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2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)最新文献

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Eye Movement Analysis for Consumer Devices 消费类设备眼动分析
2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) Pub Date : 2021-09-15 DOI: 10.1109/ICCE-TW52618.2021.9603016
Marvin Yen, Haowei Li, Chun-Wei Shen, Ren-Xiang Ying, W. Kao
{"title":"Eye Movement Analysis for Consumer Devices","authors":"Marvin Yen, Haowei Li, Chun-Wei Shen, Ren-Xiang Ying, W. Kao","doi":"10.1109/ICCE-TW52618.2021.9603016","DOIUrl":"https://doi.org/10.1109/ICCE-TW52618.2021.9603016","url":null,"abstract":"The eye tracker enables the new applications for consumer electronics. By estimating the human gaze direction in real-time as a human-machine interface (HMI), various novel computer games or remote learning systems for education purpose could be developed. In this paper, a new HMI system, which integrates a visible-spectrum gaze tracker, a fixation/saccade analyzer, an object tracking, and a score evaluation module, has been proposed. This system aims at guiding the design the computer game as well as the examination of human visual reaction for various consumer electronics.","PeriodicalId":141850,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126080360","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: 1
Reconfigurable Deep Learning Accelerator Hardware Architecture Design for Sparse CNN
2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) Pub Date : 2021-09-15 DOI: 10.1109/ICCE-TW52618.2021.9602959
Chung-Bin Wu, Chung-Hsuan Chen, Chen-Peng Kuan
{"title":"Reconfigurable Deep Learning Accelerator Hardware Architecture Design for Sparse CNN","authors":"Chung-Bin Wu, Chung-Hsuan Chen, Chen-Peng Kuan","doi":"10.1109/ICCE-TW52618.2021.9602959","DOIUrl":"https://doi.org/10.1109/ICCE-TW52618.2021.9602959","url":null,"abstract":"The design architecture proposed in this paper uses the row-sparsity -map compression method. Proposed row-leap to solve the PE balance problem. Proposed the channel-leap to increase the PE usage. The design architecture proposed in this paper can achieve 95% PE usage under the Yolo-like network. And the bandwidth is reduced by 40%.","PeriodicalId":141850,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129188266","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: 2
An Energy-efficient and Programmable RISC-V CNN Coprocessor for Real-time Epilepsy Detection and Identification on Wearable Devices 节能和可编程的RISC-V CNN协处理器用于可穿戴设备上的实时癫痫检测和识别
2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) Pub Date : 2021-09-15 DOI: 10.1109/ICCE-TW52618.2021.9602978
Yi-Wen Hung, Yao-Tse Chang, Shuenn-Yuh Lee, Chou-Ching K. Lin, G. Shieh
{"title":"An Energy-efficient and Programmable RISC-V CNN Coprocessor for Real-time Epilepsy Detection and Identification on Wearable Devices","authors":"Yi-Wen Hung, Yao-Tse Chang, Shuenn-Yuh Lee, Chou-Ching K. Lin, G. Shieh","doi":"10.1109/ICCE-TW52618.2021.9602978","DOIUrl":"https://doi.org/10.1109/ICCE-TW52618.2021.9602978","url":null,"abstract":"This paper has proposed an energy-efficient epilepsy detection framework for embedded systems. The epilepsy detection framework is implemented in 11 layers Convolution Neural Network (CNN) with a 2-stage RISC-V core and a coprocessor to accelerate CNN inferences. The CNN algorithm provides 97.8% and 93.5% accuracy on floating-point and fixedpoint operations respectively. The proposed CNN coprocessor is designed to offload CNN inference from RISC-V core to hardware with 51 nJ data transfer energy and 0.9 µJ inference energy for each 500 points input data frame. The coprocessor reduces the runtime of CNN inferences over 10^6x to perform only 0.012 s latency for each classification. According to the energy-efficient coprocessor, an AI-based solution is practical for real-time epilepsy detection on wearable devices for consumer electronics.","PeriodicalId":141850,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132883263","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: 1
Graph Signal Denoising Method Using the K-Nearest Neighbors Found by Dijkstra's Algorithm 基于Dijkstra算法的k近邻图信号去噪方法
2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) Pub Date : 2021-09-15 DOI: 10.1109/ICCE-TW52618.2021.9603077
C. Tseng, Su-Ling Lee
