The proceedings contain 28 papers. The topics discussed include: phototropic bionics: realization of intelligent machine detection and obstacle avoidance;a study of model predictive control and reinforcement learning ...
ISBN:
(纸本)9781510674721
The proceedings contain 28 papers. The topics discussed include: phototropic bionics: realization of intelligent machine detection and obstacle avoidance;a study of model predictive control and reinforcement learning control system;advancements and challenges in speech emotion recognition: a comprehensive review;revolutionizing ADHD diagnosis: deeplearning in 3D medical imaging;improving robustness in emotion recognition via adversarial training;realimage improvement study based on pivotal tuning inversion;a review of 3D printing slicing algorithms;and analysis of two variants of U-net for pulmonary nodule segmentation: attention U-net and dense-attention U-net.
The proceedings contain 16 papers. The topics discussed include: evolution of real-timeprocessing of visual information over four decades: a retrospective as outlook to the future of real-time imaging;real-time embed...
ISBN:
(纸本)9781510662629
The proceedings contain 16 papers. The topics discussed include: evolution of real-timeprocessing of visual information over four decades: a retrospective as outlook to the future of real-time imaging;real-time embedded large-scale place recognition for autonomous ground vehicles using a spatial descriptor;real-time video super-resolution reconstruction using wavelet transforms and sparse representation;development of light-field motion tracking technology for use in laboratory studies of planet formation;towards learning-based denoising of light fields;real-time onboard visual parking space detection: a performance study;an automated AI and video measurement techniques for monitoring social distancing, mask detection, and facial temperature screening for COVID-19;computational efficient deeplearning-based super resolution approach;and in-sensor neural network for real-time KWS by imageprocessing.
Traditional indoor positioning technologies mostly require advanced installation of hardware devices, resulting in high costs and long-term maintenance. With advancements in image recognition and deeplearning technol...
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The proliferation of the Internet of Things (IoT) and cloud services has given rise to the edge computing paradigm, where data is processed partly or entirely at the edge of the network, rather than solely in the clou...
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The proliferation of the Internet of Things (IoT) and cloud services has given rise to the edge computing paradigm, where data is processed partly or entirely at the edge of the network, rather than solely in the cloud. Edge computing can address problems such as latency, limited battery life of mobile devices, bandwidth costs, security, and privacy. Typical applicable scenarios based on edge computing include video analytics, smart home, smart city, and collaborative *** the development of deeplearning techniques, research on employing deeplearning to develop intelligent edge systems is emerging. In this dissertation, we aim to investigate how deeplearning can process data on source-constrained individual edge devices in realtime and how deeplearning can process data by utilizing collaborative edge devices to provide better *** build several critical systems, including video analytics, driving anomaly detection, arm posture tracking, and device orientation tracking. In the video analytics system, we combine deeplearning with traditional imageprocessing techniques to achieve real-time object detection on mobile devices without offloading. In the driving anomaly detection system, we train deeplearning models for driving anomaly detection by leveraging the information from collaborative peer devices to provide better accuracy. In the arm posture tracking system, we employ multitask learning to track the orientation and location of the wrist simultaneously, which significantly improves the latency compared to the conventional methods. In the device orientation tracking system, we develop a deep reinforcement learning framework to train an agent that adjusts the parameters of a conventional orientation tracking method in response to changing *** IoT systems continue to grow in complexity and size, preserving training data has become an increasingly important challenge. In our future work, we plan to investigate the use of representation
In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyroid nodules due to its high real-time capabilities and low invasive...
