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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RCC, ITS, WBS | 2022-12-14 11:30 |
Shiga | Ritsumeikan Univ. BKC (Primary: On-site, Secondary: Online) |
A fundamental study of a drone classification method applying CNN to range and Doppler images obtained by a millimeter-wave fast chirp MIMO radar Masashi Kurosaki, Kenshi Ogawa, Ryohei Nakamura, Hisaya Hadama (NDA)  WBS2022-46 ITS2022-22 RCC2022-46 |
In this paper, we propose a method to classifying various drones from range profile and micro Doppler images of a drone ... [more] | WBS2022-46 ITS2022-22 RCC2022-46 pp.65-70 |
| SR | 2022-01-25 11:15 |
Online | Online | An evaluation of CNN using Deep Residual Learning and Long Short-term Memory for LTE and WLAN Systems Classifications Teruji Ide (NIT, Kagoshima college), Rozeha Rashid, M A Sarijari (UTM) SR2021-75 |
In this study, we investigate and present a deep residual (ResNet) learning for modulation classification. The simulatio... [more] | SR2021-75 pp.82-89 |
| RCS, SIP, IT | 2022-01-20 14:05 |
Online | Online | Automatic Modulation Classification Based on SNR estimation using Multi-Task Learning Wataru Machida, Yosuke Sugiura, Nozomiko Yasui, Tetsuya Shimamura (Saitama Univ.) IT2021-56 SIP2021-64 RCS2021-224 |
Automatic modulation classification is a technology that identifies the modulation type used in received signals and pla... [more] | IT2021-56 SIP2021-64 RCS2021-224 pp.155-160 |
| WBS, RCC, ITS | 2021-12-13 09:00 |
Online | Online | [Poster Presentation]
Experimental study on Micro-Doppler Characteristics of Drones using a Millimeter-wave Fast Chirp Modulation Radar Masashi Kurosaki, Ryohei Nakamura, Hisaya Hadama (NDA) WBS2021-41 ITS2021-15 RCC2021-48 |
In recent years, the rapid development and spread of drones has attracted attention in a variety of fields, military, ag... [more] | WBS2021-41 ITS2021-15 RCC2021-48 pp.40-44 |
| SR | 2021-05-21 10:00 |
Online | Online | An evaluation of CNN using Deep Residual Learning for OFDM and Single Carrier Modulation Classification Teruji Ide (NIT, Kagoshima College), Rozeha A Rashid, Leon Chin, M A Sarijari, Rubita Sudirman (UTM) SR2021-9 |
In this study, we investigate and present a deep residual learning for modulation classification. The simulation results... [more] | SR2021-9 pp.57-64 |
| SR | 2020-11-18 11:15 |
Online | Online | CNN using Deep Residual Learning for Modulation Classification Teruji Ide (NIT, Kagoshima College), Rozeha A. Rashid, Leon Chin, M A Sarijari, Rubita Sudirman (UTM) SR2020-25 |
In this study, we investigate and present a deep residual learning for modulation classification. The simulation results... [more] | SR2020-25 pp.17-21 |
| RISING (2nd) |
2019-11-26 10:30 |
Tokyo | Fukutake Learning Theater, Hongo Campus, Univ. Tokyo | [Poster Presentation]
Initial Evaluation of Modulation Recognition Based on Deep Learning using Estimated SNR as Side Information Yasutaka Yamashita, Shigeru Uchida, Hiroshi Aruga (Mitsubishi Electric Corp.) |
Modulation classification is an important research subject in cognitive radios and radio monitoring systems to recognize... [more] | |
| RCS | 2019-06-21 11:40 |
Okinawa | Miyakojima Hirara Port Terminal Building | SNR Estimation by using Neural Network in Adaptive Modulation and Coding Shun Kojima, Kazuki Maruta, Chang-Jun Ahn (Chiba Univ.) RCS2019-94 |
This paper proposes a novel Adaptive Modulation and Coding (AMC) scheme enabled by Artificial Neural Network (ANN) aide... [more] |
RCS2019-94 pp.333-338 |
| RCS, RCC, ASN, NS, SR (Joint) |
2016-07-22 09:25 |
Aichi | A Novel Modulation Classification Method in Cognitive Radios using Deep Network Xu Zhu, Takeo Fujii (UEC) SR2016-50 |
This paper proposes a universal modulation classification method based on Denoise Stacked Sparse Auto-encoder (DSSA), on... [more] | SR2016-50 pp.103-106 |
|
| PRMU, IE, MI, SIP | 2016-05-19 15:40 |
Aichi | Neural Decoding of Code Modulated Visual Evoked Potentials by Spatio-Temporal Inverse Filtering for Brain Computer Interfaces Jun-ichi Sato, Yoshikazu Washizawa (UEC) SIP2016-13 IE2016-13 PRMU2016-13 MI2016-13 |
