Open Access. Powered by Scholars. Published by Universities.®
Electrical and Computer Engineering Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (209)
- Computer Engineering (201)
- Physics (190)
- Optics (189)
- Systems and Communications (171)
-
- Other Electrical and Computer Engineering (164)
- Electromagnetics and Photonics (162)
- Electrical and Electronics (159)
- Power and Energy (26)
- Signal Processing (24)
- Controls and Control Theory (22)
- Digital Communications and Networking (21)
- Other Physics (16)
- Aerospace Engineering (15)
- Computer Sciences (12)
- Computer and Systems Architecture (10)
- Medicine and Health Sciences (10)
- Nanoscience and Nanotechnology (10)
- Systems Engineering and Multidisciplinary Design Optimization (9)
- Biomedical Engineering and Bioengineering (8)
- Mechanical Engineering (6)
- Robotics (6)
- Electronic Devices and Semiconductor Manufacturing (5)
- Graphics and Human Computer Interfaces (5)
- Applied Mathematics (4)
- Biomedical (4)
- Life Sciences (4)
- Biomechanical Engineering (3)
- Institution
- Keyword
-
- University of Dayton Electro-optics and Photonics (15)
- Optimization (12)
- Energy efficiency (9)
- Biogeography-based optimization (8)
- Machine learning (8)
-
- Deep learning (7)
- Robustness (7)
- Kalman filter (6)
- State estimation (6)
- Atmospheric turbulence (5)
- Computational modeling (5)
- Estimation (5)
- Image restoration (5)
- Kalman filtering (5)
- Modeling (5)
- Smart pixel (5)
- Additive manufacturing (4)
- Batteries (4)
- Biosensor (4)
- Compressive sensing (4)
- Constrained optimization (4)
- Costs (4)
- Evolutionary algorithm (4)
- Fault tolerance (4)
- Gradient descent (4)
- Infrared imaging (4)
- Metamaterials (4)
- Microwave (4)
- Neural networks (4)
- Optical (4)
- Publication Year
Articles 31 - 60 of 691
Full-Text Articles in Electrical and Computer Engineering
Practical And Lightweight Defense Against Website Fingerprinting, Colman Mcguan, Chansu Yu, Kyoungwon Suh
Practical And Lightweight Defense Against Website Fingerprinting, Colman Mcguan, Chansu Yu, Kyoungwon Suh
Electrical and Computer Engineering Faculty Publications
Website fingerprinting is a passive network traffic analysis technique that enables an adversary to identify the website visited by a user despite encryption and the use of privacy services such as Tor. Several website fingerprinting defenses built on top of Tor have been proposed to guarantee a user's privacy by concealing trace features that are important to classification. However, some of the best defenses incur a high bandwidth and/or latency overhead. To combat this, new defenses have sought to be both lightweight - i.e., introduce a small amount of bandwidth overhead - and zero-delay to real network traffic. This work …
Accurate Synthesis Of Dysarthric Speech For Asr Data Augmentation, Mohammad Soleymanpour, Michael T. Johnson, Rahim Soleymanpour, Jeffrey Berry
Accurate Synthesis Of Dysarthric Speech For Asr Data Augmentation, Mohammad Soleymanpour, Michael T. Johnson, Rahim Soleymanpour, Jeffrey Berry
Electrical and Computer Engineering Faculty Publications
Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility through slow, uncoordinated control of speech production muscles. Automatic Speech recognition (ASR) systems can help dysarthric talkers communicate more effectively. However, robust dysarthria-specific ASR requires a significant amount of training speech, which is not readily available for dysarthric talkers. This paper presents a new dysarthric speech synthesis method for the purpose of ASR training data augmentation. Differences in prosodic and acoustic characteristics of dysarthric spontaneous speech at varying severity levels are important components for dysarthric speech modeling, synthesis, and augmentation. For dysarthric speech synthesis, a modified neural multi-talker …
Multiple-Point Metamaterial-Inspired Microwave Sensors For Early-Stage Brain Tumor Diagnosis, Nantakan Wongkasem, Gabriel Cabrera
Multiple-Point Metamaterial-Inspired Microwave Sensors For Early-Stage Brain Tumor Diagnosis, Nantakan Wongkasem, Gabriel Cabrera
Electrical and Computer Engineering Faculty Publications
