Open Access. Powered by Scholars. Published by Universities.®

Electrical and Computer Engineering Commons™

Open Access. Powered by Scholars. Published by Universities.®

Electrical and Computer Engineering Faculty Publications

Discipline
Institution
Keyword
Publication Year

Articles 91 - 120 of 691

Full-Text Articles in Electrical and Computer Engineering

Meltpondnet: A Swin Transformer U-Net For Detection Of Melt Ponds On Arctic Sea Ice, Ivan Sudakow, Vijayan K. Asari, Ruixu Liu, Denis Demchev Jan 2022

Meltpondnet: A Swin Transformer U-Net For Detection Of Melt Ponds On Arctic Sea Ice, Ivan Sudakow, Vijayan K. Asari, Ruixu Liu, Denis Demchev

Electrical and Computer Engineering Faculty Publications

High-resolution aerial photographs of Arctic region are a great source for different sea ice feature recognition, which are crucial to validate, tune, and improve climate models. Melt ponds on the surface of melting Arctic sea ice are of particular interest as they are sensitive and valuable indicators and are proxy to the processes in the Arctic climate system. Manual analysis of this remote sensing data is extremely difficult and time-consuming due to the complex shapes and unpredictable boundaries of the melt ponds, and that leads to the necessity for automatizing the processes. In this study, we propose a robust and …

Go to article

Circuit Optimization Techniques For Efficient Ex-Situ Training Of Robust Memristor Based Liquid State Machine, Alex Henderson, Christopher Yakopcic, Cory Merkel, Steven Harbour, Tarek M. Taha, Hananel Hazan Jan 2022

Circuit Optimization Techniques For Efficient Ex-Situ Training Of Robust Memristor Based Liquid State Machine, Alex Henderson, Christopher Yakopcic, Cory Merkel, Steven Harbour, Tarek M. Taha, Hananel Hazan

Electrical and Computer Engineering Faculty Publications

Spiking neural network hardware offers a high performance, power-efficient and robust platform for the processing of complex data. Many of these systems require supervised learning, which poses a challenge when using gradient-based algorithms due to the discontinuous properties of SNNs. Memristor based hardware can offer gains in portability, power reduction, and throughput efficiency when compared to pure CMOS. This paper proposes a memristor-based spiking liquid state machine (LSM). The inherent dynamics of the LSM permit the use of supervised learning without backpropagation for weight updates. To carry out the design space evaluation of the LSM for optimal hardware performance, several …

Go to article

Nondestructive Detection Of Codling Moth Infestation In Apples Using Pixel-Based Nir Hyperspectral Imaging With Machine Learning And Feature Selection, Nader Ekramirad, Alfadhl Y. Khaled, Lauren E. Doyle, Julia R. Loeb, Kevin D. Donohue, Raul T. Villanueva, Akinbode A. Adedeji Dec 2021

Nondestructive Detection Of Codling Moth Infestation In Apples Using Pixel-Based Nir Hyperspectral Imaging With Machine Learning And Feature Selection, Nader Ekramirad, Alfadhl Y. Khaled, Lauren E. Doyle, Julia R. Loeb, Kevin D. Donohue, Raul T. Villanueva, Akinbode A. Adedeji

Electrical and Computer Engineering Faculty Publications

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing …

Go to article

Uptpu: Improving Energy Efficiency Of A Tensor Processing Unit Through Underutilization Based Power-Gating, Pramesh Pandey, Noel Daniel Gundi, Koushik Chakraborty, Sanghamitra Roy Dec 2021

Uptpu: Improving Energy Efficiency Of A Tensor Processing Unit Through Underutilization Based Power-Gating, Pramesh Pandey, Noel Daniel Gundi, Koushik Chakraborty, Sanghamitra Roy

Electrical and Computer Engineering Faculty Publications

The AI boom is bringing a plethora of domain-specific architectures for Neural Network computations. Google's Tensor Processing Unit (TPU), a Deep Neural Network (DNN) accelerator, has replaced the CPUs/GPUs in its data centers, claiming more than 15 × rate of inference. However, the unprecedented growth in DNN workloads with the widespread use of AI services projects an increasing energy consumption of TPU based data centers. In this work, we parametrize the extreme hardware underutilization in TPU systolic array and propose UPTPU: an intelligent, dataflow adaptive power-gating paradigm to provide a staggering 3.5 ×-6.5× energy efficiency to TPU for different input …

