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RISC-V Accelerators, Enablement and applications for Automotive and Smart Home in the ISOLDE Project 24th

RISC-V Accelerators, Enablement and Applications for Automot...

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24th international conference on Embedded Computer Systems: Architectures, modeling, and simulation

作者： Ciobanu, Catalin Bogdan Galmeanu, Honorius Puscasu, Alexandru Gologanu, Mihai Buiu, Octavian Antonescu, Mihai Serbu, Vlad-Gabriel Moise, Vasile-Madalin Axinte, Cristian-Tiberius Popovici, Alexandru-Tudor Uleru, George-Iulian Stan, Andrei Munteanu, Mihai Drimbarean, Alexandru Nemeti, Csaba Rotar, Danut Comma Grosu, Daniel Moisa, Cosmin Ditu, Bogdan Trusca, Petre Cristian Antache, Marius Costinescu, Simona Sachian, Mari-Anais Suciu, George Gheorghe, Cristian Tudor, Cristina Koci, Kejsi Natl Inst Res & Dev Microtechnol IMT Bucharest Romania Transilvania Univ Brasov Brasov Romania Natl Univ Sci & Technol Politeh Bucharest Bucharest Romania Gheorghe Asachi Tech Univ Iasi Iasi Romania FotoNat SRL Brasov Romania Continental Automot Romania Timisoara Romania NXP Semicond Romania Bucharest Romania BEIA Consult Int Bucharest Romania FotoNat Ltd Galway Ireland

ISBN: (纸本)9783031783791;9783031783807

With Artificial Intelligence applications becoming ubiquitous, both industrial and consumer applications require more computing availability. The increasing need for power-constrained Artificial Intelligence computing asks for new and innovative solutions, on both hardware design as well as on software toolchains that leverage them. At the same time, the challenge of EU technological sovereignty encourages an industry trend oriented to developing its own solutions for power efficient High Performance Computing (HPC). In this context, as partners in the ISOLDE project started in 2023, we propose the development of new power efficient embedded HPC accelerators, HPC design platforms, as well as High Level Synthesis (HLS) approaches and toolchain technology designs for RISC-V based applications. Specifically, we envisage applications such as Fast Fourier Transform, Number Theoretic Transforms, Post Quantum Computing and artificial intelligence, as well as designing ONNX targeted toolchains based on the LLVM compiler, open solutions for Neural Networks (NN) accelerators and new OpenMP extensions. Within the ISOLDE framework, the envisaged applications to prove the readiness of these technologies are the Automotive and the Smart Home demonstrators.

关键词： RISC-V HPC Polymorphic Register File PolyMem AI Accelerator HLS AI Compiler ONNX LLVM OpenMP

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Advancements in single-image 3D modeling: a deepmesh fusion approach

Advancements in single-image 3D modeling: a deepmesh fusion ...

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2024 international conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Liu, Guanglei Shenyang Ligong University Shenyang China

ISBN: (纸本)9781510689176

DeepMesh is an innovative image-to-3D reconstruction framework that significantly enhances model reconstruction quality and detail accuracy, especially when processing single images through the application of multi-light point technology. This study introduces an advanced light source simulation method that simulates multiple light sources illuminating the object, enhancing the capture of surface details and thus achieving higher visual fidelity and geometric accuracy in the generated 3D models. DeepMesh combines Transformer-based sparse view reconstruction technology with advanced mesh processing algorithms, effectively extracting and reconstructing 3D data from complex lighting environments. Experimental results on standard datasets demonstrate that DeepMesh outperforms existing technologies in terms of Structural Similarity (SSIM) and Perceptual Image Quality Index (LPIPS), proving its potential in practical applications. Moreover, the framework's multi-light point processing technique provides a new direction for future research in 3D reconstruction under complex lighting conditions. © 2025 SPIE.

关键词： 3D reconstruction

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Industry 5.0 in Healthcare: An Integrated Framework for Human-Centered Prosthetics Design and Manufacturing 6

Industry 5.0 in Healthcare: An Integrated Framework for Huma...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Ambrogio, Giuseppina Borgia, Carmine Bortolini, Marco Catapano, Gerardo Conforti, Domenico De Napoli, Luigi Gagliardi, Francesco Galizia, Francesco Gabriele Guido, Rosita Longo, Francesco Mirabelli, Federica Mirabelli, Giovanni Mundo, Domenico Regattieri, Alberto Saffioti, Maria Rosaria Scordamaglia, Gabriele Solina, Vittorio Umbrello, Domenico Veltri, Pierpaolo University of Calabria Department of Mechanical Engineering Energy Engineering and Management Engineering Italy University of Bologna Department of Industrial Engineering Italy University of Calabria Department of Computer Engineering Modeling Electronics and Systems Engineering Italy

