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International Conference on Microelectronics, ICM

作者： Eman Hassan Meriem Bettayeb Baker Mohammad Yahya Zweiri Hani Saleh Electrical Engineering and Computer Science Department System-on-Chip (SoC) Lab Khalifa University Abu Dhabi UAE Department of Aerospace Engineering and Advanced Research and Innovation Center (ARIC) Khalifa University Abu Dhabi UAE

The interest in brain-inspired computing architectures has been growing, particularly in the context of edge devices with constrained resources for executing cognitive functions. One such approach is hyperdimensional computing (HDC), a novel concept that draws inspiration from the large representation of human neuronal activity. HDC has demonstrated effectiveness in one-dimensional tasks, like text identification and activity recognition, offering advantages in power consumption and response time over convolutional neural networks (CNNs). This paper compares HDC and CNN regarding architecture, accuracy, and hardware complexity, explicitly focusing on image classification tasks. Our findings indicate that CNNs generally outperform HDC in two-dimensional tasks but require significantly more computational resources. In contrast, HDC offers adequate results using just 16% of the data needed for training. Additionally, experiments conducted using a Raspberry Pi 4 show that HDC can enhance inference speed and energy efficiency by approximately 2.5 times relative to CNNs.

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Towards Natural Prosthetic Hand Gestures: A Common-Rig and Diffusion Inpainting Pipeline

Towards Natural Prosthetic Hand Gestures: A Common-Rig and D...

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

作者： Seungyup Ka Taemoon Jeong Sunwoo Kim Sankalp Yamsani Joohyung Kim Sungjoon Choi Department of Artificial Intelligence Korea University Seoul Korea Graphics AI Lab. NC Research NCSOFT Corporation Seongnam Korea Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Champaign IL USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Existing works on prosthetic hands focus on increasing dexterity by carrying out functional tasks. Achieving specific hand movements, such as pointing the index finger, are desired but research on generating the hand movement itself has yet to be widely explored. In this work, we propose a pipeline for generating hand motion from body motion via using the Common-Rig, a kinematic rig representation for effective motion representation, and a diffusion-based inpainting method, which has shown strengths in generalization and stability. Common rigging is applied to a motion capture dataset with both body and hands information, and hand motions are generated while conditioned on the body motions of a hand- zeroed test set. The generated results of our proposed method, compared to two baseline methods, attain smaller fingertip positional errors and diversity closer to that of the ground truth. In addition, the generated motions are implemented on a real robotic system with prosthetic hands for evaluation.

关键词： Visualization Smoothing methods Pipelines Stability criteria Fingers Real-time systems Motion capture engineering in medicine and biology Robots Prosthetic hand

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Circuit Optimization Techniques for Efficient Ex-Situ Training of Robust Memristor Based Liquid State Machine 22

Circuit Optimization Techniques for Efficient Ex-Situ Traini...

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Proceedings of the 17th ACM International Symposium on Nanoscale Architectures

作者： Alex Henderson Chris Yakopcic Steven Harbour Tarek Taha Cory Merkel Hananel Hazan Dayton Engineering Advanced Projects Lab Southwest Research Institute Beavercreek OH USA Department of Electrical and Computer Engineering University of Dayton Dayton OH USA Department of Computer Engineering Rochester Institute of Technology Rochester NY USA Allen Discovery Center Tufts University Medford MA USA

ISBN: (纸本)9781450399388

Spiking neural network hardware offers a high performance, power-efficient and robust platform for the processing of complex data. Many of these systems require supervised learning, which poses a challenge when using gradient-based algorithms due to the discontinuous properties of SNNs. Memristor based hardware can offer gains in portability, power reduction, and throughput efficiency when compared to pure CMOS. This paper proposes a memristor-based spiking liquid state machine (LSM). The inherent dynamics of the LSM permit the use of supervised learning without backpropagation for weight updates. To carry out the design space evaluation of the LSM for optimal hardware performance, several temporal signal classification tasks are performed. It is found that the binary neuron activations in the output layer improve testing accuracy by 3.7% and 5% for classification, while reducing training time. A power and energy analysis of the proposed hardware is presented, resulting in an approximately 50% reduction in power consumption and cycle energy.

