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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Likai Li Yichuan Bai Shengping Liu Yang Zhao Sunan He Yaqing Li Li Du Yuan Du Nanjing University Nanjing China United Microelectronics Center Chongqing China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

The deceleration of Moore's Law has led to increasing difficulties in advancing the computational speed and power efficiency of Complementary-Metal-Oxide-Semiconductor (CMOS) chips. As a solution to this challenge, optical computing emerges as a promising technology, boasting low energy consumption, high processing speed, and extensive bandwidth. Yet, a critical obstacle remains: the absence of a co-simulation platform that incorporates both photonic chips and peripheral electrical circuits. This paper addresses this gap by introducing a hybrid optoelectronic computing evaluation and deployment platform utilizing Simulink tools. Based on the measured data from the silicon optical computing chip, we have deployed an image filtering algorithm and a convolutional neural network onto this platform. The optical computing chip achieves an accuracy of 86.4% on the imageNet image dataset. Through evaluation, we have identified the most substantial impacts on calculation results. To achieve an image classification accuracy of 80%, the signal-to-noise ratio (SNR) of the low-speed DAC must be a minimum of 52 dB. These findings provide crucial insights into the optimization of optical computing systems.

关键词： Semiconductor device modeling Semiconductor device measurement Accuracy Software packages Optical computing Reliability theory Silicon photonics

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PhysID: Physics-based Interactive Dynamics from a Single-view image

PhysID: Physics-based Interactive Dynamics from a Single-vie...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Sourabh Vasant Gothe Ayon Chattopadhyay Gunturi Venkata Sai Phani Kiran Pratik Vibhav Agarwal Jayesh Rajkumar Vachhani Sourav Ghosh Parameswaranath VM Barath Raj KR Samsung R&D Institute India Bangalore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Transforming static images into interactive experiences remains a challenging task in computer vision. Tackling this challenge holds the potential to elevate mobile user experiences, notably through interactive and AR/VR applications. Current approaches aim to achieve this either using pre-recorded video responses or requiring multi-view images as input. In this paper, we present PhysID, that streamlines the creation of physics-based interactive dynamics from a single-view image by leveraging large generative models for 3D mesh generation and physical property prediction. This significantly reduces the expertise required for engineering-intensive tasks like 3D modeling and intrinsic property calibration, enabling the process to be scaled with minimal manual intervention. We integrate an on-device physics-based engine for physically plausible real-time rendering with user interactions. PhysID represents a leap forward in mobile-based interactive dynamics, offering real-time, non-deterministic interactions and user-personalization with efficient on-device memory consumption. Experiments evaluate the zero-shot capabilities of various Multimodal Large Language Models (MLLMs) on diverse tasks and the performance of 3D reconstruction models. These results demonstrate the cohesive functioning of all modules within the end-to-end framework, contributing to its effectiveness.

关键词： performance evaluation Solid modeling Three-dimensional displays Streaming media Aerodynamics Rendering (computer graphics) Real-time systems Mobile handsets Mesh generation Material properties

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AoI-Based Scalable Edge Computing Resource Allocation in Heterogeneous IIoT systems

AoI-Based Scalable Edge Computing Resource Allocation in Het...

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IEEE Conference on Wireless Communications and Networking

作者： Daniel S. Zakamulin Mohsen Pourghasemian Firooz B. Saghezchi Haris Gacanin Chair for Distributed Signal Processing RWTH Aachen University Aachen Germany

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Edge computing is a sustainable network paradigm that supports resource constrained Industrial Internet of Things (IIoT) devices in performing data-driven tasks. The distributed architecture of computing resources, combined with the heterogeneous and sporadic characteristics of IIoT tasks, presents a substantial challenge to achieving energy-efficient and low-latency task execution. In this paper, we demonstrate that modeling the Age of Information (AoI) as an exponential function can significantly enhance the performance of task scheduling and execution in edge computing systems. Furthermore, we introduce a low over-head Multi-Agent Deep Reinforcement Learning (DRL) algorithm with No Observation (MA-NO) for distributed edge computing resource allocation, where agents (IIoT devices) operate independently without communicating with each other. However, to prevent the divergence of the agents and ensure a Pareto-optimal decision-making, we adopt a common reward that is shared among all agents. That is, all agents receive the same reward, based on their collective performance. simulation results show that our proposed AoI model reduces the average execution latency of the tasks by 35.2% compared to a linear AoI ones. Furthermore, our proposed MA-NO algorithm reduces the total energy consumption of the IIoT devices by 59.9% and 63.4% compared to the single-agent DRL algorithm and multi-agent DRL algorithm with partial observations, respectively.