Go to article

Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya Dec 2021

Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya

Electrical and Computer Engineering Faculty Publications

In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debriscovered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the …

Go to article

Self-Learned Kernel Low Rank Approach To Accelerated High Resolution 3d Diffusion Mri, Abhijit Baul, Nian Wang, Choyi Zhang, Yuchou Chang, Leslie Ying, Yuchou Chang, Ukash Nakarmi Nov 2021

Self-Learned Kernel Low Rank Approach To Accelerated High Resolution 3d Diffusion Mri, Abhijit Baul, Nian Wang, Choyi Zhang, Yuchou Chang, Leslie Ying, Yuchou Chang, Ukash Nakarmi

Electrical and Computer Engineering Faculty Publications

Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyze the subtle changes in the tissue structure. However, the lengthy acquisition time is a major limitation in the clinical application of dMRI. Different image acquisition techniques such as parallel imaging, compressed sensing, has shortened the prolonged acquisition time but creating high-resolution 3D dMRI slices still requires a significant amount of time. In this study, we have shown that high resolution 3D dMRI can be reconstructed from the highly undersampled k-space and q-space data using a Kernel Low Rank method. Our proposed method has outperformed the conventional CS methods in …

Go to article

Ee-Acml: Energy-Efficient Adiabatic Cmos/Mtj Logic For Cpa-Resistant Iot Devices, Zachary Kahleifeh, Himanshu Thapliyal Nov 2021

Ee-Acml: Energy-Efficient Adiabatic Cmos/Mtj Logic For Cpa-Resistant Iot Devices, Zachary Kahleifeh, Himanshu Thapliyal

Electrical and Computer Engineering Faculty Publications

Internet of Things (IoT) devices have strict energy constraints as they often operate on a battery supply. The cryptographic operations within IoT devices consume substantial energy and are vulnerable to a class of hardware attacks known as side-channel attacks. To reduce the energy consumption and defend against side-channel attacks, we propose combining adiabatic logic and Magnetic Tunnel Junctions to form our novel Energy Efficient-Adiabatic CMOS/MTJ Logic (EE-ACML). EE-ACML is shown to be both low energy and secure when compared to existing CMOS/MTJ architectures. EE-ACML reduces dynamic energy consumption with adiabatic logic, while MTJs reduce the leakage power of a circuit. …

Go to article

Artificial Intelligence Method For The Forecast And Separation Of Total And Hvac Loads With Application To Energy Management Of Smart And Nze Homes, Rosemary E. Alden, Huangjie Gong, Evan S. Jones, Cristinel Ababei, Dan M. Ionel Nov 2021

Artificial Intelligence Method For The Forecast And Separation Of Total And Hvac Loads With Application To Energy Management Of Smart And Nze Homes, Rosemary E. Alden, Huangjie Gong, Evan S. Jones, Cristinel Ababei, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Separating the HVAC energy use from the total residential load can be used to improve energy usage monitoring and to enhance the house energy management systems (HEMS) for existing houses that do not have dedicated HVAC circuits. In this paper, a novel method is proposed to separate the HVAC dominant load component from the house load. The proposed method utilizes deep learning techniques and the physical relationship between HVAC energy use and weather. It employs novel long short-term memory (LSTM) encoder-decoder machine learning (ML) models, which are developed based on future weather data input in place of weather forecasts. In …

Go to article

Resampling And Super-Resolution Of Hexagonally Sampled Images Using Deep Learning, Dylan Flaute, Russell C. Hardie, Hamed Elwarfalli Oct 2021

Resampling And Super-Resolution Of Hexagonally Sampled Images Using Deep Learning, Dylan Flaute, Russell C. Hardie, Hamed Elwarfalli

Electrical and Computer Engineering Faculty Publications

Super-resolution (SR) aims to increase the resolution of imagery. Applications include security, medical imaging, and object recognition. We propose a deep learning-based SR system that takes a hexagonally sampled low-resolution image as an input and generates a rectangularly sampled SR image as an output. For training and testing, we use a realistic observation model that includes optical degradation from diffraction and sensor degradation from detector integration. Our SR approach first uses non-uniform interpolation to partially upsample the observed hexagonal imagery and convert it to a rectangular grid. We then leverage a state-of-the-art convolutional neural network (CNN) architecture designed for SR …

