharifi, Omid Hashemipour

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a unified analog to digital converter (ADC) and digital to analog converter (DAC) for multibit $\Delta\Sigma$ modulators. The unified ADC/DAC circuit provides error shaping for mismatches between DAC unit elements. Hence, the dynamic element matching (DEM) circuit or digital calibration is not required resulting in the area and power saving as well as the elimination of the excess loop delay introduced by DEM circuit. Incorporating a 6-bit unified ADC/DAC, the $\Delta\Sigma$ modulator achieves 16.15-bit resolution utilizing only a second order loop filter and oversampling ratio of 40. The proposed modulator is simulated in a 65-nm CMOS process. …

Mixed Carrier Communication For Sixth-Generation Networks :, Ahmed Fahmy Mahmoud Hussein

Legacy Theses & Dissertations (2009 - 2024)

Recently, research on sixth-generation (6G) wireless networks has gained significant interest. By 2030, it is expected that 6G will introduce revolutionary applications and services. Thus, 6G is likely to expand across all available spectrum, including terahertz (THz) and optical frequency bands. Although 5G will offer a massive upgrade to the spectrum, the technology does not provide solutions to support a vast multitude of services and devices simultaneously.Motivated by the heterogeneity of wireless technologies, devices, and services, the Mixed Carrier Communication (MCC) concept is introduced for the first time. MCC is a novel concept that supports the 6G vision by enabling …

Peer-To-Peer Energy Trading For Networked Microgrids, Jonathan David Warner

Theses, Dissertations and Capstones

Considering the limitations of the existing centralized power infrastructure, research interests have been directed to decentralized smart power systems constructed as networks of interconnected microgrids. Therefore, it has become critical to develop secure and efficient energy trading mechanisms among networked microgrids for reliability and economic mutual benefits. Furthermore, integrating blockchain technologies into the energy sector has gained significant interest among researchers and industry professionals. Considering these trends, the work in this thesis focuses on developing Peer-to-Peer (P2P) energy trading models to facilitate transactions among microgrids in a multiagent network. Price negotiation mechanisms are proposed for both islanded and grid-connected microgrid …

Enabling Machine Learning On The Edge Using Sram Conserving Efficient Neural Networks Execution Approach, Bharath Sudharsan, Pankesh Patel, John G. Breslin, Muhammad Intizar Ali

Publications

Edge analytics refers to the application of data analytics and Machine Learning (ML) algorithms on IoT devices. The concept of edge analytics is gaining popularity due to its ability to perform AI-based analytics at the device level, enabling autonomous decision-making, without depending on the cloud. However, the majority of Internet of Things (IoT) devices are embedded systems with a low-cost microcontroller unit (MCU) or a small CPU as its brain, which often are incapable of handling complex ML algorithms.

In this paper, we propose an approach for the ecient execution of already deeply compressed, large neural networks (NNs) on tiny …

Knowledge Infused Policy Gradients With Upper Confidence Bound For Relational Bandits, Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth

Publications

Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc. However, most of the algorithms use at feature vectors to represent context whereas, in the real world, there is a varying number of objects and relations among them to model in the context. For example, in a music recommendation system, the user context contains what music they listen to, which artists create this music, the artist albums, etc. Adding richer relational context representations also introduces a much larger context space making exploration-exploitation harder. To improve the efficiency of exploration-exploitation knowledge about the …

A Meta-Gradient Approach To Learning Cooperative Multi-Agent Communication Topology, Qi Zhang, Dingyang Chen

Publications

In cooperative multi-agent reinforcement learning (MARL), agents often can only partially observe the environment state, and thus communication is crucial to achieving coordination. Communicating agents must simultaneously learn to whom to communicate (i.e., communication topology) and how to interpret the received message for decision-making. Although agents can efficiently learn communication interpretation by end-to-end backpropagation, learning communication topology is much trickier since the binary decisions of whether to communicate impede end-to-end differentiation. As evidenced in our experiments, existing solutions, such as reparameterization tricks and reformulating topology learning as reinforcement learning, often fall short. This paper introduces a meta-learning framework that aims …

Ieee Access Special Section Editorial: Trends And Advances In Bio-Inspired Image-Based Deep Learning Methodologies And Applications, Peter Peer, Carlos M. Travieso-Gonzalez, Vijayan K. Asari, Malay Kishore Dutta

Electrical and Computer Engineering Faculty Publications

Many of the technological advances we enjoy today have been inspired by biological systems due to their ease of operation and outstanding efficiency. Designing technological solutions based on biological inspiration has become a cornerstone of research in a variety of areas ranging from control theory and optimization to computer vision, machine learning, and artificial intelligence. Especially in the latter few areas, biologically relevant solutions are becoming increasingly important as we look for new ways to make artificial systems more efficient, intelligent, and overall effective.

