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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Alekha Kumar Mishra Deepak Puthal Asis Kumar Tripathy Dept. of Computer Science and Engineering National Institute of Technology Jamshedpur India Center for Cyber-Physical Systems and Department of EECS Khalifa University Abu Dhabi UAE School of IT and Engineering VIT Vellore India

The sinkhole attack detection mechanisms that have been reported till date are distributed in nature. Defending a sinkhole attack requires protecting the act of forging the rank computation task. This paper propose a secure centralized mechanism to ensure protection of rank computation process against sinkhole nodes in the IoT-Based systems. The expectation maximization algorithm is used to optimally cluster the nodes into a group bearing the same rank. The distribution of ranks to the nodes is protected by the signature of the sink. The experimental results show that the optimal rank computation is secure against sinkhole nodes, however requires additional network communication between nodes and the sink node.

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A Low-overhead PUF-based Secure Scan Design

A Low-overhead PUF-based Secure Scan Design

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IEEE International Symposium on Quality Electronic Design

作者： Wei Zhou Aijiao Cui Cassi Chen Gang Qu School of Electronic and Information Engineering Harbin Institute of Technology (Shenzhen) Guangdong China University of California Berkeley USA Dept. of Electrical and Computer Engineering and Institute of Systems Research Univ. of Maryland College Park USA

Scan-based side-channel attacks have become a severe threat to the security of cryptographic chips and locking mechanisms are one of the most effective methods against these attacks. However, securing the test key that locks the scan but must be shared among test engineers arises as a new challenge. In this paper, we solve this challenge by adopting the physical unclonable function (PUF) design to generate test keys that are unique for each chip. A one-time programming structure (OTPS) is used when the PUF response is first generated to improve its reliability. The security of the PUF response is achieved by obfuscation such that it can be retrieved only when a specific validation test vector presents. We implement the proposed secure scan design by reusing the original scan chain to reduce overhead. We demonstrate that the proposed secure scan design can protect the crypto chips against all existing scan-based side-channel attacks while incurring negligibly low overhead.

关键词： Side-channel attacks Resists Programming Physical unclonable function Reliability engineering Silicon Registers

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On the Stimulation Artifact Reduction during Electrophysiological Recording of Compound Nerve Action Potentials *

On the Stimulation Artifact Reduction during Electrophysiolo...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Raphael Panskus Lukas Holzapfel Wouter A. Serdijn Vasiliki Giagka Dept. of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology The Netherlands Dept. of System Integration and Interconnection Technologies Fraunhofer Institute for Reliability and Microintegration IZM Berlin Germany Neuroscience Dept. Erasmus Medical Center Rotterdam the Netherlands

Recording neuronal activity triggered by electrical impulses is a powerful tool in neuroscience research and neural engineering. It is often applied in acute electrophysiological experimental settings to record compound nerve action potentials. However, the elicited neural response is often distorted by electrical stimulus artifacts, complicating subsequent analysis. In this work, we present a model to better understand the effect of the selected amplifier configuration and the location of the ground electrode in a practical electrophysiological nerve setup. Simulation results show that the stimulus artifact can be reduced by more than an order of magnitude if the placement of the ground electrode, its impedance, and the amplifier configuration are optimized. We experimentally demonstrate the effects in three different settings, in-vivo and in-vitro.

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Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jing Bi Ziqi Wang Haitao Yuan Jinhong Yang Jia Zhang College of Computer Beijing University of Technology Beijing China Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Lyle School of Engineering Southern Methodist University Dallas TX USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions.

关键词： Accuracy Processor scheduling Computational modeling Education Autoencoders Collaboration Search problems Space exploration Particle swarm optimization Optimization

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Matlab-based Epoch Extraction for Speaker Differentiation

arXiv

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arXiv 2024年

作者： Li, Kunlun Ferro, Daniel Zhao, Xu Vuppala, Anil K. Syed, Abdul Jabbar Syed, Azeemuddin Department of Electrical and Computer Engineering The University of Mississippi Oxford United States LTRC International Institute of Information Technology Hyderabad Hyderabad India Centre for VLSI and Embedded Systems International Institute of Information Technology Hyderabad India Dept. of ECE The University of Mississippi United States

