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arXiv 2024年

作者： Ginio, Noam Lindenbaum, Michael Fishbain, Barak Liberzon, Dan Faculty of Civil and Environmental Engineering Technion Haifa320003 Israel Faculty of Computer Science Technion Haifa320003 Israel Interdisciplinary Program for Marine Engineering Technion Haifa320003 Israel

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. The developed method training and evaluation was based on in-house made supervised dataset. Here we present this supervised dataset of polarimetric images of the water surface coupled with the water surface elevation measurements made by a linear array of resistance-type wave gauges (WG). The water waves were mechanically generated in a laboratory waves basin, and the polarimetric images were captured under an artificial light source. Meticulous camera and WGs calibration and instruments synchronization supported high spatio-temporal resolution. The data set covers several wavefield conditions, from simple monochromatic wave trains of various steepness, to irregular wavefield of JONSWAP prescribed spectral shape and several wave breaking scenarios. The dataset contains measurements repeated in several camera positions relative to the wave field propagation direction. © 2024, CC BY-NC-ND.

关键词： Water waves

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Modeling the Self-Capacitance of Individual Plates in a Multi-Conductor System using PEEC 5

Modeling the Self-Capacitance of Individual Plates in a Mult...

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5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Wolf, Henry Striker, Ryan Cleveland, Jerika Gieser, Anna Braaten, Benjamin D. Enochson, Avery Ewert, Daniel Mitra, Dipankar Allen, Jeffery Allen, Monica Vitola, Sabina North Dakota State University Department of Electrical and Computer Engineering Fargo United States Program of Minnesota State University Department of Integrated Engineering at Iron Range Engineering MankatoMN United States University of Wisconsin - la Crosse Department of Computer Science and Computer Engineering La CrosseWI United States Eglin Air Force Base Eglin Afb Air Force Research Laboratory Munitions Directorate FL32542 United States Minnesota State University Bell Program Department of Integrated Engineering MankatoMN United States

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

In many areas of electronics design, it is necessary to understand the different aspects of capacitance associated with various conducting surfaces in a particular layout. This is because as operating frequencies increase and dimensions decrease, capacitive coupling can become the dominant means in which noise is induced in a design. Therefore, having the ability to extract an equivalent circuit for modeling capacitive coupling and understanding the coupling can be very important, and challenging. This paper presents insight on capacitive coupling by evaluating the capacitance between two parallel plates, with particular attention paid to the self-capacitance (with a reference at infinity) of each of the individual plates. More specifically, the Partial Element Equivalent Circuit (PEEC) method is used to compute the self-capacitance of each individual plate in the parallel plate capacitor problem, and results are verified by comparison to values from James C. Maxwell's original works and the electrostatic solver ANSYS Maxwell 3D. Overall, it is shown how the self-capacitance of each individual plate changes as a function of distance between the parallel plates. © 2022 IEEE.

关键词： Capacitance

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A Comparison Diagnosis Algorithm for Conditional Fault Local Diagnosis of Multiprocessor Systems 25th

A Comparison Diagnosis Algorithm for Conditional Fault Loca...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan City Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9789811995811

An efficient diagnosis is very important for a multiprocessor system. In this paper, we present a (α, β) -trees combination S(u, X, α, β) and give some conclusions about the local diagnosis. Based on the (α, β) -trees combination, we give a conditional fault local diagnosis algorithm to identify the fault or fault-free status of each processor correctly under the MM ∗ model when the number of faulty nodes does not exceed α+ 2 β- 3 and every node has at least one fault-free neighboring node. According to our results, a connected network with a (α, β) -trees combination S(u, X, α, β) for a node u is conditionally locally (α+ 2 β- 3 ) -diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+ αβ2). As an application, we show that our algorithm can identify all the faulty nodes of n-dimensional star graph Sn if the faulty node number does not exceed 3 n- 8. Compared with existing algorithms, our algorithm allows more faulty node in a multiprocessor system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational complexity

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Optimizing System-on-Chip Performance Using AI and SDN: Approaches and Challenges 9

Optimizing System-on-Chip Performance Using AI and SDN: Appr...