Industry 5.0 emphasizes human-centric approaches in technological advancement, particularly crucial in healthcare applications such as prosthetics development. While individual technologies like additive manufacturing, machine learning, digital twins, and tissue engineering have shown promise in prosthetics innovation, their integration remains a significant challenge. This paper presents a novel multidisciplinary framework developed through the National Centre for High-Performance Computing (CN-HPC) project, which addresses the gap between individual technological capabilities and their coordinated application in personalized prosthetics development. The framework, implemented through collaboration between six research groups from two universities, employs the House of Quality methodology to align technological integration with patient needs. Our approach encompasses a comprehensive seven-step process, from initial patient requirements definition through virtual modeling, production process optimization, virtual sensing, machine learning enhancement, digital twin simulation, to advanced tissue engineering. A data model facilitates seamless information exchange between different technological domains and research groups. This research contributes to the field by providing a scalable, human-centered framework for technology integration in medical device development, potentially extending beyond prosthetics to other healthcare applications. The proposed framework represents a significant step toward realizing the full potential of Industry 5.0 in healthcare manufacturing through the systematic integration of advanced technologies for delivering personalized prosthetic solutions. © 2024 The Authors. Published by Elsevier B.V.

关键词： Tissue engineering

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AI for Smart Farming: Machine Learning Models for Precision Crop Yield Prediction

AI for Smart Farming: Machine Learning Models for Precision ...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Bhavana, Munugapati Nijhawan, Ginni Raju, Hemanth Bansal, Saloni LTIMindtree Limited Digital Engineering and Assurance Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Dept. of Comp. Sc. & Engg. Phagwara India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331512965

AI and other advanced technologies are gradually beginning to invade the agriculture sector to serve the increasing demand of the consumers for better methods of food production. A key component of precision agriculture, this work focuses on how to use machine learning models to predict crop yields with high accuracy. Specifically, the nature and volatility of the environmental context - such as the nature and quality of soils, as well as the climatic and water conditions - present a major challenge to the application of the conventional modeling approaches aimed at forecasting productive capacity in agriculture. Still, when comparing, the AI-driven models will allow sorting through much information gathered from different sources, including sensors, satellite imagery, and yield history. This work examines the differences of several machine learning models. Such models are the Random Forests, SVMs, and CNNs and other Deep Learning methodologies respectively. A quality model is evaluated based on scalability, computational performance, and prediction accuracy. Also in scope of the study, there is an opportunity to employ IoT together with data collected with remote sensing to make and provide realtime forecasts and evaluations. By the utilization of the results attained, it is established that the yields expected by the machine learning models are much more precise, which aids in enhancing agricultural decision-making concerning resource utilizing, crop handling, and risk minimization. The purpose of this study is focused on the promotion of the agricultural sustainability and production through the development of the innovative data driven farming practices. © 2025 IEEE.

关键词： Smart agriculture

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Barrier Lyapunov-Based Fuzzy Sliding Mode Control for Lateral Autonomous Vehicle Dynamics 1st

Barrier Lyapunov-Based Fuzzy Sliding Mode Control for Latera...

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1st international conference on Electrical Systems and Smart technologies, ICESST 2024

作者： Jennan, Najlae Mellouli, El Mehdi Arjdal, El Hanafi Laboratory of Engineering Systems and Applications Sidi Mohamed Ben Abdellah University National School of Applied Sciences Fez Morocco Laboratory of Materials Signals Systems and Physical Modeling Faculty of Sciences of Agadir Ibn Zohr University Agadir Morocco

ISBN: (纸本)9783031867040

This paper presents an advanced control strategy for autonomous vehicle lateral dynamics using Sliding Mode Control (SMC) integrated with Takagi-Sugeno (T-S) fuzzy logic and enhanced by the Barrier Lyapunov approach. The objective is to ensure fixed-time convergence and stability in the presence of uncertainties and disturbances. The mathematical model of the autonomous vehicle system is established as a preliminary step, after which control laws are designed. A comprehensive stability analysis is conducted to validate the stability and convergence of the overall system. The reaching law is adapted through the application of T-S fuzzy logic, thereby enhancing the controller's adaptability and reducing the phenomenon of chattering associated with the SMC method. The simulation results demonstrate superior performance, achieving a 33% faster convergence time compared to traditional SMC methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sliding mode control

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Comprehensive Review on Integrating Battery Management System with Digital Twin for Real-Time EV Battery State Estimation

Comprehensive Review on Integrating Battery Management Syste...

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AIML-applications for Engineering & Technology (ICAET), international conference on

作者： Kalika Patil Mangal Patil Department of Electronics & Communication Bharati Vidyapeeth Deemed to be University College of Engineering Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

The conversion of transportation has been accelerated by recent advances in battery system production and technology; yet, the industry is still confronted with substantial challenges that may be addressed by state-of-the-art digital technologies. Digital twinning of battery packs is one example of a state-of-the-art method. To enhance the performance, security, and efficient, digital twins (DTs) of batteries use progressed multidimensional models, AI, sophisticated sensor element, IoT technologies, and cloud computing strategies toward create a fundamental, active illustration about actual battery system (physical twin). An exhaustive examination of the necessary supporting technologies, demands, & several possible applications for battery DTs is presented in this review paper. Included in the scope of the review are software architectures, use cases, digital platforms that facilitate DT creation and integration, software, and hardware requirements for DT implementation with batteries, electrical considerations for modeling, and algorithmic methodologies. The present challenges are identified, and scenarios that will yield enough benefit to compensate for these challenges, such as the extra costs, are discussed.