Simple, instantaneous, contactless, multiple-point metamaterial-inspired microwave sensors, composed of multi-band, low-profile metamaterial-inspired antennas, were developed to detect and identify meningioma tumors, the most common primary brain tumors. Based on a typical meningioma tumor size of 5-20 mm, a higher operating frequency, where the wavelength is similar or smaller than the tumor target, is crucial. The sensors, designed for the microwave Ku band range (12-18 GHz), where the electromagnetic property values of tumors are available, were implemented in this study. A seven-layered head phantom, including the meningioma tumors, was defined using actual electromagnetic parametric values in the frequency range of interest …
A Simulated Annealing Approach To The Scheduling Of Battery-Electric Bus Charging, Alexander Brown, Greg Droge
A Simulated Annealing Approach To The Scheduling Of Battery-Electric Bus Charging, Alexander Brown, Greg Droge
Electrical and Computer Engineering Faculty Publications
With an increasing adoption of battery-electric bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this paper, a simulated annealing (SA) implementation is developed for a charge scheduling framework for a fixed-schedule fleet of BEBs that utilizes a proportional battery dynamics model, accounts for multiple charger types, allows partial charging, and further considers the total energy consumed by the schedule as well as peak power use. Two generation mechanisms are implemented for the SA algorithm, denoted as the "quick" and "heuristic" implementations, respectively. The model validity is demonstrated by …
Assessment Of Economic Viability Of Direct Current Fast Charging Infrastructure Investments For Electric Vehicles In The United States, Daniel Bernal, Adeeba A. Raheem, Sundeep Inti, Hongjie Wang
Assessment Of Economic Viability Of Direct Current Fast Charging Infrastructure Investments For Electric Vehicles In The United States, Daniel Bernal, Adeeba A. Raheem, Sundeep Inti, Hongjie Wang
Electrical and Computer Engineering Faculty Publications
As the global transportation sector increasingly adopts electric vehicles, the demand for advanced and accessible charging infrastructure is rising. In addition to at-home electric vehicle (EV) charging, there is a growing need for the swift development of commercial direct current fast charging (DCFC) stations to meet on-the-go EV charging demands. While government funds are available to support the expansion of the EV charging network in the United States, the establishment of a robust nationwide EV charging infrastructure requires significant private sector investment. This study was conducted to assess the economic feasibility of various business models for fast charging stations in …
Wearable Alcohol Monitoring Device For The Data-Driven Transcutaneous Alcohol Diffusion Model, Ahmed Hasnain Jalal, Sepehr Arbabi, Mohammad A. Ahad, Fahmida Alam, Md. Ashfaq Ahmed
Wearable Alcohol Monitoring Device For The Data-Driven Transcutaneous Alcohol Diffusion Model, Ahmed Hasnain Jalal, Sepehr Arbabi, Mohammad A. Ahad, Fahmida Alam, Md. Ashfaq Ahmed
Electrical and Computer Engineering Faculty Publications
Wearable alcohol monitoring devices demand noninvasive, real-time measurement of blood alcohol content (BAC) reliably and continuously. A few commercial devices are available to determine BAC noninvasively by detecting transcutaneous diffused alcohol. However, they suffer from a lack of accuracy and reliability in the determination of BAC in real time due to the complex scenario of the human skin for transcutaneous alcohol diffusion and numerous factors (e.g., skin thickness, kinetics of alcohol, body weight, age, sex, metabolism rate, etc.). In this work, a transcutaneous alcohol diffusion model has been developed from real-time captured data from human wrists to better understand the …
Entropy-Infused Deep Learning Loss Function For Capturing Extreme Values In Wind Power Forecasting, Mucun Sun, Sergio Valdez, Juan M. Perez, Kevin Garcia, Gael Galvan, Cesar Cruz, Yifeng Gao, Li Zhang
Entropy-Infused Deep Learning Loss Function For Capturing Extreme Values In Wind Power Forecasting, Mucun Sun, Sergio Valdez, Juan M. Perez, Kevin Garcia, Gael Galvan, Cesar Cruz, Yifeng Gao, Li Zhang
Electrical and Computer Engineering Faculty Publications
