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arXiv 2022年

作者： Bhattacharjee, Sukanta Banerjee, Ansuman Chakrabarty, Krishnendu Bhattacharya, Bhargab B. Department of Computer Science and Engineering Indian Institute of Technology Assam Guwahati781039 India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata700108 India Department of Electrical and Computer Engineering Duke University United States Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha769008 India

Recent advances in digital microfluidic (DMF) technologies offer a promising platform for a wide variety of biochemical applications, such as DNA analysis, automated drug discovery, and toxicity monitoring. For on-chip implementation of complex bioassays, automated synthesis tools have been developed to meet the design challenges. Currently, the synthesis tools pass through a number of complex design steps to realize a given biochemical protocol on a target DMF architecture. Thus, design errors can arise during the synthesis steps. Before deploying a DMF biochip on a safety critical system, it is necessary to ensure that the desired biochemical protocol has been correctly implemented, i.e., the synthesized output (actuation sequences for the biochip) is free from any design or realization errors. We propose a symbolic constraint-based analysis framework for checking the correctness of a synthesized biochemical protocol with respect to the original design specification. The verification scheme based on this framework can detect several post-synthesis fluidic violations and realization errors in 2D-array based or pin-constrained biochips as well as in cyberphysical systems. It further generates diagnostic feedback for error localization. We present experimental results on the polymerase chain reaction (PCR) and in-vitro multiplexed bioassays to demonstrate the proposed verification approach. © 2022, CC BY.

关键词： Polymerase chain reaction

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Cloud-Based Intelligent Patient Triage Systems with SVM Classifiers for Emergency departments

Cloud-Based Intelligent Patient Triage Systems with SVM Clas...

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Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： N V Shibu Sujatha M M. Xavier Suresh K. Sindhuja T Saju Raj C. Srinivasan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Physics School of Advanced Sciences and Languages VIT Bhopal University Bhopal Madhya Pradesh India Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Efficient patient triage in emergency departments is crucial for the timely and successful allocation of resources and treatment in the constantly changing healthcare environment. To improve patient triage systems, this research suggests a new strategy that makes use of cloud computing and support vector machine (SVM) classifiers. The proposed solution endeavors to enhance patient outcomes in emergency departments by optimizing resource allocation via the use of cloud infrastructure's scalability and accessibility in conjunction with SVM algorithms' strong classification capabilities. Quickly classifying patients according to severity, the system integrates and analyzes data in real-time, allowing healthcare providers to prioritize high-risk situations and distribute resources appropriately. Healthcare practitioners can work together more efficiently and share patient data with cloud-based architecture, which promotes a more comprehensive approach to treatment. Results from the experiments prove that the system works as advertised and can be easily scaled up, highlighting its revolutionary potential to change the way emergency healthcare facilities handle patient triage. The growing body of evidence demonstrates the value of intelligent systems for healthcare delivery optimization and emergency patient care improvement via the use of technology.

关键词： Support vector machines Cloud computing Technological innovation Accuracy Scalability Medical services Real-time systems Safety Resource management Research and development

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Cloud-Enabled Predictive Vehicle Health Monitoring and Maintenance with LSTM Networks for Time-Series Analysis

Cloud-Enabled Predictive Vehicle Health Monitoring and Maint...

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Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： Viyyapu Lokeshwari Vinya K. K. Manivannan S. K. Manivannan C. Sathiyamoorthy M. Jagadeeswaran B. Meenakshi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Physics School of Advanced Sciences and Languages VIT Bhopal University Bhopal Madhya Pradesh India Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Maximizing operational efficiency and customer satisfaction in the automobile sector depends on maintaining vehicle health and avoiding maintenance downtime. Vehicle Internet of Things (IoT) devices capture massive volumes of sensor data, enabling predictive maintenance. This research offers cloud-based predictive vehicle health monitoring and maintenance systems using Long Short-Term Memory (LSTM) networks for time-series analysis. System architecture uses IoT sensors in automobiles to gather real-time data on engine temperature, oil pressure, and vibration. This data is stored, analyzed, and predicted on clouds. To assess time-series sensor data and forecast failures or maintenance requirements, LSTM networks, a form of recurrent neural network, are used. The proposed system uses data preprocessing to manage missing values and outliers, feature extraction to find significant sensor data patterns, and LSTM-based predictive modeling to predict vehicle health conditions. Technology may predict future faults by continually monitoring and evaluating the vehicle's state, allowing proactive maintenance to avoid expensive breakdowns and downtime. The cloud-based architecture is scalable and flexible, providing smooth connection with fleet management systems and remote vehicle health monitoring across scattered sites. The suggested method optimizes maintenance schedules and resource allocation using predictive insights from real-time sensor data to improve operational efficiency and save costs. It may improve predictive vehicle health monitoring and maintenance in the automobile sector, improving dependability, safety, and cost.

