Jiaxin WU Bing LI Li ZHAO Xinzhou XU
Maaki SAKAI Kanon HOKAZONO Yoshiko HANADA
Xuecheng SUN Zheming LU
Yuanhe WANG Chao ZHANG
Jinfeng CHONG Niu JIANG Zepeng ZHUO Weiyu ZHANG
Xiangrun LI Qiyu SHENG Guangda ZHOU Jialong WEI Yanmin SHI Zhen ZHAO Yongwei LI Xingfeng LI Yang LIU
Meiting XUE Wenqi WU Jinfeng LUO Yixuan ZHANG Bei ZHAO
Rong WANG Changjun YU Zhe LYU Aijun LIU
Huijuan ZHOU Zepeng ZHUO Guolong CHEN
Feifei YAN Pinhui KE Zuling CHANG
Manabu HAGIWARA
Ziqin FENG Hong WAN Guan GUI
Sungryul LEE
Feng WANG Xiangyu WEN Lisheng LI Yan WEN Shidong ZHANG Yang LIU
Yanjun LI Jinjie GAO Haibin KAN Jie PENG Lijing ZHENG Changhui CHEN
Ho-Lim CHOI
Feng WEN Haixin HUANG Xiangyang YIN Junguang MA Xiaojie HU
Shi BAO Xiaoyan SONG Xufei ZHUANG Min LU Gao LE
Chen ZHONG Chegnyu WU Xiangyang LI Ao ZHAN Zhengqiang WANG
Izumi TSUNOKUNI Gen SATO Yusuke IKEDA Yasuhiro OIKAWA
Feng LIU Helin WANG Conggai LI Yanli XU
Hongtian ZHAO Hua YANG Shibao ZHENG
Kento TSUJI Tetsu IWATA
Yueying LOU Qichun WANG
Menglong WU Jianwen ZHANG Yongfa XIE Yongchao SHI Tianao YAO
Jiao DU Ziwei ZHAO Shaojing FU Longjiang QU Chao LI
Yun JIANG Huiyang LIU Xiaopeng JIAO Ji WANG Qiaoqiao XIA
Qi QI Liuyi MENG Ming XU Bing BAI
Nihad A. A. ELHAG Liang LIU Ping WEI Hongshu LIAO Lin GAO
Dong Jae LEE Deukjo HONG Jaechul SUNG Seokhie HONG
Tetsuya ARAKI Shin-ichi NAKANO
Shoichi HIROSE Hidenori KUWAKADO
Yumeng ZHANG
Jun-Feng Liu Yuan Feng Zeng-Hui Li Jing-Wei Tang
Keita EMURA Kaisei KAJITA Go OHTAKE
Xiuping PENG Yinna LIU Hongbin LIN
Yang XIAO Zhongyuan ZHOU Mingjie SHENG Qi ZHOU
Kazuyuki MIURA
Yusaku HIRAI Toshimasa MATSUOKA Takatsugu KAMATA Sadahiro TANI Takao ONOYE
Ryuta TAMURA Yuichi TAKANO Ryuhei MIYASHIRO
Nobuyuki TAKEUCHI Kosei SAKAMOTO Takuro SHIRAYA Takanori ISOBE
Shion UTSUMI Kosei SAKAMOTO Takanori ISOBE
You GAO Ming-Yue XIE Gang WANG Lin-Zhi SHEN
Zhimin SHAO Chunxiu LIU Cong WANG Longtan LI Yimin LIU Zaiyan ZHOU
Xiaolong ZHENG Bangjie LI Daqiao ZHANG Di YAO Xuguang YANG
Takahiro IINUMA Yudai EBATO Sou NOBUKAWA Nobuhiko WAGATSUMA Keiichiro INAGAKI Hirotaka DOHO Teruya YAMANISHI Haruhiko NISHIMURA
Takeru INOUE Norihito YASUDA Hidetomo NABESHIMA Masaaki NISHINO Shuhei DENZUMI Shin-ichi MINATO
Zhan SHI
Hakan BERCAG Osman KUKRER Aykut HOCANIN
Ryoto Koizumi Xiaoyan Wang Masahiro Umehira Ran Sun Shigeki Takeda
Hiroya Hachiyama Takamichi Nakamoto
Chuzo IWAMOTO Takeru TOKUNAGA
Changhui CHEN Haibin KAN Jie PENG Li WANG
Pingping JI Lingge JIANG Chen HE Di HE Zhuxian LIAN
Ho-Lim CHOI
Akira KITAYAMA Goichi ONO Hiroaki ITO
Koji NUIDA Tomoko ADACHI
Yingcai WAN Lijin FANG
Yuta MINAMIKAWA Kazumasa SHINAGAWA
Sota MORIYAMA Koichi ICHIGE Yuichi HORI Masayuki TACHI
Sendren Sheng-Dong XU Albertus Andrie CHRISTIAN Chien-Peng HO Shun-Long WENG
Zhikui DUAN Xinmei YU Yi DING
Hongbo LI Aijun LIU Qiang YANG Zhe LYU Di YAO
Yi XIONG Senanayake THILAK Yu YONEZAWA Jun IMAOKA Masayoshi YAMAMOTO
Feng LIU Qian XI Yanli XU
Yuling LI Aihuang GUO
Mamoru SHIBATA Ryutaroh MATSUMOTO
Haiyang LIU Xiaopeng JIAO Lianrong MA
Ruixiao LI Hayato YAMANA
Riaz-ul-haque MIAN Tomoki NAKAMURA Masuo KAJIYAMA Makoto EIKI Michihiro SHINTANI
Kundan LAL DAS Munehisa SEKIKAWA Tadashi TSUBONE Naohiko INABA Hideaki OKAZAKI
