Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10227051
Noorman Rinanto, Jirayu Petchhan, S. Su
Artificial intelligence has access to every field in this era. Currently, you have access to everything, from simple tasks to quick calculations. The construction industry is one of them. Safety work, installation, and construction are also part of the drive. Demonstrating the pipeline to date does not prepare as comprehensive an assessment as it could. To this end, we benchmark performance using several cutting-edge approaches that have recently the best performance from state-of-the-art method studies, such as YOLOv5x, YOLOv6l, YOLOv7x, and YOLOv8x. The result show that the recent YOLOv8x accomplish the most effective at generating region of interest box comprehensively. Whereas some existing approaches, like YOLOv5x and v7x, get the highest capacity at classification instead.
{"title":"Multiple Object Detection Architecture-based Comparative Performance for Safe Construction Scenario","authors":"Noorman Rinanto, Jirayu Petchhan, S. Su","doi":"10.1109/ICCE-Taiwan58799.2023.10227051","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10227051","url":null,"abstract":"Artificial intelligence has access to every field in this era. Currently, you have access to everything, from simple tasks to quick calculations. The construction industry is one of them. Safety work, installation, and construction are also part of the drive. Demonstrating the pipeline to date does not prepare as comprehensive an assessment as it could. To this end, we benchmark performance using several cutting-edge approaches that have recently the best performance from state-of-the-art method studies, such as YOLOv5x, YOLOv6l, YOLOv7x, and YOLOv8x. The result show that the recent YOLOv8x accomplish the most effective at generating region of interest box comprehensively. Whereas some existing approaches, like YOLOv5x and v7x, get the highest capacity at classification instead.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116177282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226988
Yuh Herng Choke, Weichang Yeh
In recent years, convolutional neural networks (CNNs) have been proven and widely applied in the field of image recognition, including anomaly detection in manufacturing sites, and object detection in autonomous driving. However, the parameters obtained from the CNN increase exponentially with the depth of the network. Therefore, it is difficult to deploy the model in environments with limited computing resources. This study proposes a compression method for CNN by combining Simplified Swarm Optimization(SSO) with structured pruning. Our method can compress VGG16 to approximately 8.3 times smaller without sacrificing accuracy. The more important is, our method uses a heuristic approach to find the optimal pruning scheme without the need for repeated experimental verification.
{"title":"Automatic Deep Compression Based on Simplified Swarm Optimization","authors":"Yuh Herng Choke, Weichang Yeh","doi":"10.1109/ICCE-Taiwan58799.2023.10226988","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226988","url":null,"abstract":"In recent years, convolutional neural networks (CNNs) have been proven and widely applied in the field of image recognition, including anomaly detection in manufacturing sites, and object detection in autonomous driving. However, the parameters obtained from the CNN increase exponentially with the depth of the network. Therefore, it is difficult to deploy the model in environments with limited computing resources. This study proposes a compression method for CNN by combining Simplified Swarm Optimization(SSO) with structured pruning. Our method can compress VGG16 to approximately 8.3 times smaller without sacrificing accuracy. The more important is, our method uses a heuristic approach to find the optimal pruning scheme without the need for repeated experimental verification.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116244633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a meta-learning classification weight transfer network to generate masks as a few-shot image segmentation architecture. It generates good prior masks via a pretrained classification weight transfer architecture, and uses a pretrained feature extraction architecture on query images and support images. The network architecture exploits a top-down path in a feature augmentation module to adaptively transfer information from fine to coarse features for extracting features from query images. Finally, the classification module predicts the segmentation of the query image. The experimental results show that using the mean intersection of joints (mIOU) as the evaluation mechanism, the accuracy of the 1-shot experimental results is 1.7% higher than that of the baseline. In the 5-shot experimental results, the accuracy is also improved by 2.6%. Therefore, compared with the baseline, it clearly shows that the mask generated by the meta-learning classification weight transfer network can effectively help improve the performance of few-shot image segmentation system.