Extreme scenarios in wind power generation occur with higher frequency and larger magnitude in the recent years due to the ever-increasing extreme meteorological factors. Accurate forecasting of the occurrence of extreme values in wind power generation is of great concern to ensure reliable power system operation. Recently, deep learning models have surged in popularity for wind power forecasting, with the mean squared error (MSE) loss function being commonly used. However, the MSE loss function, being sensitive to extreme values, disproportionately penalizes larger errors, cannot adequately capture the extreme values present in wind energy data, and novel loss functions have seldom …
Voltage Scaled Low Power Dnn Accelerator Design On Reconfigurable Platform, Rourab Paul, Sreetama Sarkar, Suman Sau, Sanghamitra Roy, Koushik Chakraborty, Amlan Chakrabarti
Voltage Scaled Low Power Dnn Accelerator Design On Reconfigurable Platform, Rourab Paul, Sreetama Sarkar, Suman Sau, Sanghamitra Roy, Koushik Chakraborty, Amlan Chakrabarti
Electrical and Computer Engineering Faculty Publications
The exponential emergence of Field-Programmable Gate Arrays (FPGAs) has accelerated research on hardware implementation of Deep Neural Networks (DNNs). Among all DNN processors, domain-specific architectures such as Google’s Tensor Processor Unit (TPU) have outperformed conventional GPUs (Graphics Processing Units) and CPUs (Central Processing Units). However, implementing low-power TPUs in reconfigurable hardware remains a challenge in this field. Voltage scaling, a popular approach for energy savings, can be challenging in FPGAs, as it may lead to timing failures if not implemented appropriately. This work presents an ultra-low-power FPGA implementation of a TPU for edge applications. We divide the systolic array of …
A Scalable Approach To Minimize Charging Costs For Electric Bus Fleets, Daniel Mortensen, Jacob Gunther
A Scalable Approach To Minimize Charging Costs For Electric Bus Fleets, Daniel Mortensen, Jacob Gunther
Electrical and Computer Engineering Faculty Publications
Incorporating battery electric buses into bus fleets faces three primary challenges: a BEB’s extended refuel time, the cost of charging, both by the consumer and the power provider, and large compute demands for planning methods. When BEBs charge, the additional demands on the grid may exceed hardware limitations, so power providers divide a consumer’s energy needs into separate meters even though doing so is expensive for both power providers and consumers. Prior work has developed a number of strategies for computing charge schedules for bus fleets; however, prior work has not worked to reduce costs by aggregating meters. Additionally, because …
Recent Progress And Challenges Of Implantable Biodegradable Biosensors, Fahmida Alam, Md Ashfaq Ahmed, Ahmed Hasnain Jalal, Ishrak Siddiquee, Rabeya Zinnat Adury, G M Mehedi Hossain, Nezih Pala
Recent Progress And Challenges Of Implantable Biodegradable Biosensors, Fahmida Alam, Md Ashfaq Ahmed, Ahmed Hasnain Jalal, Ishrak Siddiquee, Rabeya Zinnat Adury, G M Mehedi Hossain, Nezih Pala
Electrical and Computer Engineering Faculty Publications
Implantable biosensors have evolved to the cutting-edge technology of personalized health care and provide promise for future directions in precision medicine. This is the reason why these devices stand to revolutionize our approach to health and disease management and offer insights into our bodily functions in ways that have never been possible before. This review article tries to delve into the important developments, new materials, and multifarious applications of these biosensors, along with a frank discussion on the challenges that the devices will face in their clinical deployment. In addition, techniques that have been employed for the improvement of the …
Effect Of Eis In A 3d Printed Non-Planer Array Patterned Microfluidic Devices, Shanzida Kabir, Hector Zepeda Saenz, Nazmul Islam
Effect Of Eis In A 3d Printed Non-Planer Array Patterned Microfluidic Devices, Shanzida Kabir, Hector Zepeda Saenz, Nazmul Islam
Electrical and Computer Engineering Faculty Publications
In recent years 3D printing is becoming popular among researchers for its reliability, cost-effective materials, and ease of use without having costly clean rooms. In this work we report the process of fabrication of an array patterned microfluidic device with its effect in Electrochemical impedance spectroscopy (EIS) and particle manipulator. With the optimized design of channel geometry and electrode pattern, this device can use in different lab-on-a-chip applications. A 3D printed microfluidic channel fabrication process is presented here along with a CAD drawing with microstructural dimension analysis. EIS is an expeditiously developing method used in characterizing materials and interfaces. By …
