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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Faqrizal, Irman Liakh, Tatiana Xavier, Midhun Salaün, Gwen Vyatkin, Valeriy Univ. Grenoble Alpes Cnrs Grenoble Inp Inria Lig Grenoble France Lulea Tekniska Universitet Department Of Computer Science Computer And Space Engineering Sweden Aalto University Department Of Electrical Engineering And Automation Espoo Finland

ISBN: (纸本)9798350340266

The ever-increasing complexity of industrial control systems generates a demand for reliable development methods. IEC 61499, a recent industrial standard, helps to develop complex distributed systems based on their positive characteristics, namely reusability, reconfigurability, interoperability, and portability. Formal verification techniques, such as model checking, have been proposed to ensure the correctness of these systems during the design time. However, they do not consider the presence of the environment that can impact the application behaviour at runtime. This work combines design time and runtime analyses to apply probabilistic model checking on an IEC-61499-based manufacturing application. We present several probabilistic properties to be checked. The results are visualised graphically to be analysed, which allows one to optimise the system's quantitative features, such as productivity. © 2024 IEEE.

关键词： Formal verification

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Energy-Adaptive Checkpoint-Free Intermittent Inference for Low Power Energy Harvesting Systems 26

Energy-Adaptive Checkpoint-Free Intermittent Inference for L...

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26th International Symposium on Quality Electronic Design, ISQED 2025

作者： Islam, Sahidul Wei, Wei Banerjee, Jishnu Pan, Chen The University of Texas at San Antonio Department of Computer Science United States The University of Texas at San Antonio Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331509422

Deep neural network (DNN) inference in energy harvesting (EH) devices poses significant challenges due to resource constraints and frequent power interruptions. These power losses not only increase end-to-end latency, but also compromise inference consistency and accuracy, as existing checkpointing and restore mechanisms are prone to errors. Consequently, the quality of service (QoS) for DNN inference on EH devices is severely impacted. In this paper, we propose an energy-adaptive DNN inference mechanism capable of dynamically transitioning the model into a low-power mode by reducing computational complexity when harvested energy is limited. This approach ensures that end-to-end latency requirements are met. Additionally, to address the limitations of error-prone checkpoint-and-restore mechanisms, we introduce a checkpointfree intermittent inference framework that ensures consistent, progress-preserving DNN inference during power failures in energy-harvesting systems. © 2025 IEEE.

关键词： Deep Neural Network Energy Harvesting Pattern-based Pruning

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Incorporating Event Information for High-Quality Video Interpolation 20

Incorporating Event Information for High-Quality Video Inter...

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20th IEEE International Conference on Networking, Sensing and Control, ICNSC 2023

作者： Yang, Beibei Jiang, Guangyu Fang, Yan Li, Weiling Dongguan University of Technology School of Computer Science and Technology Dongguan China Kennesaw State University Department of Electrical and Computer Engineering MariettaGA United States

ISBN: (纸本)9798350369502

Optical flow-based video interpolation is a commonly used method to enrich video details by generating new intermediate frames from existing ones. However, it cannot accurately reproduce the trajectories of irregular and fast-moving objects. To achieve the accurate reconstruction of high-speed scenes, incorporating event information during the interpolation process is an effective method. However, existing methods are not suitable for scenarios where events are sparse. To implement high-quality video interpolation for these scenarios, this study incorporates event information into the interpolation process with three-fold ideas: a) treating the moving target as the foreground and using events to delineate the target area, b) matching foreground to background based on the event information to generate temporal frame between keyframes, and c) generating intermediate frames between keyframe and temporal frame with optical flow interpolation. Empirical studies indicate that owing to its efficient incorporation of event information, the proposed framework outperforms state-of-the-art methods in generating high-quality frames for video interpolation. © 2023 IEEE.

