Engineering Commons^™

A Wavegan Approach For Mmwave-Based Fanet Topology Optimization, Enas Odat, Hakim Ghazzai, Ahmad Alsharoa

Electrical and Computer Engineering Faculty Research & Creative Works

The integration of dynamic Flying Ad hoc Networks (FANETs) and millimeter Wave (mmWave) technology can offer a promising solution for numerous data-intensive applications, as it enables the establishment of a robust flying infrastructure with significant data transmission capabilities. However, to enable effective mmWave communication within this dynamic network, it is essential to precisely align the steerable antennas mounted on Unmanned Aerial Vehicles (UAVs) with their corresponding peer units. Therefore, it is important to design a novel approach that can quickly determine an optimized alignment and network topology. In this paper, we propose a Generative Adversarial Network (GAN)-based approach, called WaveGAN, …

Cooperative Deep $Q$ -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In This Article, We Address Two Key Challenges in Deep Reinforcement Learning (DRL) Setting, Sample Inefficiency and Slow Learning, with a Dual-Neural Network (NN)-Driven Learning Approach. in the Proposed Approach, We Use Two Deep NNs with Independent Initialization to Robustly Approximate the Action-Value Function in the Presence of Image Inputs. in Particular, We Develop a Temporal Difference (TD) Error-Driven Learning (EDL) Approach, Where We Introduce a Set of Linear Transformations of the TD Error to Directly Update the Parameters of Each Layer in the Deep NN. We Demonstrate Theoretically that the Cost Minimized by the EDL Regime is an Approximation …

Skin Lesion Segmentation In Dermoscopic Images With Noisy Data, Norsang Lama, Jason Hagerty, Anand Nambisan, Ronald Joe Stanley, William Van Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

We Propose a Deep Learning Approach to Segment the Skin Lesion in Dermoscopic Images. the Proposed Network Architecture Uses a Pretrained Efficient Net Model in the Encoder and Squeeze-And-Excitation Residual Structures in the Decoder. We Applied This Approach on the Publicly Available International Skin Imaging Collaboration (ISIC) 2017 Challenge Skin Lesion Segmentation Dataset. This Benchmark Dataset Has Been Widely Used in Previous Studies. We Observed Many Inaccurate or Noisy Ground Truth Labels. to Reduce Noisy Data, We Manually Sorted All Ground Truth Labels into Three Categories — Good, Mildly Noisy, and Noisy Labels. Furthermore, We Investigated the Effect of Such …

Machine Learning Based Pcb/Package Stack-Up Optimization For Signal Integrity, Wenchang Huang, Jiahuan Huang, Minseok Kim, Bumhee Bae, Chulsoon Hwang, Subin Kim

Electrical and Computer Engineering Faculty Research & Creative Works

PCB/package stack-up design optimization is time-consuming and requiring a great deal of experience. Although some iterative optimization algorithms are applied to implement automatic stack-up design, evaluating the results of each iteration is still time-intensive. This paper proposes a combined Bayesian optimization-artificial neural network (BO-ANN) algorithm, utilizing a trained ANN-based surrogate model to replace a 2D cross-section analysis tool for fast PCB/package stack-up design optimization. With the acceleration of ANN, the proposed BO-ANN algorithm can finish 100 iterations in 40 seconds while achieving the target characteristic impedance. To better generalize the BO-ANN algorithm, a strategy of effective dielectric calculation is applied …

Aerial Lidar-Based 3d Object Detection And Tracking For Traffic Monitoring, Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa, Hichem Besbes, Yehia Massoud

Electrical and Computer Engineering Faculty Research & Creative Works

The proliferation of Light Detection and Ranging (LiDAR) technology in the automotive industry has quickly promoted its use in many emerging areas in smart cities and internet-of-things. Compared to other sensors, like cameras and radars, LiDAR provides up to 64 scanning channels, vertical and horizontal field of view, high precision, high detection range, and great performance under poor weather conditions. In this paper, we propose a novel aerial traffic monitoring solution based on Light Detection and Ranging (LiDAR) technology. By equipping unmanned aerial vehicles (UAVs) with a LiDAR sensor, we generate 3D point cloud data that can be used for …

A Deep Learning Approach To Detect Blood Vessels In Basal Cell Carcinoma, A. Maurya, R. Joe Stanley, N. Lama, Sarangapani Jagannathan, D. Saeed, S. Swinfard, J. R. Hagerty, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

Purpose: Blood vessels called telangiectasia are visible in skin lesions with the aid of dermoscopy. Telangiectasia are a pivotal identifying feature of basal cell carcinoma. These vessels appear thready, serpiginous, and may also appear arborizing, that is, wide vessels branch into successively thinner vessels. Due to these intricacies, their detection is not an easy task, neither with manual annotation nor with computerized techniques. In this study, we automate the segmentation of telangiectasia in dermoscopic images with a deep learning U-Net approach. Methods: We apply a combination of image processing techniques and a deep learning-based U-Net approach to detect telangiectasia in …

