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2024 IEEE International Conference on Consumer Electronics (ICCE)最新文献

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Calibration-Free Gaze Estimation by Combination with Hand and Facial Features Detection for Interactive Advertising Display 结合手部和面部特征检测进行免校准凝视估计，用于交互式广告展示
2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444285
Bao-Wei Chu, Wei-Liang Ou, Robert Chen-Hao Chang, Chih-Peng Fan
{"title":"Calibration-Free Gaze Estimation by Combination with Hand and Facial Features Detection for Interactive Advertising Display","authors":"Bao-Wei Chu, Wei-Liang Ou, Robert Chen-Hao Chang, Chih-Peng Fan","doi":"10.1109/ICCE59016.2024.10444285","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444285","url":null,"abstract":"In recent years, in addition to droplet infection, hand infection is also one of the major transmission routes of the new crown epidemic. Therefore, smart advertising machines that can be operated without hand contact are an important research topic. Gaze estimation is a technology that can identify the direction of gaze, which can infer the customer’s visual attention and convert it into interactive input information. Gaze estimation has great potential in contactless interactions as it allows systems to detect gaze focus and display relevant information/advertisements based on customer interests. The proposed design methodology is divided into three steps: face/hand objects detection by YOLO, threshold estimation by feature points, and gaze region detection by SVM classifier. The experimental results show that the average FPS with YOLO-based model reaches 15 when the number of filters is reduced to a quarter and the input size is set to 416x416 pixels. In the case of 4-block gaze regions, the YOLO-based model maintains a good enough accuracy that is up to 90%, and the experimental results reveals that the proposed expectation by adding hand features can effectively raise the accuracy of gaze estimation.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"66 9","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531934","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}
引用次数: 0
Improving the Representation of Sentences with Reinforcement Learning and AMR graph 利用强化学习和 AMR 图形改进句子表示法
2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444230
Jinwoo Park, Hosoo Shin, Dahee Jeong, Junyeong Kim
{"title":"Improving the Representation of Sentences with Reinforcement Learning and AMR graph","authors":"Jinwoo Park, Hosoo Shin, Dahee Jeong, Junyeong Kim","doi":"10.1109/ICCE59016.2024.10444230","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444230","url":null,"abstract":"Sentence Embedding is a technique that represents the meaning of sentences in vector form, playing a crucial role in various natural language processing tasks such as question-answering, sentiment analysis, and information retrieval. Therefore, understanding the meaning and structure of sentences is essential. We propose a novel approach to improve the performance of Sentence Embedding by utilizing Abstract Meaning Representation(AMR) parsing and reinforcement learning. We generate Sentence Embeddings using AMRBART, a type of AMR parser, and evaluate them in Question Answering (QA) tasks. In this process, we measure the similarity between the AMR graphs of two sentences using the Weighted Walks with Lookahead score and employ the Deep Deterministic Policy Gradient algorithm, a reinforcement learning algorithm, to enhance this score. By integrating AMR syntactic analysis and reinforcement learning into the Sentence Embedding generation process, we enable a more accurate understanding of natural language sentences.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"66 3","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531970","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}
引用次数: 0
Pet Watching System with IoT Devices and Chatbots 配备物联网设备和聊天机器人的宠物看护系统
2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444223
M. Nakashige, Ryota Shibusawa, Katsutoshi Oe
{"title":"Pet Watching System with IoT Devices and Chatbots","authors":"M. Nakashige, Ryota Shibusawa, Katsutoshi Oe","doi":"10.1109/ICCE59016.2024.10444223","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444223","url":null,"abstract":"The demand for pets has increased as people spend more time at home due to the Corona disaster, and many animals are sold and kept. However, people are gradually returning to normal work and school routines, and the amount of time they spend with their pets is decreasing. Therefore, to maintain a psychological distance from pets, we devised a system that allows users to monitor and interact with the status of pets left at home even when they are away from home. Specifically, the system aggregates and manages the values of various sensors attached to the pet's rearing cage in the cloud, and a chatbot communicates with the user by interpreting the data. This prototype system was linked to a smartphone SNS application and operated for about 2 years and a half. In this paper, we report on the verification of the system's operation and the effects observed.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"62 11","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531973","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}
引用次数: 0
Performance Enhancement using Data Augmentation of Depth Estimation for Autonomous Driving 利用数据增强深度估计提高自动驾驶性能
