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TAP-CAM: A Tunable Approximate Matching Engine based on Ferroelectric Content Addressable Memory 24

TAP-CAM: A Tunable Approximate Matching Engine based on Ferr...

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Ni, Chenyu Chen, Sijie Liu, Che-Kai Liu, Liu Imani, Mohsen Kämpfe, Thomas Ni, Kai Niemier, Michael Hu, Xiaobo Sharon Zhuo, Cheng Yin, Xunzhao Zhejiang University Hangzhou China Georgia Institute of Technology GA United States University of Notre Dame IN United States University of California IrvineCA United States Fraunhofer IPMS Dresden Germany Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China

ISBN: (纸本)9798400710773

Pattern search is crucial in numerous analytic applications for retrieving data entries akin to the query. Content Addressable Memories (CAMs), an in-memory computing fabric, directly compare input queries with stored entries through embedded comparison logic, facilitating fast parallel pattern search in memory. While conventional CAM designs offer exact match functionality, they are inadequate for meeting the approximate search needs of emerging data-intensive applications. Some recent CAM designs propose approximate matching functions, but they face limitations such as excessively large cell area or the inability to precisely control the degree of approximation. In this paper, we propose TAP-CAM, a novel ferroelectric field effect transistor (FeFET) based ternary CAM (TCAM) capable of both exact and tunable approximate matching. TAP-CAM employs a compact 2FeFET-2R cell structure as the entry storage unit, and similarities in Hamming distances between input queries and stored entries are measured using an evaluation transistor associated with the matchline of CAM array. The operation, robustness and performance of the proposed design at array level have been discussed and evaluated, respectively. We conduct a case study of K-nearest neighbor (KNN) search to benchmark the proposed TAP-CAM at application level. Results demonstrate that compared to 16T CMOS CAM with exact match functionality, TAP-CAM achieves a 16.95× energy improvement, along with a 3.06% accuracy enhancement. Compared to 2FeFET TCAM with approximate match functionality, TAP-CAM achieves a 6.78× energy improvement. © 2024 Copyright is held by the owner/author(s).

关键词： Ferroelectric RAM

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An embedded Deep Learning Method for Glass Insulator Detection

An Embedded Deep Learning Method for Glass Insulator Detecti...

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Sensors and Information Technology (ICSI), international conference on

作者： Jie Xia Jiaxin Wang Zhiyong Yang Nanchang Hangkong University School of Software Nanchang China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

With the continuous advancement of neural networks and deep learning, numerous deep learning models have improved recognition accuracy and efficiency while increasingly demanding higher performance from hardware devices. However, the performance of basic equipment in the industrial field often struggles to keep pace with the rapid development of these models, resulting in the difficulty of implementing high-performing deep learning tools from the research field into industrial applications. This paper proposes an embedded deep learning model based on the YOLOv4 framework, enabling high-accuracy and real-time glass insulator detection in industrial environments on performance-constrained embedded devices. Field experiments demonstrate that the proposed model achieves a detection accuracy of 97%, with an average detection time of 120ms per image, meeting the demands of industrial production.

关键词： Deep learning Performance evaluation Accuracy Computational modeling Glass Insulators real-time systems Hardware Artificial intelligence Edge computing

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Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection and Early Warning Control System 3

Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection ...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Li, Wenshan Hunan Institute of Traffic Engineering Hunan Hengyang China

