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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Labbaf Khaniki, Mohammad Ali Mirzaeibonehkhater, Marzieh Manthouri, Mohammad K.N. Toosi University of Technology Department of Control Engineering Tehran Iran Indiana University-Purdue University Department of Electrical and Computer Engineering United States Shahed University Department of Electrical and Electronic Engineering Tehran Iran

ISBN: (纸本)9798350330151

Pneumonia is a prevalent respiratory infection with potentially life-threatening consequences. In this research, we propose a novel deep learning approach to enhance pneumonia detection using the Vision Transformer (ViT) architecture with a dynamic mapping re-attention mechanism. The re-attention mechanism in the ViT architecture facilitates the flow of information between patches of the input image, enabling contextual understanding through self-attention. This mechanism captures long-range dependencies and enhances the ViT's ability to effectively recognize and interpret images. The dynamic mapping attention mechanism allows the model to dynamically assign attention weights to different regions of the input data based on their relevance to pneumonia detection. By adaptively adjusting the attention weights, the model can capture both local details and global dependencies, enabling more accurate diagnosis. The ViT utilizes the dynamic mapping re-attention mechanism with attention dropout. Here the performance of this ViT compared to the conventional ViT and the ViT incorporating the re-attention mechanism. This comparative analysis offers valuable insights into the effectiveness of the proposed dynamic mapping re-attention mechanism. Additionally, it highlights the added benefits of attention dropout in enhancing the overall performance of the model. © 2023 IEEE.

关键词： Image enhancement

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Intelligent Railway Block System Design with Lightweight CNN and RFID

Intelligent Railway Block System Design with Lightweight CNN...

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2024 International Automatic control Conference, CACS 2024

作者： Chang, You-Yi Hung, Kuei-Jung Perng, Jau-Woei National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan

ISBN: (纸本)9798350354904

Obstacles on railways present significant safety risks. In particular, Taiwan Railways (TR) often faces natural and man-made obstructions. While advanced artificial intelligence (AI) models offer precise detection, their high computational demands limit deployment. Thus, lightweight convolutional neural network (CNN) models have been developed, balancing detection accuracy with hardware efficiency. Utilizing feature-extracting preprocessing, these models achieve F2 scores above 0.89 and recalls over 0.9, while maintaining computational complexity under 15 MFLOPs and model sizes within 25 kB. This ensures compatibility with standard PCs and low-cost, low-power systems, enabling widespread deployment in regions lacking advanced AI monitoring. Additionally, TR's aging block and train-tracking systems, which are prone to failures, are enhanced with a power-saving radio frequency identification(RFID) system compatible with the CNN detector and Bluetooth-enabled passenger displays. This modernizes TR's infrastructure, improving safety operations and travel-information reliability. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Enhancing Fault Detection in Induction Motors using LSTM-Attention Neural Networks 9

Enhancing Fault Detection in Induction Motors using LSTM-Att...

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9th International Conference on control, Instrumentation and Automation, ICCIA 2023

作者： Khaniki, Mohammad Ali Labbaf Mirzaeibonehkhater, Marzieh Manthouri, Mohammad K.N. Toosi University of Technology Department of Control Engineering Tehran Iran Indiana University-Purdue University Department of Electrical and Computer Engineering United States Shahed University Department of Electrical and Electronic Engineering Tehran Iran

ISBN: (纸本)9798350366013

Induction motors carry notable importance in modern machinery and industrial equipment. Hence, the imperative to establish an early fault detection system for discerning operational states and potential faults in these motors is evident. This paper introduces a pioneering methodology for fault detection in induction motors by leveraging the power of Long Short-Term Memory (LSTM) networks and attention mechanisms within a neural network framework. By combining the temporal sequence analysis capabilities of LSTMs with the selective focus feature of attention mechanisms, the proposed architecture enhances the accuracy and timeliness of fault detection, paving the way for improved machinery health monitoring and predictive maintenance. The proposed approach is rigorously evaluated alongside established methods, including simple Artificial Neural Networks (ANN) and LSTM networks to showcase the strengths of the LSTM-Attention approach. This comprehensive analysis reveals the additional advantages of attention mechanisms and positions the LSTM-Attention architecture as a robust solution for detecting faults in induction motors. © 2023 IEEE.

