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共 2059 条记录，以下是121-130 订阅

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Bit-Level Quantization for Efficient Layout Hotspot Detection

Bit-Level Quantization for Efficient Layout Hotspot Detectio...

引用

2023 international symposium of Electronics Design automation, ISEDA 2023

作者： Pei, Zehua Zhao, Wenqian He, Zhuolun Yu, Bei Chinese University of Hong Kong Hong Kong Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798350304510

Hotspot detection is an essential step in the physical verification flow to identify layout patterns that are sensitive to process variations. Recent advances in machine learning have enabled deep neural networks (DNNs) to achieve good performance for hotspot detection;however, these models require a high computational complexity and memory footprint. To reduce such costs, quantization provides a promising solution by compressing DNNs into low-bit inference schemes. In this paper, we propose several quantization algorithms specifically designed for a classic neural network based hotspot detector while taking into account the feature distribution of the dataset used. Our experiments show that our compressed model can achieve competitive results compared with full-precision models while significantly reducing inference runtime at the same time. © 2023 ieee.

关键词： Deep neural networks

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Multi-Scale Vision Transformer with Local-Global Attention 4

Multi-Scale Vision Transformer with Local-Global Attention

引用

4th international Conference on Industrial automation, robotics and Control Engineering, IARCE 2024

作者： Zhang, Ruomiao Du, Shengdong Hu, Jie Zhao, Xiaole Yang, Yan School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

ISBN: (纸本)9798350380323

The extraction of multi-scale features from input images is crucial for computer vision tasks. However, some backbone networks based on visual transformers lack the ability to effectively extract multi-scale features or use relatively simplistic methods to do so. This paper proposes a novel multi-scale visual transformer, called Multi-Scale Extraction Transformer (MSET), designed to proficiently tackle a range of downstream visual tasks. MSET incorporates innovative concepts of Multi-Scale feature extraction Embedding (MS Embedding) and Local-Global self-attention (LGSA). MS Embedding uses a unique architecture convolutional layer method that constructs hierarchical residual-like connections, allowing for the extraction of multi-scale features at finer granularity levels. Meanwhile, LGSA integrates local and global dependencies within the self-attention module, reducing computational complexity while facilitating connections between the global and local features of the image. The importance of position information in the embeddings and the impact of amplitude on model performance are also considered, allowing MSET to handle position bias information of variable size in the input image and to ensure stability during training. Numerous experiments have demonstrated the significant advantages of MSET over most existing state-of-the-art backbone networks across various visual tasks, including image classification, object detection, instance segmentation, and semantic segmentation tasks. © 2024 ieee.

关键词： Semantic Segmentation

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Benefits of Internet of Things (IoT) Applications in Health care - An Overview 1

Benefits of Internet of Things (IoT) Applications in Health ...

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1st international Conference on computational intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Chaudhary, Preeti Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Nicosia Artificial Intelligence Engineering Dept. Mersin10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty OfEngineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

In a nutshell, the IoT refers to the idea of integrating high-tech technology in the form of "smart"gadgets and sensors into a single setting to gather data. There are still numerous areas in which machines cannot compete favorably with people, but data collection and analysis are undeniably two of their strongest points. The Internet of Things makes it possible to fully or partially automation data, it involves related activities, which is obviously very important for the healthcare industry. What exactly is the Internet connected medical things, and how exactly might it make a significant impact on the sector? By having to apply the concept of the internet of things (IoT) to the field of medicine, several significant advancements have become possible, including the ability to automatically control the thermostat of containers while transmitting inoculations and antibiotics, the accurate tracking of the ailments of a lot of illnesses without a need for additional meetings with a general practitioner, the correct and most useful use of meds, and a great deal more. All of this is realizable because of the capability of MedTech gadgets to carry out continuous monitoring system. The potential applications of internet of things technology in healthcare are very interesting. Let's review the ways in which this technology. © 2023 ieee.

关键词： Medical applications

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Ultra-Fast Edge Computing Approach for Hand Gesture Classification Based on EIT Measurements

Ultra-Fast Edge Computing Approach for Hand Gesture Classifi...

