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检索条件"任意字段=11th International Conference on Embedded and Real-Time Computing Systems and Applications"

共 3210 条记录，以下是321-330 订阅

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An efficient real time Implementation of Motion Estimation in Video Sequences on SOC 18

An efficient Real Time Implementation of Motion Estimation i...

引用

18th international Multi-conference on systems, Signals & Devices (SSD)

作者： Ammar, Anis Ben Fredj, Hana Souani, Chokri Univ Sousse Monastir Natl Sch Engineers Sousse Fac Sci Microelect & Instrumentat Lab Sousse Tunisia Monastir Univ Fac Sci Monastir Microelect & Instrumentat Lab Monastir Tunisia

ISBN: (纸本)9781665414937

Motion estimation is considered among the most famous and important applications in the field of computer vision. Indeed, in the last decade, the optical flow has become an alternative technique to estimate motion on successive frames. However, dense and precise estimates of the optical flow al'e usually costly in computing time. In this context, our approach is focused upon calculating the moving object's optical flow through the use of the Lucas-Kanade algorithm in real time which is implemented into Raspberry Pi 4. the efficiency of our real time implementation is demonstrated by the experimental results, hence showing better performances in comparison with the conventional calculation.

关键词： motion estimation optical flow embedded system real time implementation

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An electronic platform for farm accounting and decision making 11

An electronic platform for farm accounting and decision maki...

引用

11th international conference on Information and Communication Technologies in Agriculture, Food and Environment, HAICTA 2024

作者： Tafidou, Anna Lialia, Evgenia Kouriati, Asimina Dimitriadou, Eleni Moulogianni, Christina Prentzas, Angelos Bournaris, thomas Kountios, Georgios Department of Mathematics Aristotle University of Thessaloniki Thessaloniki54124 Greece Department of Agricultural Economics Aristotle University of Thessaloniki Thessaloniki54124 Greece Department of Agriculture International Hellenic University Sindos Thessaloniki57400 Greece

In the contemporary agricultural landscape, efficient management of agricultural holdings necessitates the integration of technical and economic data. this paper introduces an electronic platform that integrates data monitoring to meet the demands of modern agricultural practices. the platform offers a comprehensive solution, enabling farmers and stakeholders to collect, analyze, and utilize both technical and economic information. through the utilization of modern technology, the platform provides user-friendly data input, processing, and visualization. It encompasses various features, including real-time monitoring of crop growth parameters, yield estimations, resource utilization, and financial performance indicators. Furthermore, the platform is designed to promote interoperability, allowing integration with existing agricultural management systems and data sources. this interoperability enhances the scalability and adaptability of the platform, ensuring its relevance across diverse agricultural contexts. By providing a unified solution for the integrated monitoring of technical and economic data, our electronic platform empowers agricultural stakeholders with the tools necessary for optimized resource management, increased productivity, and sustainable agricultural development. this paper presents the development, functionalities, and potential applications of the electronic platform, underscoring its significance in advancing agricultural practices towards greater efficiency and sustainability. © 2024 Copyright for this paper by its authors.

关键词： decision making Electronic platform farm accounting technical and economic data

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PM4Tree: A Scriptable Parametric Modeling Interface for Decorative Tree Design Using PM4VR

PM4Tree: A Scriptable Parametric Modeling Interface for Deco...

引用

international conference on Soft computing and Machine Intelligence (ISCMI)

作者： Wanwan Li University of Tulsa Tulsa Oklahoma USA

ISBN: (数字)9798331518127

ISBN: (纸本)9798331518134

this paper introduces PM4Tree, a scriptable parametric modeling interface tailored for the procedural generation of decorative trees in Virtual reality (VR). Built upon the PM4VR framework, PM4Tree combines parametric design principles with intuitive scripting capabilities to allow users to design a wide range of aesthetically pleasing and mathematically inspired tree structures. By extending PM4VR’s framework, PM4Tree enables seamless integration of parametric tree design interface into environments, making it suitable for applications in interior design, digital content generation, spatial computing, game development, and VR experiences. this interface empowers users to rapidly iterate on decorative tree designs, adjusting parameters such as branch patterns, leaf density, and tree shape, thus offering high levels of flexibility and creative control.

关键词： Solid modeling Procedural generation Shape Ecosystems Virtual environments Flowering plants real-time systems Parametric statistics Spatial computing Machine intelligence

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Video-Based Online Interactive System Using CNN Segmentation Method

Video-Based Online Interactive System Using CNN Segmentation...