关键词： Cloud computing Costs Time series analysis Real-time systems Maintenance Safety Automobiles Internet of Things Monitoring Long short term memory

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Advancing Vascular Dementia Diagnosis Through Salp Swarm Optimized Convolutional Neural Network

Advancing Vascular Dementia Diagnosis Through Salp Swarm Opt...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Nivedita Manohara Mathkunti U Ananthanagu Vidya N Abdulpur D. Mangaiyarkarasi Anitha S Department of Computer Science & Engineering Alliance School of Advanced Computing Alliance Univerisity Bengaluru Karnataka India Department of Electrical Engineering Dr.D.Y Patil Pratishthan's College of Engineering Kolhapur Maharastra India Computer Science & Engineering Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Avadi India Department of Artificial Intelligence & Machine Learning Sri Sairam College of Engineering Anekal (VTU) Bengaluru Karnataka India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Vascular Dementia (VD) is a form of cognitive impairment seen in individuals, restricting from problems with blood vessels supplying the brain. These issues lead to strokes or reduced blood flow and gradually lead to the development of vascular dementia. Since it tends to progress slowly, diagnosis often comes after significant brain damage making prevention is crucial. Early identification of risk factors are vital to mitigate the risk of irreversible cognitive decline associated with vascular dementia. Accordingly, this research proposes an optimized neural network model for predicting vascular dementia at its earlier to improve the quality of life. The initial stage in this research involves preprocessing, where the input data is cleaned and normalized. These steps ensure that the vascular dementia dataset is cleaned of inconsistencies and prepared with normalized features for accurate analysis and modeling. Following preprocessing, relevant data features are selected using the chi-square approach, making the classification task easier. Finally, classification is performed utilizing a Convolutional Neural Network (CNN) and for fine-tuning of CNN parameters Salp Swarm Optimization (SSO) Algorithm is implemented. The execution of proposed work is processed using Python software and the outcome reveals superior performance in terms of accuracy and precision. Thereby the proposed method provides better performance with an accuracy of 94.5%, contrasted to existing methods in predicting VD.

关键词： Accuracy Software algorithms Data preprocessing Predictive models Software Classification algorithms Convolutional neural networks Biological neural networks Dementia Python

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Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking

arXiv

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arXiv 2024年

作者： Zhu, Yujiao Chen, Mingzhe Wang, Sihua Hu, Ye Liu, Yuchen Yin, Changchuan The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and System Engineering University of Miami Coral GablesFL33146 United States The Department of Computer Science North Carolina State University RaleighNC27695 United States

In this paper, the problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated. In the considered model, each passive UAV receives reflection signals from the target UAV, which are initially transmitted by the active UAV. The received reflection signals allow each passive UAV to estimate the signal transmission distance which will be transmitted to a base station (BS) for the estimation of the position of the target UAV. Due to the movement of the target UAV, each active/passive UAV must optimize its trajectory to continuously localize the target UAV. Meanwhile, since the accuracy of the distance estimation depends on the signal-to-noise ratio of the transmission signals, the active UAV must optimize its transmit power. This problem is formulated as an optimization problem whose goal is to jointly optimize the transmit power of the active UAV and trajectories of both active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards thus finding better transmit power of the active UAV and trajectories for both active and passive UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 39.4% and 64.6%, compared to VD-RL and independent deep RL methods, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Effect of Calculating Pointwise Mutual Information using a Fuzzy Sliding Window in Topic Modeling

Effect of Calculating Pointwise Mutual Information using a F...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Emil Rijcken Kalliopi Zervanou Marco Spruit Floortje Scheepers Uzay Kaymak Jheronimus Academy of Data Science Eindhoven University of Technology Eindhoven The Netherlands Department of Information and Computing Sciences Utrecht University Utrecht The Netherlands Leiden Institute of Advanced Computer Science Leiden University Leiden The Netherlands Psychiatry University Medical Centre Utrecht Utrecht The Netherlands