引用

international symposium on Robotic and Sensors Environments (ROSE)

作者： Mnif, Mandi Sahnoun, Salwa Kaaniche, Marouene Ben Atitallah, Bilel Fakhfakh, Ahmed Kanoun, Olfa Univ Sfax Natl Sch Elect & Telecommun Sfax Sfax Tunisia Univ Carthage Natl Inst Appl Sci & Technol Carthage Tunisia Digital Res Ctr Sfax CRNS Lab Signals Syst Artificial Intelligence & Networ Sfax Tunisia Tech Univ Chemnitz Fac Elect Engn & Informat Technol Measurements & Sensor Technol Chemnitz Germany

ISBN: (纸本)9798350362367;9798350362374

Gesture-based robot control offers intuitive interaction between humans and robots, with applications ranging from industrial automation to assistive robotics. However, existing solutions face challenges in achieving real-time requirements while ensuring accurate gesture recognition. This paper presents a new edge computing-based approach for real-time control of robots using Electrical Impedance Tomography (EIT) measurements to classify hand gesture numbers in American Sign Language (ASL). Existing solutions for gesture recognition struggle to achieve real-time performance while maintaining accuracy and energy efficiency. This challenge becomes higher in the case of EIT because of its relative complexity. We focus therefore on leveraging the capabilities of the edge device to implement effectively the Convolutional Neural Network (CNN) acceleration. The proposed solution combines hardware-aware optimization techniques to achieve fast and accurate gesture recognition by enabling rapid inference while minimizing energy consumption on a low-power resource-constrained device with Tiny Machine Learning (TinyML) capabilities. The lightweight CNN model required only 10.2 s to train using the Keras library of TensorFlow and achieved an accuracy of 89.37% for 10 sign language classes, with only 66 mu s taken to run inference on the hardware-accelerated microcontroller-based device.

关键词： Edge Computing Tiny ML Hardware Accelerator Hand Gesture Recognition EIT Deep Learning Quantization Inference Time Energy Efficiency

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A method for determining the analytical solution for Bagley Torvik equations with fractional derivatives 16

A method for determining the analytical solution for Bagley ...

引用

16th ieee international symposium on Applied computational intelligence and Informatics, SACI 2022

作者： Pasca, Madalina Sofia Caruntu, Bogdan Albu, Adriana Politehnica University Timisoara Mathematics Department Timisoara Romania West University of Timisoara Timisoara Romania Politehnica University Timisoara Department of Automation and Applied Informatics Timisoara Romania

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

This paper presents the method called Piecewise Polynomial Least Squares Method (PWPLSM) for determining the approximate analytical solution for Bagley Torvik fractional differential equation. The equation models the deformation resistance characteristics of different polymers, having applicability in various branches of engineering. The paper focuses on the operation of the PWPLSM method and its accuracy. © 2022 ieee.

关键词： Least squares approximations

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Energy Consumption Analysis for Raspberry Pi Based Smart Mirrors and Proposed Solutions 16

Energy Consumption Analysis for Raspberry Pi Based Smart Mir...

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16th ieee international symposium on Applied computational intelligence and Informatics, SACI 2022

作者： Rat, Alexandru Stangaciu, Cristina Micea, Mihai Politechnica University of Timisoara Faculty of Automation and Computers Timisoara Romania Politechnica University of Timisoara Computers and Information Technology Department Timisoara Romania

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

This article presents an analysis regarding energy consumption of a type of smart devices, namely smart mirrors using a reference implementation based on Raspberry Pi and providing features like Internet connectivity, access to different applications, voice command and geolocation. This paper includes a comparative study between different software implementations and configurations proposed for the smart mirror. © 2022 ieee.

关键词： Energy utilization

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A Survey of Behavior Tree-Based Task Planning Algorithms for Autonomous Robotic Systems 15

A Survey of Behavior Tree-Based Task Planning Algorithms for...

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15th international Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Shin, Mingyu Jung, Soyi Ajou University Dept. Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Ajou University Dept. Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350364637

Behavior trees (BTs) have gained recognition for their modularity and scalability, establishing them as a robust framework for task automation and planning in various domains, including robotics, game AI, and autonomous systems. This paper presents a comprehensive review of the integration of BTs with reinforcement learning (RL) and learning-from-demonstration (LfD) to enhance decision making and task planning in robotic systems. The review emphasizes the advantages of BTs, such as increased adaptability and efficiency through RL integration and the simplification of robot programming via LfD. Despite these benefits, challenges persist in the areas of computational complexity, scalability in multi-agent systems, and the automatic generation of BTs. This paper concludes by identifying key areas for future research to address these challenges and further advance the development of autonomous robotic systems. © 2024 ieee.

