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
Shunsuke YAMAKI Kazuhiro FUKUI Masahide ABE Masayuki KAWAMATA
This paper proposes statistical analysis of phase-only correlation (POC) functions under the phase fluctuation of signals due to additive Gaussian noise. We derive probability density function of phase-spectrum differences between original signal and its noise-corrupted signal with additive Gaussian noise. Furthermore, we evaluate the expectation and variance of the POC functions between these two signals. As the variance of Gaussian noise increases, the expectation of the peak of the POC function monotonically decreases and variance of the POC function monotonically increases. These results mathematically guarantee the validity of the POC functions used for similarity measure in matching techniques.
Riku AKEMA Masao YAMAGISHI Isao YAMADA
Approximate Simultaneous Diagonalization (ASD) is a problem to find a common similarity transformation which approximately diagonalizes a given square-matrix tuple. Many data science problems have been reduced into ASD through ingenious modelling. For ASD, the so-called Jacobi-like methods have been extensively used. However, the methods have no guarantee to suppress the magnitude of off-diagonal entries of the transformed tuple even if the given tuple has an exact common diagonalizer, i.e., the given tuple is simultaneously diagonalizable. In this paper, to establish an alternative powerful strategy for ASD, we present a novel two-step strategy, called Approximate-Then-Diagonalize-Simultaneously (ATDS) algorithm. The ATDS algorithm decomposes ASD into (Step 1) finding a simultaneously diagonalizable tuple near the given one; and (Step 2) finding a common similarity transformation which diagonalizes exactly the tuple obtained in Step 1. The proposed approach to Step 1 is realized by solving a Structured Low-Rank Approximation (SLRA) with Cadzow's algorithm. In Step 2, by exploiting the idea in the constructive proof regarding the conditions for the exact simultaneous diagonalizability, we obtain an exact common diagonalizer of the obtained tuple in Step 1 as a solution for the original ASD. Unlike the Jacobi-like methods, the ATDS algorithm has a guarantee to find an exact common diagonalizer if the given tuple happens to be simultaneously diagonalizable. Numerical experiments show that the ATDS algorithm achieves better performance than the Jacobi-like methods.
Masayuki ODAGAWA Takumi OKAMOTO Tetsushi KOIDE Toru TAMAKI Bisser RAYTCHEV Kazufumi KANEDA Shigeto YOSHIDA Hiroshi MIENO Shinji TANAKA Takayuki SUGAWARA Hiroshi TOISHI Masayuki TSUJI Nobuo TAMBA
In this paper, we present a hardware implementation of a colorectal cancer diagnosis support system using a colorectal endoscopic video image on customizable embedded DSP. 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 computer-aided diagnosis (CAD) system for colorectal endoscopic images with Narrow Band Imaging (NBI) magnification with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification. Since CNN and SVM need to perform many multiplication and accumulation (MAC) operations, we implement the proposed hardware system on a customizable embedded DSP, which can realize at high speed MAC operations and parallel processing with Very Long Instruction Word (VLIW). Before implementing to the customizable embedded DSP, we profile and analyze processing cycles of the CAD system and optimize the bottlenecks. We show the effectiveness of the real-time diagnosis support system on the embedded system for endoscopic video images. The prototyped system demonstrated real-time processing on video frame rate (over 30fps @ 200MHz) and more than 90% accuracy.
Hiryu KAMOSHITA Daichi KITAHARA Ken'ichi FUJIMOTO Laurent CONDAT Akira HIRABAYASHI
This paper proposes a high-quality computed tomography (CT) image reconstruction method from low-dose X-ray projection data. A state-of-the-art method, proposed by Xu et al., exploits dictionary learning for image patches. This method generates an overcomplete dictionary from patches of standard-dose CT images and reconstructs low-dose CT images by minimizing the sum of a data fidelity and a regularization term based on sparse representations with the dictionary. However, this method does not take characteristics of each patch, such as textures or edges, into account. In this paper, we propose to classify all patches into several classes and utilize an individual dictionary with an individual regularization parameter for each class. Furthermore, for fast computation, we introduce the orthogonality to column vectors of each dictionary. Since similar patches are collected in the same cluster, accuracy degradation by the orthogonality hardly occurs. Our simulations show that the proposed method outperforms the state-of-the-art in terms of both accuracy and speed.