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Information and Education Technology (ICIET), international conference on

作者： Tsung-Han Tsai Wei-Chung Wan Dept. of Electrical Engineering National Central University Taoyuan Taiwan

In recent years, the COVID-19 has made it difficult for people to interact with each other face-to-face, but various kinds of social interactions are still needed. therefore, we have developed an online interactive system based on the image processing method, that allows people in different places to merge the human region of two images onto the same image in real-time. the system can be used in a variety of situations to extend its interactive applications. the system is mainly based on the task of Human Segmentation in the CNN (convolution Neural Network) method. then the images from different locations are transmitted to the computing server through the Internet. In our design, the system ensures that the CNN method can run in real-time, allowing both side users can see the integrated image to reach 30 FPS when the network is running smoothly.

关键词： Image segmentation three-dimensional displays Interactive systems Transfer learning Streaming media Feature extraction Visual effects

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EarlyBird: Energy belongs to those who wake up early

EarlyBird: Energy belongs to those who wake up early

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IEEE international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Hugo Reymond Jean-Luc Béchennec Mikaël Briday Sébastien Faucou Isabelle Puaut Erven Rohou Univ Rennes Inria CNRS IRISA France Nantes Université École Centrale Nantes CNRS LS2N Nantes France

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

By relying on ambient energy, battery-less devices significantly increase the autonomy of IoT devices, enabling maintenance-free operation in remote locations. However, due to the scarcity of ambient energy, these devices rely on capacitors to buffer energy, and alternate between power-off phases where the device is harvesting energy and computation bursts. In most existing techniques, the device resumes execution only when the capacitor is full. However, we argue that doing so is sub-optimal. Instead, we advocate that waking-up the device sooner may yield better performance since the microcontroller consumes less power when operating at lower voltage. To this extent, we introduce EarlyBird, a technique that automatically computes a fine-tuned wake-up voltage for each resume point. EarlyBird leverages static analysis to determine how much energy is needed before resuming from a given program location, and provides a runtime library to enforce the early wake-up strategy. We evaluated how EarlyBird improves existing checkpointing techniques and results show an increase in the number of benchmarks executed per minute of up to 5.65×.

关键词： Performance evaluation Checkpointing Runtime library Microcontrollers Capacitors Voltage Static analysis Benchmark testing real-time systems Internet of things

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Towards WiFi-based real-time Sensing Model Deployed on Low-power Devices 19

Towards WiFi-based Real-time Sensing Model Deployed on Low-p...

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19th IEEE international conference on Mobile Ad Hoc and Smart systems (MASS)

作者： Lu, Xinxin Wang, Lei Tian, Yuan Hu, Jiayi Pan, Yuhao Zhang, Enze Zhang, Yichi Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116024 Peoples R China Ctr Underwater Robot Peng Cheng Lab Shenzhen 518066 Peoples R China OPPO Us Res Ctr Palo Alto CA 94303 USA

ISBN: (数字)9781665471800

ISBN: (纸本)9781665471800

WiFi-based human activity recognition (HAR) is becoming popular due to its non-invasiveness and far-reaching applications such as security surveillance and health monitoring. Existing deep learning methods, while promising, consist of extremely large-scale neural network architectures with massive model parameters and high computational complexity. thereby, real-time monitoring can be achieved only by leveraging power-hungry GPUs, making it difficult to recognize the performed activities timely. Towards this end, we propose LiST, a real-time and effective WiFi-based HAR model capable of quickly inferring an accurate human activity on a CPU, even on an embedded system. Specifically, a two-stream spatial-temporal structure is utilized to capture sub-carrier and time information from 2D data converted from 3DWiFi Channel State Information (CSI) readings, and uses the dilated convolution to expand the convolution perception range without increasing the number of parameters. To improve the model's generalization ability, we introduce a label smoothing technique to soft labels, enhancing the intra-class distance and separating the inter-class relationship. Extensive experiments demonstrate that LiST obtains excellent performance of over 95.80% with lower model parameters in three scenes and enables to infer activity in about 0.37 s on the Raspberry Pi 4, outperforming the state-of-the-art solutions.