Epoch extraction has become increasingly popular in recent years for speech analysis research because accurately detecting the location of the Epoch is crucial for analyzing speech signals. The Epoch, occurring at the instant of excitation in the vocal tract system, particularly during glottal closure, plays a significant role in differentiating speakers in multi-speaker conversations. However, the extraction of the Epoch poses a challenge due to the time-varying factors in the vocal tract system, which makes deconvolution for obtaining the original excitation location more complex. In this paper, various methods for Epoch extraction, including Zero Frequency Filtering (ZFF) and Zero Frequency Resonator (ZFR), will be discussed, and their pros and cons evaluated. In addition, the stability, accuracy, and feasibility of each method will be compared. The evaluation will involve a Matlab-based locking algorithm, and a proposed hardware implementation using Raspberry pi for speaker differentiation. The experiment includes six individuals uttering the phrase"The University of Mississippi," with one person acting as the reference or"lock" speaker. The number of epochs occurring at similar positions to the reference speaker will be counted as Delta, with larger Delta values indicating greater speaker similarity. Experimental results demonstrate that when the speaker remains the same, the average number of Delta is 7.5, while for different speakers, the average number of Delta decreases to 3, 2, 2, 2, and 1, respectively, representing a decrease of approximately 73% in the number of epochs at similar positions compared to the reference *** Codes 94A12 (Primary), 68T10 (Secondary) Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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Tracking cellular and subcellular dynamics with quantitative oblique back-illumination microscopy 10

Tracking cellular and subcellular dynamics with quantitative...

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Quantitative Phase Imaging X 2024

作者： Filan, Caroline E. Bharadwaj, Srinidhi Casteleiro Costa, Paloma Davarzani, Amin Cappabianca, Dan Tommasi, Anna Sarko, Lauren La Vonne Denne, Nina Mao, Leidong Saha, Krishanu Karumbaiah, Lohitash Robles, Francisco E. George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology 801 Ferst Dr. NW AtlantaGA United States Wallace H. Coulter Dept. of Biomedical Engineering Georgia Institute of Technology and Emory University 313 Ferst Dr. NW AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology 266 Ferst Drive AtlantaGA United States School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA United States Regenerative Bioscience Center University of Georgia AthensGA United States Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI United States Wisconsin Institute for Discovery University of Wisconsin-Madison MadisonWI United States Division of Neuroscience Biomedical and Health Sciences Institute University of Georgia AthensGA United States Edgar L. Rhodes Center for Animal and Dairy Science College of Agriculture and Environmental Science University of Georgia AthensGA United States

ISBN: (纸本)9781510669635

Quantitative oblique back-illumination microscopy (qOBM) is a label-free imaging technique that enables tomographic phase imaging of thick scattering samples with epi-illumination. Here, we propose the use of two forms of functional imaging with qOBM to study tissue and cell cultures. In doing so, we obtain the spatiotemporal and quantitative functional information associated with the phase values extrapolated from qOBM imaging. We have applied this process to study the efficacy of individual immune T cells to kill glioblastoma spheroid cultures in 3D spheroids. Data show that we can effectively distinguish between cell phenotypes and characterize the dynamic motion of these cells in 3D cultures. This work offers a distinct advantage in tracking 3D cellular dynamics in thick tissue as many function imaging modalities are limited to 2D samples. Further, this technology can be expanded to analyze a wide variety of cellular and subcellular dynamics non-invasively in thick tissue. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Tissue

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Ensuring Model Fairness via Stratified Training: TP53 Mutation Prediction with Estrogen Receptor Stratification in Breast Histopathology

Ensuring Model Fairness via Stratified Training: TP53 Mutati...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Nikos Tsiknakis Kang Wang Dimitrios Salgkamis Evangelos Tzoras Georgios C. Manikis Emmanouil Sifakis Jonas Bergh Ioannis Zerdes Kostas Marias Alexios Matikas Theodoros Foukakis Oncology-Pathology Karolinska Institutet Stockholm Sweden Breast Cancer Center Karolinska University Hospital Stockholm Sweden Institute of Computer Science Foundation for Research and Technology Hellas Heraklion Greece Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Developing AI models on medical images as decision support systems has seen a huge increase in interest during the last few years. However, most published studies have neglected testing the model’s robustness against certain dataset-related biases and unbalanced variables. For example, although the prevalence of TP53 mutations is higher in Estrogen Receptor (ER)-negative breast cancer, while most ER-positive tumors are not mutated, published models have been developed on the entirety of the available data without testing for such intrinsic biases that can lead to overfitting. In this study we show that models trained for TP53 mutation prediction overfit on ER status and that stratification of training on the basis of ER is beneficial for all subgroups while it reduces bias and increases generalizability and fairness. (Implementation: https://***/tsikup/er-stratified-training-tp53-prediction)

关键词： Training Decision support systems Histopathology Predictive models Data models Robustness Breast cancer Testing Tumors Biomedical imaging