关键词： Industries Cloud computing Reviews Biological system modeling Battery management systems Real-time systems Software Batteries Digital twins State estimation

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27th international Symposium on VLSI Design and Test, VDAT 2023

27th International Symposium on VLSI Design and Test, VDAT 2...

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27th international Symposium on VLSI Design and Test, VDAT 2023

ISBN: (纸本)9789819752683

The proceedings contain 27 papers. The special focus in this conference is on VLSI Design and Test. The topics include: A 0.1–4.71 GHz Integer-N CP-PLL-Based Low-Power Frequency Synthesizer for High-Speed applications;A Current-Mode-Logic-Based PFD–Charge Pump Circuit for Low-Reference Spur PLLs;an Energy Efficient Mixed Logic 2-to-4 Decoder for Embedded Memory applications;Analysis and modeling of Self-heating and Substrate-Induced Transitions in 5-nm Stacked Nanosheet FET;optimization of Sol–Gel TiO2 Thin Films for Photovoltaic Organic Photodetector;Design and Analysis of Differential Configuration Based Active Inductor for 5G Sub-6 GHz applications;A Low Jitter and High-Speed Flash TDC with PVT Calibration and Its Testing Methodology;a Dual-Mode High-Frequency Grounded Memristor Emulator Circuit;Analysis and Reduction of GOTFET Capacitances Using Physics-Based Compact modeling;A Fast Transient Low Iq Capless LDO with Protection Circuits;Demonstration of Doped-HfO2 Ferroelectric Based Double Layer Stacked NC FinFET;High-Precision Programmable Thermistor Linearization ASIC for Electro-Optical Payload applications;microwave-Assisted Fe3O4-Based Memristor for Brain-Inspired Computing;Current-Sensing ADC with Power Consumption of 57 nW and FoM of 336 fJ/Conversion at 28 nm CMOS Technology;Design and Analysis of Low-Power Protection Circuits for LDO Regulators;Comparative Study of FET Based Biosensors with Metal Oxide Stacked Sensing Membrane;preventing Costly Iterations by Delivering SoC Congestion Aware Standard Cell Lib Using Pin Accessibility Checker;A 0.076-MW 9.8-ENOB 1-MS/s SAR ADC Using Novel R-C Hybrid DAC;Efficient 1-Bit Hybrid Full Adder Design with Low Power Delay Product Using FinFET-TGDI Technology: simulation and Comparative Study;high Throughput Multiple Device Diagnosis System for Hierarchical Test Designs;Characterization of Multi-Vt Transistors for Analog IC Design in Sub-nanometer technologies.

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27th international Symposium on VLSI Design and Test, VDAT 2023

27th International Symposium on VLSI Design and Test, VDAT 2...

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27th international Symposium on VLSI Design and Test, VDAT 2023

ISBN: (纸本)9789819737550

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Exploring Hand Gesture Recognition: Trends, technologies, and Application

Exploring Hand Gesture Recognition: Trends, Technologies, an...

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Intelligent Data Communication technologies and Internet of Things (IDCIoT), international conference on

作者： Ayesha Pohekar Ajitkumar Pundge Vishal Shirsath School of Engineering Ajeenkya DY Patil University Pune Maharashtra India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Hand Gesture Recognition (HGR) systems have garnered significant attention due to their applications in human-computer interaction, virtual reality, and assistive technologies. However, achieving adaptable, accurate, and user-friendly HGR solutions remains a challenge due to issues such as environmental variability and user adaptability. This survey explores state-of-the-art HGR techniques, encompassing traditional methods and advanced deep learning-based methodologies. Classification techniques are analyzed based on sensory-based, vision-based, and hybrid input approaches, highlighting their respective strengths and limitations. The impact of feature extraction methods, dataset quality, and real-time processing on the performance of classification algorithms is discussed. By addressing these challenges, this work identifies potential pathways for advancing HGR systems to enhance their usability and reliability across diverse applications.

关键词： Human computer interaction Hands Deep learning Solid modeling Adaptation models Accuracy Gesture recognition Feature extraction Real-time systems Reliability

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Digital Twins for Monitoring and simulation of Ship Hull Structures: A Maritime Engineering Perspective

Digital Twins for Monitoring and Simulation of Ship Hull Str...

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international Scientific-Professional conference on Information Technology (IT)

作者： Tahir Mavrić Špiro Ivošević Faculty of Maritime Studies Kotor University of Montenegro Kotor Montenegro

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

The key challenges in the maritime industry revolve around digital transformation, reducing carbon emissions (decarbonization), and the implementation of smart and autonomous technologies. Smart Shipping technologies, Electronic Charts and e-Navigation are key elements of modern digitization. Digital twins have emerged as transformative tools in maritime engineering, enabling real-time monitoring, advanced simulations, and predictive maintenance of ship hull structures. This paper explores methodologies for integrating digital twin technology in ship monitoring and simulation, discusses its practical applications, and addresses challenges in implementation.

关键词： Industries Digital transformation Predictive models Low carbon economy Real-time systems Digital twins Marine vehicles Information technology Monitoring Predictive maintenance

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