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

共 36882 条记录，以下是411-420 订阅

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A 0.75mm2 407μW real-time Speech Audio Denoiser with Quantized Cascaded Redundant Convolutional Encoder-Decoder for Wearable IoT Devices

A 0.75mm2 407μW Real-Time Speech Audio Denoiser with Quanti...

引用

international conference on VLSI Design

作者： Dimple Vijay Kochar Maitreyi Ashok Anantha P. Chandrakasan Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

Audio denoising is crucial for delivering high-quality sound in applications ranging from communication devices to entertainment systems. On-device denoising is critical for en-suring consistent performance across various host platforms. Machine learning (ML) models exhibit strong audio processing performance in the frequency domain but require efficient hardware design. This paper focuses on enhancing audio quality using convolutional encoder-decoder ML models with low power consumption while meeting real-time processing constraints. We achieve this by developing a quantized network that optimally reduces computational costs without compromising enhancement quality. Furthermore, our hardware quantization scheme reduces memory usage by up to 75% while maintaining accuracy. Next, we design a complementary processing element activation routing scheme tailored to our algorithm, significantly reducing the on-chip memory accesses by $\mathbf{5}-\mathbf{9}\times$ . Fabricated in 28nm CMOS process, our chip demonstrates real-time audio denoising, processing each frame within 8ms while consuming only $407\ \mathbf{\mu} \mathbf{W}$ or 3.24 μJ/frame at 0.65V, 18.5 MHz, making it ideal for battery-powered IoT devices. In terms of performance, our chip also achieves the highest evaluation score for audio quality (PESQ), outperforming previous works.

关键词： Performance evaluation Quantization (signal) Power demand Accuracy Computational modeling Noise reduction Very large scale integration real-time systems Hardware Internet of Things

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An Effective Approach for Deepfake Video Detection using Binarized Neural Network

An Effective Approach for Deepfake Video Detection using Bin...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Praveen K Atul Pandey Bhawana Rudra Department of Information Technology National Institute of Technology Surathkal Karnataka India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The rise of DeepFake technologies, especially in audio and video, poses significant threats to information integrity, security, and privacy. Artificially driven Artificial Intelligence (AI) methods and their advancement make it difficult to trace synthetic media through deepfakes that closely approximate real speech, facial expressions, and body movements. Consequently, traditional methods of detecting these are losing the race because they cannot compete with the newly invented methods that are more advanced in comparison. This paper proposes a lightweight and scalable approach to deepfake video detection using Binarized Neural Networks (BNNs). We integrate BNNs with Convolutional Neural Networks (CNNs) and Multi-task Cascaded Convolutional Networks (MTCNN) to boost feature extraction and analysis while making sure that this is done at a computational efficiency, especially to be deployed in resource-constrained systems such as mobile and embedded devices. The binarization of network weights and activations naturally deals with the trade-off regarding detection accuracy and computational cost. Our approach introduces a practical solution for real-time deepfake detection, thus advancing toward more secure and trusted digital environments. Our proposed model has achieved an accuracy of 80%.

关键词： Deepfakes Technological innovation Accuracy Neural networks Streaming media real-time systems Computational efficiency Convolutional neural networks Security Artificial intelligence

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SenStick-Eye: A Sensing Platform to Observe Behavioral Insights from Small Everyday Objects

SenStick-Eye: A Sensing Platform to Observe Behavioral Insig...

引用

ieee international conference on Consumer Electronics (ICCE)

作者： Yugo Nakamura Yuki Matsuda Yutaka Arakawa Kyushu University Fukuoka Fukuoka Japan Okayama University Okayama Okayama Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

SenStick-Eye is a novel sensing platform that captures behavioral insights from small everyday objects, such as cutlery, toothbrushes, and pens, by integrating a G-sensor (accelerometer) and a monocular RGB camera (480×480 pixels). This compact system enables continuous monitoring of human-object interactions, facilitating object-centric behavioral analysis. By collecting both motion and visual data, SenStick-Eye reveals patterns and triggers in habitual behaviors that often go unnoticed. The platform is designed for applications in just-in-time interventions, habit formation, and behavior change technologies, offering new opportunities for AIoT-based systems to support healthier habits.