Understanding Timing Error Characteristics From Overclocked Systolic Multiply–Accumulate Arrays In Fpgas, Andrew Chamberlin, Andrew Gerber, Mason Palmer, Tim Goodale, Noel Daniel Gundi, Koushik Chakraborty, Sanghamitra Roy
Understanding Timing Error Characteristics From Overclocked Systolic Multiply–Accumulate Arrays In Fpgas, Andrew Chamberlin, Andrew Gerber, Mason Palmer, Tim Goodale, Noel Daniel Gundi, Koushik Chakraborty, Sanghamitra Roy
Electrical and Computer Engineering Faculty Publications
Artificial Intelligence (AI) hardware accelerators have seen tremendous developments in recent years due to the rapid growth of AI in multiple fields. Many such accelerators comprise a Systolic Multiply–Accumulate Array (SMA) as its computational brain. In this paper, we investigate the faulty output characterization of an SMA in a real silicon FPGA board. Experiments were run on a single Zybo Z7-20 board to control for process variation at nominal voltage and in small batches to control for temperature. The FPGA is rated up to 800 MHz in the data sheet due to the max frequency of the PLL, but the …
Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu
Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu
Electrical and Computer Engineering Faculty Publications
Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can …
Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks, Hamed Elwarfalli, Dylan Flaute, Russell C. Hardie
Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks, Hamed Elwarfalli, Dylan Flaute, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel …
H-Nobs: Achieving Certified Fairness And Robustness In Distributed Learning On Heterogeneous Datasets, Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian
H-Nobs: Achieving Certified Fairness And Robustness In Distributed Learning On Heterogeneous Datasets, Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian
Electrical and Computer Engineering Faculty Publications
Fairness and robustness are two important goals in the design of modern distributed learning systems. Despite a few prior works attempting to achieve both fairness and robustness, some key aspects of this direction remain underexplored. In this paper, we try to answer three largely unnoticed and unaddressed questions that are of paramount significance to this topic: (i) What makes jointly satisfying fairness and robustness difficult? (ii) Is it possible to establish theoretical guarantee for the dual property of fairness and robustness? (iii) How much does fairness have to sacrifice at the expense of robustness being incorporated into the system? To …
Approximating Discrimination Within Models When Faced With Several Non-Binary Sensitive Attributes, Yijun Bian, Yujie Luo, Ping Xu
Approximating Discrimination Within Models When Faced With Several Non-Binary Sensitive Attributes, Yijun Bian, Yujie Luo, Ping Xu
Electrical and Computer Engineering Faculty Publications
Discrimination mitigation with machine learning (ML) models could be complicated because multiple factors may interweave with each other including hierarchically and historically. Yet few existing fairness measures are able to capture the discrimination level within ML models in the face of multiple sensitive attributes. To bridge this gap, we propose a fairness measure based on distances between sets from a manifold perspective, named as ‘harmonic fairness measure via manifolds (HFM)’ with two optional versions, which can deal with a fine-grained discrimination evaluation for several sensitive attributes of multiple values. To accelerate the computation of distances of sets, we further propose …
Studying Ht-Mcss In Ieee 802.11n Networks Via Simulations, Jun Peng
Studying Ht-Mcss In Ieee 802.11n Networks Via Simulations, Jun Peng
Electrical and Computer Engineering Faculty Publications