ISBN: (纸本)9798331508456

Traditional methods of monitoring vital signs in vehicles often fall short when it comes to response time, especially in fast-moving situations. To tackle this, our paper introduces a groundbreaking solution: leveraging millimeter-wave radar technology to monitor vital signs in real time within the vehicle, providing timely alerts when necessary. This approach not only enhances the accuracy of readings but also ensures a swift response that is crucial during vehicle operation. By tapping into the unique abilities of millimeter-wave radar to detect subtle human signs, we propose a novel method that processes radar signals to monitor vital signs like heart rate and respiratory rate. This technique is highly effective in meeting the stringent demands of real-time accuracy and speed, especially while driving. Our system includes three main components: radar sensors, a signal processing unit, and an early-warning control module, working together seamlessly to ensure optimal monitoring. The radar sensor continuously transmits and receives millimeter-wave signals, allowing it to track vital signs without interruption. A specially designed feature extraction algorithm filters out noise and extracts critical information, including heart and respiratory rates. Then, advanced algorithms analyze this data in real time, spotting any abnormalities and triggering alerts through the control module. What sets this system apart is its rigorous testing under various vehicle speeds and environmental conditions, confirming its stability, reliability, and fast response times. In real-world applications, the system responds to a high heart rate in 3.7 to 4.0 seconds and to a rapid breathing rate in 4.1 to 4.3 seconds. This millimeter-wave radar-based system offers a fresh approach to vehicle safety, combining practicality with promising future potential. © 2025 ieee.

关键词： Electronic guidance systems

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Performance Analysis of BLDC Motors for Flywheel energy storage applications with Nonmagnetic vs. Magnetic core Stator using Finite element time stepping method

Performance Analysis of BLDC Motors for Flywheel energy stor...

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2025 international conference on Intelligent Control, computing and Communications, IC3 2025

作者： Pasa, Alok Kumar Raghavan, K.R.S. Bhabha Atomic Research Centre Mumbai India Bhabha Atomic Research Centre Mysuru India

ISBN: (纸本)9798331527495

This paper presents a comparative performance analysis for selecting high-speed and efficient Brushless DC (BLDC) motors, with a focus on different stator core materials suitable for applications such as flywheel energy storage systems and servo motors. Traditionally, motor design and selection are performed through computational analysis using Finite Element Analysis (FEA) tools prior to implementation. However, to gain a deeper understanding of performance parameters for specific applications, this study employs a circuit field coupled Finite Element Method (FEM) analysis in the time domain to investigate the dynamic performance of BLDC motors equipped with non-magnetic and magnetic stator core materials. Preliminary results reveal significant differences in motor efficiency, torque production, and electromagnetic properties between the two configurations. The findings highlight the potential benefits of using non-magnetic materials in BLDC motors for flywheel applications, offering advantages in terms of efficiency, weight reduction, and cost-effectiveness. © 2025 ieee.

关键词： Flywheels

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IoT and 5G Integration: Transforming real-time applications in Smart Industries 3

IoT and 5G Integration: Transforming Real-Time Applications ...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Nandy, Manish Dubey, Ahilya Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798331542375

Modern M2M communication engaged with IoT and 5G technology is reinventing real time applications in smart industries through provision of efficient connectivity, low latency and large number of devices connection. This synchronizes makes it possible to transform industries, and hence lay down the groundwork for Industry 4.0 and more. The IM connectivity along with increased reliability and velocity made available by 5G technology improve IoT networks;key service use cases include predictive maintenance, robotic control, and real-time information analytics. Through network slicing, the key application areas can be prioritized to get the right of way and optimal resource consumptions in the 5G applications. In addition, the compatibility of IoT systems with high 5G networks resolves the difficulties that arise from various industrial settings, including the coordination of large amounts of data produced by the IoT devices. It also enhances energy use efficiency, decreases operational expenses and fosters sustainability, due to the use of improved resource optimisation. It notes that manufacturing logistics and healthcare key industrial areas have experienced changes through areas such as remote monitoring, digital twins, and augmented reality applications. This paper examines the technological convergence between IoT and 5G, showcases innovative industrial use cases, and advances opportunities and threats of the integration to establish intelligent and efficient sectors in the context of the internet of things. © 2025 ieee.

关键词： Predictive maintenance

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Enhancing Traceability and Security in mHealth systems: A Proximal Policy Optimization-Based Multi-Authority Attribute-Based Encryption Approach 29

Enhancing Traceability and Security in mHealth Systems: A Pr...

