2022 IEEE International Conference on Consumer Electronics (ICCE)最新文献_第10页

Deep Reinforcement Learning based Resource Allocation in Dense Sliced LoRaWAN Networks 基于深度强化学习的密集切片LoRaWAN网络资源分配

2022 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2022-01-07 DOI: 10.1109/ICCE53296.2022.9730234

Amine Tellache, Abdelkader Mekrache, Abbas Bradai, Ryma Boussaha, Y. Pousset

{"title":"Deep Reinforcement Learning based Resource Allocation in Dense Sliced LoRaWAN Networks","authors":"Amine Tellache, Abdelkader Mekrache, Abbas Bradai, Ryma Boussaha, Y. Pousset","doi":"10.1109/ICCE53296.2022.9730234","DOIUrl":"https://doi.org/10.1109/ICCE53296.2022.9730234","url":null,"abstract":"Long-Range Wide Area Network (LoRaWAN) is a rapidly expanding communication system for Low Power Wide Area Network (LPWAN) in the Internet of Things (IoTs) deployments. It employs an Adaptive Data Rate (ADR) scheme that optimizes data rate, airtime, and energy consumption. Recently, the use of Network Slicing (NS) in LoRa Wannetworks is being widely studied and a hot topic for the latest research in the literature. Network resources must be efficiently assigned to IoT devices in an isolated manner in order to handle and support specific Quality of Service (QoS) requirements for each slice. However, in dense LoRaWAN networks, the ADR scheme is insufficient for efficient resource allocation to meet the QoS requirements of each slice. In this article, we propose a DRL-based approach for intra-slicing resource allocation in dense LoRa Wannetworks. In each slice, we implemented multi-agent DRL that allocates Spreading Factor (SF) and Transmission Power (TP) to IoT devices to meet QoS requirements, i.e. we replaced the conventional ADR scheme with multi-agent DQN with different reward function design for each slice according to QoS requirements. Experimental results realized in real conditions show that our approach outperforms the existing ADR scheme for all the slices.","PeriodicalId":350644,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics (ICCE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132952148","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: 7

Educational Robotics in the Service of the Gestalt Similarity Principle 为格式塔相似原理服务的教育机器人

2022 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2022-01-07 DOI: 10.1109/ICCE53296.2022.9730479

Aphrodite Sophokleous, A. Amanatiadis, S. Gkelios, S. Chatzichristofis

引用次数: 1

Fault Detection of Electric Motor Coil by YOLOv3 with Spatial Attention 基于空间注意力的YOLOv3电机线圈故障检测

2022 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2022-01-07 DOI: 10.1109/ICCE53296.2022.9730324

Mizuki Kato, Y. Iwamoto, Yen-Wei Chen, Toru Aiba, T. Sugimoto

引用次数: 0

Local-Sound Visualizations for Presence Control of Telepresence Robots 远程呈现机器人临场感控制的局部声音可视化

2022 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2022-01-07 DOI: 10.1109/ICCE53296.2022.9730341

Takayuki Arakawa, Haruya Takase, Hanako Ishida, Takeshi Sugimoto, S. Horii, T. Kamachi, Osamu Hoshuyama

引用次数: 0

Low Error Approximate Absolute Difference Hardware 低误差近似绝对差硬件

2022 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2022-01-07 DOI: 10.1109/ICCE53296.2022.9730564

Ilker Hamzaoglu, Berke Ayrancioglu, Hasan Azgin

引用次数: 0

Gait Analysis: Head Vertical Movement Leads to Lower Limb Joint Angle Movements 步态分析:头部垂直运动导致下肢关节角度运动

2022 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2022-01-07 DOI: 10.1109/ICCE53296.2022.9730350

Tong-Hun Hwang, A. Effenberg

{"title":"Gait Analysis: Head Vertical Movement Leads to Lower Limb Joint Angle Movements","authors":"Tong-Hun Hwang, A. Effenberg","doi":"10.1109/ICCE53296.2022.9730350","DOIUrl":"https://doi.org/10.1109/ICCE53296.2022.9730350","url":null,"abstract":"Supported by the increasing number of head-worn devices, such as earbuds and smart glasses, research on gait analysis using head-worn sensors has been emerged. These head-worn sensor solutions can be used to analyze natural gait patterns in daily life at lower costs. However, these approaches are limited to spatial-temporal gait parameters, and it is so far impossible to measure joint angle movements of the lower body because angles are normally measured by at least two sensors. To overcome the limitation, it is necessary to scrutinize the relationship between the head and lower body joints. In this paper, therefore, the causality between head vertical movement and lower limb joint movements was estimated using a transfer entropy analysis during walking. In total, 12 participants' gait patterns were analyzed. Strikingly, the transfer entropy direction from the head movements to the joint angle movements was more dominant than vice versa, which was the most obvious between the head and hip. This finding can lay the groundwork for simple secondary measurement of lower limb joint problems with the simple use of head-worn sensors.","PeriodicalId":350644,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics (ICCE)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129592653","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