Go to article

Equivalent Electric And Heat-Pump Water Heater Models For Aggregated Community-Level Demand Response Virtual Power Plant Controls, Huangjie Gong, Tim Rooney, Oluwaseun M. Akeyo, Brian T. Branecky, Dan M. Ionel Oct 2021

Equivalent Electric And Heat-Pump Water Heater Models For Aggregated Community-Level Demand Response Virtual Power Plant Controls, Huangjie Gong, Tim Rooney, Oluwaseun M. Akeyo, Brian T. Branecky, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Advanced control techniques may be used to establish a virtual power plant to regulate the operation of electric water heaters, which may be regarded as a “uni-directional battery” and a major component of a hybrid residential energy storage system. In order to estimate the potential of regulating water heaters at the aggregated level, factors including user behavior, number of water heaters, and types of water heaters must be considered. This study develops generic water heater load curves based on the data retrieved from large experimental projects for resistive electric water heaters (EWHs) and heat pump water heaters (HPWHs). A community-level …

Go to article

Simple, Cost-Effective Fabrication, And Flow Dynamics Analysis Of A Passive Microfluidic Mixer Using 3d Printing And Soft Lithography, Md Fazlay Rubby, Mohammad Salman Parvez, Nazmul Islam Oct 2021

Simple, Cost-Effective Fabrication, And Flow Dynamics Analysis Of A Passive Microfluidic Mixer Using 3d Printing And Soft Lithography, Md Fazlay Rubby, Mohammad Salman Parvez, Nazmul Islam

Electrical and Computer Engineering Faculty Publications

Simple and low-cost fabrication of microfluidic devices has attracted considerable attention among researchers. The traditional soft lithography fabrication method requires expensive equipment like a UV exposure system and mask fabrication facility. In this work, an alternative and low-cost UV exposure system was introduced along with an alternative mask fabrication system. A previously reported passive microfluidic mixer was fabricated successfully using this modified soft lithography method. Challenges were presented during this modified fabrication method. Another emerging potential alternative for the fabrication of microfluidic mixers is 3D printing. It was also used in this experiment to fabricate a passive micromixer. This method …

Go to article

Effect Of Non-Planar Tungsten V-Electrode Pattern In A 3d Printed Microfluidic System, Mohammad Salman Parvez, Md Fazlay Rubby, Shanzida Kabir, Meah Imtiaz Zulkarnain, Nazmul Islam Oct 2021

Effect Of Non-Planar Tungsten V-Electrode Pattern In A 3d Printed Microfluidic System, Mohammad Salman Parvez, Md Fazlay Rubby, Shanzida Kabir, Meah Imtiaz Zulkarnain, Nazmul Islam

Electrical and Computer Engineering Faculty Publications

Manipulation, guiding, and focusing of particles is an important phenomenon in the area of biomedical research. In most cases, particles are suspended in a microfluidic environment. These microfluidic environments can be high or low conductive. Most importantly these flows seeded with the micro-particles are manipulated and guided in microfluidic channels. Microfluidic channels have very low dimensions and considering the flow rate the characteristic of the flow in a microfluidic channel is laminar in nature. There are many micromachining methods available for fabricating microfluidic channels such as soft-lithography, wet etching, electroforming, PDMS molding, laser ablation followed by wet etching but in …

Go to article

A Unified Framework Of Deep Learning-Based Facial Expression Recognition System For Diversified Applications, Sanoar Hossain, Saiyed Umer, Vijayan K. Asari, Ranjeet Kumar Rout Oct 2021

A Unified Framework Of Deep Learning-Based Facial Expression Recognition System For Diversified Applications, Sanoar Hossain, Saiyed Umer, Vijayan K. Asari, Ranjeet Kumar Rout

