Engineering Commons^™

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 …

Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram …

Imnets: Deep Learning Using An Incremental Modular Network Synthesis Approach For Medical Imaging Applications, Redha A. Ali, Russell C. Hardie, Barath Narayanan Narayanan, Temesguen Messay

Electrical and Computer Engineering Faculty Publications

Deep learning approaches play a crucial role in computer-aided diagnosis systems to support clinical decision-making. However, developing such automated solutions is challenging due to the limited availability of annotated medical data. In this study, we proposed a novel and computationally efficient deep learning approach to leverage small data for learning generalizable and domain invariant representations in different medical imaging applications such as malaria, diabetic retinopathy, and tuberculosis. We refer to our approach as Incremental Modular Network Synthesis (IMNS), and the resulting CNNs as Incremental Modular Networks (IMNets). Our IMNS approach is to use small network modules that we call SubNets …

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 …

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 …

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 …

Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named"MitosisNet". MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region …

Ensemble Lung Segmentation System Using Deep Neural Networks, Redha A. Ali, Russell C. Hardie, Hussin K. Ragb

Electrical and Computer Engineering Faculty Publications

Lung segmentation is a significant step in developing computer-aided diagnosis (CAD) using Chest Radiographs (CRs). CRs are used for diagnosis of the 2019 novel coronavirus disease (COVID-19), lung cancer, tuberculosis, and pneumonia. Hence, developing a Computer-Aided Detection (CAD) system would provide a second opinion to help radiologists in the reading process, increase objectivity, and reduce the workload. In this paper, we present the implementation of our ensemble deep learning model for lung segmentation. This model is based on the original DeepLabV3+, which is the extended model of DeepLabV3. Our model utilizes various architectures as a backbone of DeepLabV3+, such as …

A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language …

Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep …

Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai

Electrical and Computer Engineering Faculty Publications

To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic …

Recursive Non-Local Means Filter For Video Denoising, Redha A. Ali, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

In this paper, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on non-local means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patches, within a given search window.

The weights are based on the patch intensity vector distances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video …

Identity-Based Schemes For A Secured Big Data And Cloud Ict Framework In Smart Grid System, Feng Ye, Yi Qian, Rose Qingyang Hu

Electrical and Computer Engineering Faculty Publications

Smart grid is an intelligent cyber physical system (CPS). The CPS generates a massive amount of data for efficient grid operation. In this paper, a big data-driven, cloud-based information and communication technology (ICT) framework for smart grid CPS is proposed. The proposed ICT framework deploys hybrid cloud servers to enhance scalability and reliability of smart grid communication infrastructure. Because the data in the ICT framework contains much privacy of customers and important data for automated controlling, the security of data transmission must be ensured. In order to secure the communications over the Internet in the system, identity-based schemes are proposed …

Identity‐Based Schemes For A Secured Big Data And Cloud Ict Framework In Smart Grid System, Feng Ye, Yi Qian, Rose Qingyang Hu

Electrical and Computer Engineering Faculty Publications

Smart grid is an intelligent cyber physical system (CPS). The CPS generates a massive amount of data for efficient grid operation. In this paper, a big data‐driven, cloud‐based information and communication technology (ICT) framework for smart grid CPS is proposed. The proposed ICT framework deploys hybrid cloud servers to enhance scalability and reliability of smart grid communication infrastructure. Because the data in the ICT framework contains much privacy of customers and important data for automated controlling, the security of data transmission must be ensured. In order to secure the communications over the Internet in the system, identity‐based schemes are proposed …

Negative Index In Chiral Metamaterials Under Conductive Loss And First-Order Material Dispersion Using Lorentzian, Condon And Drude Models, Monish Ranjan Chatterjee, Tarig A. Algadey

Electrical and Computer Engineering Faculty Publications

Emergence of negative index (NIM) in chiral materials with conductive loss using standard dispersive models is reported. Positive and negative phase and group indices are realized as expected for NIM behavior for sidebands with opposite polarities.

Analysis Of Various Classification Techniques For Computer Aided Detection System Of Pulmonary Nodules In Ct, Barath Narayanan Narayanan, Russell C. Hardie, Temesguen Messay

Electrical and Computer Engineering Faculty Publications

Lung cancer is the leading cause of cancer death in the United States. It usually exhibits its presence with the formation of pulmonary nodules. Nodules are round or oval-shaped growth present in the lung. Computed Tomography (CT) scans are used by radiologists to detect such nodules. Computer Aided Detection (CAD) of such nodules would aid in providing a second opinion to the radiologists and would be of valuable help in lung cancer screening. In this research, we study various feature selection methods for the CAD system framework proposed in FlyerScan. Algorithmic steps of FlyerScan include (i) local contrast enhancement (ii) …