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29th international conference on Information Technology, IT 2025

作者： Konda, Bhargavi Yadulla, Akhila Reddy Kasula, Vinay Kumar Yenugula, Mounica Adupa, Chakradhar HRIS Systems Analyst IV AuburndaleMA02466 United States University of the Cumberlands Department of Information Technology WilliamsburgKY40769 United States VISA Inc Sr. Systems Applications Analyst AshburnVA20147 United States SR University Department of Electronics and Communication Engineering TG Hanamkonda506371 India

ISBN: (纸本)9798331517649

Mobile healthcare (mHealth) is an emerging technology that facilitates the sharing of personal health records (PHR), but it also introduces risks related to the security and privacy of PHRs. To address these concerns, Attribute-Based Encryption (ABE) has been proposed as an advanced cryptographic solution to enable fine-grained access control over encrypted data. However, existing attribute-based mHealth systems often lack efficient traceability or are limited to a single authority. This paper presents a novel traceable multi-authority ABE scheme, incorporating Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning algorithm, to enhance traceability and security. The scheme is built on composite order groups and supports arbitrary monotonic access structures defined by Linear Secret Sharing Schemes (LSSS). The security of the system is validated under adaptive security assumptions, with PPO optimizing the access control decisions and identifying potential vulnerabilities in real-time. Additionally, the performance of the system is evaluated, demonstrating its efficiency, scalability, and availability for practical mHealth applications. This approach not only ensures robust access control but also guarantees high adaptability and traceability, effectively addressing privacy concerns in the sharing of sensitive health data. © 2025 ieee.

关键词： Electronic health record

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Cyber-Physical systems in Disaster Management: real-time Data Collection and Analysis for Improved Response 3

Cyber-Physical Systems in Disaster Management: Real-Time Dat...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Chaudhary, Pavan Pachayappan, R. Soni, Ishika Pattanaik, Anmol Marwaha, Pradeep Sudhamsu, Gadug Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

Disaster management is one of the applications where cyber-physical systems play a significant role by allowing real-time data collection and analysis. Such systems consist of physical, computational and communication elements and enable the automatic exchange of information between machines and humans and decision-making in disaster situations. This technology can change the method of disaster response to a more automated one, and it could reduce the amount of manual data collection and analysis. In disaster management, CPS consists of a set of interconnected devices, including sensors, drones and mobile devices monitoring different factors of the disaster: assessing damage, environmental conditions and survivor whereabouts. This information is sent to a central hub for processing and real-time analysis. This information is then fed into models to assess risk, identify hot spots and guide rescue and relief operations. CPS enables disaster response teams to utilize live data, which means decisions can be made faster and more accurately. © 2025 ieee.

关键词： Emergency services

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Nanophotonic Edge computing System For Ultra-Low Latency Human-Machine Interface

Nanophotonic Edge Computing System For Ultra-Low Latency Hum...

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ieee international conference on Micro Electro Mechanical systems

作者： Zhihao Ren Zixuan Zhang Yangyang Zhuge Zian Xiao Siyu Xu Jingkai Zhou Chengkuo Lee Department of Electrical & Computer Engineering National University of Singapore Singapore National Centre for Advanced Integrated Photonics (NCAIP) Singapore

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper introduces a nanophotonic edge computing (NEC) system built on a bilayer AlN/Si waveguide platform, optimized for AI processing in wearable sensors. Utilizing AlN electro-optic micro-ring resonators (EOMRRs) for feature extraction and Si thermo-optic Mach-Zehnder interferometers (TOMZIs) for neural network computations, the NEC chip demonstrates high classification accuracy for gesture (96.77%) and gait (98.31%) analysis, ultra-low latency (<100 ps), and minimal energy consumption (<0.34 pJ). This breakthrough exemplifies the potential of nanophotonic technology to significantly enhance real-time, low-power, and high-accuracy AIoT applications, pushing the boundaries of edge computing capabilities.