2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444235
Jisang Yoo, Woomin Jun, Sungjin Lee
{"title":"Performance Enhancement using Data Augmentation of Depth Estimation for Autonomous Driving","authors":"Jisang Yoo, Woomin Jun, Sungjin Lee","doi":"10.1109/ICCE59016.2024.10444235","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444235","url":null,"abstract":"For autonomous driving, various sensors such as cameras, LiDAR, and radar are required to accurately perceive the surrounding environment. These sensors provide information for tasks like object recognition, lane detection, path planning, and distance estimation. However, processing information from these multiple sensors for perception tasks demands significant costs, computational resources, and latency. These challenges hinder the practical implementation of real-time edge computing in autonomous driving systems. Consequently, research is actively exploring methods to perform perception using only cameras, particularly to alleviate the computational burden and cost associated with 3D point cloud data from LiDAR or radar sensors. In this study, we investigate techniques to optimize the performance of Monocular Depth Estimation (MDE) methods, which utilize a single camera to extract 3D information about the surrounding environment. We focus on enhancing accuracy through classical data augmentation techniques and synthetic data generation methods. Additionally, we explore the selection of an optimal loss function. Experimental results demonstrate that employing our proposed data augmentation approach reduces REL by approximately 3.9%, showcasing the potential of this method.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"2 5","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531634","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}
引用次数: 0
Enhancing Scene Understanding in VR for Visually Impaired Individuals with High-Frame Videos and Event Overlays 利用高帧视频和事件叠加增强视障人士对 VR 场景的理解
2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444301
Dehao Han, Shijie Yang, Ping Zhao, Xiaoming Chen, Chen Wang, Yuk Ying Chung
{"title":"Enhancing Scene Understanding in VR for Visually Impaired Individuals with High-Frame Videos and Event Overlays","authors":"Dehao Han, Shijie Yang, Ping Zhao, Xiaoming Chen, Chen Wang, Yuk Ying Chung","doi":"10.1109/ICCE59016.2024.10444301","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444301","url":null,"abstract":"Virtual reality (VR) technology has undergone significant development, with various applications of VR gradually integrating into people’s lives, providing them with novel audio-visual experiences. However, visually impaired individuals have faced challenges in utilizing VR due to their impaired motion perception. This paper explores enhancing the scene understanding of the visually impaired population in VR environments. Event cameras, inspired by biological vision, represent a novel type of visual sensor that excels at capturing motion information within scenes. In this paper, a methodology is proposed that involves overlaying event information captured by an event camera onto the video obtained by a conventional camera. This fusion aims to augment the motion information within the video, thus improving the motion perception experience for visually impaired individuals. A comparative experiment is designed, contrasting the proposed method with both the original video and the event-overlaid video. The experimental results are quantified and evaluated, demonstrating that the introduction of event information leads to enhanced the scene understanding of visually impaired individuals.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"112 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531640","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}
引用次数: 0
ICCE 2024 News ICCE 2024 新闻
2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/icce59016.2024.10444132
{"title":"ICCE 2024 News","authors":"","doi":"10.1109/icce59016.2024.10444132","DOIUrl":"https://doi.org/10.1109/icce59016.2024.10444132","url":null,"abstract":"","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"111 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531641","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}
引用次数: 0
Classification and Source Location Indication of Jamming Attacks Targeting UAVs via Multi-output Multiclass Machine Learning Modeling 通过多输出多类别机器学习建模对以无人机为目标的干扰攻击进行分类和来源位置指示
2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444388
M. Alkhatib, M. McCormick, L. Williams, A. Leon, L. Camerano, K. Al, V. Devabhaktuni, N. Kaabouch, Discriminative Svm, LR Regularization
{"title":"Classification and Source Location Indication of Jamming Attacks Targeting UAVs via Multi-output Multiclass Machine Learning Modeling","authors":"M. Alkhatib, M. McCormick, L. Williams, A. Leon, L. Camerano, K. Al, V. Devabhaktuni, N. Kaabouch, Discriminative Svm, LR Regularization","doi":"10.1109/ICCE59016.2024.10444388","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444388","url":null,"abstract":"This paper introduces machine learning (ML) as a solution for the detection and range localization of jamming attacks targeting the global positioning system (GPS) technology, with applications to unmanned aerial vehicles (UAVs). Different multi-output multiclass ML models are trained with GPS-specific sample datasets obtained from exhaustive feature extraction and data collection routines that followed a set of realistic experimentations of attack scenarios. The resulting models enable the classification of four attack types (i.e., barrage, single-tone, successive-pulse, protocol-aware), the jamming direction, and the distance from the jamming source by yielding a detection rate (DR), misdetection rate (MDR), false alarm rate (FAR), and F-score (FS) of 98.9%, 1.39%, 0.28%, and 0.989, respectively.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"14 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531793","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}