Histogram Of Oriented Phase (Hop): A New Descriptor Based On Phase Congruency, Hussin Ragb, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In this paper we present a low level image descriptor called Histogram of Oriented Phase based on phase congruency concept and the Principal Component Analysis (PCA). Since the phase of the signal conveys more information regarding signal structure than the magnitude, the proposed descriptor can precisely identify and localize image features over the gradient based techniques, especially in the regions affected by illumination changes. The proposed features can be formed by extracting the phase congruency information for each pixel in the image with respect to its neighborhood. Histograms of the phase congruency values of the local regions in the image …

Diffractive Propagation And Recovery Of Modulated (Including Chaotic) Electromagnetic Waves Through Uniform Atmosphere And Modified Von Karman Phase Turbulence, Monish Ranjan Chatterjee, Fathi H.A. Mohamed

Electrical and Computer Engineering Faculty Publications

In a parallel approach to recently-used transfer function formalism, a study involving diffraction of modulated electromagnetic (EM) waves through uniform and phase-turbulent atmospheres is reported in this paper. Specifically, the input wave is treated as a modulated optical carrier, represented by use of a sinusoidal phasor with a slowly timevarying envelope. Using phasors and (spatial) Fourier transforms, the complex phasor wave is transmitted across a uniform or turbulent medium using the Kirchhoff-Fresnel integral and the random phase screen.

Some preliminary results are presented comparing non-chaotic and chaotic information transmission through turbulence, outlining possible improvement in performance utilizing the robust features …

Large-Area Object Search And Recovery Using Sector-Based Aerial Acousto-Optic Scanning And Reflection Sensing, Monish Ranjan Chatterjee, Salaheddeen G. Bugoffa

Electrical and Computer Engineering Faculty Publications

A sector-based angular scanning system intended to identify and spatially locate relatively small objects scattered over a large terrain is described in this paper. The system is modeled as a planar surface on the horizontal (XY) plane, with an acousto-optic Bragg cell on board an unmanned aerial vehicle (UAV) operating in the XZ plane.

The Bragg cell is excited by a chirped RF signal with a designed frequency ramp. As the scanning beam reflects off the horizontal surface, a detector placed strategically at a suitable altitude (in the analysis shown to be on board the UAV itself) picks up the …

A New Architecture For Application-Aware Cognitive Multihop Wireless Networks, Trenton Evans, Kossivi Tossou, Feng Ye, Zhihui Shu, Yi Qian, Yaoqing Yang, Hamid Sharif

Electrical and Computer Engineering Faculty Publications

In this article, we propose a new architecture for AC-MWN. Cognitive radio is a technique to adaptively use the spectrum so that the resource can be used more efficiently in a low-cost way. A multihop wireless network can be deployed quickly and flexibly without fixed infrastructure. In our proposed new architecture, we study backbone routing schemes with network cognition, and a routing scheme with network coding and spectrum adaptation. A testbed is implemented to test the proposed schemes for AC-MWN. In addition to basic measurements, we implement a video streaming application based on the proposed AC-MWN architecture using cognitive radios. …

A Real-Time Information Based Demand-Side Management System In Smart Grid, Feng Ye, Yi Qian, Rose Qingyang Hu

Electrical and Computer Engineering Faculty Publications

In this paper, we study a real-time information based demand-side management (DSM) system with advanced communication networks in smart grid. DSM can smooth peak-to-average ratio (PAR) of power usage in the grid, which in turn reduces the waste of fuel and the emission of greenhouse gas. We first target to minimize PAR with a centralized scheme. To motivate power suppliers, we further propose another centralized scheme targeting minimum power generation cost. However, customers may not be motivated by a centralized scheme since such a scheme requires total control and privacy from them. A centralized scheme also requires too much real-time …

Histogram Of Oriented Phase And Gradient (Hopg) Descriptor For Improved Pedestrian Detection, Hussin Ragb, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

This paper presents a new pedestrian detection descriptor named Histogram of Oriented Phase and Gradient (HOPG) based on a combination of the Histogram of Oriented Phase (HOP) features and the Histogram of Oriented Gradient features (HOG).

The proposed descriptor extracts the image information using both the gradient and phase congruency concepts. Although the HOG based method has been widely used in the human detection systems, it lacks to deal effectively with the images impacted by the illumination variations and cluttered background. By fusing HOP and HOG features, more structural information can be identified and localized in order to obtain more …

An Adaptive Security Protocol For A Wireless Sensor‐Based Monitoring Network In Smart Grid Transmission Lines, Xuping Zhang, Feng Ye, Sucheng Fan, Jinghong Guo, Guoliang Xu, Yi Qian

Electrical and Computer Engineering Faculty Publications

In this paper, we propose a new security protocol for a wireless sensor network, which is designed for monitoring long range power transmission lines in smart grid. Part of the monitoring network is composed of optical fiber composite over head ground wire (OPGW), thus it can be secured with conventional security protocol. However, the wireless sensor network between two neighboring OPGW gateways remains vulnerable. Our proposed security protocol focuses on the wireless sensor network part, it provides mutual authentication, data integrity, and data confidentiality for both uplink and downlink transmissions between the sensor nodes and the OPGW gateway. Besides, our …