Topic modeling is a popular method for analysing large amounts of unstructured text data and extracting meaningful insights. The coherence of the generated topics is a critical metric for determining the model quality and measuring the semantic relatedness of the words in a topic. The distributional hypothesis, a fundamental theory in linguistics, states that words occurring in the same contexts tend to have similar meanings. Based on this theory, word co-occurrence in a given context is often used to reflect word association in coherence scores. To this end, many coherence scores use Normalised Pointwise Mutual Information (NPMI), which uses a sliding window to describe the neighbourhood that defines the context. It is assumed that there is no other structure in the neighbourhood except for the presence of words. Inspired by the distributional hypothesis, we hypothesise the word distance to be relevant for determining the word association. Hence, we propose using a fuzzy sliding window to define a neighbourhood in which the association between words depends on the membership of the words in the fuzzy sliding window. To this end, we propose Fuzzy Normalized Pointwise Mutual Information (FNPMI) to calculate fuzzy coherence scores. We implement two different neighbourhood structures by the definition of the membership function of the sliding window. In the first implementation, the association between two words correlates positively with the distance, whereas the correlation is negative in the second. We compare the correlation of our proposed new coherence metrics with human judgment. We find that the use of a fuzzy sliding window correlates less with human judgment than a crisp sliding window. This finding indicates that word distance within a window is less important than defining the window size itself.

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Approximate Shortest Paths in Polygons with Violations

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International Journal of Computational Geometry and Applications 2020年第1期30卷 79-95页

作者： Dutta, Binayak Roy, Sasanka Department of Computer Science and Engineering Tezpur University Tezpur India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India

We study the shortest k-violation path problem in a simple polygon. Let P be a simple polygon in 2 with n vertices and let s,t be a pair of points in P. Let int(P) represent the interior of P. Let P = 2(P) represent the exterior of P. For an integer k ≥ 0, the shortest k-violation path problem in P is the problem of computing the shortest path from s to t in P, such that at most k path segments are allowed to be in P. The subpaths of a k-violation path are not allowed to bend in P. For any k, we present a (1 + 2) factor approximation algorithm for the problem that runs in O(n2σ2klog n2σ2 + n2σ2k) time. Here σ = O(log(L r)) and, L, r are geometric parameters. © 2020 World Scientific Publishing Company.

关键词： approximation polygons shortest paths Steiner points triangulation violations

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xuefei Li Mingzhe Chen Danpu Liu Zhilong Zhang Tony Q. S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs).

关键词： Base stations Correlation Network topology Simulation Collaboration Reinforcement learning Vectors Sensors Global communication Optimization

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SpGesture: source-free domain-adaptive sEMG-based gesture recognition with jaccard attentive spiking neural network 24

SpGesture: source-free domain-adaptive sEMG-based gesture re...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Weiyu Guo Ying Sun Yijie Xu Ziyue Qiao Yongkui Yang Hui Xiong Thrust of Artificial Intelligence HKUST (Guangzhou) China School of Computing and Information Technology Great Bay University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Thrust of Artificial Intelligence HKUST (Guangzhou) China and Department of Computer Science and Engineering HKUST Hong Kong SAR China

ISBN: (纸本)9798331314385

Surface electromyography (sEMG) based gesture recognition offers a natural and intuitive interaction modality for wearable devices. Despite significant advancements in sEMG-based gesture recognition models, existing methods often suffer from high computational latency and increased energy consumption. Additionally, the inherent instability of sEMG signals, combined with their sensitivity to distribution shifts in real-world settings, compromises model robustness. To tackle these challenges, we propose a novel SpGesture framework based on Spiking Neural Networks, which possesses several unique merits compared with existing methods: (1) Robustness: By utilizing membrane potential as a memory list, we pioneer the introduction of Source-Free Domain Adaptation into SNN for the first time. This enables SpGesture to mitigate the accuracy degradation caused by distribution shifts. (2) High Accuracy: With a novel Spiking Jaccard Attention, SpGesture enhances the SNNs' ability to represent sEMG features, leading to a notable rise in system accuracy. To validate SpGesture's performance, we collected a new sEMG gesture dataset which has different forearm postures, where SpGesture achieved the highest accuracy among the baselines (89.26%). Moreover, the actual deployment on the CPU demonstrated a latency below 100ms, well within real-time requirements. This impressive performance showcases SpGesture's potential to enhance the applicability of sEMG in real-world scenarios. The code is available at https://***/guoweiyu/SpGesture/.

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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