关键词： spiking neural network liquid state machine memristor

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GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data

arXiv

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arXiv 2023年

作者： Zhang, Xinwei Hong, Mingyi Chen, Jie Department of Electrical and Computer Engineering University of Minnesota United States MIT-IBM Watson AI Lab IBM Research United States MIT-IBM Watson AI Lab United States

Vertical federated learning (VFL) is a distributed learning paradigm, where computing clients collectively train a model based on the partial features of the same set of samples they possess. Current research on VFL focuses on the case when samples are independent, but it rarely addresses an emerging scenario when samples are interrelated through a graph. In this work, we train a graph neural network (GNN) through VFL, where each client owns a part of the node features and a different edge set. This data scenario incurs a significant communication overhead, not only because of the handling of distributed features but also due to neighborhood aggregation in a GNN. Moreover, the training analysis is faced with a challenge caused by the biased stochastic gradients. We propose a model-splitting method that splits a backbone GNN across the clients and the server and a communication-efficient algorithm, GLASU, to train such a model. GLASU adopts lazy aggregation and stale updates to skip communication in neighborhood aggregation and in model updates, respectively, greatly reducing communication while enjoying convergence guarantees. We conduct extensive numerical experiments on real-world datasets, showing that GLASU effectively trains a GNN that matches the accuracy of centralized training, while using only a fraction of the time due to communication saving. © 2023, CC BY.

关键词： Graph neural networks

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Towards Faster Graph Partitioning via Pre-training and Inductive Inference

arXiv

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arXiv 2024年

作者： Qin, Meng Zhang, Chaorui Gao, Yu Ding, Yibin Jiang, Weipeng Zhang, Weixi Han, Wei Bai, Bo Department of Computer Science & Engineering The Hong Kong University of Science & Technology Hong Kong Theory Lab Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. China

Graph partitioning (GP) is a classic problem that divides the node set of a graph into densely-connected blocks. Following the IEEE HPEC Graph Challenge and recent advances in pre-training techniques (e.g., large-language models), we propose PR-GPT (Pre-trained & Refined Graph ParTitioning) based on a novel pre-training & refinement paradigm. We first conduct the offline pre-training of a deep graph learning (DGL) model on small synthetic graphs with various topology properties. By using the inductive inference of DGL, one can directly generalize the pre-trained model (with frozen model parameters) to large graphs and derive feasible GP results. We also use the derived partition as a good initialization of an efficient GP method (e.g., InfoMap) to further refine the quality of partitioning. In this setting, the online generalization and refinement of PR-GPT can not only benefit from the transfer ability regarding quality but also ensure high inference efficiency without re-training. Based on a mechanism of reducing the scale of a graph to be processed by the refinement method, PR-GPT also has the potential to support streaming GP. Experiments on the Graph Challenge benchmark demonstrate that PR-GPT can ensure faster GP on large-scale graphs without significant quality degradation, compared with running a refinement method from scratch. We will make our code public at https://***/KuroginQin/PRGPT. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Modeling and Simulation of Electric Vehicle using MATlab

Modeling and Simulation of Electric Vehicle using MATLAB

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Neeraj Kumar Bahuguna Govind Sati Raj Gusain Anurag Vidyarthi Rishi Prakash R. Gowri Department of Electrical Engineering GB Pant University of Agriculture and Technology Pantnagar India Department of Instrumentation Engineering Vaaman Engineers India Ltd. Hindustan Zinc PMP Pantnagar India Department of Electronics & Communication Engineering Propagation Research Lab Graphic Era (Deemed to be University) Dehradun India Department of Computer Science Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

With the progression of technology, the automobile industry has experienced a significant transformation due to the emergence of Electric Vehicles (EVs). EVs offer a sustainable means of transportation that doesn’t harm the environment. The decline of global oil reserves and the detrimental effects of air pollution caused by conventional motor vehicles have motivated numerous researchers to explore alternative energy sources for vehicle propulsion. This study aims to investigate power flow calculations to ensure that the amount of electrical energy aligns with the requirements of the electric vehicle. Using MATlab/Simulink software, a model of a small electric vehicle is designed to achieve optimal power flow response within the EV energy system and this article outlines the process of modeling an Electric Vehicle using MATlab and Simulink, elucidating different components such as the Motor Model, Battery Model, Power Model, and Regenerative System. By employing Simulink, the designed Electric Vehicle is simulated, and important metrics such as Motor Torque, Vehicle Speed, and State of Charge of the Battery are calculated using a given Driving Cycle. The collected data is then analyzed to enhance the model’s performance.