关键词： Smoothing methods embedded systems Convolution Computational modeling Feature extraction real-time systems Sensors

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An Artificial Intelligence Hearing Aid Based on Two-level Neural Network 11

An Artificial Intelligence Hearing Aid Based on Two-level Ne...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced computing systems - Technology and applications (IDAACS)

作者： Zhao, Chaoyang Liu, Chun Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781665426053

Hearing aids have become an indispensable part of the lives of some hearing-impaired people. Traditional hearing aids will be adjusted according to the personal hearing curve and allowing patients to avoid noise-induced harm. However, there is no sound classification or intelligent noise reduction, which cannot meet the higher demand for hearing aids. this paper designed a hearing aid based on a two-level neural network, and the Urbansound8K data set was used to train the neural network. It can simulate the human auditory attention mechanism and intelligently control the output volume. At the same time, the noise reduction model is used to perform corresponding noise reduction processing on different speech streams. Experimental results show that the hearing aid can differentially amplify various sounds in different scenes. the noise part of the sound heard by the user will be suppressed to a certain extent, which can improve the comfort of long-term wearing.

关键词： embedded hearing aids neural networks speech enhancement noise cancellation

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Federated Learning in Robotic and Autonomous systems 18

Federated Learning in Robotic and Autonomous Systems

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18th international conference on Mobile systems and Pervasive computing, MobiSPC 2021, the 16th international conference on Future Networks and Communications, FNC 2021 and the 11th international conference on Sustainable Energy Information Technology, SEIT 2021

作者： Xianjia, Yu Queralta, Jorge Peña Heikkonen, Jukka Westerlund, Tomi Turku Intelligent Embedded and Robotic Systems Lab University of Turku Turku Finland

Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with blockchain and distributed ledger technologies (DLTs) are playing a key role. At the same time, advances in deep learning (DL) have significantly raised the degree of autonomy and level of intelligence of robotic and autonomous systems. While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. However, FL by itself does not provide the levels of security and robustness required by today's standards in distributed autonomous systems. this survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research. © 2021 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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How Lightweight Deep Learning Enhances Performance in DPU-Accelerated Autonomous Driving on Zynq SoC

How Lightweight Deep Learning Enhances Performance in DPU-Ac...

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Mechatronics and Robotics Engineering (ICMRE), international conference on

作者： Gyu Hyeon Hwang HoBin Oh Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

this study presents a lightweight deep learning model developed for DPU-accelerated systems. It aims to provide real-time autonomous driving on resource-constrained systems such as the Ultra96v2. A customized kids electric car served as the platform. Custom power supply and steering control systems were set up in the car to enable real-world testing. To enhance inference performance, various methods were used. these included input size reduction, channel-pruning, and quantization. As a consequence, the pruned and quantized YOLOv3-Tiny model produced a frame rate of 67.592 FPS. this is roughly a 25x increase over the original YOLOv3's 2.715 FPS on Ultra96v2's PL domain. these results show that real-time deployment is feasible on FPGA-based platforms. the work offers insights for creating efficient and scalable embedded systems for self-driving vehicle system.

关键词： Deep learning YOLO Quantization (signal) Power supplies real-time systems Automobiles Autonomous vehicles Robots Optimization Testing

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Model Inference Using TVM on AMD GPU: Image Classification and LLM Case Studies

Model Inference Using TVM on AMD GPU: Image Classification a...

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Electrical, Electronic and computing Engineering (IcETRAN), international conference on

作者： Andrija Malbaša Nenad Petrović Branko Arsić Faculty of Electornic Engineering Syrmia LLC University of Niš Serbia Faculty of Science Syrmia LLC University of Kragujevac Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

Deep learning models have revolutionized the field of artificial intelligence by exhibiting exceptional capabilities in understanding complex patterns and making accurate predictions. However, the growing complexity of these models presents a challenge in achieving real-time inference, which is crucial for practical applications. As deep learning models become more popular, there is an increasing demand for faster inference times to enable real-time decision-making in critical scenarios, such as fire detection and classification. In recent years, the advancement in parallel computing hardware, such as graphics processing units (GPUs), specialized tensor processing units (TPUs) and application-specific integrated circuits (ASICs), has provided a significant boost to deep learning inference. One of the challenges in accelerating deep learning inference on diverse hardware platforms is the lack of optimized implementations of mathematical operations specifically tailored for each platform. this limitation can significantly hinder the performance of deep learning models on these platforms. In this paper, we focus on Advanced Micro Devices (AMD) GPU graphics cards and explore the utilization of the Apache Tensor Virtual Machine (TVM) compiler framework to accelerate deep learning inference for Generative Pre-trained Transformer 2 (GPT-2) large language model (LLM) and ResNet-18 image classification model, represented in PyTorch and Open Neural Network Exchange (ONNX) formats, respectively.

关键词： Deep learning Tensors Computational modeling Graphics processing units Predictive models Transformers real-time systems

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