关键词： Optical flows

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Multicarrier Waveforms for Moon-to-Earth RF Transmission 10

Multicarrier Waveforms for Moon-to-Earth RF Transmission

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10th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2023

作者： Rahman, Talha Faizur Marojevic, Vuk Sacchi, Claudio Mississippi State University Dept. of Electrical and Computer Engineering Mississippi StateMS United States University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9781665456906

More than 50 years after the last Apollo mission, the race to the Moon has started again. In the framework of new missions, it will be of paramount importance to establish a reliable communication link between the vehicles (or astronauts) present on the Moon's surface and an Earth station. Alternative choices consider radio-frequency (RF), optical, and hybrid optical-RF solutions. In this paper, we focus on the RF transmission case. Recent works evidenced how diversity is required to face multipath fading issues due to the reflection of electromagnetic rays on the Lunar surface. For this reason, suitable waveforms, which are robust against multi path distortion should be considered. In this work, we propose multicarrier Orthogonal Frequency Division Multiplexing (OFDM), Single-Carrier OFDM (SC-OFDM), and Constant-Envelope OFDM (CE-OFDM). The comparative results will be discussed in order to select the best solution to cope with the various tradeoffs between link performance, spectral efficiency, and computational complexity. © 2023 IEEE.

关键词： 5G mobile communication systems

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Beamforming Optimization for Continuous Aperture Array (CAPA)-Based Communications

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5099-5113页

作者： Wang, Zhaolin Ouyang, Chongjun Liu, Yuanwei Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Kyung Hee University Department of Electronic Engineering 17104 Korea Republic of

The beamforming optimization in continuous aperture array (CAPA)-based multi-user communications is studied. In contrast to conventional spatially discrete antenna arrays, CAPAs can exploit the full spatial degrees of freedom (DoFs) by emitting information-bearing electromagnetic (EM) waves through continuous source current distributed across the aperture. Nevertheless, such an operation renders the beamforming optimization problem as a non-convex integral-based functional programming problem, which is challenging for conventional discrete optimization methods. A couple of low-complexity approaches are proposed to solve the functional programming problem. 1) Calculus of variations (CoV)-based approach: Closed-form structure of the optimal continuous source patterns are derived based on CoV, inspiring a low-complexity integral-free iterative algorithm for solving the functional programming problem. 2) Correlation-based zero-forcing (Corr-ZF) approach: Closed-form ZF source current patterns that completely eliminate the inter-user interference are derived based on the channel correlations. By using these patterns, the original functional programming problem is transformed to a simple power allocation problem, which can be solved using the classical water-filling approach with reduced complexity. Our numerical results validate the effectiveness of the proposed designs and reveal that: 1) compared to the state-of-the-art Fourier-based discretization approach, the proposed CoV-based approach not only improves communication performance but also reduces computational complexity by up to hundreds of times for large CAPA apertures and high frequencies, and 2) the proposed Corr-ZF approach achieves asymptotically optimal performance compared to the CoV-based approach. © 2002-2012 IEEE.

关键词： Iterative methods

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Neural Network Controlled Grid Synchronization of Single-Phase Inverter Using SOGI-PLL for Efficient Solar Power Integration 13

Neural Network Controlled Grid Synchronization of Single-Pha...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Mishra, Vinayak Singh, Narendra Pal Singh, Umesh Kumar Jain, Akshit Kumar, Doshi Saumya Rakesh Teerthanker Mahaveer University Moradabad Faculty of Engineering Electrical Engineering Department Uttar Pradesh India Invertis University Bareilly Department of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350380583

This paper presents a comprehensive Artificial Neural Network (ANN)-based control scheme for single-phase grid-connected inverters, emphasizing efficient and accurate synchronization. Using Echo State Networks (ESN) within the ANN framework, this method generates a precise modulating wave for Pulse Width Modulation (PWM) control. Combined with a Second order generalised algorithms (SOGI) Phase-Locked Loop (PLL) system, the approach effectively aligns the inverter's output with grid phase and frequency, achieving low Total Harmonic Distortion (THD). Simulations in MATLAB/Simulink verify the superior performance of this method over traditional Proportional Integral (PI) and PR control systems in terms of steady-state and transient performance. © 2024 IEEE.

关键词： Phase locked loops

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Undersampled Random Forest: A Green Approach to Imbalanced Learning 3

Undersampled Random Forest: A Green Approach to Imbalanced L...