Fast Impedance Prediction For Power Distribution Network Using Deep Learning, Ling Zhang, Jack Juang, Zurab Kiguradze, Bo Pu, Shuai Jin, Songping Wu, Zhiping Yang, Jun Fan, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

Modeling and simulating a power distribution network (PDN) for printed circuit boards with irregular board shapes and multi-layer stackup is computationally inefficient using full-wave simulations. This paper presents a new concept of using deep learning for PDN impedance prediction. A boundary element method (BEM) is applied to efficiently calculate the impedance for arbitrary board shape and stackup. Then over one million boards with different shapes, stackup, integrated circuits (IC) location, and decap placement are randomly generated to train a deep neural network (DNN). The trained DNN can predict the impedance accurately for new board configurations that have not been used …

A Deep Learning Approach To Design And Discover Sustainable Cementitious Binders: Strategies To Learn From Small Databases And Develop Closed-Form Analytical Models, Taihao Han, Sai Akshay Ponduru, Rachel Cook, Jie Huang, Gaurav Sant, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

To reduce the energy-intensity and carbon footprint of Portland cement (PC), the prevailing practice embraced by concrete technologists is to partially replace the PC in concrete with supplementary cementitious materials [SCMs: geological materials (e.g., limestone); industrial by-products (e.g., fly ash); and processed materials (e.g., calcined clay)]. Chemistry and content of the SCM profoundly affect PC hydration kinetics; which, in turn, dictates the evolutions of microstructure and properties of the [PC + SCM] binder. Owing to the substantial diversity in SCMs' compositions–plus the massive combinatorial spaces, and the highly nonlinear and mutually-interacting processes that arise from SCM-PC interactions–state-of-the-art computational models are …

Chimeranet: U-Net For Hair Detection In Dermoscopic Skin Lesion Images, Norsang Lama, Reda Kasmi, Jason R. Hagerty, R. Joe Stanley, Reagan Harris Young, Jessica Miinch, Januka Nepal, Anand Nambisan, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

Hair and ruler mark structures in dermoscopic images are an obstacle preventing accurate image segmentation and detection of critical network features. Recognition and removal of hairs from images can be challenging, especially for hairs that are thin, overlapping, faded, or of similar color as skin or overlaid on a textured lesion. This paper proposes a novel deep learning (DL) technique to detect hair and ruler marks in skin lesion images. Our proposed ChimeraNet is an encoder-decoder architecture that employs pretrained EfficientNet in the encoder and squeeze-and-excitation residual (SERes) structures in the decoder. We applied this approach at multiple image sizes …

Forecasting Nodal Price Difference Between Day-Ahead And Real-Time Electricity Markets Using Long-Short Term Memory And Sequence-To-Sequence Networks, Ronit Das, Rui Bo, Haotian Chen, Waqas Ur Rehman, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Price forecasting is at the center of decision making in electricity markets. Much research has been done in forecasting energy prices for a single market while little research has been reported on forecasting price difference between markets, which presents higher volatility and yet plays a critical role in applications such as virtual trading. To this end, this paper takes the first attempt at it and employs novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units and Sequence-to-Sequence (Seq2Seq) architecture to forecast nodal price difference between day-ahead and real-time markets. In addition to value prediction, these deep learning architectures …

Enhanced Pml Based On The Long Short Term Memory Network For The Fdtd Method, He Ming Yao, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a new absorbing boundary condition (ABC) computation approach based on the deep learning technique. Benefited from the sequence dependence feature, the Long Short-Term Memory (LSTM) network is employed to replace the conventional perfectly matched layer (PML) ABC for the Finite-Difference Time-Domain (FDTD) solving process. The newly proposed LSTM based PML model is trained by the electromagnetic field data on the interface of the conventional PML. Different from the conventional PML, the newly proposed model only needs one cell layer as the boundary. Hence, the newly proposed method conveniently reduces both the algorithm's complexity and the area of …

Machine Learning Methodology Review For Computational Electromagnetics, He Ming Yao, Lijun Jiang, Huan Huan Zhang, Wei E.I. Sha

Electrical and Computer Engineering Faculty Research & Creative Works

While machine learning is revolutionizing every corner of modern technologies, we have been attempting to explore whether machine learning methods could be used in computational electromagnetic (CEM). In this paper, five efforts in line with this direction are reviewed. They include forward methods such as the method of moments (MoM) solved by the artificial neural network training process, FDTD PML (perfectly matched layer) using the hyperbolic tangent basis function (HTBF), etc. There are also inverse problems that use the deep ConvNets for the effective source reconstruction and subwavelength imaging in the far-field. Benchmarks are provided to demonstrate the feasibility of …

Deep Learning Nuclei Detection In Digitized Histology Images By Superpixels, Sudhir Sornapudi, R. Joe Stanley, William V. Stoecker, Haidar Almubarak, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R. Frazier

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades.

Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network.

Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with …