{"title":"Mask Generation with Meta-Learning Classifier Weight Transformer Network for Few-Shot Image Segmentation","authors":"Fong-Ci Jhou, Kai-Wen Liang, Chung-Hsun Lo, Chien-Yao Wang, Yung-Fang Chen, Jia-Ching Wang, P. Chang","doi":"10.1109/ICCE-Taiwan58799.2023.10226989","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226989","url":null,"abstract":"This paper proposes a meta-learning classification weight transfer network to generate masks as a few-shot image segmentation architecture. It generates good prior masks via a pretrained classification weight transfer architecture, and uses a pretrained feature extraction architecture on query images and support images. The network architecture exploits a top-down path in a feature augmentation module to adaptively transfer information from fine to coarse features for extracting features from query images. Finally, the classification module predicts the segmentation of the query image. The experimental results show that using the mean intersection of joints (mIOU) as the evaluation mechanism, the accuracy of the 1-shot experimental results is 1.7% higher than that of the baseline. In the 5-shot experimental results, the accuracy is also improved by 2.6%. Therefore, compared with the baseline, it clearly shows that the mask generated by the meta-learning classification weight transfer network can effectively help improve the performance of few-shot image segmentation system.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116277582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226739
S. Javaid, I. Ioannou, A. Pitsillides, Yasuo Tan
Green energy sources including wind turbines and solar generating systems play a significant role in power systems due to their low environmental impact. However, their generated power is highly variable, posing an unpredictable danger of power fluctuations. As a result, power fluctuations can affect the quality and stability of the power grid, hence making the power imbalance a formidable problem. Traditional control and management techniques have many limitations in measuring, transmitting, and controlling power data between power devices. To achieve power balancing in each time instance, artificial intelligence (AI) techniques are needed to realize truly real-time power control. This paper promotes the application of distributed AI framework for a power system with renewable power sources, loads, and storage devices to achieve power balancing.
{"title":"Motivating an Agent-based Distributed AI Framework for Renewable Integration: Power Balancing","authors":"S. Javaid, I. Ioannou, A. Pitsillides, Yasuo Tan","doi":"10.1109/ICCE-Taiwan58799.2023.10226739","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226739","url":null,"abstract":"Green energy sources including wind turbines and solar generating systems play a significant role in power systems due to their low environmental impact. However, their generated power is highly variable, posing an unpredictable danger of power fluctuations. As a result, power fluctuations can affect the quality and stability of the power grid, hence making the power imbalance a formidable problem. Traditional control and management techniques have many limitations in measuring, transmitting, and controlling power data between power devices. To achieve power balancing in each time instance, artificial intelligence (AI) techniques are needed to realize truly real-time power control. This paper promotes the application of distributed AI framework for a power system with renewable power sources, loads, and storage devices to achieve power balancing.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116488009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226789
Yun-Fan Huang, Wei-Kuo Chiang
Optimization to Refactoring reduces the queuing delay between the 5G core Network Functions and considers the allocation cost of 5G core Network Functions. This paper studies the 5G core Network Functions Refactoring Optimization problem. The problem is formulated as the Mixed Integer Quadratic Constraint Programming (MIQCP) and two-objective problem. We implement the model by using the optimization tool GUROBI. Finally, we compare and analyze the performance of the other architectures designed by different 5GC refactoring methods. The performance result shows that 5GC refactoring by GUROBI can improve the performance, with less queuing delay and allocation cost.
{"title":"Gurobi Optimization for 5GC Refactoring","authors":"Yun-Fan Huang, Wei-Kuo Chiang","doi":"10.1109/ICCE-Taiwan58799.2023.10226789","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226789","url":null,"abstract":"Optimization to Refactoring reduces the queuing delay between the 5G core Network Functions and considers the allocation cost of 5G core Network Functions. This paper studies the 5G core Network Functions Refactoring Optimization problem. The problem is formulated as the Mixed Integer Quadratic Constraint Programming (MIQCP) and two-objective problem. We implement the model by using the optimization tool GUROBI. Finally, we compare and analyze the performance of the other architectures designed by different 5GC refactoring methods. The performance result shows that 5GC refactoring by GUROBI can improve the performance, with less queuing delay and allocation cost.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123614702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226669
Kuei-Chung Chang, Yi-Ching Liao
Violent incidents have happened regularly and usually cause tragedies due to without rescued immediately. Therefore, we expect to study a violence detection and alarm system based on human body pose recognition of images captured from public CCTVs. The system uses OpenPose for real-time skeleton recognition to detect poses based on the abnormal angles or speeds of movement between the various joints of the limbs to determine whether it is violent behavior or not. If possible violent events are detected, the system will trigger an alarm and notify the police or security guard to deter the attacker. Victims will be able to get assistance as soon as possible through this recognition system even if they have obstacles for calling help by themselves. Experimental results show that the target violence behaviors can be detected successfully.