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3rd International Conference on Sustainable Mobility Applications, Renewables and Technology, SMART 2024

作者： Kamalov, Firuz Elnaffar, Said El Khatib, Ziad Cherukuri, Aswani Kumar Jonnalagadda, Annapurna Department of Electrical Engineering Canadian University Dubai Dubai United Arab Emirates Department of Computer Science Canadian University Dubai Dubai United Arab Emirates School of IT & Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331532925

The increasing size of machine learning models and the datasets used for training has resulted in significantly higher computational demands. Modern large language models, in particular, consume vast amounts of energy during training, raising serious environmental concerns. In this paper, we propose a low-energy classifier, the Undersampled Random Forest (URF), designed specifically for imbalanced data. The URF method leverages undersampling in combination with a parallelizable random forest architecture. This approach reduces the size of the training data and accelerates computation, leading to more energy-efficient training processes. Moreover, the proposed method consistently achieves high recall rates on minority class instances, making it both effective and environmentally conscious. © 2024 IEEE

关键词： Adversarial machine learning

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Bipartite Graph based Multi-view Clustering (Extended Abstract) 39

Bipartite Graph based Multi-view Clustering (Extended Abstra...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Li, Lusi He, Haibo Old Dominion University Department of Computer Science Norfolk United States University of Rhode Island Department of Electrical Computer and Biomedical Engineering Kingston United States

ISBN: (纸本)9798350322279

In existing graph-based multi-view clustering algorithms, consensus cluster structures are explored by constructing similarity graphs of multiple views and then fusing them into a unified superior graph. However, they overlook consensus information when learning each graph independently, resulting in the undesirable unified graph with biases. To this end, we proposed a framework named bipartite graph based multi-view clustering (BIGMC) in [1] to tackle this challenge. To summarize, the key idea of BIGMC is to employ a small number of uniform anchors to represent the consensus information across views. In this way, BIGMC creates a bipartite graph between data points and anchors for each view, which are then fused to generate a unified bipartite graph. The unified graph would in turn improve each view bipartite graph and the anchor set. Finally, the clusters are formed directly using the unified graph. In this extended abstract, we also summarize the effectiveness of BIGMC as shown in experimental results originally presented in [1]. © 2023 IEEE.

关键词： Clustering algorithms

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Reusing Deep Learning Models: Challenges and Directions in Software engineering

Reusing Deep Learning Models: Challenges and Directions in S...

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2023 IEEE John Vincent Atanasoff Symposium on Modern Computing, JVA 2023

作者： Davis, James C. Jajal, Purvish Jiang, Wenxin Schorlemmer, Taylor R. Synovic, Nicholas Thiruvathukal, George K. Purdue University Department of Electrical & Computer Engineering West LafayetteIN United States Loyola University Chicago Department of Computer Science ChicagoIL United States

ISBN: (纸本)9798350328899

Deep neural networks (DNNs) achieve state-of-The-Art performance in many areas, including computer vision, system configuration, and question-Answering. However, DNNs are expensive to develop, both in intellectual effort (e.g., devising new architectures) and computational costs (e.g., training). Re-using DNNs is a promising direction to amortize costs within a company and across the computing industry. As with any new technology, however, there are many challenges in re-using DNNs. These challenges include both missing technical capabilities and missing engineering practices. This vision paper describes challenges in current approaches to DNN re-use. We summarize studies of re-use failures across the spectrum of re-use techniques, including conceptual (e.g., re-using based on a research paper), adaptation (e.g., re-using by building on an existing implementation), and deployment (e.g., direct re-use on a new device). We outline possible advances that would improve each kind of re-use. © 2023 IEEE.

关键词： Deep neural networks

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Interpreting Multiclass Lung Cancer from CT Scans using Grad-CAM on Lightweight CNN Layers 6

Interpreting Multiclass Lung Cancer from CT Scans using Grad...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Muntasir, Fahim Datta, Ayon Mahmud, Shakib Brac University Department of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Bangladesh University of Engineering and Technology Department of Biomedical Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Lung cancer is one of the most prevalent and deadly cancer kinds. CT scan imaging has made it easier to observe in-depth at different lung cancer conditions. Convolutional Neural Networks have already been utilized for detecting objects in images. This research implements a lightweight CNN architecture in classifying two types of cancerous images and distinguishing them from healthy ones. AlexNet, ResNet101 V2, DenseNet201, MobileNet V2, and other sizable deep learning and transfer learning models were tested against this unique architecture with global max pooling. The proposed CNN model overlaps in performance with much bigger models whilst using about eight times fewer parameters, thus being efficient. Apart from making the architecture smaller, Grad-CAM was used to explain the convolutional flow over different convolution layers. This integration of the Grad-CAM technique into the analysis adds a crucial layer of transparency to the deep-learning models. By vi-sualizing the regions within lung cancer images that significantly influence predictions, we aim to enhance the interpretability of our model, fostering trust and better understanding among healthcare professionals. © 2024 IEEE.

关键词： Convolutional neural networks

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