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Bipartite perfect matching is in quasi-nc

Bipartite perfect matching is in quasi-nc

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作者： FENNER, STEPHEN GURJAR, ROHIT THIERAUF, THOMAS Dept. of Computer Science and Engineering University of South Carolina ColumbiaSC29208 United States Center for the Mathematics of Information California Institute of Technology PasadenaCA91125 United States Dept. of Electrical Engineering and Computer Science Aalen University Aalen73430 Germany

We show that the bipartite perfect matching problem is in quasi-NC2. That is, it has uniform circuits of quasi-polynomial size nO(log n), and O(log2 n) dept.. Previously, only an exponential upper bound was known on the size of such circuits with poly-logarithmic dept.. We obtain our result by an almost complete derandomization of the famous Isolation Lemma when applied to yield an efficient randomized parallel algorithm for the bipartite perfect matching problem. © 2021 Society for Industrial and Applied Mathematics Publications. All rights reserved.

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Achieving Gaussian Vector Broadcast Channel Capacity with Scalar Lattices

arXiv

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arXiv 2024年

作者： Şener, M. Yusuf Kramer, Gerhard Shamai, Shlomo Böhnke, Ronald Xu, Wen School of Computation Information and Technology Technical University of Munich Munich80333 Germany Munich Research Center Huawei Technologies Duesseldorf GmbH Munich80992 Germany Dept. of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa3200003 Israel

A coding scheme with scalar lattices is applied to K-receiver, Gaussian, vector broadcast channels with K independent messages, one for each receiver. The method decomposes each receiver channel into parallel scalar channels with known interference and applies dirty paper coding with a modulo interval, amplitude shift keying (ASK), and probabilistic shaping to each scalar channel. The achievable rate tuples include all points inside the capacity region by choosing truncated Gaussian shaping, large ASK alphabets, and large modulo intervals. Copyright © 2024, The Authors. All rights reserved.

关键词： MIMO systems

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Visual Interpretability of Deep Learning Models in Glaucoma Detection Using Color Fundus Images

Visual Interpretability of Deep Learning Models in Glaucoma ...

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2022 IET International Conference on engineering Technologies and Applications, IET-ICETA 2022

作者： Lu, Da-Wen Hsu, Wei-Wen Huang, Yu-Chuan Lee, Lung-Chi Guo, Jing-Ming Hsiao, Yu-Ting Lin, I-Hung Chang, Yao-Chung Ko, Mei-Lan National Defense Medical Center Tri-Service General Hospital Dept. of Ophthalmology Taipei Taiwan National Taitung University Dept. of Computer Science and Information Engineering Taitung Taiwan School of Pharmacy National Defense Medical Center Taipei Taiwan National Taiwan University of Science and Technology Dept. of Electrical Engineering Taipei Taiwan Taipei Medical University Hospital Dept. of Ophthalmology Taipei Taiwan Graduate Institute of Clinical Medicine College of Medicine National Taiwan University Taipei Taiwan National Taiwan University Hospital Dept. of Ophthalmology Hsinchu Taiwan National Tsing Hua University Dept. of Biomedical Engineering and Environmental Science Hsinchu Taiwan

ISBN: (数字)9781665491389

ISBN: (纸本)9781665491389

With the advancement of deep learning in many computer vision applications, the computer-aided diagnosis (CAD) systems were developed using the deep learning approaches to assist physicians in clinical diagnosis as a second opinion. Many studies have reported plausible performance of the deep learning models on glaucoma detection simply based on fundus images. However, the mechanism of the deep learning models in detecting glaucoma using color fundus is still not clear. Therefore, the deep features extracted by the deep learning models were discussed in this study to provide the visual interpretability for the frameworks of deep convolutional neural network (DCNN) in glaucoma assessment. In our experiments, 986 fundus photographs were collected from National Taiwan University Hospital Hsin-Chu Branch, categorized into two groups: the experimental group of 512 glaucomatous cases with ganglion cell complex (GCC) impairment and the control group of 474 non-glaucomatous cases with normal GCC thickness. The experimental results show the deep learning models mainly focus on the areas of optic nerve head (ONH) for the diagnosis of glaucoma, which is accordant to the clinical rules in glaucoma assessment. Surprisingly, the DCNN models can still achieved high prediction accuracy in detecting the glaucomatous cases with the cropped images of macular areas only. Several cases show the model's focus areas correspond to the region with GCC impairment. The results imply the deep learning models can detect the morphologically detailed alterations in fundus photographs, which may be beyond experts' visualization. © 2022 IEEE.

关键词： Eye protection

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