Zedong SUN Chunxiang GU Yonghui ZHENG
Sieve algorithms are regarded as the best algorithms to solve the shortest vector problem (SVP) on account of its good asymptotical quality, which could make it outperform enumeration algorithms in solving SVP of high dimension. However, due to its large memory requirement, sieve algorithms are not practical as expected, especially on high dimension lattice. To overcome this bottleneck, TupleSieve algorithm was proposed to reduce memory consumption by a trade-off between time and memory. In this work, aiming to make TupleSieve algorithm more practical, we combine TupleSieve algorithm with SubSieve technique and obtain a sub-exponential gain in running time. For 2-tuple sieve, 3-tuple sieve and arbitrary k-tuple sieve, when selecting projection index d appropriately, the time complexity of our algorithm is O(20.415(n-d)), O(20.566(n-d)) and $O(2^{rac{kmathrm{log}_2p}{1-k}(n-d)})$ respectively. In practice, we propose a practical variant of our algorithm based on GaussSieve algorithm. Experimental results show that our algorithm implementation is about two order of magnitude faster than FPLLL's GuassSieve algorithm. Moreover, techniques such as XOR-POPCNT trick, progressive sieving and appropriate projection index selection can be exploited to obtain a further acceleration.
This paper addresses pilot contamination in massive multiple-input multiple-output (MIMO) uplink. Pilot contamination is caused by reuse of identical pilot sequences in adjacent cells. To solve pilot contamination, the base station utilizes differences between the transmission frames of different users, which are detected via joint channel and data estimation. The joint estimation is regarded as a bilinear inference problem in compressed sensing. Expectation propagation (EP) is used to propose an iterative channel and data estimation algorithm. Initial channel estimates are attained via time-shifted pilots without exploiting information about large scale fading. The proposed EP modifies two points in conventional bilinear adaptive vector approximate message-passing (BAd-VAMP). One is that EP utilizes data estimates after soft decision in the channel estimation while BAd-VAMP uses them before soft decision. The other point is that EP can utilize the prior distribution of the channel matrix while BAd-VAMP cannot in principle. Numerical simulations show that EP converges much faster than BAd-VAMP in spatially correlated MIMO, in which approximate message-passing fails to converge toward the same fixed-point as EP and BAd-VAMP.
Yih-Cherng LEE Hung-Wei HSU Jian-Jiun DING Wen HOU Lien-Shiang CHOU Ronald Y. CHANG
Automatic tracking and classification are essential for studying the behaviors of wild animals. Owing to dynamic far-shooting photos, the occlusion problem, protective coloration, the background noise is irregular interference for designing a computerized algorithm for reducing human labeling resources. Moreover, wild dolphin images are hard-acquired by on-the-spot investigations, which takes a lot of waiting time and hardly sets the fixed camera to automatic monitoring dolphins on the ocean in several days. It is challenging tasks to detect well and classify a dolphin from polluted photos by a single famous deep learning method in a small dataset. Therefore, in this study, we propose a generic Cascade Small Object Detection (CSOD) algorithm for dolphin detection to handle small object problems and develop visualization to backbone based classification (V2BC) for removing noise, highlighting features of dolphin and classifying the name of dolphin. The architecture of CSOD consists of the P-net and the F-net. The P-net uses the crude Yolov3 detector to be a core network to predict all the regions of interest (ROIs) at lower resolution images. Then, the F-net, which is more robust, is applied to capture the ROIs from high-resolution photos to solve single detector problems. Moreover, a visualization to backbone based classification (V2BC) method focuses on extracting significant regions of occluded dolphin and design significant post-processing by referencing the backbone of dolphins to facilitate for classification. Compared to the state of the art methods, including faster-rcnn, yolov3 detection and Alexnet, the Vgg, and the Resnet classification. All experiments show that the proposed algorithm based on CSOD and V2BC has an excellent performance in dolphin detection and classification. Consequently, compared to the related works of classification, the accuracy of the proposed designation is over 14% higher. Moreover, our proposed CSOD detection system has 42% higher performance than that of the original Yolov3 architecture.