{"title":"Graph Signal Denoising Method Using the K-Nearest Neighbors Found by Dijkstra's Algorithm","authors":"C. Tseng, Su-Ling Lee","doi":"10.1109/ICCE-TW52618.2021.9603077","DOIUrl":"https://doi.org/10.1109/ICCE-TW52618.2021.9603077","url":null,"abstract":"In this paper, graph signal denoising problem is investigated. First, conventional graph signal denoising method using graph Laplacian matrix (GLM) is described to show that a big matrix inversion is needed in this method. To reduce computational load, a modified Dijkstra's algorithm is presented to find the K-nearest neighbors (K-NN) of a given vertex in the graph and a local graph Laplacian matrix (LGLM) of the sub-graph around this vertex is constructed by using the K-NN information and graph adjacency matrix. Then, based on the local smoothness property of graph signal and LGLM, the denoised signal at the given vertex can be computed by a Cramer's rule method. Finally, real temperature data is used to show the effectiveness of the proposed denoising method and performance comparison with conventional method is made.","PeriodicalId":141850,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"36 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132287069","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: 3
Fatigue Estimation using Nonlinear Disturbance Observer for Tele-Rehabilitation System with Electrical Stimulation 电刺激远程康复系统的非线性干扰观测器疲劳估计
2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) Pub Date : 2021-09-15 DOI: 10.1109/ICCE-TW52618.2021.9603123
Y. Kawai, F. Sugimoto, Kenshin Fujie, H. Kawai, T. Miyoshi
{"title":"Fatigue Estimation using Nonlinear Disturbance Observer for Tele-Rehabilitation System with Electrical Stimulation","authors":"Y. Kawai, F. Sugimoto, Kenshin Fujie, H. Kawai, T. Miyoshi","doi":"10.1109/ICCE-TW52618.2021.9603123","DOIUrl":"https://doi.org/10.1109/ICCE-TW52618.2021.9603123","url":null,"abstract":"This paper considers a muscle fatigue estimation using a nonlinear disturbance observer for a tele-rehabilitation system with an electrical stimulation. The main contribution is to apply the nonlinear disturbance observer to detect the muscle fatigue. First, the patient’s knee joint model is proposed. Next, the nonlinear disturbance observer is derived by using the knee joint model. Finally, the experiment of the tele-rehabilitation with the constant time delay is implemented. Though the range of the patient’s knee angle is same, the force provided by the physical therapist is increased. Then the magnitude of the estimated disturbance is increased in the negative direction. Therefore, the muscle fatigue can be estimated by using the proposed nonlinear disturbance observer.","PeriodicalId":141850,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130512166","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: 1
Binary- and Multi-class Network Intrusion Detection with Adaptive Synthetic Sampling and Deep Learning 基于自适应综合采样和深度学习的二类和多类网络入侵检测
2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) Pub Date : 2021-09-15 DOI: 10.1109/ICCE-TW52618.2021.9603206
Jehn-Ruey Jiang, Chia-Lin Li
{"title":"Binary- and Multi-class Network Intrusion Detection with Adaptive Synthetic Sampling and Deep Learning","authors":"Jehn-Ruey Jiang, Chia-Lin Li","doi":"10.1109/ICCE-TW52618.2021.9603206","DOIUrl":"https://doi.org/10.1109/ICCE-TW52618.2021.9603206","url":null,"abstract":"Intrusion detection system (IDS) is becoming more and more important for detecting network intrusions, anomalies or attacks. This paper proposes a method that first uses adaptive synthetic (ADASYN) sampling to oversample data in small-size class, then uses deep learning models, such as the variational autoencoder (VAE), long short-term memory (LSTM) network, and deep neural network (DNN), for network intrusion detection. The well-known NSL-KDD dataset is applied to evaluate the effectiveness and superiority of the proposed method.","PeriodicalId":141850,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132348883","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: 2
An RSSI-Based Device-Free Localization System for Smart Wards 基于rssi的智能病房无设备定位系统
2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) Pub Date : 2021-09-15 DOI: 10.1109/ICCE-TW52618.2021.9603249
Yu-Siang Feng, Hsiao-Yu Liu, Mei-Hui Hsieh, Hsiao-Chun Fung, Chan-Yi Chang, Chi-Cheng Yu, Chih-Wie Huang
{"title":"An RSSI-Based Device-Free Localization System for Smart Wards","authors":"Yu-Siang Feng, Hsiao-Yu Liu, Mei-Hui Hsieh, Hsiao-Chun Fung, Chan-Yi Chang, Chi-Cheng Yu, Chih-Wie Huang","doi":"10.1109/ICCE-TW52618.2021.9603249","DOIUrl":"https://doi.org/10.1109/ICCE-TW52618.2021.9603249","url":null,"abstract":"Patient monitoring during hospitalization and intime assistance are essential tasks. However, it is always challenging for medical personnel to efficiently monitor duties, especially under raising attention to privacy issues. Advances in devicefree localization (DFL) technologies and the evolution of machine learning technologies made localization more accurate than ever. We take advantage of easily accessible Wi-Fi signals around the wards and perform privacy-preserving localization on patients using multi-scale convolutional neural network (CNN) and long short-term memory (LSTM) models. The results demonstrate high localization accuracy. Also, the system can be extended for emergent event detection, enabling medical personnel to react promptly.","PeriodicalId":141850,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128326778","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: 3
