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IEEE Symposium on Nuclear science (NSS/MIC)

作者： G. Lessard P. Arsenault P. Martel-Dion S. Prentice T. Rossignol F. Retière S. A. Charlebois J.-F. Pratte Department of Electrical and Computer Engineering Interdisciplinary Institute for Technological Innovation Université de Sherbrooke Sherbrooke Canada Science Technology Department Physical Science Division TRIUMF Vancouver Canada

Particle physics experiments, like nEXO and ARGO, require a large-scale data acquisition system capable of supporting high data rates. One can expect a total data rate of approximately 400 Gb/s for nEXO, whereas ARGO estimates are on a scale of Pb/s. To support those experiments, which require a low-power and high-bandwidth communication (≈1 Gb/s per link), a modular Silicon-Photonics (SiP) communication module is in development to connect the front-end electronics to the data acquisition system (DAQ). This research focuses on the interface between more than 100 optical transceivers and the DAQ. To eliminate the need for custom hardware in the servers of the DAQ, the system is compatible with a standard Ethernet network. Targetting an Ethernet interface allows the use of commodity off-the-shelf equipment to connect custom electronics to the DAQ and simplifies integration, deployment, and maintenance. This system is composed of a Zynq system-on-chip (SoC), where the FPGA receives data from a set of transceivers and wraps the frame received in UDP datagrams sent to the DAQ; the processor handles various configurations and commands. To match the requirements on data rates and modularity, this study proposes a proof of concept demonstrating a 100 Gb/s link using a FPGA to transfer the data from the custom SiP transceivers to a server running DAQ software, implemented using the MIDAS framework. Following this work, the implemented Ethernet link will constitute a system ready to integrate with the SiP communication module. This integration will provide a platform to deploy large-scale and high data-rate DAQs for the targeted experiments, namely nEXO and ARGO.

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Qiang Zhang, Shuxin Dong, Suyu Xu, Long Dong, Weihe Li, Xiaokun Sun, Pengzhong Jiang, Feng Zhang, Xianyu Luo, Gongning Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology School of Medicine and Health Harbin China Heilongjiang University School of Computer Science and Technology Harbin China Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Medical University Cancer Hospital Department of Breast Surgery Harbin China King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences & Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9798350337488

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level. © 2023 IEEE.

关键词： gene identification graph convolutional network inductive matrix completion lncRNA-disease association

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Torque Ripple Reduction by Injecting q-axis Suppression Current for Half-wave Rectified Brushless Synchronous Motors

Torque Ripple Reduction by Injecting q-axis Suppression Curr...

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International Conference on electrical Machines and Systems (ICEMS)

作者： Kohei Kanaida Tetsuji Daido Shin-Ichi Hamasaki Takashi Abe Division of Electrical Engineering and Computer Science Nagasaki University Japan

ISBN: (数字)9781665493024

ISBN: (纸本)9781665493031

In recent years, electric vehicles have become widespread from the viewpoint of environmental issues. Variable magnetic field motors have been proposed as a solution to environmental issues. The half-wave rectified variable field brushless synchronous motor uses a high-frequency current that varies the field magnetic flux by superimposing it on the stator current. One of the problems of this motor is that torque pulsation occurs by the high-frequency exciting current. In this study, we propose a method to suppress torque pulsation. The q-axis compensation current for torque pulsation is derived by a mathematical model, and its effectiveness is verified through a prototype machine.

关键词： Magnetic flux Torque Brushless motors Prototypes Stators Synchronous motors Mathematical models

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Cyber-physical security of biochips: A perspective

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Biomicrofluidics 2025年第3期19卷 031304页

作者： Baban, Navajit Singh Bhattacharjee, Sukanta Song, Yong-Ak Chakrabarty, Krishnendu Karri, Ramesh Division of Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati India School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Electrical and Computer Engineering New York University New YorkNY85287 United States

Microfluidic biochips (MBs) are transforming diagnostics, healthcare, and biomedical research. However, their rapid deployment has exposed them to diverse security threats, including structural tampering, material degradation, sample-level interference, and intellectual property (IP) theft, such as counterfeiting, overbuilding, and piracy. This perspective highlights emerging attack vectors and countermeasures aimed at mitigating these risks. Structural attacks, such as stealthy design code modifications, can result in faulty diagnostics. To address this, deep learning -based anomaly detection leverages microstructural changes, including optical changes such as shadows or reflections, to identify and resolve faults. Material-level countermeasures, including mechano-responsive dyes and spectrometric watermarking, safeguard against subtle chemical alterations during fabrication. Sample-level protections, such as molecular barcoding, ensure bio-sample integrity by embedding unique DNA sequences for authentication. At the IP level, techniques like watermarking, physically unclonable functions, fingerprinting, and obfuscation schemes provide robust defenses against reverse engineering and counterfeiting. Together, these approaches offer a multi-layered security framework to protect MBs, ensuring their reliability, safety, and trustworthiness in critical applications. © 2025 Author(s).