Recently, control theory using machine learning, which is useful for the control of unknown systems, has attracted significant attention. This study focuses on such a topic with optimal control problems for unknown nonlinear systems. Because optimal controllers are designed based on mathematical models of the systems, it is challenging to obtain models with insufficient knowledge of the systems. Kernel functions are promising for developing data-driven models with limited knowledge. However, the complex forms of such kernel-based models make it difficult to design the optimal controllers. The design corresponds to solving Hamilton-Jacobi (HJ) equations because their solutions provide optimal controllers. Therefore, the aim of this study is to derive certain kernel-based models for which the HJ equations are solved in an exact sense, which is an extended version of the authors' former work. The HJ equations are decomposed into tractable algebraic matrix equations and nonlinear functions. Solving the matrix equations enables us to obtain the optimal controllers of the model. A numerical simulation demonstrates that kernel-based models and controllers are successfully developed.
Riku AKEMA Masao YAMAGISHI Isao YAMADA
The Canonical Polyadic Decomposition (CPD) is the tensor analog of the Singular Value Decomposition (SVD) for a matrix and has many data science applications including signal processing and machine learning. For the CPD, the Alternating Least Squares (ALS) algorithm has been used extensively. Although the ALS algorithm is simple, it is sensitive to a noise of a data tensor in the applications. In this paper, we propose a novel strategy to realize the noise suppression for the CPD. The proposed strategy is decomposed into two steps: (Step 1) denoising the given tensor and (Step 2) solving the exact CPD of the denoised tensor. Step 1 can be realized by solving a structured low-rank approximation with the Douglas-Rachford splitting algorithm and then Step 2 can be realized by solving the simultaneous diagonalization of a matrix tuple constructed by the denoised tensor with the DODO method. Numerical experiments show that the proposed algorithm works well even in typical cases where the ALS algorithm suffers from the so-called bottleneck/swamp effect.
Masayuki ODAGAWA Takumi OKAMOTO Tetsushi KOIDE Toru TAMAKI Shigeto YOSHIDA Hiroshi MIENO Shinji TANAKA
In this paper, we present a classification method for a Computer-Aided Diagnosis (CAD) system in a colorectal magnified Narrow Band Imaging (NBI) endoscopy. In an endoscopic video image, color shift, blurring or reflection of light occurs in a lesion area, which affects the discrimination result by a computer. Therefore, in order to identify lesions with high robustness and stable classification to these images specific to video frame, we implement a CAD system for colorectal endoscopic images with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification on the embedded DSP core. To improve the robustness of CAD system, we construct the SVM learned by multiple image sizes data sets so as to adapt to the noise peculiar to the video image. We confirmed that the proposed method achieves higher robustness, stable, and high classification accuracy in the endoscopic video image. The proposed method also can cope with differences in resolution by old and new endoscopes and perform stably with respect to the input endoscopic video image.
