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IEEE Research and Applications of Photonics In Defense Conference (RAPID)

作者： J. Krzyston D. Lippiatt R. Bhattacharjea A. Stark S.E. Ralph School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Electro-Optical Systems Laboratory Georgia Tech Research Institute Atlanta USA

Simulating nonlinear systems featuring a dynamic DSP module is cumbersome, e.g., fiber transport with digital coherent receivers. Machine Learning is used to quickly and accurately estimate waveforms transported through multi-span fiber links over multiple launch powers and OSNRs. Replacing simulation techniques with ML is considered.

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Hyper-Skin: a hyperspectral dataset for reconstructing facial skin-spectra from RGB images 23

Hyper-Skin: a hyperspectral dataset for reconstructing facia...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Pai Chet Ng Zhixiang Chi Yannick Verdie Juwei Lu Konstantinos N. Plataniotis The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Huawei Noah's Ark Laboratory Huawei Canada

We introduce Hyper-Skin, a hyperspectral dataset covering wide range of wavelengths from visible (VIS) spectrum (400nm - 700nm) to near-infrared (NIR) spectrum (700nm - 1000nm), uniquely designed to facilitate research on facial skin-spectra reconstruction. By reconstructing skin spectra from RGB images, our dataset enables the study of hyperspectral skin analysis, such as melanin and hemoglobin concentrations, directly on the consumer device. Overcoming limitations of existing datasets, Hyper-Skin consists of diverse facial skin data collected with a pushbroom hyperspectral camera. With 330 hyperspectral cubes from 51 subjects, the dataset covers the facial skin from different angles and facial poses. Each hyperspectral cube has dimensions of 1024× 1024×448, resulting in millions of spectra vectors per image. The dataset, carefully cu- rated in adherence to ethical guidelines, includes paired hyperspectral images and synthetic RGB images generated using real camera responses. We demonstrate the efficacy of our dataset by showcasing skin spectra reconstruction using state-of-the-art models on 31 bands of hyperspectral data resampled in the VIS and NIR spectrum. This Hyper-Skin dataset would be a valuable resource to NeurIPS community, encouraging the development of novel algorithms for skin spectral reconstruction while fostering interdisciplinary collaboration in hyper- spectral skin analysis related to cosmetology and skin's well-being. Instructions to request the data and the related benchmarking codes are publicly available at: https://***/hyperspectral-skin/Hyper-Skin-2023.

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Computational Intelligence in Remote Sensing Image Registration:A survey

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International Journal of Automation and computing 2021年第1期18卷 1-17页

作者： Yue Wu Jun-Wei Liu Chen-Zhuo Zhu Zhuang-Fei Bai Qi-Guang Miao Wen-Ping Ma Mao-Guo Gong Key Laboratory of Big Data and Intelligent Vision School of Computer Science and TechnologyXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Electronic EngineeringXidian UniversityXi′an 710071China

In recent years,computational intelligence has been widely used in many fields and achieved remarkable *** computing and deep learning are important branches of computational *** methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image *** paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep *** the part of remote sensing image registration based on evolutionary calculation,the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is *** application of deep learning in remote sensing image registration is also *** the same time,the development status and future of remote sensing image registration are summarized and their prospects are examined.

关键词： Computational intelligence evolutionary computation neural network deep learning remote sensing image registration

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A Novel Scalable Array Design for III-V Compound Semiconductor-based Nonvolatile Memory (UltraRAM) with Separate Read-Write Paths

A Novel Scalable Array Design for III-V Compound Semiconduct...

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IEEE International Symposium on Quality Electronic Design

作者： Shamiul Alam Kazi Asifuzzaman Ahmedullah Aziz Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA Oak Ridge National Laboratory Oak Ridge TN USA

The dream of achieving a universal memory that can provide robust non-volatile memory states along with low-energy operation has been the key driving force of memory research. Despite dominating the memory market, conventional charge-based memories cannot satisfy these requirements. However, UltraRAM, an oxide-free charge-based memory cell, aims to achieve both of these requirements. This device achieves non-volatility (with an endurance of over 10 7 cycles and a retention of over 1000 years) along with switching at low-voltage (±2.3 V) utilizing a triple-barrier resonant tunneling (TBRT) structure made of InAs/AlSb. In this work, we propose an array design for UltraRAM-based memory devices. Our proposed memory array features separate read-write path and eliminates the possibility of accidentally switching the memory states stored in the array. Moreover, our design allows us to read all the cells in a column in one cycle without imposing any limit on the scalability. Besides, since the read operation in our proposed design is independent of the write mechanism, there is flexibility to optimize the read operation for memory and in-memory computing applications.

