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IEEE Transactions on Circuits and systems for Artificial Intelligence 2024年第1期1卷 37-52页

作者： Viviane Potocnik Luca Colagrande Tim Fischer Luca Bertaccini Daniele Jahier Pagliari Alessio Burrello Luca Benini Integrated Systems Laboratory (IIS) of ETH Zürich ETZ Züurich Switzerland Department of Control and Computer Engineering Politecnico di Torino Turin Italy Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

Transformer-based foundation models have become crucial for various domains, most notably natural language processing (NLP) or computer vision (CV). These models are predominantly deployed on high-performance GPUs or hardwired accelerators with highly customized, proprietary instruction sets. Until now, limited attention has been given to RISC-V-based general-purpose platforms. In our work, we present the first inference results of transformer models on an open-source many-tiny-core RISC-V platform implementing distributed Softmax primitives and leveraging ISA extensions for SIMD floating-point operand streaming and instruction repetition, as well as specialized DMA engines to minimize costly main memory accesses and to tolerate their latency. We focus on two foundational transformer topologies, encoder-only and decoder-only models. For encoder-only models, we demonstrate a speedup of up to 12.8 $\times$ between the most optimized implementation and the baseline version. We reach over 79% FPU utilization and 294 GFLOPS/W, outperforming State-of-the-Art (SoA) accelerators by more than 2 $\times$ utilizing the HW platform while achieving comparable throughput per computational unit. For decoder-only topologies, we achieve 16.1 $\times$ speedup in the Non-Autoregressive (NAR) mode and up to 35.6 $\times$ speedup in the Autoregressive (AR) mode compared to the baseline implementation. Compared to the best SoA dedicated accelerator, we achieve 2.04 $\times$ higher FPU utilization.

关键词： Frequency modulation Transformers computer architecture Computational modeling Kernel Artificial intelligence Hardware

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Geometric Parameterized Macromodel of Uniaxial Capacitance Accelerometer Based on Modal Superposition 16

Geometric Parameterized Macromodel of Uniaxial Capacitance A...

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16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Gridchin, Aleksandr V. Koleda, Aleksei N. Barbin, Evgenii S. Nesterenko, Tamara G. Kolchuzhin, Vladimir A. Department of Semiconductor Devices and Microelectronics Novosibirsk State Technical University Novosibirsk Russia Department of Electronic Engineering National Research Tomsk Polytechnic University Tomsk Russia Laboratory of Intelligent Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Advanced Cellular Group Qorvo Munich GmbH Munich Germany

ISBN: (纸本)9798350330885

The paper presents a piecewise geometric parameterization method and a parametric analysis of uniaxial capacitance accelerometer using a reduced model formulated on the modal superposition method. The extraction of parameters for the macromodel such as modal frequencies, modal masses, modal stiffnesses, strain energy, and capacitances are described. The reduced model consists of the two modes: torsion mode, and transversal mode, three electrodes in the electrostatic domain, and eight master nodes in the geometrical domain located at the edges of the plate. The generation of the discrete macromodels is executed using commercial software. The thickness of the plate has been selected as a design parameter. Linear piecewise interpolation is used for macromodel parameterization which have been done inside VHDL-AMS code. The accelerometer's dynamic response simulated using of parametrized macromodel considering the mechanical contact phenomena and the nonlinear electrostatic interaction are presented and discussed. The developed approach can be applied for fast system level simulations in view of the variation of the geometric parameters. © 2023 IEEE.

关键词： Capacitance

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MLFusion: Multilevel Data Fusion using CNNS for atrial fibrillation detection

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computers in Biology and Medicine 2025年 189卷 109901-109901页

作者： John, Arlene Parhi, Keshab K. Cardiff, Barry John, Deepu Biomedical Signals and Systems Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7522 NB Netherlands Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States School of Electrical and Electronic Engineering University College Dublin DublinD04 FX62 Ireland

Data fusion, involving the simultaneous integration of signals from multiple sensors, is an emerging field that facilitates more accurate inferences in instrumentation applications. This paper presents a novel fusion methodology for multi-sensor multimodal data using convolutional neural networks (CNNs), which introduces a data-driven approach to automatically select the optimal level of information abstraction for fusion. Unlike traditional methods, the proposed model allows for a self-learning fusion process, eliminating the need for manual selection of the fusion level by the designer. The model uniquely integrates feature extraction and fusion into a unified framework, enhancing efficiency and performance. Additionally, the fusion network incorporates signal quality indicators (SQIs) as input streams, enabling the model to account for the quality of each input signal during the fusion process. The methodology is evaluated on the task of atrial fibrillation detection through the fusion of two multimodal signal inputs taken from a portion of the MIMIC III database. The fusion network, using electrocardiogram (ECG) and photoplethsymogram (PPG) signals as inputs, and trained with an average loss function, achieved an accuracy of 99.33% and a sensitivity of 99.74%. The model's robustness in the presence of noisy inputs is also analyzed, demonstrating the effectiveness of the SQI-based multi-level fusion approach. This novel methodology represents a significant advancement in data fusion by offering a fully automated, quality-aware approach to multi-sensor multimodal signal fusion. © 2025