Reona TAKEMOTO Takayuki NOZAKI
Maximum run-length limited codes are constraint codes used in communication and data storage systems. Insertion/deletion correcting codes correct insertion or deletion errors caused in transmitted sequences and are used for combating synchronization errors. This paper investigates the maximum run-length limited single insertion/deletion correcting (RLL-SIDC) codes. More precisely, we construct efficiently encodable and decodable RLL-SIDC codes. Moreover, we present its encoding and decoding algorithms and show the redundancy of the code.
Shoichiro YAMASAKI Tomoko K. MATSUSHIMA
The present paper proposes orthogonal variable spreading factor codes over finite fields for multi-rate communications. The proposed codes have layered structures that combine sequences generated by discrete Fourier transforms over finite fields, and have various code lengths. The design method for the proposed codes and examples of the codes are shown.
We analyze the effect of window choice on the zero-padding method and corrected quadratically interpolated fast Fourier transform using a harmonic signal in noise at both high and low signal-to-noise ratios (SNRs) on a theoretical basis. Then, we validate the theoretical analysis using simulations. The theoretical analysis and simulation results using four traditional window functions show that the optimal window is determined depending on the SNR; the estimation errors are the smallest for the rectangular window at low SNR, the Hamming and Hanning windows at mid SNR, and the Blackman window at high SNR. In addition, we analyze the simulation results using the signal-to-noise floor ratio, which appears to be more effective than the conventional SNR in determining the optimal window.
Masayuki ODAGAWA Tetsushi KOIDE Toru TAMAKI Shigeto YOSHIDA Hiroshi MIENO Shinji TANAKA
This paper presents examination result of possibility for automatic unclear region detection in the CAD system for colorectal tumor with real time endoscopic video image. We confirmed that it is possible to realize the CAD system with navigation function of clear region which consists of unclear region detection by YOLO2 and classification by AlexNet and SVMs on customizable embedded DSP cores. Moreover, we confirmed the real time CAD system can be constructed by a low power ASIC using customizable embedded DSP cores.
In this letter, we study low-density parity-check (LDPC) codes for noisy channels with insertion and deletion (ID) errors. We first propose a design method of irregular LDPC codes for such channels, which can be used to simultaneously obtain degree distributions for different noise levels. We then show the asymptotic/finite-length decoding performances of designed codes and compare them with the symmetric information rates of cascaded ID-noisy channels. Moreover, we examine the relationship between decoding performance and a code structure of irregular LDPC codes.
Xiaoyu CHEN Huanchang LI Yihan ZHANG Yubo LI
A new construction of shift sequences is proposed under the condition of P|L, and then the inter-group complementary (IGC) sequence sets are constructed based on the shift sequence. By adjusting the parameter q, two or three IGC sequence sets can be obtained. Compared with previous methods, the proposed construction can provide more sequence sets for both synchronous and asynchronous code-division multiple access communication systems.
This letter proposes an adaptive beamforming switch algorithm for realistic massive multiple-input multiple-output (MIMO) systems through prototypes. It is analyzed and identified that a rigid single-mode beamforming regime is hard to maintain superior performance all the time due to no adaption to the inevitable channel variation in practice. In order to cope with this practical issue, the proposed systematic beamforming mechanism is investigated to enable dynamic selection between minimum mean-squared error and grid-of-beams beamforming algorithms, which improves system downlink performance, including throughput and block error rate. The significant performance benefits and realistic feasibility have been validated through the field tests in live networks and theoretical analyses. Meanwhile, the adaptive beamforming switch algorithm is applicable to both fourth and fifth generation time-division duplexing cellular communication system using massive-MIMO technology.
Expectation propagation (EP) is a powerful algorithm for signal recovery in compressed sensing. This letter proposes correction of a variance message before denoising to improve the performance of EP in the high signal-to-noise ratio (SNR) regime for finite-sized systems. The variance massage is replaced by an observation-dependent consistent estimator of the mean-square error in estimation before denoising. Massive multiple-input multiple-output (MIMO) is considered to verify the effectiveness of the proposed correction. Numerical simulations show that the proposed variance correction improves the high SNR performance of EP for massive MIMO with a few hundred transmit and receive antennas.
Shinnosuke KURATA Toshinori OTAKA Yusuke KAMEDA Takayuki HAMAMOTO
We propose a HDR (high dynamic range) reconstruction method in an image sensor with a pixel-parallel ADC (analog-to-digital converter) for non-destructively reading out the intermediate exposure image. We report the circuit design for such an image sensor and the evaluation of the basic HDR reconstruction method.