关键词： Low voltage Nonvolatile memory Scalability Force Switches In-memory computing Resonant tunneling devices

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A Subtype Classification of Hematopoietic Cancer Using Machine Learning Approach 13th

A Subtype Classification of Hematopoietic Cancer Using Machi...

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13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021

作者： Park, Kwang Ho Pham, Van Huy Davagdorj, Khishigsuren Munkhdalai, Lkhagvadorj Ryu, Keun Ho Database and Bioinformatics Laboratory School of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9789811616846

Hematopoietic cancer is the malignant transformation in immune system cells. This cancer usually occurs in areas such as bone marrow and lymph nodes, the hematopoietic organ, and is a frightening disease that collapses the immune system with its own mobile characteristics. Hematopoietic cancer is characterized by the cells that are expressed, which are usually difficult to detect in the hematopoiesis process. For this reason, we focused on the five subtypes of hematopoietic cancer and conducted a study on classifying by applying machine learning algorithms both contextual approach and non-contextual approach. First, we applied PCA approach for extracting suited feature for building classification model for subtype classification. And then, we used four machine learning classification algorithms (support vector machine, k-nearest neighbor, random forest, neural network) and synthetic minority oversampling technique for generating a model. As a result, most classifiers performed better when the oversampling technique was applied, and the best result was that oversampling applied random forest produced 95.24% classification performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Gene expression

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Stable Implementation Of Voice Activity Detector using Zero-Phase Zero Frequency Resonator On FPGA

Stable Implementation Of Voice Activity Detector using Zero-...

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Real Time Communications at IIT (RTC), IEEE International Conference and Expo on

作者： Syed Abdul Jabbar Purva Barche Krishna Gurugubelli Syed Azeemuddin Anil Kumar Vuppala Speech processing Laboratory LTRC IIIT-H Hyderabad India CVEST and Department of Electrical and Computer Engineering IIIT-Hyderabad and The University of Mississippi Mississippi USA

Voice activity detection (VAD), is a signal processing technique used to determine whether a given speech signal contains voiced or unvoiced segments. VAD is used in various applications such as Speech Coding, Voice Controlled Systems, speech feature extraction, etc. For example, in Adaptive multi-rate (AMR) speech coding, VAD is used as an efficient way of coding different speech frames at different bit rates. In this paper, we implemented the application of a Zero-Phase Zero Frequency Resonator (ZP-ZFR) as VAD on hardware. ZP-ZFR is an Infinite Impulse Response (IIR) filter that offers the advantage of requiring a lower filter order, making it suitable for hardware implementation. The proposed system is implemented on the TIMIT database using the Nexys Video Artix-7 FPGA board. The hardware design is carried out using Vivado 2021.1, a popular tool for FPGA development. The Hardware Description Language (HDL) used for implementation is Verilog. The proposed system achieves good performance with low complexity. Therefore this work is implemented on hardware, which can be used in various applications.

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A Deep Neural Network-Assisted Diagnosis Method for Cardiovascular Disease Based on the Optimization of the TAdam Algorithm

A Deep Neural Network-Assisted Diagnosis Method for Cardiova...

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2023 China Automation Congress, CAC 2023

作者： Hu, Yumei Li, Xing Zhang, Yang Zhu, Yuxuan Sun, Yuechao College of Computer Science and Technology Dongguan University of Technology Dongguan China School of Electrical Engineering & Intelligentization Dongguan University of Technology Dongguan China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang China School of Mechanical Engineering of Dgut Dongguan University of Technology Dongguan China Northeastern University College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350303759

Cardiovascular disease is a sudden and extremely disabling and deadly disease that hinders the development of public health care in China and poses a serious threat to the health of users. Therefore, this paper proposes a deep neural network-assisted diagnosis of cardiovascular disease based on TAdam algorithm optimization under a distributed medical monitoring network to enable rapid identification of cardiovascular patients, timely detection and reporting of the condition. This paper addresses the problems that deep neural network models are susceptible to the influence of their structure and hyperparameters and poor generalization convergence, firstly, the mainstream optimization algorithms based on deep learning are sorted out, and finally the T Adam algorithm is used to optimize the model. By comparing the common gradient optimization algorithms SGD (Stochastic Gradient Descent), Adagrad (Adaptive Gradient Algorithm), RMSprop (Root Mean Square propagation), Adam (Adaptive Moment Estimation) and the improved TAd am optimization algorithm trained and tested on the same dataset, the experimental results show that the TAdam algorithm exhibits the fastest convergence speed and the best generalization performance on the cardiovascular disease dataset. The method proposed in this paper enables rapid diagnosis of the user's disease risk and feedback to the relevant personnel to maximise user safety. © 2023 IEEE.