关键词： Convolutional neural networks

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Contrast enhancement of optical videoscope images

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Iran Journal of computer Science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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PZT Cantilever for Energy Harvesting and Vibration Sensing

PZT Cantilever for Energy Harvesting and Vibration Sensing

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Eric Danson Dong Sam Ha Bradley Department of Electrical and Computer Engineering Multifunctional Integrated Circuits and Systems (MICS) Group Blacksburg VA USA

The proposed two-stage ensemble machine learning model aims to bridge the gap between energy harvesting and vibration sensing applications for lead zirconate titanate (PZT) and similar piezoceramic materials by enabling one device to perform both functions simultaneously. Two PZT cantilever configurations were tested: one without a tip mass for maximum linearity at low frequencies and one with a tip mass for maximum energy output. The highest absolute prediction error on the testing set is 19% and 7%, respectively. While the R2 score remained nearly 1, the PZT cantilever with the tip mass showed an 11% lower mean absolute error (MAE) and 38% lower mean squared error (MSE) compared to the PZT without, suggesting that PZT cantilevers in energy harvesting configurations can be used to predict acceleration with acceptable accuracy.

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment 28

Integration of ANN for Accurate Estimation and Control in Wa...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ţiru, Andreea Elena Vasiliev, Iulian Diaconu, Larisa Vilanova, Ramon Voipan, Daniel Ratnaweera, Harsha Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain Dunǎrea de Jos University of Galaţi Department of Computer Science and Information Technology Galaţi Romania Ås Norway

ISBN: (纸本)9798350339918

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so. © 2023 IEEE.

关键词： Neural networks

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Robust control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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Quantifying, Activating and Rewarding Flexibility in Renewable Energy Communities

Quantifying, Activating and Rewarding Flexibility in Renewab...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Julien Allard François Vallée Zacharie De Grève Thomas Stegen Mevludin Glavic Bertrand Cornélusse Power Systems and Markets Research Group Electrical Power Engineering Unit University of Mons Mons Belgium Department of Electrical Engineering and Computer Science University of Liège Liège Belgium

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

Demand-side flexibility from renewable energy community members increases the benefits of local production and exchanges. To effectively harness this flexibility, end-users must be rewarded for their efforts regarding the shifted energy volumes and the associated discomfort. This work quantifies these last two quantities for three types of loads (electric vehicles, thermal loads and wet appliances). Then, a two-step model is proposed which first optimally activates the offered flexibility and then shares the benefits of this activation among the renewable energy community members through a rule-based or an optimization-based approach. Results show that the community framework can further increase the benefits brought by flexible demand.

关键词： Renewable energy sources Europe Tariffs Production Pricing Thermal management Electric vehicles Smart grids Thermal loading Load modeling

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Electromagnetic-Thermal Performance Trade-Offs in Phased Arrays with Shaped Elevation Patterns

Electromagnetic-Thermal Performance Trade-Offs in Phased Arr...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Feza Turgay Celik Changxu Zhao Alexander Yarovoy Yanki Aslan Department of Microelectronics Microwave Sensing Signals and Systems Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

Both thermal and electromagnetic performance of substrate-integrated waveguide (SIW) and microstrip line-fed shaped-beam arrays with slot and patch radiating elements are conducted. Three array types operating at 26 GHz band, namely SIW slot array, SIW array with patches, and proximity coupled patch array, are considered. The array performances regarding shaped radiation pattern stability with frequency and maximal temperature at the power amplifier chips are discussed. The study highlights intriguing trade-offs between radiation pattern performance and cooling ability in phased arrays.

关键词： Phased arrays Microstrip antenna arrays Time-frequency analysis Electromagnetic waveguides Thermal conductivity Thermal analysis Microstrip

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Android-Based Visualization of Vehicle Trip Data for Accident Reconstruction

Android-Based Visualization of Vehicle Trip Data for Acciden...

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International Conference on electrical engineering and Informatics (ICELTICs)

作者： Ahmad Mufadhdhal Alkifani Maya Fitria Muhammad Daffa Adrian Sitorus T. Muhammad Caesar Maulana Humaira Rahmad Dawood Department of Electrical and Computer Engineering Universitas Syiah Kuala Interactive Intelligent Systems Research Group Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350366822

ISBN: (纸本)9798350366839

Vehicle traffic accident investigations often depend on eyewitness testimony, which can be unreliable, especially in remote areas where no closed circuit television (CCTV) footage is available. A mobile data logger was developed, equipped with a range of sensors, including an accelerometer and gyroscope, to collect and store data related to vehicle movements. An additional Flutter-driven program was developed to extract and visualize data from the mobile logger, and thus improve the accuracy of accident detection and allow incidents to be rebuilt. These programs were subjected to black-box testing, which produced results consistent with the anticipated outcomes for both the recording and data visualization tests. The data visualization application was subjected to testing using the system usability scale (SUS) technique and obtained a score of 89.5, corresponding to an A grade, the highest possible score of the 5 grade levels.

关键词： Visualization Data visualization Closed box Recording Sensors Gyroscopes Usability Vehicle dynamics Accidents Testing

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