关键词： Accelerometers Visualization Electric potential Cameras real-time systems Sensors Behavioral sciences Monitoring Consumer electronics

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An FFT Accelerator IP Core Implemented on FPGA

An FFT Accelerator IP Core Implemented on FPGA

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Vamsi Krishna Challagundla Jayaraj U Kidav Shashank Kumar Singh Anirban Jyoti Hati Sai Kumar Mandiga Dept. of ESE National Institute of Electronics and Information Technology (NIELIT) Aurangabad India Scientist G Dept. of ESE National Institute of Electronics and Information Technology (NIELIT) Aurangabad India Scientist B Dept. of ESE National Institute of Electronics and Information Technology (NIELIT) Aurangabad India Scientist C Dept. of ESE National Institute of Electronics and Information Technology (NIELIT) Aurangabad India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The main objective of this project is to design and implement a Fast Fourier Transform (FFT) IP core. Due to its ability to provide accurate frequency domain analysis of time domain signals, the FFT technique is indispensable in many applications related to digital signal processing. The implementation is based on the radix-2 method, whose lower computational complexity and hardware resource efficiency make it a good fit for FPGA platforms. With its robust FPGA and ARM CortexA53 processors, the ZCU104 board provides an adaptable environment for creating unique IP cores and incorporating them into more complex *** objective of this project is to use the Zynq SoC’s hardware acceleration capabilities to improve the FFT core for speed and resource consumption. The ZCU104 board is used to test and validate the built IP core, proving its usefulness for real-time signal processing applications. As compared to software-based FFT implementations, the results demonstrate a notable boost in performance, underscoring the potential of FPGA based solutions in high performance computing jobs.

关键词： Program processors Fast Fourier transforms Signal processing algorithms Throughput real-time systems Radar signal processing IP networks time-domain analysis Signal analysis Field programmable gate arrays

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Shadow Dexterous Hand Control via MediaPipe and BioIK Integration

Shadow Dexterous Hand Control via MediaPipe and BioIK Integr...

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ieee international conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Bruno Santos Gil Gonçalves Vítor H. Pinto SYSTEC (DIGI2) ARISE & ECE Dept. Faculdade de Engenharia Universidade do Porto Porto Portugal SYSTEC (DIGI2) ARISE & ME Dept. Faculdade de Engenharia Universidade do Porto Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

This paper presents a study on the teleoperation of an anthropomorphic robotic hand. The proposed system integrates MediaPipe for human hand tracking and BioIK for translating the captured data to the robotic hand. Two methods for acquiring three-dimensional hand positions are evaluated: direct estimation via MediaPipe and a stereo vision-based technique. Experimental results reveal that the stereo technique significantly improves the accuracy and fidelity of the hand position replication, albeit at the cost of increased teleoperation instability. The algorithm achieves an end-to-end delay of one second, ensuring real-time usability within certain constraints. The system's effectiveness is further validated through successful object-grasping experiments, highlighting its potential for advanced teleoperation applications.

关键词： Hands Costs Accuracy Estimation Kinematics real-time systems Delays Usability Autonomous robots

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Atlas: Ensuring Accuracy for Privacy-Preserving Federated IoT applications 25

Atlas: Ensuring Accuracy for Privacy-Preserving Federated Io...

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Proceedings of the ACM/ieee 16th international conference on Cyber-Physical systems (with CPS-IoT Week 2025)

作者： Jiechao Gao Mingyue Tang Wenpeng Wang Tushar Routh Bradford Campbell University of Virginia Charlottesville VA USA University of Illinois Urbana-Champaign Champaign IL USA

ISBN: (纸本)9798400714986

In smart Internet of Things (IoT) applications, edge devices often collect and store limited data, which is insufficient for training modern deep learning models. Collaborative training methods like cloud computing and federated learning enable robust models for IoT applications, yet introduce data privacy concerns due to central data collection and model inversion attacks. Remedies such as differential privacy can bring data privacy protection but dramatically degrade the accuracy performance of IoT applications. To safeguard user data privacy while maintaining application quality, it is imperative to establish a framework capable of preserving user privacy without compromising accuracy *** this paper, we present Atlas, a private and accurate personalized federated local differential privacy (LDP) framework for IoT applications. We first design a layer-sharing mechanism called the layer importance mask to separate the local model into global and personalized layers. Second, we design a weighted LDP mechanism and add noise to the global layers before transmitting them to the federated learning framework for aggregation. Third, we combine local personalized layers and aggregated global layers to perform IoT tasks. Our experiments on five real-world IoT application datasets and the CIFAR-10 dataset show that our privacy-preserving approach only sacrifices 2% to 6% of accuracy compared to the state-of-the-art non-privacy preserving FL frameworks among various IoT applications and outperforms the current LDP-based FL framework by 8% to 13%.

关键词： Federated Learning

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Research on Optimizing Economic Decision Support System Using Data Mining Technology

Research on Optimizing Economic Decision Support System Usin...