关键词： Energy consumption Accuracy Human-machine systems Neural networks Virtual reality real-time systems Safety Low latency communication Monitoring Edge computing

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embedded Hardware Implementation of Noise Gate on STM32 Processor Using MATLAB/Simulink

Embedded Hardware Implementation of Noise Gate on STM32 Proc...

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2024 international conference on Photonics Solutions, ICPS 2024

作者： Jongsataporn, Thitaphan Chivapreecha, Sorawat Department of Industrial Physic and Medical Instrumentation King Mongkut's University of Technology North Bangkok 1518 Pracharat 1 Road WongsawangBangsue Bangkok10800 Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang 1 Chalong Krung Lat Krabang Bangkok10520 Thailand

ISBN: (数字)9781510688308

ISBN: (纸本)9781510688292

This paper presents a model-based approach for developing a digital signal processing (DSP) system, specifically focusing on a Noise Gate application, using MATLAB/Simulink, and deploying it on the STM32F769I-Discovery board. The study thoroughly examines the advantages of model-based hardware implementation, particularly in facilitating rapid prototyping for real-time applications in educational contexts. The STM32F769I is powered by a high-performance Arm® Cortex®-M7 core, which can execute DSP instructions efficiently. Its ample memory and built-in debugging tools enhance its suitability for complex signal processing tasks. The implementation leverages MATLAB/Simulink and its robust compatibility with the STM32 hardware, allowing for seamless software and hardware component integration. The powerful model-based development features streamline the transition from simulation to physical hardware, reducing development time and enhancing reliability. The Noise Gate application is designed to control audio signals effectively, eliminating unwanted background noise through user-defined parameters such as threshold, attack time, and release time, which optimize performance based on specific audio environments. Additionally, the design applies a first-order IIR low-pass as a smoothing filter, which dynamically adjusts the gain according to the characteristics of the incoming audio signal. This ensures gradual opening and closing of the gate, resulting in smoother audio transitions. The results demonstrate significant noise suppression and overall audio quality enhancement, highlighting the practicality of employing model-based design methodologies for DSP applications. Furthermore, the insights gained from this study contribute to efficient hardware prototype development and offer implications for future projects in the field of digital signal processing. © 2025 SPIE.

关键词： Program debugging

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Big Data for Enhancing Cybersecurity in Enterprise Environments Proactive Threat Detection and Prevention 3

Big Data for Enhancing Cybersecurity in Enterprise Environme...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Chityala, Upender Reddy Shnain, Ammar H. Govindaraj, Manoj Johri, Prashant Kuppuraj, T. Devi, N.Lavanya Equinox It Solutions Llc United States College of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Management Studies Chennai India School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India Psna College of Engineering and Technology Department of Ece India

ISBN: (纸本)9798331542375

This research explores the integration of big data technologies and machine learning to enhance cybersecurity in enterprise environments focusing on proactive threat detection and prevention. Traditional security systems often struggle to keep up with the growing complexity and volume of cyber threats, necessitating a more advanced, data-driven approach. The proposed methodology leverages real-time data processing, predictive modeling, and anomaly detection to improve threat detection accuracy, reduce response times, and enhance system scalability. By collecting and analyzing data from various network sources, including logs from security devices and endpoint protection software, the system detects potential threats more efficiently than conventional methods. Machine learning algorithms such as supervised and unsupervised learning, along with big data platforms like Apache Spark, enable real-time analysis of large volumes of data, ensuring timely identification of both known and unknown cyber threats. The results demonstrate significant improvements in detection accuracy, system scalability, and incident resolution time, with the proposed methodology outperforming traditional security systems in key performance metrics. This research highlights the potential of big data-driven cybersecurity solutions in addressing modern challenges and offers a framework for organizations to implement more effective, scalable, and proactive security measures to safeguard their digital assets. © 2025 ieee.

关键词： Cyber attacks

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