关键词： performance evaluation Training Energy consumption Processor scheduling Computational modeling simulation Energy efficiency Resource management Industrial Internet of Things Edge computing

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Integration Design of Motion Capture and Sensor Technology in Home Fitness

Integration Design of Motion Capture and Sensor Technology i...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： He Chen Xu Hong Kaiwen Xiao Sujie Mao Sangmyung University Seoul South Korea Jiangsu Second Normal University Nanjing China Nanjing Institute of Physical Education Nanjing China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

Background and Purpose: This study aims to design an integrated home fitness system combining motion capture (Mocap) and sensor technology (Sensing Tech). With remote work and home-based lifestyles becoming the norm, traditional home fitness methods face challenges in personalized guidance and real-time feedback. This system aims to provide personalized fitness advice and instant corrections by monitoring users' movement quality and physiological state in real time, thereby enhancing the safety, effectiveness, and overall health level of home fitness. Technical Overview: Mocap captures human motion data through devices such as cameras and sensors, generating precise three-dimensional data models, primarily divided into optical, inertial, and hybrid systems. Sensing Tech utilizes accelerometers, gyroscopes, heart rate sensors, etc., to detect and respond to physical, chemical, or biological changes in the environment, providing real-time data for monitoring health conditions. System Design: The system comprises four parts: architecture, data acquisition, data processing, and user interface. It adopts a hierarchical architecture including the data acquisition layer, data processing layer, and user interface layer. User actions and physiological data are collected using IMU sensors, heart rate monitors, and camera systems. Integrated signal processing, feature extraction, behavior recognition algorithms run on microprocessors or cloud servers. Real-time feedback and personalized training advice are provided through a graphical user interface (GUI). Technical Implementation and simulation: Software such as MATLAB/Simulink is used for system modeling and simulation to validate feasibility. Machine learning algorithms are developed for action recognition and health monitoring, optimizing model parameters and conducting validation. performance evaluation: Model performance is evaluated using metrics like confusion matrices, accuracy, recall rate, and F1 scores. User feedba

关键词： Heart rate Accuracy System performance signal processing algorithms Computer architecture Cameras Real-time systems Motion capture Biomedical monitoring Monitoring

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1st International Scientific and Practical Conference on Information Technologies and Intelligent Decision Making systems, ITIDMS 2021

1st International Scientific and Practical Conference on Inf...

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1st International Scientific and Practical Conference on Information Technologies and Intelligent Decision Making systems, ITIDMS 2021

ISBN: (纸本)9783031213397

The proceedings contain 13 papers. The special focus in this conference is on Information Technologies and Intelligent Decision Making systems. The topics include: Tone image processing with Discrete Data Structure;the Concept of Client-Server Architecture for Remote Control of a Distributed Knowledge Base;experimental Study of Russian-Language News Text Categorization performance Using Machine Learning Algorithms;experimental evaluation of the Efficiency of Compression of Files by Fractal-Spectral Codec;an Intelligent Decision Support System for Solving Complex Territorial Planning Problems;fault Tolerance of Distributed Worker Processes in Corporate Information systems and Technologies;functioning of the Thermal Memory Cell;using Data Mining Technology in Monitoring and modeling the Epidemiological Situation of the Human Immunodeficiency Virus in Kazakhstan;information Technology and Artificial Intelligence in Improving of Center Pivot Irrigation Control;KOA Pathology Screening Using Multi-channel VAG signal Fusion Method;prioritization of Distributed Worker Processes Based on Etcd Locks.

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Empirical evaluation and simulation of the Impact of Global Navigation Satellite System Solutions on Uncrewed Aircraft System-Structure from Motion for Shoreline Mapping and Charting

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DRONES 2024年第11期8卷 646-646页

作者： Pilartes-Congo, Jose A. Simpson, Chase Starek, Michael J. Berryhill, Jacob Parrish, Christopher E. Slocum, Richard K. Texas A&M Univ Coll Engn & Comp Sci Corpus Christi TX 78412 USA Conrad Blucher Inst Surveying & Sci Measurement Analyt Lab Corpus Christi TX 78412 USA Oregon State Univ Sch Civil & Construct Engn Corvallis OR 97331 USA

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without us

关键词： geomatics engineering GNSS shoreline charting UAS-SfM photogrammetry

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A CNN-Based Classifier for Quality image Selection from Sky Cameras

A CNN-Based Classifier for Quality Image Selection from Sky ...

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International Conference on signal processing and Information Security (ICSPIS)

作者： Mahmoud Rezk Varun Kumar Tiwari Prajowal Manandhar Vineeth Krishnan Luis Martin Pomares Dubai Electricity and Water Authority Research and Development Centre Dubai United Arab Emirates

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

This paper presents quality enhancement techniques for all sky images in solar parks that uses sky camera-based systems. This study addresses the real-world challenges associated with deploying these systems, particularly focusing on external factors such as weather conditions, and interference due to soiling and dust accumulated on top of the domes of the sky cameras installed in desert conditions as well as human, birds, or insects that can compromise the quality of sky images used for irradiance estimation. To tackle these issues, a Convolutional Neural Network (CNN)-based classifier capable of identifying and excluding invalid images from the data processing pipeline is proposed. The proposed classifier achieves an accuracy exceeding 98% in detecting invalid images and is more than seven times smaller than traditionally used pre-trained models like VGG-16 and ResNet50, while also being over 16% faster. This research paves the way for more reliable operations in solar parks, leveraging ML-based methodologies for enhanced solar energy management.