Electrical and Computer Engineering Faculty Publications

This work proposes a facial expression recognition system for a diversified field of appli- cations. The purpose of the proposed system is to predict the type of expressions in a human face region. The implementation of the proposed method is fragmented into three components. In the first component, from the given input image, a tree-structured part model has been applied that predicts some landmark points on the input image to detect facial regions. The detected face region was normalized to its fixed size and then down-sampled to its varying sizes such that the advantages, due to the effect of multi-resolution …

Go to article

Improving The Power Outage Resilience Of Buildings With Solar Pv Through The Use Of Battery Systems And Ev Energy Storage, Huangjie Gong, Dan M. Ionel Sep 2021

Improving The Power Outage Resilience Of Buildings With Solar Pv Through The Use Of Battery Systems And Ev Energy Storage, Huangjie Gong, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Buildings with solar photovoltaic (PV) generation and a stationary battery energy storage system (BESS) may self-sustain an uninterrupted full-level electricity supply during power outages. The duration of off-grid operation is dependent on the time of the power fault and the capabilities of the home energy management system (HEMS). In this paper, building resilience is quantified by analyzing the self-sustainment duration for all possible power outages throughout an entire year. An evaluation method is proposed and exercised on a reference house in California climate zone 9 for which the detailed electricity usage is simulated using the EnergyPlus software. The influence of …

Go to article

Application Of Tilt Correlation Statistics To Anisoplanatic Optical Turbulence Modeling And Mitigation, Russell C. Hardie, Michael A. Rucci, Santasri Bose-Pillai, Richard Van Hook Sep 2021

Application Of Tilt Correlation Statistics To Anisoplanatic Optical Turbulence Modeling And Mitigation, Russell C. Hardie, Michael A. Rucci, Santasri Bose-Pillai, Richard Van Hook

Electrical and Computer Engineering Faculty Publications

Atmospheric optical turbulence can be a significant source of image degradation, particularly in long range imaging applications. Many turbulence mitigation algorithms rely on an optical transfer function (OTF) model that includes the Fried parameter. We present anisoplanatic tilt statistics for spherical wave propagation. We transform these into 2D autocorrelation functions that can inform turbulence modeling and mitigation algorithms. Using these, we construct an OTF model that accounts for image registration. We also propose a spectral ratio Fried parameter estimation algorithm that is robust to camera motion and requires no specialized scene content or sources. We employ the Fried parameter estimation …

Go to article

Scene Motion Detection In Imagery With Anisoplanatic Optical Turbulence Using A Tilt-Variance-Based Gaussian Mixture Model, Richard L. Van Hook, Russell C. Hardie Sep 2021

Scene Motion Detection In Imagery With Anisoplanatic Optical Turbulence Using A Tilt-Variance-Based Gaussian Mixture Model, Richard L. Van Hook, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

In long-range imaging applications, anisoplanatic atmospheric optical turbulence imparts spatially- and temporally varying blur and geometric distortions in acquired imagery. The ability to distinguish true scene motion from turbulence warping is important for many image-processing and analysis tasks. The authors present a scenemotion detection algorithm specifically designed to operate in the presence of anisoplanatic optical turbulence. The method models intensity fluctuations in each pixel with a Gaussian mixture model (GMM). The GMM uses knowledge of the turbulence tilt-variance statistics. We provide both quantitative and qualitative performance analyses and compare the proposed method to several state-of-the art algorithms. The image data …

Go to article

Simulation Of Anisoplanatic Lucky Look Imaging And Statistics Through Optical Turbulence Using Numerical Wave Propagation, Michael A. Rucci, Russell C. Hardie, Richard K. Martin Sep 2021

Simulation Of Anisoplanatic Lucky Look Imaging And Statistics Through Optical Turbulence Using Numerical Wave Propagation, Michael A. Rucci, Russell C. Hardie, Richard K. Martin

Electrical and Computer Engineering Faculty Publications

This paper investigates anisoplanatic numerical wave simulation in the context of lucky look imaging. We demonstrate that numerical wave propagation can produce root mean square (RMS) wavefront distributions and probability of lucky look (PLL) statistics that are consistent with Kolmogorov theory. However, the simulated RMS statistics are sensitive to the sampling parameters used in the propagation window. To address this, we propose and validate a new sample spacing rule based on the point source bandwidth used in the propagation and the level of atmospheric turbulence. We use the tuned simulator to parameterize the wavefront RMS probability density function as a …