We study the High Throughput Modulation and Coding Schemes (HT-MCS) in IEEE 802.11n In networks in this paper. The HT-MCSs are designed to deal with various channel conditions in the networks. We used ns-3 simulations to study HT-MCS 0 to HT-MCS 31 under various radio propagation models. The simulation results are presented in this paper. In our simulations the HT-MCSs were tested in networks with Friis, Nakagami, and log-distance propagation models for studying their performance under various signal attenuation and fading effects. The HT-MCSs provided consistent performance under most channel conditions in our simulations. However, they showed significantly degraded performance …
Robust Denoising And Densenet Classification Framework For Plant Disease Detection, Kevin Zhou, Dimah Dera
Robust Denoising And Densenet Classification Framework For Plant Disease Detection, Kevin Zhou, Dimah Dera
Electrical and Computer Engineering Faculty Publications
Plant disease is one of many obstacles encountered in the field of agriculture. Machine learning models have been used to classify and detect diseases among plants by analyzing and extracting features from plant images. However, a common problem for many models is that they are trained on clean laboratory images and do not exemplify real conditions where noise can be present. In addition, the emergence of adversarial noise that can mislead models into wrong predictions poses a severe challenge to developing preserved models against noisy environments. In this paper, we propose an end-to-end robust plant disease detection framework that combines …
Generalized Conditional Feedback System With Model Uncertainty, Chengbo Dai, Zhiqiang Gao, Yangquan Chen, Donghai Li
Generalized Conditional Feedback System With Model Uncertainty, Chengbo Dai, Zhiqiang Gao, Yangquan Chen, Donghai Li
Electrical and Computer Engineering Faculty Publications
Model uncertainty creates a largely open challenge for industrial process control, which causes a trade-off between robustness and performance optimality. In such a case, we propose a generalized conditional feedback (GCF) system to largely eliminate conflicts between robustness and performance optimality. This approach leverages a nominal model to design an optimal control in the virtual domain and defines an ancillary feedback controller to drive the physical process to track the trajectory of the virtual domain. The effectiveness of the proposed GCF scheme is demonstrated in a simulation for six typical industrial processes and three model-based control methods, and in a …
Qc-Odkla: Quantized And Communication-Censored Online Decentralized Kernel Learning Via Linearized Admm, Ping Xu, Yue Wang, Xiang Chen, Zhi Tian
Qc-Odkla: Quantized And Communication-Censored Online Decentralized Kernel Learning Via Linearized Admm, Ping Xu, Yue Wang, Xiang Chen, Zhi Tian
Electrical and Computer Engineering Faculty Publications
This article focuses on online kernel learning over a decentralized network. Each agent in the network receives online streaming data and collaboratively learns a globally optimal nonlinear prediction function in the reproducing kernel Hilbert space (RKHS). To overcome the curse of dimensionality issue in traditional online kernel learning, we utilize random feature (RF) mapping to convert the nonparametric kernel learning problem into a fixed-length parametric one in the RF space. We then propose a novel learning framework, named online decentralized kernel learning via linearized ADMM (ODKLA), to efficiently solve the online decentralized kernel learning problem. To enhance communication efficiency, we …
Blockchain-Based Applications For Smart Grids: An Umbrella Review, Wenbing Zhao, Quan Qi, Jiong Zhou, Xiong Luo Qi
Blockchain-Based Applications For Smart Grids: An Umbrella Review, Wenbing Zhao, Quan Qi, Jiong Zhou, Xiong Luo Qi
Electrical and Computer Engineering Faculty Publications
This article presents an umbrella review of blockchain-based smart grid applications. By umbrella review, we mean that our review is based on systematic reviews of this topic. We aim to synthesize the findings from these systematic reviews and gain deeper insights into this discipline. After studying the systematic reviews, we find it imperative to provide a concise and authoritative description of blockchain technology because many technical inaccuracies permeate many of these papers. This umbrella review is guided by five research questions. The first research question concerns the types of blockchain-based smart grid applications. Existing systematic reviews rarely used a systematic …