关键词： Multi agent systems

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A dipole model-based approach for magnetic disturbance compensation of mobile robots using magnetometer arrays 23

A dipole model-based approach for magnetic disturbance compe...

引用

23rd ieee international symposium on computational intelligence and Informatics, CINTI 2023

作者： Stefanoni, Massimo Odry, Akos Sarcevic, Peter Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary University of Szeged Department of Mechatronics and Automation Szeged Hungary

ISBN: (纸本)9798350342949

Magnetic sensors are used for localization purposes in both outdoor and indoor applications. In indoor applications, three-axis magnetometers cannot be used as compasses due to the presence of hard and soft-iron effects that require to be neglected. This paper proposes a new method for magnetic compensation for an unknown metallic object. It considers the source of the disturbance as a dipole, uses an array of four no-coplanar magnetometers, and exploits an Artificial Neural Network (ANN). The ANN receives as inputs the measurement differences of each pairwise sensor and provides as output the disturbance components related to one sensor of the array. In such a way, by estimating the magnetic disturbances, it is possible to evaluate the real value of the Earth's magnetic field. The ANN is trained by exploiting a database generated by using the dipole model and by evaluating the model for different intensities and orientations. The simulation results show how the method can effectively reduce the introduced disturbances in the surroundings of the object with a Mean Absolute Error (MAE) in the order of 0.3 μ T, and a maximum error in the range from 1.8767 μ T to 2.5252 μ T. The proposed method contributes to the development of improved localization algorithms, where the proposed disturbance rejection technique enables the utilization of reliable measurements for mobile robot pose estimation. © 2023 ieee.

关键词： Neural networks

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Digital Twins for Space robotics Applications

Digital Twins for Space Robotics Applications

引用

international symposium on computational intelligence and Informatics

作者： József Kövecses McGill University Montréal QC Canada

ISBN: (数字)9798350353433

ISBN: (纸本)9798350353440

A digital twin is a virtual replica of a physical system. It is based on mathematical models of the system, the related algorithms and computer implementations. robotics generally plays a key role in space exploration. Physical contact and its characteristics are of the utmost importance for the execution of robotic tasks. In this presentation, we will discuss mechanics-based digital twins for robotic contact, particularly in the space operations context for robotic arms and vehicles.

关键词： Space vehicles Manipulators Mathematical models Digital twins Space exploration Informatics computational intelligence

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A Review on Implementation of AES Algorithm Using Parallelized Architecture on FPGA Platform 6

A Review on Implementation of AES Algorithm Using Paralleliz...

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6th ieee international Conference on Advanced Systems and Emergent Technologies, IC_ASET 2023

作者： Mohammed, Nada Qasim Amir, Amiza Ahmad, Badlishah Salih, Muataz Hammed Arrfou, Hana Thalji, Nisrean Matem, Rema Abbas, Jamal Kamil K. Hussien, Qasim Mohammed Abdulhassan, Maki Mahdi University Malaysia Perlis Electronic Engineering & Technology Arau2600 Malaysia Design and Engineering Automation Group Flex Penang 11500 Malaysia College of Qatar Business Administration Doha122104 Qatar Jadara University Robotics and Artificial Intelligence Irbid Jordan Arau 2600 Malaysia Al-Nisour University College Computer Engineering Techniques Baghdad10036 Iraq

ISBN: (纸本)9798350321029

High-security cryptography algorithms like AES require high computational capabilities to achieve information security. Therefore, it is necessary to use parallel computing architectures that exploit modern technologies to obtain the most conceivable computational power. Various methods have been introduced to achieve parallel processing. One of them is field-programmable gate arrays (FPGAs), which have good characteristics suitable for implementing parallel architectures with lower power consumption. This paper will focus on the most important FPGA boards that were used to implement the AES cryptographic algorithm. In addition, it demonstrates the general scheme of building architecture with multiple computing processing engines to get high performance and better throughput, which is reflected in the reduced cost and energy consumption of IoT devices. © 2023 ieee.

关键词： Cryptography

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