Go to article

Multi-Operator Cell Tower Locations Prediction From Crowdsourced Data, Mostafizur Rahman, Mohammad Arif Hossain, Murat Yuksel Aug 2021

Multi-Operator Cell Tower Locations Prediction From Crowdsourced Data, Mostafizur Rahman, Mohammad Arif Hossain, Murat Yuksel

Electrical and Computer Engineering Faculty Publications

Cell tower locations are not publicly available due to business interests of wireless providers. Very often wireless providers provide exaggerated coverage maps that may mislead the public. In addition to providing a neutral check on the coverage maps, prediction of cell tower locations hosting multiple operators’ access nodes could also be helpful in disaster communications and public safety in general. The localization of the disaster-affected towers can be very conducive to respond and reach to the victims. Further, victims’ devices could utilize this knowledge to initiate device-to-device (D2D) or unmanned aerial vehicular (UAV) communications as alternatives to the damaged cellular …

Go to article

Premium-Cnn: Propagating Uncertainty Towards Robust Convolutional Neural Networks, Dimah Dera, Nidhal Carla Bouaynaya, Ghulam Rasool, Roman Shterenberg, Hassan M. Fathallah-Shaykh Jul 2021

Premium-Cnn: Propagating Uncertainty Towards Robust Convolutional Neural Networks, Dimah Dera, Nidhal Carla Bouaynaya, Ghulam Rasool, Roman Shterenberg, Hassan M. Fathallah-Shaykh

Electrical and Computer Engineering Faculty Publications

Deep neural networks (DNNs) have surpassed human-level accuracy in various learning tasks. However, unlike humans who have a natural cognitive intuition for probabilities, DNNs cannot express their uncertainty in the output decisions. This limits the deployment of DNNs in mission-critical domains, such as warfighter decision-making or medical diagnosis. Bayesian inference provides a principled approach to reason about model's uncertainty by estimating the posterior distribution of the unknown parameters. The challenge in DNNs remains the multi-layer stages of non-linearities, which make the propagation of high-dimensional distributions mathematically intractable. This paper establishes the theoretical and algorithmic foundations of uncertainty or belief propagation …

Go to article

Investigation Of Variable Switching Frequency In Finite Control Set Model Predictive Control On Grid-Connected Inverters, Luocheng Wang, Tiefu Zhao, Jiangbiao He Jun 2021

Investigation Of Variable Switching Frequency In Finite Control Set Model Predictive Control On Grid-Connected Inverters, Luocheng Wang, Tiefu Zhao, Jiangbiao He

Electrical and Computer Engineering Faculty Publications

Finite control set model predictive control (FCS-MPC) has been widely studied and applied to the power converters and motor drives. It provides the power electronics system with fast dynamic response, nonlinear system formulation, and flexible objectives and constraints integration. However, its variable switching frequency feature also induces severe concerns on the power loss, the thermal profile, and the filter design. Stemming from these concerns, this article investigates the variable switching frequency characteristics of FCS-MPC on the grid-connected inverters. An intuitive relationship between the switching frequency and the magnitude of the converter output voltage is proposed through the geometry analysis, where …

Go to article

A Game-Theoretic Framework To Regulate Freeriding In Inter-Provider Spectrum Sharing, Mostafizur Rahman, Murat Yuksel, Thomas Quint Jun 2021

A Game-Theoretic Framework To Regulate Freeriding In Inter-Provider Spectrum Sharing, Mostafizur Rahman, Murat Yuksel, Thomas Quint

Electrical and Computer Engineering Faculty Publications

Primary-secondary spectrum sharing is limited in terms of design space, and may not be sufficient to meet the ever-increasing demand of connectivity and high signal quality. The next step to increase spectrum sharing efficiency is to design markets where sharing takes place among primary providers rather than leaving it to the limited case where the primary licensee is idle. Attaining contractual spectrum sharing among primary providers, a.k.a. co-primary or inter-provider sharing, involves additional costs for the users, e.g., roaming fee. Co-primary spectrum sharing without additional charge to the users poses two major challenges: a) regulatory approaches must be introduced to …