关键词： Deep neural networks

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Data Compression for Ultrasonic Microstructure Scattering Signals using Unsupervised Neural Networks

Data Compression for Ultrasonic Microstructure Scattering Si...

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IEEE Symposium (IUS) Ultrasonics

作者： Xin Zhang Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago Illinois U.S.A.

Ultrasonic Nondestructive Evaluation (NDE) has been extensively used to characterize the microstructure of metallic structures for early exposure of materials integrity. However, industrial NDE requires the processing, storage, and real-time transmission of large volumes of ultrasonic data. Therefore, it is indispensable to compress ultrasonic data with high fidelity. In this study, we explore the development of Unsupervised Learning (UL) based Neural Network (NN) models for massive ultrasonic data compression and an innovative multilayer perceptron residual autoencoder: Ultrasonic Residual Compressive Autoencoder (URCA), is introduced to compress ultrasonic data with high compression performance. This URCA can be fast-trained and utilizes the sparsity penalty with residual connection to optimize compression performance. UL-based NNs allow for memory-efficient training and rapid online augmentation of the model. To examine the results, a high-performance ultrasonic signal acquisition system was assembled to automatically collect ultrasonic data from heat-treated 1,018 steel blocks for microstructure characterization. Compression performance is analyzed based on compression ratio, reconstruction accuracy and model training time. The reconstruction accuracy was measured using the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR). By training the URCA NN for a high reconstruction performance of 0.96 SSIM, we obtained 91.25% memory space-saving. For a higher compression performance of 0.80 SSIM, we obtained 96.04% memory space-saving.

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How We Ruined the Internet

arXiv

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arXiv 2023年

作者： Beck, Micah D. Moore, Terry R. Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville United States Innovative Computing Laboratory University of Tennessee Knoxville United States

At the end of the 19th century the logician C.S. Peirce coined the term "fallibilism" for the "… the doctrine that our knowledge is never absolute but always swims, as it were, in a continuum of uncertainty and of indeterminacy" [1]. In terms of scientific practice, this means we are obliged to reexamine the assumptions, the evidence, and the arguments for conclusions that subsequent experience has cast into doubt. In this paper we examine an assumption that underpinned the development of the Internet architecture, namely that a loosely synchronous point-to-point datagram delivery service could adequately meet the needs of all network applications, including those which deliver content and services to a mass audience at global scale. We examine how the inability of the Networking community to provide a public and affordable mechanism to support such asynchronous point-to-multipoint applications led to the development of private overlay infrastructure, namely CDNs and Cloud networks, whose architecture stands at odds with the Open Data Networking goals of the early Internet advocates. We argue that the contradiction between those initial goals and the monopolistic commercial imperatives of hypergiant overlay infrastructure operators is an important reason for the apparent contradiction posed by the negative impact of their most profitable applications (e.g., social media) and strategies (e.g., targeted advertisement). We propose that, following the prescription of Peirce, we can only resolve this contradiction by reconsidering some of our deeply held assumptions. Copyright © 2023, The Authors. All rights reserved.

关键词： Open Data

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Dead-time Compensation Method for Bus-clamping Modulated Voltage Source Inverter

arXiv

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arXiv 2023年

作者： Ghaderloo, Reza Asrar Shen, Yidi Singhabahu, Chanaka Resalayyan, Rakesh Khaligh, Alireza Maryland Power Electronics Laboratory Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

Bus-clamping Pulse Width Modulation (PWM) is an effective method to reduce the switching loss in a three-phase voltage source inverter (VSI). In bus-clamping PWM scheme, the phase legs are switched using high frequency PWM signals for two-third of the line cycle, while for the remaining duration of cycle, the pole voltage is clamped to either positive or negative rail of the DC bus. In PWM operation of a half bridge, a dead-time is applied between the gate signals of complementary switches to ensure safe and reliable operation. However, introduction of dead-time leads to poor power quality, increased Total Harmonic Distortion (THD) and variation in actual voltage compared to the intended pole voltage. Moreover, when the bus-clamping technique is used, the PWM has both high frequency switching region and clamped region in a line cycle, and consequently, the undesired effects of dead-time are further aggravated. Therefore, in order to enhance the quality of output voltage, this paper presents a dead-time compensation strategy for a VSI operating with bus-clamping PWM. The proposed method calculates the required compensation term to be added on the modulation signal considering wide range of operating conditions. Additionally, the compensation includes a new strategy for low current conditions near zero-crossing to avoid distortion. The proposed method is verified by simulation and experiments in a three-phase VSI with a switching frequency of 100 kHz and a fundamental frequency of 60Hz. Copyright © 2023, The Authors. All rights reserved.

关键词： Pulse width modulation

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