In the cardiac operating room, several operators are essential to assist the surgeon, including the physician managing and monitoring the artificial heart-lung machine. The custodian must interpret the patient's v...
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The development of Industrial Internet of Things (IIoT) technology and network infrastructures has enabled the acquisition of substantial data, enabling data-driven condition monitoring and analysis. Detecting anomali...
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The development of Industrial Internet of Things (IIoT) technology and network infrastructures has enabled the acquisition of substantial data, enabling data-driven condition monitoring and analysis. Detecting anomalies in machinery equipment is crucial in IIoT environments for safety enhancement, productivity, and reliability. To provide effective anomaly detection at IIoT edge nodes without delay, it is necessary to efficiently collect and process vast amounts of data from various sensors. While this demands a significant amount of computing resources, edge nodes only have limited data storage and processing capabilities. Therefore, our focus is on developing a lightweight anomaly detection algorithm for acoustic signal processing, considering the computational resources of the IIoT edge node. In this article, we propose the parallel discrete wavelet transform (PDWT) as an efficient method for compressing and processing acoustic signals received at edge nodes. This approach significantly alleviates memory consumption and reduces the computational time at the edge. In addition, by harnessing preprocessed features through PDWT, we can develop lightweight anomaly detection models suitable for deployment at the edge, making them highly practical for real-world implementation. The experimental results using real-world data collected from industrial machines confirm the effectiveness of the proposed solution.
In order to meet the real-time detection and processing requirements of on-board targets in the field of remote sensing imageprocessing, this paper carries out relevant research from the perspective of software optim...
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Object detection plays an important role on various mobile robot tasks. However, directly applying existing detectors on videos from a mobile robot will cause a sharp accuracy decline, because such videos introduce so...
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Object detection plays an important role on various mobile robot tasks. However, directly applying existing detectors on videos from a mobile robot will cause a sharp accuracy decline, because such videos introduce some extra difficulties on accurate detection. This paper proposes a viewpoint-based memory mechanism to handle detection performance deterioration and improve detection accuracy of the videos in realtime. The mechanism positively organizes previous results from multiple viewpoints of target objects as prior knowledge to enhance detection accuracy for succeeding frames, and it is designed as an extension module of an existing image detector. In experiments, we collect testing dataset from an indoor mobile robot, and compare performance of several sole image detectors and the same detectors extended by the extension module. The result shows the mechanism module achieves 20.7% object localization rate margin in average at a cost of 18.1 ms, and the mechanism can give positive impact on various existing detectors. The result indicates the proposed method achieves good accuracy margin, has acceptable time cost, and gets a degree of universal applicability.
With the increasing demand for Cloud-based video Surveillance as a Service (VSaaS), the efficient processing of vast amounts of video data poses significant challenges. The framework leverages Fog computing at the net...
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This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive ethernet technology. The proposed system provides a design and optimization algorithm of in-vehicle net...
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This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive ethernet technology. The proposed system provides a design and optimization algorithm of in-vehicle networking technologies related Ethernet Audio video Bridge (AVB) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machin to Machine (RLMM) plays a significant role in reducing the latency between devices. The approach of RLMM on realistic test cases indicated that there was a latency reduction about 30.41% It is expected that the optimized settings for the actual automotive network environment can greatly shorten the time period in the development and design process. The results achieved from the experiments on the latency present in each function are trustworthy since average values are obtained via repeated tests for several months. It would considerably benefit the industry because analyzing the delay between each function in a short period of time is tremendously significant. In addition, through the proposed real-time camera and video streaming via optimized settings of AVB system, it is expected that AI (Artificial Intelligence) algorithms in autonomous driving will be of great help in understanding and analyzing images in realtime.
This paper introduces an efficient prediction algorithm tailored for advanced and high efficiency video coding, encompassing both H.264 and H.265. The proposed approach aims at replacing the standard intra prediction ...
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ISBN:
(纸本)9798350385434;9798350385427
This paper introduces an efficient prediction algorithm tailored for advanced and high efficiency video coding, encompassing both H.264 and H.265. The proposed approach aims at replacing the standard intra prediction methodology by employing a streamlined prediction mode, which significantly reduces computational overhead and system complexity while eliminating the requirement for mode decision. By leveraging block comparison criteria, the designed method combines neighboring blocks in a linear fashion to accurately represent the target block. Extensive comparisons are conducted with the H.264 intra prediction using various video sequences and multiple evaluation criteria. The results demonstrate substantial time savings of up to 60% compared to the H.264 standard intra prediction algorithm, with a minor peak signal-to-noise ratio drop. The proposed algorithm holds promise for enhancing real-timevideoprocessing and compression in video coding systems, offering notable efficiency gains without sacrificing predictive accuracy.
real-timevideo surveillance system is commonly employed to aid security professionals in preventing *** use of deep learning(DL)technologies has transformed real-timevideo surveillance into smart video surveillance ...
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real-timevideo surveillance system is commonly employed to aid security professionals in preventing *** use of deep learning(DL)technologies has transformed real-timevideo surveillance into smart video surveillance systems that automate human behavior *** recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant *** action recognition(HAR)is treated as a crucial issue in several applications areas and smart video surveillance to improve the security *** advancements of the DL models help to accomplish improved recognition *** this view,this paper presents a smart deep-based human behavior classification(SDL-HBC)model for real-timevideo *** proposed SDL-HBC model majorly aims to employ an adaptive median filtering(AMF)based pre-processing to reduce the noise ***,the capsule network(CapsNet)model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam ***,the differential evolution(DE)with stacked autoencoder(SAE)model is applied for the classification of human activities in the intelligent video surveillance *** performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH *** experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922.
Modern day computer vision applications are frequently implemented using machine learning approaches. While these implementations can perform very well, the performance is heavily dependent on sufficient and accurate ...
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