On The Mandelbrot Set For I**2 = ±1 And Imaginary Higgs Fields, Jonathan Blackledge

Articles

We consider the consequence of breaking with a fundamental result in complex analysisby lettingi2=±1wherei=√−1is the basic unit of all imaginary numbers. An analysis of theMandelbrot set for this case shows that a demarcation between a Fractal and a Euclidean object ispossible based oni2=−1andi2= +1, respectively. Further, we consider the transient behaviourassociated with the two cases to produce a range of non-standard sets in which a Fractal geometricstructure is transformed into a Euclidean object. In the case of the Mandelbrot set, the Euclideanobject is a square whose properties are investigate. Coupled with the associated Julia sets and othercomplex plane mappings, this …

Re-Designing Main Memory Subsystems With Emerging Monolithic 3d (M3d) Integration And Phase Change Memory Technologies, Chao-Hsuan Huang

Theses and Dissertations--Electrical and Computer Engineering

Over the past two decades, Dynamic Random-Access Memory (DRAM) has emerged as the dominant technology for implementing the main memory subsystems of all types of computing systems. However, inferring from several recent trends, computer architects in both the industry and academia have widely accepted that the density (memory capacity per chip area) and latency of DRAM based main memory subsystems cannot sufficiently scale in the future to meet the requirements of future data-centric workloads related to Artificial Intelligence (AI), Big Data, and Internet-of-Things (IoT). In fact, the achievable density and access latency in main memory subsystems presents a very fundamental …

Design Project: Smart Headband, John Michel, Jack Durkin, Noah Lewis

Williams Honors College, Honors Research Projects

Concussion in sports is a prevalent medical issue. It can be difficult for medical professionals to diagnose concussions. With the fast pace nature of many sports, and the damaging effects of concussions, it is important that any concussion risks are assessed immediately. There is a growing trend of wearable technology that collects data such as steps and provides the wearer with in-depth information regarding their performance. The Smart Headband project created a wearable that can record impact data and provide the wearer with a detailed analysis on their risk of sustaining a concussion. The Smart Headband uses accelerometers and gyroscopes …

Automated Blind Control, Daniel Nahra, Matthew Lacek, Timothy Kurczewski, William Daulton Baksa

Williams Honors College, Honors Research Projects

The objective of this project would be to design and prototype an automated, light and temperature sensing window blinds system. The device would detect temperature, both inside and outside, and incoming sunlight to determine proper window blind position for maximum energy savings. The user would also have the ability to change the settings of the blind from a remote device to a setting that they desire at any given time

Soil Sensor Network, Andrea Wyder, Ross Klonowski, Alexis Alves, Luke Farnsworth

Williams Honors College, Honors Research Projects

Water management during crop irrigation is a problem for the agricultural industry. To help farmers better maintain water usage, a wireless soil sensor network comprised of a sensor pod and wireless communication has been designed and implemented. It was proven that the sensor pod can be installed 6-8 inches below the ground and communicate up to at least a 6km distance back to the gateway. The senor pod shells have a 2 mm thick shell to prevent the pod from shattering when coming into contact with the ground after being released from the planter, as calculated through the force of …

Light Loaded Automated Guided Vehicle, Marcus Radtka, Nazar Paramashchuk, Lawrence Shevock

Williams Honors College, Honors Research Projects

The objective of the locomotion system was to design and implement the mechanical, electrical, and software related functions to ensure the LLAGV had the capability of maneuvering its surroundings. The LLAGV’s motors were represented in an open loop transfer function to utilize RPM feedback and a compensator when needed. The modeled compensator helped control the LLAGV’s speed and acceleration, enabling further control of the LLAGV. The internal circuitry has the means to properly distributed power to all components and allowed the user to control the LLAGV to their desire. The application software within the LLAGV locomotion system (LLAGV-LS) had consideration …