3D-to-2D-to-3D Conscious Learning 3d到2d到3d的有意识学习

2022 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2022-01-07 DOI: 10.1109/ICCE53296.2022.9730174

J. Weng

{"title":"3D-to-2D-to-3D Conscious Learning","authors":"J. Weng","doi":"10.1109/ICCE53296.2022.9730174","DOIUrl":"https://doi.org/10.1109/ICCE53296.2022.9730174","url":null,"abstract":"This is a theoretical paper on conscious learning for thoughts and creativity through general-purpose and autonomous imitation of demonstrations. This conscious learning is end-to-end (3D-to-2D-to-3D) and free from annotations of 2D images and 2D motor images (e.g., a bounding box to be attended to). The conscious learning algorithm directly takes that of the Developmental Networks that has been previously published extensively with rich experimental results. Apparently, humans and animals do this type of fully automated learning daily, but it is unclear a robot can do the same. Recently, [1], [2] presented a theory of conscious learning rooted in emergent universal Turing machines. It appeared to be the first algorithmic level theory of holistic consciousness, other than many papers in the literature about piecemeal consciousness. However, [1], [2] proved only conscious learning in motor-imposed training mode, namely 3D-to-2D taught by 2D motor impositions, free from 2D annotations. This paper fills the challenging gap in [1], [2] so the conscious learning is 3D-to-2D-to-3D (end-to-end) without motor-impositions or computing “inverse kinematics”. This is a major departure from traditional AI-handcrafting symbolic labels that tend to be brittle (e.g., for driverless cars) and then “spoon-feeding” pre-collected “big data”. Autonomous imitations drastically reduce the teaching complexity compared to pre-collected “big data”, especially because no annotations of training data are needed. Furthermore, conscious learning allows creativity beyond what is taught. This work is directly related to consumer electronics because it requires large-scale on-the-fly brainoid chips in future wearable robots/devices for consumers.","PeriodicalId":350644,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics (ICCE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129860320","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

Proposal of a Web of Things Integration Pattern on the Edge-Cloud Environment 边缘云环境下物联网集成模式的提出

2022 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2022-01-07 DOI: 10.1109/ICCE53296.2022.9730762

Yoshiyuki Masuda, S. Shimojo, Matsuki Yamamoto

引用次数: 0

Creative Intent Based Ambient Compensation for HDR10+ Content Using Metadata 基于元数据的HDR10+内容的创造性意图环境补偿

2022 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2022-01-07 DOI: 10.1109/ICCE53296.2022.9730381

D. Vo, Chenguang Liu, McClain Nelson, Bill Mandel, Suzie Hyun

引用次数: 0

Evaluating Electric Guitar Strumming Form as Musically Correct Rhythm And Sharpness Using Wrist-Wom Inertial Motion-Tracking Device 利用腕带惯性运动跟踪装置评估电吉他弹奏形式的音乐正确节奏和锐度

2022 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2022-01-07 DOI: 10.1109/ICCE53296.2022.9730320

Fumiyoshi Kamo, Soichiro Matsushita

{"title":"Evaluating Electric Guitar Strumming Form as Musically Correct Rhythm And Sharpness Using Wrist-Wom Inertial Motion-Tracking Device","authors":"Fumiyoshi Kamo, Soichiro Matsushita","doi":"10.1109/ICCE53296.2022.9730320","DOIUrl":"https://doi.org/10.1109/ICCE53296.2022.9730320","url":null,"abstract":"A wrist-worn inertial motion-tracking device using a commercially available sensor chip has been investigated for a motion evaluation on a chord strumming technique of electric guitar. Several motion parameters such as elbow-swinging angle, wrist-twisting angle, and angular jerk on the wrist-twisting axis were examined for the strumming diagnosis. Three professional guitarists and amateur players having a wide range of playing experience participated in the chord strumming evaluation experiments. As a result, it was found that musically important parameters including playing rhythm deviation and strumming sharpness were able to be estimated without using sound recorders or cameras. A newly developed evaluation algorithm for the strumming sharpness may clearly distinguish the professional and amateur guitarists. In addition, application software for PC enabled the guitar players to compare their performances with those of professional guitarists as well as of themselves in the past. A guitar strumming task finder based on the elbow-swinging angle signal enabled the user to locate a specific timing for evaluation. An experimental guitar lesson class with the easy-to-use motion analysis system showed that both the task annotation and the performance evaluation can be performed without introducing significant cost in terms of time and environmental care.","PeriodicalId":350644,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics (ICCE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121270771","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