关键词： Reverse engineering

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Operation Optimization of Laser-Powered Aerial Data Harvesting for Passive IoT Networks

Operation Optimization of Laser-Powered Aerial Data Harvesti...

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IEEE Conference on Wireless Communications and Networking

作者： Amr M. Abdelhady Abdulkadir Çelik Carles Diaz-Vilor Hamid Jafarkhani Ahmed M. Eltawil Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Makkah Province Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Center for Pervasive Communications and Computing University of California Irvine CA USA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

This paper investigates the maximization of har-vested data in a laser-powered uncrewed aerial vehicle (UAV) supporting Internet of Things (IoT) deployment. The system enables battery-free IoT devices to establish communication links with the UAV via bistatic backscattering with the aid of a power beacon source. Upon considering an unspecified flying time, we adopt path discretization and resort to the single-block successive convex approximation (SCA) to solve the data collection maximization problem. In addition to considering the UAV dynamics and power budget, two novel SCA-compatible bounds are introduced for the product of mixed convex/concave positive functions. Finally, the simulations conducted show that the proposed algorithm provides 90% increase in collected data under different operation conditions.

关键词： Wireless communication Heuristic algorithms Power lasers Data collection Autonomous aerial vehicles Trajectory Internet of Things

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Prediction and Electricity Forecasting on the Individual Household Level based on PSO-LSSVM Approach 7

Prediction and Electricity Forecasting on the Individual Hou...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Padmaja, Voleti Tamizhselvi, A. Kathir, I. Mohan, Arvind Pal, Subharun Rastogi, Ravi Geethanjali College of Engineering and Technology Electrical and Electronics Engineering Hyderabad India St. Joseph's College of Engineering Department of Information Technology Chennai India V.S.B. Engineering College Department of Electrical & Electronics Engineering Karur India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India Indian Institute of Technology Jammu Department of Computer Science and Engineering Jammu & Kashmir Jagti India NIELIT Electronics Division Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350340600

When smart metering systems are used to encourage energy conservation at the residential level, new research challenges arise in the areas of monitoring usage and providing accurate load forecasts. Intelligent smart meters rely on accurate predictions of future electricity use. Many energy and operational improvements, such as more efficient appliances, renewable energy sources, and advanced metering systems, are included into micro-grids. Here is an alternative approach to estimating annual energy use in single-family homes. Incorporating the effects of residents' daily activities and appliance usages on overall home power consumption improves the accuracy of the forecasting model. Data preparation, feature selection, and model training are the three phases of the suggested approach. Data preprocessing usually entails cleaning the data and preparing the features to be used. When it comes to feature selection, the K-means clustering approach is frequently utilized. After using information gain for feature selection, the models are trained using PSO-LSSVM. The proposed method outperforms both GRU and LSSVM, two well-established competitors. © 2023 IEEE.

关键词： K-means clustering

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Towards Detection of Hirschsprung's Disease using Dielectric Properties: Feasibility in thin mice colon

Towards Detection of Hirschsprung's Disease using Dielectric...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Samaddar, Poulami Gaddam, Sunil Gopalakrishnan, Keerthy Cherukuri, Akhila Modi, Vaishnavi Dey, Shuvashis Roy, Sayan Mitra, Dipankar Arunachalam, Shivaram P. Division of Gastroenterology and Hepatology Dept. of Medicine RochesterMN United States North Dakota State University Dept. of Electrical and Computer Engineering FargoND United States South Dakota Mines Dept. of Electrical Engineering and Computer Science Rapid CitySD United States University of Wisconsin-La Crosse Dept. of Computer Science La CrosseWI United States Mayo Clinic Department of Radiology RochesterMN United States

ISBN: (纸本)9781665442282

Hirschsprung disease is a congenital disability due to the defect in migration of colonic ganglion. Definitive diagnosis is based on the findings of histopathological evaluation of rectal biopsies which can be highly variable. Post-op perforations and bleeding are major complications of rectal biopsy. Difficulty in establishing a definitive diagnosis leads to severe complications. Research studies on the myoelectrical activity in Hirschsprung's disease have shown disorganized electrical activities and variations in spontaneous membrane activity in the affected colon. electrical properties can be measured as complex permittivity using dielectric spectroscopy as an attempt to diagnose Hirschsprung's disease. Dielectric spectroscopy measurements on thin biological samples offer challenges due to the requirements of sample size and probe positioning. In this work, the feasibility of dielectric property measurement of thin colon of early postnatal mice using the coaxial probe method is investigated. Complex permittivity of colon tissue was calculated from simulated return loss using a previously published mathematical model and compared with measured results. The results showed very good agreement from 500 MHz to 8 GHz range and demonstrate the potential for using dielectric properties as a biomarker for Hirschsprung disease. © 2023 IEEE.