Masaaki ISEKI Takamichi NAKAMOTO
An olfactory display is a device to present smells. Temporal characteristics of three types of olfactory displays such as one based upon high-speed switching of solenoid valves, desktop-type one based on SAW atomizer and wearable-type one based on SAW atomizer were evaluated using three odorants with different volatilities. The sensory test revealed that the olfactory displays based on SAW atomizer had the presentation speeds faster than that of solenoid valves switching. Especially, the wearable one had an excellent temporal characteristic. These results largely depend on the difference in the odor delivery method. The data obtained in this study provides basic knowledge when we make olfactory contents.
Jingjing SI Wenwen SUN Chuang LI Yinbo CHENG
Deep learning is playing an increasingly important role in signal processing field due to its excellent performance on many inference problems. Parametric bilinear generalized approximate message passing (P-BiG-AMP) is a new approximate message passing based approach to a general class of structure-matrix bilinear estimation problems. In this letter, we propose a novel feed-forward neural network architecture to realize P-BiG-AMP methodology with deep learning for the inference problem of compressive sensing under matrix uncertainty. Linear transforms utilized in the recovery process and parameters involved in the input and output channels of measurement are jointly learned from training data. Simulation results show that the trained P-BiG-AMP network can achieve higher reconstruction performance than the P-BiG-AMP algorithm with parameters tuned via the expectation-maximization method.
Di YAO Aijun LIU Hongzhi LI Changjun YU
In the user-congested high-frequency band, radio frequency interference (RFI) is a dominant factor that degrades the detection performance of high-frequency surface wave radar (HFSWR). Up to now, various RFI suppression algorithms have been proposed while they are usually inapplicable to the compact HFSWR because of the minimal array aperture. Therefore, this letter proposes a novel RFI mitigation scheme for compact HFSWR, even for single antenna. The scheme utilized the robust principal component analysis to separate RFI and target, based on the time-frequency distribution characteristics of the RFI. The effectiveness of this scheme is demonstrated by the measured data, which can effectively suppress RFI without losing target signal.
In this letter, a low latency, high throughput and hardware efficient sorted MMSE QR decomposition (MMSE-SQRD) for multiple-input multiple-output (MIMO) systems is presented. In contrast to the method of extending the complex matrix to real model and thereafter applying real-valued QR decomposition (QRD), we develop a highly parallel decomposition scheme based on coordinate rotation digital computer (CORDIC) which performs the QRD in complex domain directly and then converting the complex result to its real counterpart. The proposed scheme can greatly improve the processing parallelism and curtail the nullification and sorting procedures. Besides, we also design the corresponding pipelined hardware architecture of the MMSE-SQRD based on highly parallel Givens rotation structure with CORDIC algorithm for 4×4 MIMO detectors. The proposed MMSE-SQRD is implemented in SMIC 55nm CMOS technology achieving up to 50M QRD/s throughput and a latency of 59 clock cycles with only 218 kilo-gates (KG). Compared to the previous works, the proposed design achieves the highest normalized throughput efficiency and lowest processing latency.
Shucong TIAN Meng YANG Jianpeng WANG
Z-complementary pairs (ZCPs) were proposed by Fan et al. to make up for the scarcity of Golay complementary pairs. A ZCP of odd length N is called Z-optimal if its zero correlation zone width can achieve the maximum value (N + 1)/2. In this letter, inserting three elements to a GCP of length L, or deleting a point of a GCP of length L, we propose two constructions of Z-optimal ZCPs with length L + 3 and L - 1, where L=2α 10β 26γ, α ≥ 1, β ≥ 0, γ ≥ 0 are integers. The proposed constructions generate ZCPs with new lengths which cannot be produced by earlier ones.