Go to article

Centralized Thermal Stress Oriented Dispatch Strategy For Paralleled Grid-Connected Inverters Considering Mission Profiles, Luocheng Wang, Tiefu Zhao, Jiangbiao He May 2021

Centralized Thermal Stress Oriented Dispatch Strategy For Paralleled Grid-Connected Inverters Considering Mission Profiles, Luocheng Wang, Tiefu Zhao, Jiangbiao He

Electrical and Computer Engineering Faculty Publications

One of the major failure causes in the power modules comes from the severe thermal stress in power semiconductor devices. Recently, some local control level methods have been developed to balance the power loss, dealing with the harsh mission profile, in order to reduce the thermal stress. However, there is not any specific system level strategy to leverage these local control level methods responding to the multiple inverters situation. Besides, the impacts of these methods on the thermal cycle and lifetime of the power modules in the long-term time scale have not been evaluated and compared yet. Hence, in this …

Go to article

Development Of A Wearable Finger Exoskeleton For Rehabilitation, Carlos Hernandez-Santos, Yasser A. Davizón, Alejandro Said, Rogelio Soto, L. C. Felix-Harran, Adriana Vargas-Martinez May 2021

Development Of A Wearable Finger Exoskeleton For Rehabilitation, Carlos Hernandez-Santos, Yasser A. Davizón, Alejandro Said, Rogelio Soto, L. C. Felix-Harran, Adriana Vargas-Martinez

Electrical and Computer Engineering Faculty Publications

This research work shows a new architecture of a novel wearable finger exoskeleton for rehabilitation; the proposed design consists of a one degree of freedom mechanism that generates the flexion and extension movement for the proximal, medial and distal phalange of the fingers to assist patients during the rehabilitation process, after neurological trauma, such as a stroke. The anatomy and anthropometric measures for the hand were used to define the design of the mechanism. In the analytic part, the representative equations for the forward and inverse kinematic analysis of the fingers are obtained, also a dynamic analysis is presented. The …

Go to article

A Photoanode With Hierarchical Nanoforest Tio 2 Structure And Silver Plasmonic Nanoparticles For Flexible Dye Sensitized Solar Cell, Brishty Deb Choudhury, Chen Lin, Sk Md Ali Zaker Shawon, Javier Soliz-Martinez, Hasina Huq, Mohammed Jasim Uddin Apr 2021

A Photoanode With Hierarchical Nanoforest Tio 2 Structure And Silver Plasmonic Nanoparticles For Flexible Dye Sensitized Solar Cell, Brishty Deb Choudhury, Chen Lin, Sk Md Ali Zaker Shawon, Javier Soliz-Martinez, Hasina Huq, Mohammed Jasim Uddin

Electrical and Computer Engineering Faculty Publications

Due to unique photovoltaic properties, the nanostructured morphologies of TiO2 on flexible substrate have been studied extensively in the recent years for applications in dye sensitized solar cells (DSSCs). Microstructured electrode materials with high surface area can facilitate rapid charge transport and thus improve the light-to-current conversion efficiency. Herein we present an improved photoanode with forest like photoactive TiO2 hierarchical microstructure using a simple and facile hydrothermal route. To utilize the surface plasmon resonance (SPR) and hence increase the photon conversion efficiency, a plasmonic nanoparticle Ag has also been deposited using a very feasible photoreduction method. The branched structure of …

Go to article

Guest Editorial: Edge Intelligence For Beyond 5g Networks, Yan Zhang, Zhiyong Feng, Hassnaa Moustafa, Feng Ye, Usman Javaid, Chunfen Cui Apr 2021

Guest Editorial: Edge Intelligence For Beyond 5g Networks, Yan Zhang, Zhiyong Feng, Hassnaa Moustafa, Feng Ye, Usman Javaid, Chunfen Cui

Electrical and Computer Engineering Faculty Publications

Beyond fifth-generation (B5G) networks, or so-called "6G", is the next-generation wireless communications systems that will radically change how Society evolves. Edge intelligence is emerging as a new concept and has extremely high potential in addressing the new challenges in B5G networks by providing mobile edge computing and edge caching capabilities together with Artificial Intelligence (AI) to the proximity of end users. In edge intelligence empowered B5G networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with …