关键词： Long short-term memory

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Late Contribution: VeriSide: A Modified Verilator for Leakage Assessment at the RTL Level

Late Contribution: VeriSide: A Modified Verilator for Leakag...

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Latin American Test Workshop, LATW

作者： Behnam Farnaghinejad Antonio Porsia Annachiara Ruospo Alessandro Savino Stefano Di Carlo Ernesto Sanchez Department of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

Leakage assessment at the Register Transfer Level (RTL) is essential for identifying vulnerabilities in various designs, including cryptographic systems, AI models, and other applications handling sensitive data during the design phase. This paper introduces VeriSide, an innovative framework built as a modified version of Verilator to generate compact format files that directly capture side-oriented information, such as Hamming Distance (HD) or Hamming Weight (HW) of the signals. VeriSide streamlines the power side-channel (PSC) analysis process by providing efficient and scalable solutions for large-scale designs. Traditional methods relying on verbose Value Change Dump (VCD) or Switching Activity Interchange Format (SAIF) files face significant scalability and resource challenges, especially for complex systems-on-chip (SoCs). These methods incur substantial storage and processing overheads. VeriSide overcomes these limitations by drastically reducing file size and eliminating post-simulation memory usage, while maintaining analysis accuracy.

关键词： Accuracy Scalability Switches Register transfer level Data models Hamming distances Cryptography Hamming weight Artificial intelligence Faces

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Analyzing the Structural and Operational Impact of Faults in Floating-Point and Posit Arithmetic Cores for CNN Operations

Analyzing the Structural and Operational Impact of Faults in...

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IEEE European Test Symposium (ETS)

作者： Josie E. Rodriguez Condia Juan-David Guerrero-Balaguera Robert Limas Sierra Matteo Sonza Reorda Department of Control and Computer Engineering (DAUIN) Politecnico di Torino

ISBN: (数字)9798350349320

ISBN: (纸本)9798350349337

1 This work reports a first attempt to evaluate the fine-grain impact of permanent faults in the structures of arithmetic hardware cores implementing two number formats (Posit and FP). We assess and analyze errors in the cores for two operations (Add, and Multiply), which are the most used in several modern applications, including machine learning. The results show that Posit cores are structurally more vulnerable to fault propagation and induce more output corruptions than FP cores (from 3.3% up to 6.2%). Moreover, we found that the average absolute error in faulty FP cores is higher by up to 2 orders of magnitude than in Posit ones.

关键词： Europe Machine learning Hardware Circuit faults Reliability Arithmetic

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Understanding the Effects of Permanent Faults in GPU's Parallelism Management and control Units 23

Understanding the Effects of Permanent Faults in GPU's Paral...

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2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Guerrero Balaguera, Juan David Rodriguez Condia, Josie Esteban Fernandes Dos Santos, Fernando Sonza Reorda, Matteo Rech, Paolo Politecnico di Torino Department of Control and Computer Engineering Turin Italy Inria Rennes Bretagne Atlantique Research Centre Rennes France University of Trento Department of Industrial Engineering Trento Italy

ISBN: (纸本)9798400701092

Modern Graphics Processing Units (GPUs) demand life expectancy extended to many years, exposing the hardware to aging (i.e., permanent faults arising after the end-of-manufacturing test). Hence, techniques to assess permanent fault impacts in GPUs are strongly required, especially in safety-critical *** paper presents a method to evaluate permanent faults in the GPU's scheduler and control units, together with the first figures to quantify these effects. We inject 5.83x105 permanent faults in the gate-level units of a GPU model. Then, we map the observed error categories as software errors by instrumenting 13 applications and two convolutional neural networks, injecting more than 1.65x105 permanent errors (1,000 errors per application), reducing evaluation times from several years to hundreds of hours. Our results highlight that faults in GPU parallelism management units impact software execution parameters. Moreover, errors in resource management or instructions codes hang the code, while 45% of errors induce silent data corruption. © 2023 Owner/Author(s).