引用次数: 0
Long-Range Drone Detection Dataset 远程无人机探测数据集
2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444135
Amirreza Rouhi, Himanshu Umare, Sneh Patal, Ritik Kapoor, Namit Deshpande, Solmaz Arezoomandan, Princie Shah, David K. Han
{"title":"Long-Range Drone Detection Dataset","authors":"Amirreza Rouhi, Himanshu Umare, Sneh Patal, Ritik Kapoor, Namit Deshpande, Solmaz Arezoomandan, Princie Shah, David K. Han","doi":"10.1109/ICCE59016.2024.10444135","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444135","url":null,"abstract":"For the safe and efficient deployment of unmanned aerial vehicles (UAVs) in complex urban landscapes, robust collision avoidance mechanisms are imperative. Although several methodologies exist for drone detection, current solutions are suboptimal for long-range detection, primarily due to the scarcity of comprehensive training datasets. In this paper, we present a novel long-range drone detection dataset, encompassing a set of different UAV types, flight patterns, and environmental conditions. Utilizing this dataset, we trained a state-of-the-art YOLO object detection algorithm, demonstrating the ability to identify drones at distances up to 60 meters with a high mean average precision (mAP). Extensive real-world tests affirm the efficacy of our approach, achieving a detection accuracy exceeding 75%. This dataset and the accompanying machine learning model contribute a significant advancement in the realm of long-range drone detection, particularly well-suited for urban deployments. For access to the complete Long-Range Drone Detection Dataset (LRDD), please visit https://research.coe.drexel.edu/ece/imaple/long-range-drone-detection-dataset/.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"67 5","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531967","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}
引用次数: 0
Design of Miniature Automatic Driving Vehicle Controlled by Camera Image Processing 设计由摄像头图像处理控制的微型自动驾驶汽车
2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444163
Akira Kojima
{"title":"Design of Miniature Automatic Driving Vehicle Controlled by Camera Image Processing","authors":"Akira Kojima","doi":"10.1109/ICCE59016.2024.10444163","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444163","url":null,"abstract":"We are developing a miniature self-driving car for the design contest at ICCE2024, which will be driven automatically by image processing of camera images. Our implementation uses a AMD/Xilinx SoC FPGA as the central controller. The hardware of the programmable logic part of the SoC FPGA is used for object detection in the camera image and PWM control of the motors. Other processes are software-controlled by the processor of the SoC FPGA.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"110 4","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531644","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}
引用次数: 0
A Dedicated Consensus Algorithm for Improving Performance of Futuristic Energy Blockchain 提高未来能源区块链性能的专用共识算法
2024 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2024-01-06 DOI: 10.1109/ICCE59016.2024.10444184
C. Yapa, C. D. Alwis, U. Wijewardhana, Madhusanka Liyanage
{"title":"A Dedicated Consensus Algorithm for Improving Performance of Futuristic Energy Blockchain","authors":"C. Yapa, C. D. Alwis, U. Wijewardhana, Madhusanka Liyanage","doi":"10.1109/ICCE59016.2024.10444184","DOIUrl":"https://doi.org/10.1109/ICCE59016.2024.10444184","url":null,"abstract":"Integrating renewable energy generation in the consumer end has transformed users acting as both buyers and sellers. Uncoordinated interconnections lead to degraded power quality, which demands for continuous network monitoring. This study evaluates the applicability of the network monitoring process to develop a novel consensus protocol, customized for blockchain platforms integrated with smart grids. The motivation for integrating grid monitoring with the blockchain consensus mechanism lies in the significance of decentralized architectures such as distributed ledger technologies for future energy grids. Existing blockchain consensus mechanisms have exhibited excessive energy usage and low scalability due to the additional workload associated. Hence, the study proposes a customised solution, which has the advantage of combining grid monitoring with the blockchain consensus protocol. This eliminates the additional computation burden, reduces the cost of execution, and improves the transaction throughput of the blockchain consensus mechanism.","PeriodicalId":518694,"journal":{"name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","volume":"94 5","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531654","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}
引用次数: 0
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