Go to article

Piezo-Tribo Dual Effect Hybrid Nanogenerators For Health Monitoring, Sk Md Ali Zaker Shawon, Andrew Xu Sun, Valeria Suarez Vega, Brishty Deb Chowdhury, Phong Tran, Zaida D. Carballo, Jim Aica Tolentino, Jianzhi Li, Muhammad Sufian Rafaqut, Mohammed Jasim Uddin Apr 2021

Piezo-Tribo Dual Effect Hybrid Nanogenerators For Health Monitoring, Sk Md Ali Zaker Shawon, Andrew Xu Sun, Valeria Suarez Vega, Brishty Deb Chowdhury, Phong Tran, Zaida D. Carballo, Jim Aica Tolentino, Jianzhi Li, Muhammad Sufian Rafaqut, Mohammed Jasim Uddin

Electrical and Computer Engineering Faculty Publications

Over the years, nanogenerators for health monitoring have become more and more attractive as they provide a cost-effective and continuous way to successfully measure vital signs, physiological status, and environmental changes in/around a person. Using such sensors can positively affect the way healthcare workers diagnose and prevent life-threatening conditions. Recently, the dual piezo-tribological effect of hybrid nanogenerators (HBNGs) have become a subject of investigation, as they can provide a substantial amount of data, which is significant for healthcare. However, real-life exploitation of these HBNGs in health monitoring is still marginal. This review covers piezo-tribo dual-effect HBNGs that are used as …

Go to article

On The Impact Of Gravity Compensation On Reinforcement Learning In Goal-Reaching Tasks For Robotic Manipulators, Jonathan Fugal, Hasan A. Poonawala, Jihye Bae Mar 2021

On The Impact Of Gravity Compensation On Reinforcement Learning In Goal-Reaching Tasks For Robotic Manipulators, Jonathan Fugal, Hasan A. Poonawala, Jihye Bae

Electrical and Computer Engineering Faculty Publications

Advances in machine learning technologies in recent years have facilitated developments in autonomous robotic systems. Designing these autonomous systems typically requires manually specified models of the robotic system and world when using classical control-based strategies, or time consuming and computationally expensive data-driven training when using learning-based strategies. Combination of classical control and learning-based strategies may mitigate both requirements. However, the performance of the combined control system is not obvious given that there are two separate controllers. This paper focuses on one such combination, which uses gravity-compensation together with reinforcement learning (RL). We present a study of the effects of gravity …

Go to article

Color-Compressive Bilateral Filter And Nonlocal Means For High-Dimensional Images, Christina Karam, Kenjiro Sugimoto, Keigo Hirakawa Mar 2021

Color-Compressive Bilateral Filter And Nonlocal Means For High-Dimensional Images, Christina Karam, Kenjiro Sugimoto, Keigo Hirakawa

Electrical and Computer Engineering Faculty Publications

We propose accelerated implementations of bilateral filter (BF) and nonlocal means (NLM) called color-compressive bilateral filter (CCBF) and color-compressive nonlocal means (CCNLM). CCBF and CCNLM are random filters, whose Monte-Carlo averaged output images are identical to the output images of conventional BF and NLM, respectively. However, CCBF and CCNLM are considerably faster because the spatial processing of multiple color channels are combined into a single random filtering process. This implies that the complexity of CCBF and CCNLM is less sensitive to color dimension (e.g., hyperspectral images) relatively to other BF and NLM methods. We experimentally verified that the execution time …

Go to article

Deep Learning For Anisoplanatic Optical Turbulence Mitigation In Long-Range Imaging, Matthew A. Hoffmire, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Barry K. Karch Mar 2021

Deep Learning For Anisoplanatic Optical Turbulence Mitigation In Long-Range Imaging, Matthew A. Hoffmire, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Barry K. Karch

Electrical and Computer Engineering Faculty Publications

We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as …

Go to article

Deep Learning For Anisoplanatic Optical Turbulence Mitigation In Long-Range Imaging, Matthew A. Hoffmire, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Barry K. Karch Mar 2021

Deep Learning For Anisoplanatic Optical Turbulence Mitigation In Long-Range Imaging, Matthew A. Hoffmire, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Barry K. Karch

Electrical and Computer Engineering Faculty Publications

We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as …

Go to article

First Prev Next Last