关键词： Electromagnetic simulation

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Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching

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IEEE Transactions on Communications 2025年

作者： Tharakan, Krishnendu S. Dahrouj, Hayssam Kouzayha, Nour Elsawy, Hesham Al-Naffouri, Tareq Y. Queen's University School of Computing Kingston Canada University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955-6900 Saudi Arabia

Delivering an immersive experience to virtual reality (VR) users through wireless connectivity offers the freedom to engage from anywhere at any time. Nevertheless, it is challenging to ensure seamless wireless connectivity that delivers real-time and high-quality videos to the VR users. This paper proposes a field of view (FoV) aware caching for mobile edge computing (MEC)-enabled wireless VR network. In particular, the FoV of each VR user is cached/prefetched at the base stations (BSs) based on the caching strategies tailored to each BS. Specifically, decentralized and personalized federated learning (DP-FL) based caching strategies with guarantees are presented. Considering VR systems composed of multiple VR devices and BSs, a DP-FL caching algorithm is implemented at each BS to personalize content delivery for VR users. The utilized DP-FL algorithm guarantees a probably approximately correct (PAC) bound on the conditional average cache hit. Further, to reduce the cost of communicating gradients, one-bit quantization of the stochastic gradient descent (OBSGD) is proposed, and a convergence guarantee of O(1/√T) is obtained for the proposed algorithm, where T is the number of iterations. Additionally, to better account for the wireless channel dynamics, the FoVs are grouped into multicast or unicast groups based on the number of requesting VR users. The performance of the proposed DP-FL algorithm is validated through realistic VR head-tracking dataset, and the proposed algorithm is shown to have better performance in terms of average delay and cache hit as compared to baseline algorithms. © 1972-2012 IEEE.

关键词： Federated learning

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Study on ESS maximum capacity calculation and operation algorithm based on load pattern of substations in urban railway

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Transactions of the Korean Institute of electrical Engineers 2021年第9期70卷 1405-1412页

作者： Yun, Chi-Myeong Cho, Gyu-Jung Kim, Hyungchul Kim, Chul-Hwan Jung, Hosung Smart Electrical & Signaling Division Korea Railroad Research Institute Korea Republic of Dept. of Transportation Engineering Korea University of Science and Technology Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In this paper, we introduce the method of reducing only the morning peak of the ESS and the method of calculating the maximum ESS capacity according to the method of reducing the entire day by using the characteristic that the load of the urban railway substation shows a different load pattern depending on weekdays, weekends, and holidays. Furthermore, an operation method suitable for urban railroad is introduced using the operation algorithm for each of the five points of time of the ESS according to the load pattern. To verify the introduced maximum capacity calculation method and ESS operation algorithm, power analysis program, PSCAD/EMTDC was used. Copyright © The Korean Institute of electrical Engineers.

关键词： Cost reduction

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On biased compression for distributed learning

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The Journal of Machine Learning Research 2023年第1期24卷 12974-13023页

作者： Aleksandr Beznosikov Samuel Horváth Peter Richtárik Mher Safaryan Computer Electrical and Math. Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal KSA and Skolkovo Institute of Science and Technology Moscow Russia and School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow Russia Computer Electrical and Math. Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal KSA

In the last few years, various communication compression techniques have emerged as an indispensable tool helping to alleviate the communication bottleneck in distributed learning. However, despite the fact biased compressors often show superior performance in practice when compared to the much more studied and understood unbiased compressors, very little is known about them. In this work we study three classes of biased compression operators, two of which are new, and their performance when applied to (stochastic) gradient descent and distributed (stochastic) gradient descent. We show for the first time that biased compressors can lead to linear convergence rates both in the single node and distributed settings. We prove that distributed compressed SGD method, employed with error feedback mechanism, enjoys the ergodic rate $O\left( \delta L \exp[-\frac{\mu K}{\delta L}] + \frac{(C + \delta D)}{K\mu}\right)$, where δ ≥1 is a compression parameter which grows when more compression is applied, L and µ are the smoothness and strong convexity constants, C captures stochastic gradient noise (C = 0 if full gradients are computed on each node) and D captures the variance of the gradients at the optimum (D = 0 for over-parameterized models). Further, via a theoretical study of several synthetic and empirical distributions of communicated gradients, we shed light on why and by how much biased compressors outperform their unbiased variants. Finally, we propose several new biased compressors with promising theoretical guarantees and practical performance.

关键词： compression operators biased compressors distributed learning linear convergence error feedback

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