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9th International Conference on Software Defined Systems, SDS 2022

作者： Alja'afreh, Mohammad Obaidat, Muath Karime, Ali Alouneh, Sahel United Arab Emirates Computer Science- City University of New York United States The Royal Military College of Canada Department of Electrical and Computer Engineering Canada Al Ain University German Jordanian University Cybersecurity Program Jordan Jordan

ISBN: (纸本)9798350346718

The advancement of modern multimedia and data-intensive classes of applications demands the development of hardware that delivers better performance. Due to the evolution of 5G, Edge-Computing, the Internet of Things, Software-Defined networks, etc., the data produced by the devices such as sensors are increasing. A software-Defined network is a powerful paradigm that is capable of automating networking and cloud computing. Software-Defined Network has controllers, devices, and applications which produce a huge amount of data. The processing of data inside the device as well as between the devices needs a better hardware architecture with more cores to ensure speedy performance. The System-on-Chip approach alone will not be capable to handle this dense core comprised of hardware. We have to blend Network-on-Chip along with System-on-Chip to increase the potential to include more cores capable to handle more threads. Artificial Intelligence, a key enabler in next-generation devices is capable of producing a better architecture design with optimized performance. In this paper, we are discussing and endeavouring how System-on-Chip, Network-on-Chip, Software-Defined Networks, and Artificial Intelligence can be physically, logically, and contextually incorporated to deliver improved computation and networking outcomes. © 2022 IEEE.

关键词： Network-on-chip

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Long short-Term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control 7

Long short-Term memory (LSTM) model-based reinforcement lear...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Wijaya, Santo Heryadi, Yaya Arifin, Yulyani Suparta, Wayan Lukas Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta55281 Indonesia Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance. © 2022 Elsevier B.V.. All rights reserved.

关键词： Model predictive control

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Fish weight prediction using empirical and data-driven models in aquaculture systems

Fish weight prediction using empirical and data-driven model...

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Control Technology and Applications (CCTA),

作者： Xiao Chen Ibrahima N’Doye Fahad Aljehani Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Science and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Saudi Arabia Environmental Science and Engineering Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Saudi Arabia National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

Predicting fish growth trajectory is crucial in achieving better control for the growing phase of the fish in aquaculture systems. However, fish biomass sensors that directly measure the fish’s weight are currently unavailable. Additionally, the fish growth model that relies on the fish weight is affected by environmental and feeding management factors, raising the difficulty of predicting the weight. This paper proposes comparative empirical and data-driven models for fish weight prediction. The empirical fish growth model relies directly on the growth ratio, an essential component for optimizing fish growth, while the data-driven model is based on long short-term memory (LSTM). Since the exact daily food ratio, proportional to the body weight of a growing fish, cannot be prescribed, we optimize the growth ratio of the empirical model in a sliding window framework based on the previous growth periods and then predict the fish weights a few days ahead through a nonlinear exponential regression approach. The LSTM method uses a normal distribution to generate multiple datasets over the experimental fish weight range and interpolate these data to provide a good prediction. To this end, we propose a weighted sum optimization method that combines the individual cost of the LSTM and empirical methods to achieve better short-term prediction performance. The simulation results demonstrate the effectiveness of the proposed models in predicting the fish weight and show that the combined weighted sum approach reduces the testing errors by 61.3% and 70.9% compared to the empirical and LSTM methods, respectively. Furthermore, an increasing specific growth rate (SGR) for both LSTM and combined models was observed in the short-term predictions.

关键词： Weight measurement Costs Biological system modeling Simulation Predictive models Fish Trajectory

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Neuronal Modeling Tool Using DynaSim 5

Neuronal Modeling Tool Using DynaSim

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5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Gieser, Anna Vitola, Sabina Enochson, Avery Wolf, Henry Striker, Ryan Cleveland, Jerika Braaten, Benjamin D. Ewert, Daniel Mitra, Dipankar Allen, Jeffery Allen, Monica North Dakota State University Department of Electrical and Computer Engineering Fargo United States Minnesota State University Bell Program Department of Integrated Engineering MankatoMN United States Program of Minnesota State University Department of Integrated Engineering at Iron Range Engineering MankatoMN United States University of Wisconsin - la Crosse Department of Computer Science and Computer Engineering La CrosseWI United States Munitions Directorate Air Force Research Laboratory Eglin Air Force Base FL32542 United States