A High Performance Ceramic-Polymer Separator For Lithium Batteries, Jitendra Kumar, Padmakar Kichambare, Amarendra K. Rai, Rabi Bhattacharya, Stanley J. Rodrigues, Guru Subramanyam

Electrical and Computer Engineering Faculty Publications

A three-layered (ceramic-polymer-ceramic) hybrid separator was prepared by coating ceramic electrolyte [lithium aluminum germanium phosphate (LAGP)] over both sides of polyethylene (PE) polymer membrane using electron beam physical vapor deposition (EB-PVD) technique. Ionic conductivities of membranes were evaluated after soaking PE and LAGP/PE/LAGP membranes in a 1 Molar (1M) lithium hexafluroarsenate (LiAsF₆) electrolyte in ethylene carbonate (EC), dimethyl carbonate (DMC) and ethylmethyl carbonate (EMC) in volume ratio (1:1:1). Scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques were employed to evaluate morphology and structure of the separators before and after cycling performance tests to better understand structure-property correlation. …

Translation Of Rabindranath Tagore's 'Ode To Africa', Monish Ranjan Chatterjee

Electrical and Computer Engineering Faculty Publications

During his illustrious lifetime, Rabindranath Tagore travelled extensively around the world, spreading inspiration and gaining veneration in most destinations as the emissary of the East and of a deeply futuristic Universalist philosophy. An assessment of the intellectuals and cultural icons of the world whom Tagore encountered, interacted with, and influenced, is both astonishing and indeed still waiting to be adequately evaluated. His exchanges with Einstein, Wells, Rolland, Gide, Freud, Durant, Yeats, Rothenstein, Andrews, Noguchi, Gandhi, Radhakrishnan, Nehru, Bose and numerous others are well-documented. Tagore's literary works and public life centered on rejoicing in and celebrating everything unique and artistic in …

Industry-University Collaboration: A University Of Dayton Model, Guru Subramanyam

Electrical and Computer Engineering Faculty Publications

This paper introduces industry-university collaboration activities currently in place at the University of Dayton's School of Engineering. These collaborations are important to prepare industry-ready graduates who excel in technical, entrepreneurial, and leadership skills. One of the key curricular components is the industry-sponsored multidisciplinary projects. Industry involvement in advisory committee, strategic research partnerships, and other forms are discussed.

Spectral And Performance Analysis For The Propagation And Retrieval Of Signals From Modulated Chaos Waves Transmitted Through Modified Von Karman Turbulence, Fathi H.A. Mohamed, Monish Ranjan Chatterjee

Electrical and Computer Engineering Faculty Publications

A transfer function formalism is applied to track propagation of modulated chaos waves through modified von Karman phase turbulence; the demodulated signal is examined vis-à-vis performance relative to turbulence strength in comparison with non-chaotic propagation.

Negative Index In A Lossy Chiral Metamaterial Under First-Order Material Dispersion Using A Drude Model, Tarig A. Algadey, Monish Ranjan Chatterjee

Electrical and Computer Engineering Faculty Publications

Using the Drude model for complex conductivity,phase and group velocities, and indices are calculated under first-order dispersion in chiral metamaterials. Conditions are derived for negative index, and the results compared with parametric analyses.

Vanadium Oxide Thin-Film Variable Resistor-Based Rf Switches, Kuanchang Pan, Weisong Wang, Eunsung Shin, Kelvin Freeman, Guru Subramanyam

Electrical and Computer Engineering Faculty Publications

Vanadium dioxide (VO2) is a unique phase change material (PCM) that possesses a metal-to-insulator transition property. Pristine VO2 has a negative temperature coefficient of resistance, and it undergoes an insulator-to-metal phase change at a transition temperature of 68°C. Such a property makes the VO2 thin-film-based variable resistor (varistor) a good candidate in reconfigurable electronics to be integrated with different RF devices such as inductors, varactors, and antennas. Series single-pole single-throw (SPST) switches with integrated VO2 thin films were designed, fabricated, and tested. The overall size of the device is 380 μm × 600 μm. The SPST switches were fabricated on …

Creation Of Carbon Nanotube Based Biosensors Through Dielectrophoretic Assembly, Nilan S. Mani, Steve Kim, Kaushik Annam, Danielle Bane, Guru Subramanyam

Electrical and Computer Engineering Faculty Publications

Due to their excellent electrical, optical, and mechanical properties, nanosized single wall carbon nanotubes (SWNTs) have attracted significant attention as a transducing element in nano-bio sensor research. Controlled assembly, device fabrication, and bio-functionalization of the SWNTs are crucial in creating the sensors. In this study, working biosensor platforms were created using dielectrophoretic assembly of single wall carbon nanotubes (SWNTs) as a bridge between two gold electrodes. SWNTs in a commercial SDS surfactant solution were dispensed in the gap between the two gold electrodes, followed by applying an ac voltage across the two electrodes. The dielectrophoresis aligns the CNTs and forms …