{"title":"Design of Violence Event Detection System Based on CCTVs by Human Body Pose Recognition","authors":"Kuei-Chung Chang, Yi-Ching Liao","doi":"10.1109/ICCE-Taiwan58799.2023.10226669","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226669","url":null,"abstract":"Violent incidents have happened regularly and usually cause tragedies due to without rescued immediately. Therefore, we expect to study a violence detection and alarm system based on human body pose recognition of images captured from public CCTVs. The system uses OpenPose for real-time skeleton recognition to detect poses based on the abnormal angles or speeds of movement between the various joints of the limbs to determine whether it is violent behavior or not. If possible violent events are detected, the system will trigger an alarm and notify the police or security guard to deter the attacker. Victims will be able to get assistance as soon as possible through this recognition system even if they have obstacles for calling help by themselves. Experimental results show that the target violence behaviors can be detected successfully.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123687507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226770
Chung-Bin Wu, Yi-Yen Lai, Yen-Ren Hou
This paper proposes a system integration and optimized hardware acceleration design for the lightweight YOLOV3 model in the object detection network architecture, including the Convolution Layer, the Maxpooling Layer, the Detection Layer, the Shortcut layer, and the optimized i output layers. In addition, this paper is verified and implemented in hardware on the Xilinx Zynq UltraScale+MPSoc ZCU102FPGA platform. The operating frequency is 180 MHz. The usage of bandwidth for the Convolution and Maxpooling Layer Fusion and Shortcut and Convolution Layer Fusion can be reduced by 85.33% and 45.27%, respectively. While optimizing Maxpooling Layer and Shortcut Layer, the running time is faster than ARM CortaxA53 15 and 26 times, respectively. Furthermore, the realization and the results of the system integration are exhibited through the HDMI monitor.
{"title":"System Integration and Optimization of AI Hardware Acceleration Architecture for Object Detection","authors":"Chung-Bin Wu, Yi-Yen Lai, Yen-Ren Hou","doi":"10.1109/ICCE-Taiwan58799.2023.10226770","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226770","url":null,"abstract":"This paper proposes a system integration and optimized hardware acceleration design for the lightweight YOLOV3 model in the object detection network architecture, including the Convolution Layer, the Maxpooling Layer, the Detection Layer, the Shortcut layer, and the optimized i output layers. In addition, this paper is verified and implemented in hardware on the Xilinx Zynq UltraScale+MPSoc ZCU102FPGA platform. The operating frequency is 180 MHz. The usage of bandwidth for the Convolution and Maxpooling Layer Fusion and Shortcut and Convolution Layer Fusion can be reduced by 85.33% and 45.27%, respectively. While optimizing Maxpooling Layer and Shortcut Layer, the running time is faster than ARM CortaxA53 15 and 26 times, respectively. Furthermore, the realization and the results of the system integration are exhibited through the HDMI monitor.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121973174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226734
Chao-Hung Chiang, Wei-Sui Tu, S. Kuo, Cheng-Xu Ma, Qi-Hao Wu
The carbon reduction of transportation is very important. This work determines the key factors influencing intelligent transport tools for future design. The experts’ data collection was 15 (sea transportation) and 12 (air transportation). The two-dimensional mean value matrix (TDMVM) tool is used to analyse the importance, energy savings and carbon reduction. The results showed that sea and air intelligent transportation could efficiently solve energy savings and carbon emissions.
{"title":"Study on Intelligence in Sea and Air Transportation to Energy Saving and Carbon Emissions","authors":"Chao-Hung Chiang, Wei-Sui Tu, S. Kuo, Cheng-Xu Ma, Qi-Hao Wu","doi":"10.1109/ICCE-Taiwan58799.2023.10226734","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226734","url":null,"abstract":"The carbon reduction of transportation is very important. This work determines the key factors influencing intelligent transport tools for future design. The experts’ data collection was 15 (sea transportation) and 12 (air transportation). The two-dimensional mean value matrix (TDMVM) tool is used to analyse the importance, energy savings and carbon reduction. The results showed that sea and air intelligent transportation could efficiently solve energy savings and carbon emissions.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"24 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120867646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Video-based point cloud compression(V-PCC)is a point cloud compression standard formulated by the Moving Picture Experts Group(MPEG) organization. The concept of this standard is to project 3D point cloud information onto a 2D plane and generate 2D image, namely geometry map, texture map, and occupancy map. Overlap 2D images to form 3D depth from three images, combine color and position information, and then encode and decode using High Efficiency Video Coding (HEVC) compression. However, the projected image will have obvious holes. These holes are regarded as high-frequency signal in the image, which will have a bad impact on the subsequent compression rate. It is necessary to use image filling to smooth the image, reduce high-frequency signal, and facilitate subsequent compression processing. Therefore, the purpose of this research is to develop a series of anti-noise procedures to fill and smooth images with High-Efficiency Video Coding(HEVC), including mean filter, Smooth Pull Push Algorithm(SPP), etc. This algorithm has been implemented in mpeg-pcc-tmc2-release-v8.0 [1], and the obtained data proves that although PSNR needs to be sacrificed, it can effectively reduce the number of compressed bytes after texture map filling.