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Electronics, Energy systems and Power Engineering (EESPE), ieee international conference on

作者： Yang Meng Shuzhu Tang Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper studies how to use data mining technology to optimize economic decision support system. Traditional economic decision models often rely on static rules and assumptions, which are difficult to cope with the ever-changing economic environment. This study proposes a data mining optimization algorithm based on multi-model forecast fusion, which combines regression analysis, SVM and neural network, and improves the adaptability and accuracy of the model through a hierarchical structure. Experimental results show that the proposed model is significantly better than the traditional model in multiple economic forecasting tasks, especially when the economic fluctuations are large, it can provide more accurate decision support. In the specific experiment, historical economic data was used for training and verification, and the mean square error (MSE) of the model was reduced by more than 15% compared with the traditional model, and the real-time and accuracy of the decision were greatly improved. Through comparative experiments, this paper proves the effectiveness of the multi-model fusion strategy in economic decision-making and demonstrates its great potential in practical applications.

关键词： Economics Support vector machines Adaptation models Accuracy Fluctuations Biological system modeling Predictive models Data models real-time systems Data mining

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Identifying Surgical Instruments in Pedagogical Cataract Surgery Videos through an Optimized Aggregation Network

Identifying Surgical Instruments in Pedagogical Cataract Sur...

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international Image Processing, applications and systems conference (IPAS)

作者： Sanya Sinha Michal Balazia Francois Bremond Dept. of Surgery and Cancer Imperial College London London United Kingdom Team STARS INRIA d’Université Côte d’Azur Sophia Antipolis France

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Instructional cataract surgery videos are crucial for ophthalmologists and trainees to observe surgical details repeatedly. This paper presents a deep learning model for real-time identification of surgical instruments in these videos, using a custom dataset scraped from open-access sources. Inspired by the architecture of YOLOV9, the model employs a Programmable Gradient Information (PGI) mechanism and a novel Generally-Optimized Efficient Layer Aggregation Network (Go-ELAN) to address the information bottleneck problem, enhancing Minimum Average Precision (mAP) at higher Non-Maximum Suppression Intersection over Union (NMS IoU) scores. The Go-ELAN YOLOV9 model, evaluated against YOLO v5, v7, v8, v9 vanilla, Laptool and DETR, achieves a superior mAP of 73.74 at IoU 0.5 on a dataset of 615 images with 10 instrument classes, demonstrating the effectiveness of the proposed model.

关键词： Cataracts YOLO Deep learning Instruments Image processing Surgery real-time systems Videos

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AI-Powered Augmented reality for Satellite Assembly, Integration and Test

AI-Powered Augmented Reality for Satellite Assembly, Integra...

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ieee international conference on Artificial Intelligence and Virtual reality (AIVR)

作者： Álvaro Patrício João Valente Atabak Dehban Inês Cadilha Daniel Reis Rodrigo Ventura Institute for Systems and Robotics University of Lisbon Lisbon Portugal Lusospace Lisbon Portugal

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

The integration of Artificial Intelligence (AI) and Augmented reality (AR) is set to transform satellite Assem-bly, Integration, and Testing processes by enhancing precision, minimizing human error, and improving operational efficiency in cleanroom environments. This paper presents a technical description of the European Space Agency's (ESA) project “AI for AR in Satellite AIT” which combines real-time computer vision and AR systems to assist technicians during satellite assembly. Leveraging Microsoft HoloLens 2 as the AR interface, the system delivers context-aware instructions and real-time feedback, tackling the complexities of object recognition and 6D pose estimation in AIT workflows. All AI models demonstrated over 70% accuracy, with the detection model exceeding 95% accuracy, indicating a high level of performance and reliability. A key contribution of this work lies in the effective use of synthetic data for training AI models in AR applications, addressing the significant challenges of obtaining real-world datasets in the highly dynamic satellite environments, as well as the creation of the Segmented Anything Model for Automatic Labelling (SAMAL), which facilitates the automatic annotation of real data, achieving speeds up to 20 times faster than manual human annotation. The findings demonstrate the efficacy of AI-driven AR systems in automating critical satellite assembly tasks, setting a foundation for future innovations in the space industry.

关键词： Solid modeling Satellites Annotations Computational modeling European Space Agency real-time systems Data models Artificial intelligence Assembly Augmented reality

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Collecting Restaurant Information and Urban Analysis for Foreign Tourists to realise Smart City

Collecting Restaurant Information and Urban Analysis for For...

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ieee international conference on Consumer Electronics (ICCE)

作者： Yasuki Tsuchiya Yuuko Horita School of Sustainable Design University of Toyama Toyama Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

In order to realise a smart city for foreign tourists, it is necessary to suggest which areas have an attractive concentration of restaurants. For this purpose, a survey was conducted in Tokyo using private gourmet information websites to investigate areas with a high concentration of restaurants for foreign tourists. By incorporating this method into smart city applications, foreign tourists can smoothly enjoy food culture.

关键词： Surveys Photography Translation Smart cities Web services real-time systems Satellite images Consumer electronics

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