Generative Adversarial Networks-based Face Hallucination with Identity-Preserving 基于生成对抗网络的人脸幻觉与身份保护
2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) Pub Date : 2021-09-15 DOI: 10.1109/ICCE-TW52618.2021.9603171
C. Yeh, Daniel Chiu, Li-Wei Kang, Chih-Chung Hsu, Chen Lo
{"title":"Generative Adversarial Networks-based Face Hallucination with Identity-Preserving","authors":"C. Yeh, Daniel Chiu, Li-Wei Kang, Chih-Chung Hsu, Chen Lo","doi":"10.1109/ICCE-TW52618.2021.9603171","DOIUrl":"https://doi.org/10.1109/ICCE-TW52618.2021.9603171","url":null,"abstract":"This paper presents a novel generative adversarial networks-based face hallucination framework for producing high-resolution face images from very low-resolution (LR) ones. We propose a multi-scale generator architecture with multi-scale loss functions for different upscaling factors and a triplet-based identity preserving loss for extracting multi-scale identity-aware facial representations. Experimental results have verified that our method can well super-resolve very LR face images (e.g., 8×8) quantitatively and qualitatively.","PeriodicalId":141850,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"541 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128623643","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
Facial Age Estimation by Learning Label Distribution CNN 基于学习标签分布的面部年龄估计
2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) Pub Date : 2021-09-15 DOI: 10.1109/ICCE-TW52618.2021.9602958
Kuan-Hsien Liu, Chun-Te Chang, Tsung-Jung Liu
{"title":"Facial Age Estimation by Learning Label Distribution CNN","authors":"Kuan-Hsien Liu, Chun-Te Chang, Tsung-Jung Liu","doi":"10.1109/ICCE-TW52618.2021.9602958","DOIUrl":"https://doi.org/10.1109/ICCE-TW52618.2021.9602958","url":null,"abstract":"In this paper, we propose a new deep convolutional neural networks based architecture with learning label distributions for human facial age estimation. Our proposed method first takes the Xception model with the use of data augmentation, and then Label Distribution Learning (LDL) is adopted for age encoding, KLD is used in loss function, and dropout is considered at fully connected layers. Finally, it takes the expected value of the model output as the solution in estimating ages. The IMDB-WIKI dataset is used as our pretraining dataset and the training set of APPA-REAL dataset is used to fine-tune our proposed model. In the experiment, the state-of-the-art results of MAE 3.09, 2.78, and 3.628 years are attained on MORPH-II, FG-NET, and APPA-REAL datasets, respectively.","PeriodicalId":141850,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132964430","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: 2
Lossless Image/Video Embedded Compression for Memory Bandwidth Saving of AI Applications 无损图像/视频嵌入式压缩的内存带宽节省的人工智能应用程序
2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) Pub Date : 2021-09-15 DOI: 10.1109/ICCE-TW52618.2021.9603234
Yu-Hsing Chiu, Szu-Hsuan Lai, Yu-Hsuan Lee
{"title":"Lossless Image/Video Embedded Compression for Memory Bandwidth Saving of AI Applications","authors":"Yu-Hsing Chiu, Szu-Hsuan Lai, Yu-Hsuan Lee","doi":"10.1109/ICCE-TW52618.2021.9603234","DOIUrl":"https://doi.org/10.1109/ICCE-TW52618.2021.9603234","url":null,"abstract":"Artificial Intelligence (AI) has gradually become a part of our daily life. Nevertheless, it also causes considerable memory bandwidth and memory access power especially for image/video AI applications. In this paper, an efficient lossless embedded compression (EC) is proposed to save the memory bandwidth of an AI system. It consists of two core techniques: Hybrid Prediction and Partition-based Grouping. Hybrid Prediction can transform all pixels of an 8x8 block to efficient residuals. Partition-based Grouping can further classify them into smaller groups for better compression ratio. The experiment results show that this study presents better performance than the other sophisticated EC algorithms. This study achieves a better lossless compression ratio of 2.21, saving the memory bandwidth of an AI system by 54.8%. In addition, the visual quality of image/video can be fully preserved.","PeriodicalId":141850,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132076662","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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