The presentation time will be different based on the type of papers as follows: Notes Oral session A: Embedded System & Edge Computing<\/strong> Oral Session B: IoT and Robotics<\/strong> Oral Session C: Society5.0\/Beyond 5G<\/strong> Oral Session D: Innovations in Affective Computing<\/strong> Information for Oral Presenters The presentation time will be different based on the type of papers as follows:1. Regular paper: 15mins presentation + 5mins Q&A2. WiP paper: 12mins presentation + 3mins Q&A Notes– For on-site presenters1. Presentations shall be conducted using each presenter’s personal computer. We kindly request that you prepare your presentation materials in … <\/p>\n
1. Regular paper<\/strong>: 15mins presentation + 5mins Q&A
2. WiP paper<\/strong>: 12mins presentation + 3mins Q&A<\/p>\n\n\n\n
– For on-site presenters<\/strong><\/em>
1. Presentations shall be conducted using each presenter’s personal computer. We kindly request that you prepare your presentation materials in advance of the day of the presentation.
2. For on-site presentations, the output will be projected or displayed via HDMI. Although we will provide a Type-C to HDMI adapter, we ask that each presenter ensure they have their own suitable HDMI adapter for their PC if a different type is required.
– For online presenters<\/em><\/strong>
1. Please access the online presentation link 10 minutes before the starting time of the session to check the screen sharing. (The link will be announced shortly.)
2. During the presentation, you will be utilizing Zoom’s screen-sharing feature. We kindly request that you participate from a location with a stable internet connection.<\/p>\n\n\n\n
Nov. 1st 11:00<\/p>\n\n\n\n<\/td> Title<\/td> Author<\/td> Type<\/td><\/tr> A1<\/td> Using Low Power Coprocessors in an FRP Language for Embedded Systems<\/td> Go Suzuki, Akihiko Yokoyama, Sosuke Moriguchi and Takuo Watanabe<\/td> Regular paper<\/td><\/tr> A2<\/td> Exploring the Training Performance of Deep Neural Networks on Embedded Many-core Processors<\/td> Masahiro Hasumi and Takuya Azumi<\/td> Regular paper<\/td><\/tr> A3<\/td> Offloading Image Recognition Processing to FPGA Using Resource Manager for Multi-access Edge Computing<\/td> Hayato Mori, Eisuke Okazaki, Gai Nagahashi, Mikiko Sato, Takeshi Ohkawa and Midori Sugaya<\/td> Regular paper<\/td><\/tr> A4<\/td> Proposal of inter-node communication method using P2P communication in multi-node computing platform<\/td> Jun Liang, Yanzhi Li and Midori Sugaya<\/td> WiP<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Nov. 1st 14:30<\/p>\n\n\n\n<\/td> Title<\/td> Author<\/td> Type<\/td><\/tr> B1<\/td> Decentralized Network Booting for Geographical-distributed IoT Device Clusters<\/td> Taito Morikawa, Katsuya Matsubara and Shun Kumagai<\/td> Regular paper<\/td><\/tr> B2<\/td> Design of Necessary Safety Functions and Fault Detection in Electric Vehicle Control Unit based on Software-in-the-Loop<\/td> Kiet Vo, Danai Phaoharuhansa and Chadchai Srisurangkul<\/td> Regular paper<\/td><\/tr> B3<\/td> Decentralized Systems to Improve Inter-Robot Communication in Hazardous Chemical Incidents<\/td> Liangwen Wang, Yanzi Li and Midori Sugaya<\/td> WiP<\/td><\/tr> B4<\/td> Rethinking Human-Robot Emotional Interaction: The Case of Socially Assistive Robots<\/td> Manishk Gawande and Gabriele Trovato<\/td> WiP<\/td><\/tr> B5<\/td> ToDo: Plant Robot to Support Habit Formation and Task Management<\/td> Mehriell Eliana Siajuat Ang and Gabriele Trovato<\/td> WiP<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Nov. 2nd 10:45<\/p>\n\n\n\n<\/td> Title<\/td> Author<\/td> Type<\/td><\/tr> C1<\/td> PyHARK: A HARK-based python package for robot audition and computational auditory scene analysis<\/td> Kazuhiro Nakadai, Masayuki Takigahira and Katsutoshi Itoyama<\/td> WiP<\/td><\/tr> C2<\/td> PyHARK Acceleration: A GPU-based Approach<\/td> Zirui Lin, Katsutoshi Itoyama, Kazuhiro Nakadai, Masayuki Takigahira, Haris Gulzar, Takeharu Eda, Monikka Roslianna Busto and Hideharu Amano<\/td> WiP<\/td><\/tr> C3<\/td> Sound event localization and detection utilizing overlapping end-to-end learning<\/td> Yanke Long, Riku Yasuda, Yui Sudo, Katsutoshi Itoyama, Kazuhiro Nakadai, Hideharu Amano and Kenji Nishida<\/td> WiP<\/td><\/tr> C4<\/td> Evaluation of the Second FPGA-IP Prototype for MEC Devices and its Improvements<\/td> Morihiro Kuga, Masahiro Sumita, Sen Gen and Masahiro Iida<\/td> WiP<\/td><\/tr> C5<\/td> The information transfer and sharing platform for remote control over B5G infrastructure<\/td> Kazuhiro Kosaka, Tetsuya Yokotani and Koichi Ishibashi<\/td> WiP<\/td><\/tr> C6<\/td> Automatic Generation of communication paths between multiple FPGA boards<\/td> Hiroaki Suzuki, Kensuke Iizuka, Hideharu Amano, Wataru Takahashi and Kazutoshi Wakabayashi<\/td> WiP<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Nov. 2nd 15:15<\/p>\n\n\n\n<\/td> Title<\/td> Author<\/td> Type<\/td><\/tr> D1<\/td> Feature Selection of EEG and Heart Rate Variability Indexes for Estimation of Cognitive Function in the Elderly<\/td> Kentarou Kanai, Yuri Nakagawa and Midori Sugaya<\/td> Regular paper<\/td><\/tr> D2<\/td> Swin Transformer Based Depression Detection Model Learning Only Single Channel EEG Signal <\/td> Kei Suzuki and Midori Sugaya<\/td> WiP<\/td><\/tr> D3<\/td> Investigating Effects of Stimuli on Attentional Focus during Cognitive Test using EEG<\/td> Felipe Yudi Fulini, Chen Feng, Keiji Tatani, Masashi Sakagami and Midori Sugaya<\/td> WiP<\/td><\/tr> D4<\/td> Analysis of Driver\u2019s Emotion and Driving Performance during Music Listening using Physiological Indexes<\/td> Narumon Jadram and Midori Sugaya<\/td> WiP<\/td><\/tr> D5<\/td> A Proposal of Emotion Estimation Method in Social Robots Using Facial Expression Recognition Model with Graph-based Techniques<\/td> Nopphakorn Subsa-Ard, Felipe Yudi Fulini, Tipporn Laohakangvalvit, Kaoru Suzuki and Midori Sugaya<\/td> WiP<\/td><\/tr> D6<\/td> Automated Review Tool for Educational Models Utilizing Generative AI <\/td> Daisuke Saito, Taiyou Minagawa and Kenji Hisazumi<\/td> WiP<\/td><\/tr> D7<\/td> Cuff-Less Blood Pressure Classification from ECG and PPG Signals using Deep Learning<\/td> Rakkrit Duangsoithong, Kulika Pahukarn and Dujdow Buranapanichkit<\/td> WiP<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"