关键词： Torque Software packages Transportation Electric vehicles Motors Mathematical models Batteries

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USED: Universal Speaker Extraction and Diarization

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2024年 33卷 96-110页

作者： Junyi Ao Mehmet Sinan Yıldırım Ruijie Tao Meng Ge Shuai Wang Yanmin Qian Haizhou Li Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Shenzhen Research Institute of Big Data Shenzhen China Auditory Cognition and Computational Acoustics Lab Department of Computer Science and Engineering and the MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Speaker extraction and diarization are two enabling techniques for real-world speech applications. Speaker extraction aims to extract a target speaker's voice from a speech mixture, while speaker diarization demarcates speech segments by speaker, annotating ‘who spoke when’. Previous studies have typically treated the two tasks independently. In practical applications, it is more meaningful to have knowledge about ‘who spoke what and when’, which is captured by the two tasks. The two tasks share a similar objective of disentangling speakers. Speaker extraction operates in the frequency domain, whereas diarization is in the temporal domain. It is logical to believe that speaker activities obtained from speaker diarization can benefit speaker extraction, while the extracted speech offers more accurate speaker activity detection than the speech mixture. In this paper, we propose a unified model called Universal Speaker Extraction and Diarization (USED) to address output inconsistency and scenario mismatch issues. It is designed to manage speech mixtures with varying overlap ratios and variable number of speakers. We show that the USED model significantly outperforms the competitive baselines for speaker extraction and diarization tasks on LibriMix and SparseLibriMix datasets. We further validate the diarization performance on CALLHOME, a dataset based on real recordings, and experimental results indicate that our model surpasses recently proposed approaches.

关键词： Speech recognition Data mining Training Multitasking Speech enhancement Time-domain analysis Recording Predictive models Particle separators Oral communication

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Performance optimization of microbial electrolysis cell (MEC) for palm oil mill effluent (POME) wastewater treatment and sustainable Bio-H2 production using response surface methodology (RSM)

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第34期47卷 15464-15479页

作者： Chandrasekhar, Abudukeremu Kadier ab Junying Wang ab K. Wang, Junying Chandrasekhar, K. Abdeshahian, Peyman Islam, M. Amirul Ghanbari, Farshid Bajpai, Mukul Katoch, Surjit Singh Bhagawati, Prashant Basavaraj Li, Hui Kalil, Mohd Sahaid Hamid, Aidil Abdul Abu Hasan, Hassimi Ma, Peng-Cheng Laboratory of Environmental Science and Technology The Xinjiang Technical Institute of Physics and Chemistry Key Laboratory of Functional Materials and Devices for Special Environments Chinese Academy of Sciences Urumqi 830011 China Center of Material and Opto-electronic Research University of Chinese Academy of Sciences Beijing 100049 China School of Civil and Environmental Engineering Yonsei University Seoul 03722 Republic of Korea Department of Biotechnology Engineering School of Lorena University of São Paulo São Paulo 12602-810 Brazil Laboratory for Quantum Semiconductors and Photon-based BioNanotechnology Department of Electrical and Computer Engineering Faculty of Engineering Université de Sherbrooke Sherbrooke Québec J1K 2R1 Canada Research Center for Environmental Contaminants (RCEC) Abadan University of Medical Sciences Abadan Iran Environmental Engineering Lab Civil Engineering Department National Institute of Technology Hamirpur Hamirpur Himachal Pradesh 177005 India Faculty of Engineering Department of Civil Engineering Annasaheb Dange College of Engineering and Technology Ashta Maharashtra 416301 India Department of Chemical and Process Engineering Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia School of Biosciences and Biotechnology Faculty of Science and Technology National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia Research Centre for Sustainable Process Technology Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia

Microbial electrolysis cells (MECs) are a new bio-electrochemical method for converting organic matter to hydrogen gas (H-2). Palm oil mill effluent (POME) is hazardous wastewater that is mostly formed during the crude oil extraction process in the palm oil industry. In the present study, POME was used in the MEC system for hydrogen generation as a feasible treatment technology. To enhance biohydrogen generation from POME in the MEC, an empirical model was generated using response surface methodology (RSM). A central composite design (CCD) was utilized to perform twenty experimental runs of MEC given three important variables, namely incubation temperature, initial pH, and influent dilution rate. Experimental results from CCD showed that an average value of 1.16 m(3) H-2/m(3) d for maximum hydrogen production rate (HPR) was produced. A second-order polynomial model was adjusted to the experimental results from CCD. The regression model showed that the quadratic term of all variables tested had a highly significant effect (P < 0.01) on maximum HPR as a defined response. The analysis of the empirical model revealed that the optimal conditions for maximum HPR were incubation temperature, initial pH, and influent dilution rate of 30.23 ?, 6.63, and 50.71%, respectively. Generated regression model predicted a maximum HPR of 1.1659 m(3) H-2/m(3) d could be generated under optimum conditions. Confirmation experimentation was conducted in the optimal conditions determined. Experimental results of the validation test showed that a maximum HPR of 1.1747 m(3) H-2/m(3) d was produced. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Microbial electrolysis cell (MEC) Hydrogen production Palm oil mill effluent (POME) Wastewater Optimization Response surface methodology (RSM)

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Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformation

arXiv

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arXiv 2024年

作者： Luo, Chu Fei Shayanfar, Radin Bhambhoria, Rohan Dahan, Samuel Zhu, Xiaodan Department of Electrical and Computer Engineering Ingenuity Labs Research Institute Queen’s University Canada Conflict Analytics Lab. Queen’s University Canada Cornell Law School United States

Misinformation, defined as false or inaccurate information, can result in significant societal harm when it is spread with malicious or even innocuous intent. The rapid online information exchange necessitates advanced detection mechanisms to mitigate misinformation-induced harm. Existing research, however, has predominantly focused on assessing veracity, overlooking the legal implications and social consequences of misinformation. In this work, we take a novel angle to consolidate the definition of misinformation detection using legal issues as a measurement of societal ramifications, aiming to bring interdisciplinary efforts to tackle misinformation and its consequence. We introduce a new task: Misinformation with Legal Consequence (MisLC), which leverages definitions from a wide range of legal domains covering 4 broader legal topics and 11 fine-grained legal issues, including hate speech, election laws, and privacy regulations. For this task, we advocate a two-step dataset curation approach that utilizes crowd-sourced checkworthiness and expert evaluations of misinformation. We provide insights about the MisLC task through empirical evidence, from the problem definition to experiments and expert involvement. While the latest large language models and retrieval-augmented generation are effective baselines for the task, we find they are still far from replicating expert performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Design of a LiFi System for Short-Distance Communications

Design of a LiFi System for Short-Distance Communications

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International Conference on Computing and Networking Technology (ICCNT)

作者： Ashutaosh Devrani Divya Darshan Anurag Vidyarthi Rishi Prakash R. Gowri Department of Electronics & Communication Engineering Propagation Research Lab Graphic Era (Deemed to be University) Dehradun India Department of Electronics & Communication Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Science Engineering Graphic Era Hill University Dehradun India

LiFi technology introduces Visible Light communication that uses light as a source to deliver high data speed, which is much greater than that of Wi-Fi. The research paper proposes a communication system that utilizes Light-Fidelity (Li-Fi) technology for short-range wireless communication. The article begins with an introduction to Li-Fi technology and its advantages over traditional wireless communication systems. The paper also highlights the limitations of Li-Fi, including its short range. The authors then present their proposed approach, which includes a transmitter section, a receiver section, and two modes of operation (text and audio). Additionally, the system consists of two applications to demonstrate its functionality: an SOS application and a device on/off application. The paper concludes with a detailed explanation of how the proposed system works and its potential applications in various fields.

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