关键词： Graphics processing unit

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Energy-Free Sensing and Context Recognition Using Photovoltaic Cells

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IEEE Sensors Journal 2025年第9期25卷 15598-15611页

作者： Shintani, Kaede Rizk, Hamada Yamaguchi, Hirozumi Osaka University Graduate School of Information Science and Technology Department of Information Networking Suita565-0871 Japan Tanta University Tanta31733 Egypt RIKEN Center for Computational Science Kobe650-0047 Japan RIKEN Center for Computational Science Department of Computer and Control Engineering Kobe650-0047 Japan

The field of energy-free sensing and context recognition has recently gained significant attention as it allows operating systems without external power sources. Photovoltaic cells can convert light energy into electrical energy to power sensing devices, but their power may not be sufficient to ensure energy-free sensing due to the varying power needs of sensors and high computational demands. In this article, we propose the use of photovoltaic cells as a standalone sensor for the recognition of different contexts, including user identification, step counting, and location tracking. The system utilizes the photocurrent readings generated by the photovoltaic cells to capture the unique mobility patterns of different users. By analyzing these patterns, the system can accurately identify the user, count the number of steps taken, and track the user’s location. We propose a computationally efficient dynamic time warping (DTW) to match the variable length sequences of photocurrent readings to a database of known patterns and identify the closest subject and location matches. The system was rigorously evaluated in a realistic environment, and the results indicate that it can accurately estimate step count, identify subjects, and localize them with an accuracy of 88%, 90%, and 43 cm, respectively. This is achieved while the proposed system is nonintrusive and can operate without external power sources, making it a promising technology for energy-free sensing and context recognition. © 2001-2012 IEEE.

关键词： Photovoltaics

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A Modern Take on Visual Relationship Reasoning for Grasp Planning

arXiv

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arXiv 2024年

作者： Rabino, Paolo Tommasi, Tatiana Department of Control and Computer Engineering Politecnico di Torino Italy

Interacting with real-world cluttered scenes poses several challenges to robotic agents that need to understand complex spatial dependencies among the observed objects to determine optimal pick sequences or efficient object retrieval strategies. Existing solutions typically manage simplified scenarios and focus on predicting pairwise object relationships following an initial object detection phase, but often overlook the global context or struggle with handling redundant and missing object relations. In this work, we present a modern take on visual relational reasoning for grasp planning. We introduce D3GD, a novel testbed that includes bin picking scenes with up to 35 objects from 97 distinct categories. Additionally, we propose D3G, a new end-to-end transformer-based dependency graph generation model that simultaneously detects objects and produces an adjacency matrix representing their spatial relationships. Recognizing the limitations of standard metrics, we employ the Average Precision of Relationships for the first time to evaluate model performance, conducting an extensive experimental benchmark. The obtained results establish our approach as the new state-of-the-art for this task, laying the foundation for future research in robotic manipulation. We publicly release the code and dataset at https://paolotron. ***/***. © 2024, CC BY.

关键词： Robot programming

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GenSUMO: GenAI-Creation of Critical Scenarios for Autonomous Vehicle Testing

GenSUMO: GenAI-Creation of Critical Scenarios for Autonomous...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Sergio Augusto Angelini Diego Gasco Claudio Casetti Department of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Recent advancements in autonomous vehicle research highlight the importance of Machine Learning (ML) models in tasks like motion planning, trajectory prediction, and emergency management. To support AI development, we propose a novel approach for generating on-demand datasets using the Simulator of Urban Mobility (SUMO) and a Generative Adversarial Network (GAN). Our method focuses on capturing critical events such as sudden pedestrian crossings, near-misses, and collisions, providing essential data to improve vehicle models' responses to emergency situations.

关键词： Pedestrians Machine learning Predictive models Generative adversarial networks Emergency services Data models Trajectory Planning Autonomous vehicles Testing

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Lasso-based state estimation for cyber-physical systems under sensor attacks

arXiv

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arXiv 2024年

作者： Cerone, V. Fosson, Sophie M. Regruto, D. Ripa, F. Department of Control and Computer Engineering Politecnico di Torino Italy

The development of algorithms for secure state estimation in vulnerable cyber-physical systems has been gaining attention in the last years. A consolidated assumption is that an adversary can tamper a relatively small number of sensors. In the literature, block-sparsity methods exploit this prior information to recover the attack locations and the state of the system. In this paper, we propose an alternative, Lasso-based approach and we analyse its effectiveness. In particular, we theoretically derive conditions that guarantee successful attack/state recovery, independently of established time sparsity patterns. Furthermore, we develop a sparse state observer, by starting from the iterative soft thresholding algorithm for Lasso, to perform online estimation. Through several numerical experiments, we compare the proposed methods to the state-of-the-art algorithms. © 2024, CC BY-NC-ND.

关键词： State estimation

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