关键词： performance evaluation Accuracy Computational modeling Pipelines Estimation Cameras Real-time systems Convolutional neural networks Thermal stability Residual neural networks

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ROI-Based Deep image Compression with Swin Transformers

ROI-Based Deep Image Compression with Swin Transformers

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Binglin Li Jie Liang Haisheng Fu Jingning Han School of Engineering Science Simon Fraser University Canada School of Microelectronics Xian Jiaotong University China Google LLC Mountain View CA USA

Encoding the Region Of Interest (ROI) with better quality than the background has many applications including video conferencing systems, video surveillance and object-oriented vision tasks. In this paper, we propose a ROI-based image compression framework with Swin transformers as main building blocks for the autoencoder network. The binary ROI mask is integrated into different layers of the network to provide spatial information guidance. Based on the ROI mask, we can control the relative importance of the ROI and non-ROI by modifying the corresponding Lagrange multiplier λ for different regions. Experimental results show our model achieves higher ROI PSNR than other methods and modest average PSNR for human evaluation. When tested on models pre-trained with original images, it has superior object detection and instance segmentation performance on the COCO validation dataset.

关键词： image segmentation image coding Object oriented modeling Object detection signal processing Transformers Video surveillance

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Exploring New Horizons: Automated Machine Learning for image Classification Networks

Exploring New Horizons: Automated Machine Learning for Image...

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International Conference on Imaging, signal processing and Communication (ICISPC)

作者： Kangda Cheng Jinlong Liu Zhilu Wu Junkai Wang Zhenqian Zhang Haiyan Jin School of Electronic and Information Engineering Harbin Institute of Technology Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798350367157

ISBN: (纸本)9798350367164

As machine learning technologies advance, simple image classification networks have become commonplace for developers like us in the field of neural networks. However, for humanities and social science researchers who are unfamiliar with artificial intelligence technologies, image classification remains a labor-intensive and error-prone task. The emergence of Automated Machine Learning (AutoML) technology has removed the barriers to deploying high-performance machine learning models for artists and art gallery managers who lack expertise in machine learning. For the traditional problem of image classifying, AutoML provides a simple and practical approach for building the most appropriate model based on relevant data in just one pass. In this study, we constructed a dataset for the classification of Traditional Chinese paintings and Western paintings. We then use the Tree-based Pipeline Optimization Tool for Automating Machine Learning (TPOT) algorithm to generate 1000 networks and select the model with the highest accuracy for the dataset. Our model attained a $99.29 \%$ accuracy rate on the dataset encompassing Traditional Chinese and Western paintings, demonstrating superior performance compared to commonly employed baseline models. We provide a standard AutoML practice paradigm, which simplifies the construction process of a complex machine learning model into four steps: data collection, feature engineering, model building, and model evaluation. This allows humanities researchers to deploy high-performance models without requiring profound knowledge in machine learning.

关键词： Accuracy Machine learning algorithms Computational modeling Search problems Data models Classification algorithms Standards

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MultiRAT DYMO: Enhanced Routing Protocols for VANETs

MultiRAT DYMO: Enhanced Routing Protocols for VANETs

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Communication, image and signal processing (CCISP), International Conference on

作者： Chakkaphong Suthaputchakun Tianru Li Chuan Foh Zhili Sun Periklis Chatzimisios Electrical and Computer Engineering Bangkok University Phathumthani Thailand Institute for Communication Systems University of Surrey Surrey UK School of Science & Technology International Hellenic University Thessaloniki Greece

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

This paper describes our design of multihop multiRAT routing protocol based on the traditional DYMO (Dynamic MANET On Demand) routing protocol. Since the tradition routing protocols including DYMO only support singleRAT communications. With the increasing demand of multiRAT communications, there is a need for a multihop multiRAT routing protocol. Among the well-accepted routing protocols, DYMO is one of the popular routing protocols, which is also a successor of the widely-used AODV (Ad-Hoc on Demand Distance Vector). To support multiRAT, a route discovery process in DYMO is enhanced. A route request message RREQ will be broadcasted from the source to its neighbors through multiRAT instead of singleRAT as done traditionally. Then, the intermediate hosts also perform the same procedure by broadcasting the received RREQ to the next hop via all RATs they have in hands. The process continues until reaching the destination. The destination then sends a route reply message RREP back to the source through the reverse order of the intermediate hosts and the specified RATs. Therefore, a routing table stored on each host has be enhanced to include the information regarding the RAT used for each route entry, so that the RREP can be successfully sent through the right RATs along the discovered route back to the source. With the performance evaluation based on the simulation in OMNET++, the result shows that the proposed enhanced multiRAT DYMO achieves high QoS in terms of reception rate and end-to-end delay, and successfully create routes in multiRAT environment with the mix use of different RATs in each data route.

关键词： performance evaluation Quality of service Broadcasting signal processing Routing Routing protocols Vectors Topology Delays Mobile ad hoc networks

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