Distributed Cooperative Control Of Multiple Uavs With Uncertainty, Shihab Ahmed, Wenjie Dong
Distributed Cooperative Control Of Multiple Uavs With Uncertainty, Shihab Ahmed, Wenjie Dong
Electrical and Computer Engineering Faculty Publications
This paper considers the formation flying of multiple quadrotors with a desired orientation and a leader. In the formation flying control, it is assumed that the desired formation is time-varying and there are the system uncertainty and the information uncertainty. In order to deal with different uncertainties, a backstepping-based approach is proposed for the controller design. In the proposed approach, different types of uncertainties are considered in different steps. By integrating adaptive/robust control results and Laplacian algebraic theory, distributed robust adaptive control laws are proposed such that the formation errors exponentially converge to zero and the attitude of each quadrotor …
Board 87: Work In Progress Wip Comparing The Most Demanded Skills For Electrical And Computer Engineers (Ece) Graduates In The United States From The Perspective Of Ece Academic Department Heads And Ece Professional Engineers, Mohammad Al Mestiraihi, Kurt Henry Becker
Board 87: Work In Progress Wip Comparing The Most Demanded Skills For Electrical And Computer Engineers (Ece) Graduates In The United States From The Perspective Of Ece Academic Department Heads And Ece Professional Engineers, Mohammad Al Mestiraihi, Kurt Henry Becker
Electrical and Computer Engineering Faculty Publications
When students graduate from an Electrical and Computer Engineering (ECE) program, there is a discrepancy or imbalance between the job-related competencies that firms require and what academic institutions deliver. As a result, there are more graduates who lack the skills that the market dictates. Due to the skills gap, recently recruited engineers may still need more training to gain necessary competencies, costing companies both time and money. The primary purpose of this study is to compare the skills ECE graduates should have upon graduation from ECE industry perspective and ECE academic department heads’ perspectives. In this context, this paper presents …
Work In Progress: Assessing Engineering Students' Behavioral Engagement And Learning; Survey Development And Validation, Ibukun Samuel Osunbunmi, Kurt Henry Becker, Young Min Kim, Mohammad Al Mestiraihi
Work In Progress: Assessing Engineering Students' Behavioral Engagement And Learning; Survey Development And Validation, Ibukun Samuel Osunbunmi, Kurt Henry Becker, Young Min Kim, Mohammad Al Mestiraihi
Electrical and Computer Engineering Faculty Publications
Studies have shown that the more involved students are with their learning, the better their achieved desired educational outcome. However, little is known about the extent to which the existing engineering curriculum and pedagogical strategy implemented elicit engineering students’ engagement in the Middle East and North Africa region (MENA) of the world. The goal of this study is to examine to what extent the existing engineering curriculum elicits student engagement in self-learning and collaborative learning. This effort is part of the ongoing re-evaluation and redesign of the water engineering curriculum of one of the countries in the MENA region by …
Experimental Evaluation Of Smart Electric Meters’ Resilience Under Cyber Security Attacks, Harsh Kumar, Oscar A. Alvarez, Sanjeev Kumar
Experimental Evaluation Of Smart Electric Meters’ Resilience Under Cyber Security Attacks, Harsh Kumar, Oscar A. Alvarez, Sanjeev Kumar
Electrical and Computer Engineering Faculty Publications
For the first time, commercial grade smart meters have been subjected to cyber security attacks to understand their operation and security resilience under different attack scenarios. Cyber security is a matter of top concern for utility companies installing smart meters for remote collection of power usage data from customer premises. Keeping power-usage data secure and to maintain system’s resiliency under cyber security attacks is very important. In Smart electric grids, the power usage data from smart meters are periodically reported to the utility company. Reporting and remote monitoring of power usage data requires the use of data network protocols, which …