c-VEP has been investigated resently, and and applied to brain computer interfaces (BCIs). c-VEP BCI exhibits faster com... [more] | SIP2016-13 IE2016-13 PRMU2016-13 MI2016-13 pp.65-70 |
|
| SIS | 2015-03-06 09:50 |
Tokyo | Meiji Univ. Nakano Campus (Tokyo) | Sub-Optimum Maximum Likelihood Modulation Classification Algorithm Using Multiple Antennas Kenta Homma, Tetsuya Shimamura (Saitama Univ.) SIS2014-104 |
Blind modulation classification (MC) plays an important role in many wireless communication systems such as cognitive r... [more] |
SIS2014-104 pp.73-78 |
| RCC, ASN, NS, RCS, SR (Joint) |
2014-07-30 16:10 |
Kyoto | Kyoto Terrsa | Time-Frequency Analysis based PSK Modulation Classification Xu Zhu, Takeo Fujii (Univ. of Electro-Comm.) SR2014-21 |
In order to realize improving signal detection probability using interference canceler and multi-system detection techni... [more] | SR2014-21 pp.7-11 |
| SAT, WBS (Joint) |
2014-05-15 13:00 |
Aichi | Nagoya Institute of Technology | Block Length Estimation of Block Modulations by Cycrostationarity Shengdi Jin, Ikuo Oka, Shingo Ata (Osaka City Univ.) SAT2014-1 |
The modulation identifications is one of the key issues in the cognitive radios, and many works were devoted to the modu... [more] | SAT2014-1 pp.1-4 |
| ISEC, IT, WBS | 2014-03-10 11:00 |
Aichi | Nagoya Univ., Higashiyama Campus | Classification of Partitions of Permutations by Dominant Sets for Rank Modulation Yusuke Takahashi, Hiroshi Kamabe (Gifu Univ.) IT2013-56 ISEC2013-85 WBS2013-45 |
A coing scheme using a rank modulation was proposed for storing data in flash memories.There are two fundamental operati... [more] | IT2013-56 ISEC2013-85 WBS2013-45 pp.13-18 |
| RCS, SR, SRW (Joint) |
2014-03-03 11:40 |
Tokyo | Waseda Univ. | Performance evaluation of high-order modulation classification based on moment of IQ components with optimized median Ryosuke Miyauchi, Hideki Ochiai (Yokohama National Univ.) RCS2013-313 |
Modulation classification is expected to play an important role in the recent wireless communications applications such ... [more] | RCS2013-313 pp.43-48 |
| RCS, SR, SRW (Joint) |
2014-03-04 11:30 |
Tokyo | Waseda Univ. | A Study on Modulation Classification Method for Unknown Received Signal Analysis Takashi Asahara, Hideto Aikawa (Mitsubishi Electric) SRW2013-50 |
Various technology has been developed such as the technology of recognizing the radio channel condition in the cognitive... [more] | SRW2013-50 pp.37-42 |
| SIP, RCS | 2013-01-31 09:55 |
Hiroshima | Viewport-Kure-Hotel (Kure) | QAM Modulation Classification Based on Instantaneous Power Moments Using Adjustable Median Ryosuke Miyauchi, Hideki Ochiai (Yokohama National Univ.) SIP2012-91 RCS2012-248 |
Modulation classification is expected to play an important role in the recent wireless communications applications such ... [more] | SIP2012-91 RCS2012-248 pp.61-66 |
| SIP, RCS | 2013-02-01 09:00 |
Hiroshima | Viewport-Kure-Hotel (Kure) | Low Complexity Automatic Modulation Classification Technique for Multiple Modulation Schemes Yong Jin, Shuichi Ohno (Hiroshima Univ.) SIP2012-98 RCS2012-255 |
In this article, we present a low computational complexity automatic modulation classification (AMC) method for multiple... [more] | SIP2012-98 RCS2012-255 pp.103-108 |
| SR, RCS, SRW (Joint) |
2012-03-09 13:00 |
Kanagawa | YRP | Symbol Rate Estimation utilizing Spectral Correlation for Automatic Modulation Classification Azril Haniz, Md. Abdur Rahman, Minseok Kim, Jun-ichi Takada (Tokyo Inst. of Tech.) SR2011-130 |
Many modulation schemes exhibit a peak along the cross-section of the spectral correlation density (SCD) at the carrier ... [more] | SR2011-130 pp.181-188 |
| SR | 2011-07-28 15:05 |
Kanagawa | YRP | [Technology Exhibit]
Implementation Issues of Automatic Modulation Classification on Software Defined Radio Platform Md. Abdur Rahman, Azril Haniz, Minseok Kim, Jun-ichi Takada (Tokyo Inst. of Tech.) SR2011-32 |
Automatic modulation classification (AMC) is used in many military and civilian applications such as surveillance, spect... [more] | SR2011-32 pp.93-100 |
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