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

Human cognitive processes remain an area of strong interest and ongoing research. One tool to gain greater insight into this process is neuronal modeling. The following features are desirable in a neuronal modeling tool: a library of known parameters for different neurons and species, the ability to select the neuron and species of interest, the ability to quickly simulate neuronal behavior, and the ability to calculate metabolic requirements and efficiencies for various neuronal activity. Many of these can be found in software today, but not all elements can be found in one entity. The goal of this work is to develop a neuronal modeling tool that incorporates these features. © 2022 IEEE.

关键词： Neurons

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Electrical Characteristic and Power Fluctuations of GAA Si NS CFETs by Simultaneously Considering Six Process Variation Factors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 229-238页

作者： Kola, Sekhar Reddy Li, Yiming National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Characteristic variability induced by process variation effect (PVE) is one of technological challenges in semiconductor industry. In this work, we computationally study electrical characteristic and power fluctuations induced by six factors of PVE of the gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field-effect-transistors (CFETs) which are formed by vertically stacking n-FET on top of p-FET. Among the six factors, NS thickness (TNS), NS width (WNS), and gate length (LG) are identified as crucial factors contributing to large variations in device characteristics. The p-FET exhibits substantial off-state current fluctuation (about 151%) due to the bottom parasitic channel leakages. Compared with the magnitudes of dynamic and short circuit powers, the static power is marginal, but it possesses the largest fluctuation (up to 148%). If we assume that each factor of PVE has the same probability distribution as the others and all are mutually independent, the statistical sum of their power fluctuations will exhibit more than 50% overestimations, compared with the results when all factors are considered simultaneously. © 2020 IEEE.

关键词： III-V semiconductors

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Cancer Prediction Using Clinical and Genomic Data Fusion: A Systematic Review

Cancer Prediction Using Clinical and Genomic Data Fusion: A ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Kelvin Julian Kuncahyo Setyo Nugroho Rudi Nirwantono Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies.

关键词： Accuracy Machine learning algorithms Genomics Data integration Medical services Predictive models Data models Bioinformatics Cancer Systematic literature review

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Mechanism of Threshold Voltage Instability in Double Gate α-IGZO Nanosheet TFT under Bias and Temperature Stress

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IEEE Journal of the Electron Devices Society 2024年 12卷 464-471页

作者： Aslam, Muhammad Chang, Shu-Wei Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Lee, Wen-Hsi Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan City701401 Taiwan Institute of Pioneer Semiconductor Innovation Industry Academic Innovation School National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Amorphous indium gallium zinc oxide (a-IGZO)-based thin film transistors (TFTs) are increasingly becoming popular because of their potential in futuristic applications, including CMOS technology. Given the demand for CMOS-compatible, ultra-scaled, reliable, and high-performing devices, we fabricate and analyze scaled-channel a-IGZO-TFTs with an optimal double-gate structure, a thin nanosheet-based channel, and an effective high- κ dielectric namely HfO2. The reliably reported double gate IGZO nanosheet TFTs (DG-IGZO-NS-TFTs) are tested under positive and negative bias stress at variant temperatures, and the resulting modulations are analyzed critically. The reported DG-IGZO-NSTFTs exhibit a negative side threshold voltage shift (Δ Vth) accompanied by an increase in Ion/Ion(0) under negative bias temperature stress (NBTS) at elevated temperatures, which indicates the presence of additional charges. An anomalous negative side shifting of the Vth is observed under positive bias temperature stress (PBTS), where diffused hydrogen atoms are identified as introducing excess n-type carriers into the channel and causing the observed Δ Vth. The spectroscopic analysis is performed to establish evidence for the assumed mechanisms, and the role of individual gates is investigated in the context of performance variance under temperature-bias stress. Moreover, the partial reversibility of the stress-induced degradation is experimentally established and methodically discussed. Overall, the reported results offer a comprehensive understanding of scaled-channel DG-NS-IGZO-TFTs, which help shape performance-enhancement strategies, control degradation mechanisms, and define appropriate application scenarios. © 2013 IEEE.

关键词： Thin film transistors

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