基于视频的点云压缩(V-PCC)是由运动图像专家组(MPEG)组织制定的一种点云压缩标准。该标准的概念是将三维点云信息投影到二维平面上，生成二维图像，即几何图、纹理图和占用图。将三幅二维图像重叠形成三维深度，结合颜色和位置信息，然后使用HEVC (High Efficiency Video Coding)压缩进行编码解码。然而，投影图像会有明显的漏洞。这些孔洞被认为是图像中的高频信号，会对后续的压缩率产生不良影响。有必要使用图像填充来平滑图像，减少高频信号，便于后续压缩处理。因此，本研究的目的是开发一系列抗噪程序，以实现高效视频编码(High-Efficiency Video Coding, HEVC)对图像的填充和平滑，包括均值滤波(mean filter)、平滑拉推算法(smooth Pull Push Algorithm, SPP)等。该算法已在mpeg-pcc-tmc2-release-v8.0中实现[1]，获得的数据证明，虽然需要牺牲PSNR，但可以有效减少纹理贴图填充后的压缩字节数。
{"title":"High Compression Rate Architecture For Texture Padding Based on V-PCC","authors":"Cheng-Lin Lu, He-Sheng Chou, Yabo Huang, Mei-Ling Chan, Szu-Yin Lin, Shih-Lun Chen","doi":"10.1109/ICCE-Taiwan58799.2023.10227018","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10227018","url":null,"abstract":"Video-based point cloud compression(V-PCC)is a point cloud compression standard formulated by the Moving Picture Experts Group(MPEG) organization. The concept of this standard is to project 3D point cloud information onto a 2D plane and generate 2D image, namely geometry map, texture map, and occupancy map. Overlap 2D images to form 3D depth from three images, combine color and position information, and then encode and decode using High Efficiency Video Coding (HEVC) compression. However, the projected image will have obvious holes. These holes are regarded as high-frequency signal in the image, which will have a bad impact on the subsequent compression rate. It is necessary to use image filling to smooth the image, reduce high-frequency signal, and facilitate subsequent compression processing. Therefore, the purpose of this research is to develop a series of anti-noise procedures to fill and smooth images with High-Efficiency Video Coding(HEVC), including mean filter, Smooth Pull Push Algorithm(SPP), etc. This algorithm has been implemented in mpeg-pcc-tmc2-release-v8.0 [1], and the obtained data proves that although PSNR needs to be sacrificed, it can effectively reduce the number of compressed bytes after texture map filling.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125984537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Obstacle detection can help human to reduce risk on underwater transportation activities. In our work, we use compressive sensing technique to estimate the Direction of Arrival (DOA) and range for spatial targets especially in underwater transportation system. According to underwater acoustic propagation ray theory, the signal model for horizontal L-Shaped array is first constructed. Then, the joint DOA and range estimation is converted into an optimizing procedure, which can be finally solved by an efficient toolbox for Disciplined Convex Programming (CVX). The performance of our proposed method is discussed in the computer simulation results.
{"title":"A Joint DOA and Range Estimation Technique for Underwater Transportation System based on Compressive Sensing","authors":"Haiyan Song, Chang-Yi Yang, Yu-Jie Zhang, Jia-Xin Tang, Jia-Qi Gao","doi":"10.1109/ICCE-Taiwan58799.2023.10227015","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10227015","url":null,"abstract":"Obstacle detection can help human to reduce risk on underwater transportation activities. In our work, we use compressive sensing technique to estimate the Direction of Arrival (DOA) and range for spatial targets especially in underwater transportation system. According to underwater acoustic propagation ray theory, the signal model for horizontal L-Shaped array is first constructed. Then, the joint DOA and range estimation is converted into an optimizing procedure, which can be finally solved by an efficient toolbox for Disciplined Convex Programming (CVX). The performance of our proposed method is discussed in the computer simulation results.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129577733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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