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In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyroid nodules due to its high real-time capabilities and low invasiveness. However, thyroid images obtained from current ultrasound tests often have low resolution and are plagued by significant noise interference. Regional differences in medical conditions and varying levels of physician experience can impact the accuracy and efficiency of diagnostic results. With the advancement of deeplearning technology, deeplearning models are used to identify whether a nodule in a thyroid ultrasound image is benign or malignant. This helps to close the gap between doctors' experience and equipment differences, improving the accuracy of the initial diagnosis of thyroid nodules. To cope with the problem that thyroid ultrasound images contain complex background and noise as well as poorly defined local features. in this paper, we first construct an improved ResNet50 classification model that uses a two-branch input and incorporates a global attention lightening module. This model is used to improve the accuracy of benign and malignant nodule classification in thyroid ultrasound images and to reduce the computational effort due to the two-branch *** constructed a U-net segmentation model incorporating our proposed ACR module, which uses hollow convolution with different dilation rates to capture multi-scale contextual information for feature extraction of nodules in thyroid ultrasound images and uses the results of the segmentation task as an auxiliary branch for the classification task to guide the classification model to focus on the lesion region more efficiently in the case of weak local features. The classification model is guided to focus on the lesion region more efficiently, and the classification and segmentation sub-networks are respectively improved specifically for this study, which is used to improve the accurac
Engineered Cementitious Composites also known as Strain-hardening cementitious composites (SHCCs) has unique cracking patterns like cracks that have tiny widths and showcase high density. All of this makes it difficul...
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Engineered Cementitious Composites also known as Strain-hardening cementitious composites (SHCCs) has unique cracking patterns like cracks that have tiny widths and showcase high density. All of this makes it difficult and laborious to compute crack parameters from crack patterns. Unfortunately, this is an essential part of assessing durability and micromechanical modeling. SHSnet is developed to perform end-to-end semantic segmentation of SHCC cracks. SHSnet is efficient, attention based deep encoder-decoder network with large receptive field. Loss function based on Tversky function were used for training the model. SHSnet with loss function shows promising result with mPrecision, mF1Score and mIoU of 0.87, 0.84 and 0.83 respectively for complex SHCC cracks while requiring at least an order of fewer computational parameters than those in the literature. An imageprocessing unit is then used to estimate the width, number, and length of the cracks from the segmentation mask. Test results show that the computed crack parameters with SHSnet are exactly the same as that computed with an optical microscope but require similar to 100x less time. Results demonstrate that SHSnet works equally well in SHCCs with different surface textures, crack density, and widths;the final result was far superior to a conventional technique. This technique also shows promising results in an automatic evaluation of crack parameters relevant to durability and visualizing crack patterns even in the presence of artifacts during progressive testing. The results also demonstrate the necessity to accurately and densely calculate crack length and maximum crack width;else the durability results are expected to be significantly more conservative than the actual value.
In the era of IoT, numerous frameworks and cutting-edge models have been introduced to enhance user experience and privacy and reduce the risk of data breaches. Over time, IoT device usage has grown tremendously, and ...
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This project takes the typical failure forms of pumps in nuclear power plants such as abnormal vibration, friction and wear as the research object. The most readily available pump housing acceleration signal frequency...
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Video salient object detection(VSOD)aims at locating the most attractive objects in a video by exploring the spatial and temporal *** poses a challenging task in computer vision,as it involves processing complex spati...
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Video salient object detection(VSOD)aims at locating the most attractive objects in a video by exploring the spatial and temporal *** poses a challenging task in computer vision,as it involves processing complex spatial data that is also influenced by temporal *** the progress made in existing VSOD models,they still struggle in scenes of great background diversity within and between ***,they encounter difficulties related to accumulated noise and high time consumption during the extraction of temporal features over a long-term *** propose a multi-stream temporal enhanced network(MSTENet)to address these *** investigates saliency cues collaboration in the spatial domain with a multi-stream structure to deal with the great background diversity challenge.A straightforward,yet efficient approach for temporal feature extraction is developed to avoid the accumulative noises and reduce time *** distinction between MSTENet and other VSOD methods stems from its incorporation of both foreground supervision and background supervision,facilitating enhanced extraction of collaborative saliency *** notable differentiation is the innovative integration of spatial and temporal features,wherein the temporal module is integrated into the multi-stream structure,enabling comprehensive spatial-temporal interactions within an end-to-end *** experimental results demonstrate that the proposed method achieves state-of-the-art performance on five benchmark datasets while maintaining a real-time speed of 27 fps(Titan XP).Our code and models are available at https://***/RuJiaLe/MSTENet.
In the field of underwater wireless optical communication, optical transmitters and optical receivers need to track and align in realtime, which poses challenges to the real-time and accuracy of the measurement metho...
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