Participation Of Electric Vehicle Aggregators In Wholesale Electricity Markets: Recent Works And Future Directions, Saeed Salimi Amiri, Fazlur Rahman Bin Karim, Pedro Cesar Lopes Gerum
Participation Of Electric Vehicle Aggregators In Wholesale Electricity Markets: Recent Works And Future Directions, Saeed Salimi Amiri, Fazlur Rahman Bin Karim, Pedro Cesar Lopes Gerum
Electrical and Computer Engineering Faculty Publications
Electric Vehicles are key to reducing carbon emissions while bringing a revolution to the transportation sector. With the massive increase of EVs in road networks and the growing demand for charging services, the electric power grid faces enormous system reliability and operation stability challenges. Demand and supply disparities create inconsistency in the smooth delivery of electrical power. As a potential solution, EVs and their charging infrastructure can be aggregated to prevent the unwanted effects on power systems and also facilitate ancillary services to the power grid. When not need for transportation purposes, EVs can leverage their batteries for power grid …
Towards An Evolved Immersive Experience: Exploring 5g-And Beyond-Enabled Ultra-Low-Latency Communications For Augmented And Virtual Reality, Ananya Hazarika, Mehdi Rahmati
Towards An Evolved Immersive Experience: Exploring 5g-And Beyond-Enabled Ultra-Low-Latency Communications For Augmented And Virtual Reality, Ananya Hazarika, Mehdi Rahmati
Electrical and Computer Engineering Faculty Publications
Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in …
Compressive Sensing Via Variational Bayesian Inference Under Two Widely Used Priors: Modeling, Comparison And Discussion, Mohammad Shekaramiz, Todd K. Moon
Compressive Sensing Via Variational Bayesian Inference Under Two Widely Used Priors: Modeling, Comparison And Discussion, Mohammad Shekaramiz, Todd K. Moon
Electrical and Computer Engineering Faculty Publications
Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying priors such as Bernoulli-Gaussian-inverse Gamma (BGiG) and Gaussian-inverse Gamma (GiG) priors on the compounds of the signal. With the introduction of variational Bayesian inference, the sparse Bayesian learning (SBL) methods for solving the inverse problem of compressive sensing have received significant interest as the SBL methods become more efficient in terms of execution time. In this paper, we consider the sparse signal recovery …
Graphene-Conductive Polymer-Based Electrochemical Sensor For Dopamine Detection, Dipannita Ghosh, Md Ashiqur Rahman, Ali Ashraf, Nazmul Islam
Graphene-Conductive Polymer-Based Electrochemical Sensor For Dopamine Detection, Dipannita Ghosh, Md Ashiqur Rahman, Ali Ashraf, Nazmul Islam
Electrical and Computer Engineering Faculty Publications
The central nervous system's (CNS) dopaminergic system dysfunction has been linked to neurological illnesses like schizophrenia and Parkinson's disease. As a result, sensitive and selective detection of dopamine is critical for the early diagnosis of illnesses associated with aberrant dopamine levels. In this research, we have investigated the performance of electrochemical screen-printed sensors for different concentrations of dopamine detection using graphene-based conductive PEDOT: PSS(G-PEDOT: PSS) and Polyaniline(GPANI) inks on the working electrode and compared the sensitivity. SEM characterization technique has been performed to visualize the microstructures of the proposed inks. We have investigated cyclic voltammetry (CV) electrochemical techniques with ferri/ferrocyanide …
Study Of Spatial Reuse In Ieee 802.11ax Networks Over Propagation Models, Jun Peng, Paola Miller
Study Of Spatial Reuse In Ieee 802.11ax Networks Over Propagation Models, Jun Peng, Paola Miller
Electrical and Computer Engineering Faculty Publications
The paper studies the spatial reuse in IEEE 802.11ax networks over propagation models in ns3. The propagation models used in the study include the Friis model, the Nakagami model, and the combination of the two models. The results show that not all access points in an area benefit from the spatial reuse, and some even lose throughput. These results introduce the question of how to justify the spatial reuse for those access points who lose throughput, although the total throughput of the access points in the area benefits from the spatial reuse in general. The results also show that the …