


The research and education activities in the laboratory focus on the theoretical foundations of computers and computations, including broad applications in computer science and engineering. Our work covers algorithms and computation, programming languages, discrete mathematics, statistical signal processing, cryptography, neurocomputing, optimization, simulated acoustics and related topics.
Areas of our research interest include
Combinatorics and its applications;
Spread spectrum communication;
Sparse representation and sparse component analysis;
ContentAware networking;
Secure Cloud Computing, SOA, xAAS;
Quantum cryptography,
Signal processing;
Image analysis for measurement;
Enterprise Integration and Messaging Network:
Information Security Management;
Networks;
GRID as a Service Oriented Architecture Based System;
Security and management of computer system for eGovernment;
Blind source separation and independent component analysis, and their applications in acoustic signals and vital signs;
Neural computing and brainstyle signal processing;
Optimization and machine learning;
Timereversal wave propagation in ergodic environment and its applications in acoustics, ultrasonics and telecommunications;
Information theory and algorithmic complexity.
smart grid
radar pulse compression
The following combined research is running:
independent component analysis and sequence design.
independent component analysis and network anomaly detection.
distributed data store grids loosely coupled integration and cryptography.
Faculty of the FCS laboratory teach Computer Literacy, Programming I, Algorithms and Data Structures, Advanced Algorithms, Digital Signal Processing, Linear Systems, Information Security, SCCPs and other selective courses. Students join faculty research and also develop their own research themes. We participate in various research projects of JSPS, NIFS, RIKEN etc.
Y. Sun, Y. Tang, S. Ding, S. Lv, and Y. Cui. Diagnose the mild cognitive impairment by constructing Bayesian network with missing data. Expert Systems with Applications, 38(1):442449, 2011.
Mild Cognitive Impairment (MCI) is thought to be the prodromal phase to Alzheimer’s disease (AD), which is the most common form of dementia and leads to irreversible neurogenerative damage of the brain. In order to further improve the diagnostic quality of the MCI, we developed a MCI expert system to address MCI’ s prediction and inference question, consequently, assist the diagnosis of doctor. In this system, we mainly deal with following problems: (1) Estimate missing data in the experiment by utilizing mutual information and Newton interpolation. (2) Make certain the prior feature ordering in constructing Bayesian network. (3) Construct the Bayesian network (We term the algorithm as MNBN). The experimental results indicate that MNBN algorithm achieved better results than some existing methods in most instances. The mean square error comes to 0.0173 in the MCI experiment. Our results shed light on the potential application in MCI diagnosis.
Z. Tang, S. Ding, and Z. Yang. Dictionary Learning for Sparse Representation by Nonnegative Matrix Factorization with Constraint of DeterminantType of Maximization. ICIC Express Letters, 6(2):509515, 2012.
How to learn an effective dictionary for a sparse representation of signals is an important topic in machine learning, sparse coding, blind source separation, etc. Recently, a signal analysis method based on the nonnegative matrix factorization (NMF), in which one data matrix is factorized into two factor matrices with different properties, attracts more and more attentions. Interestingly, the factorization process by NMF can correspond with the sparse representation of signals in some extent. In this paper, we propose a novel method based on NMF for overcomplete dictionary learning of sparse representation. And we present a determinanttype criterion to constrain the solutions of nonnegative matrix factorization to satisfy some kind of sparsity property. Numerical experiments with synthetic signals and realworld images are performed, which show the effectiveness of the proposed method.
W. Liu, Q. Liu, M. Jin, and S. Ding. An Iterative Algorithm for Joint Beamforming and DoA Estimation. International Journal of Innovative Computing, Information and Control, 7(5B):30193032, 2011.
The Multiple Signal Classification (MUSIC) algorithm for DoA is known to degrade due to imprecise knowledge about the array manifold. In this paper, we present a theorem to show how imprecise knowledge affects the performance of the MUSIC algorithm. This theorem proves that performance of the MUSIC algorithm degrades less if the array responses of the sources impinging on the array are less correlated with each other, or if just a single source exists. This result inspired us to develop a method for improving DoA estimation. That is, in estimating a specific source’s DoA, we try to remove the influences of other sources from the array output, so that the input includes only a single source approximately. If so, the MUSIC algorithm should be relatively robust, because only one source is approximately involved in the estimation. A beamformer, at least approximately, can serve this purpose. On the other hand, more exact DoA estimation can further improve beamforming. As these two steps iteratively continue, we can obtain much more exact beamforming and DoA estimation. On the basis of this idea, we propose an iterative algorithm for intercooperative beamforming and DoA estimation. Our numerical experiments show the validity of the proposed algorithm.
Z. Tang, Z. Yang, and S. Ding. A New Dictionary Learning Method for Sparse Representation. ICIC Express Letters, 5(5):15351540, 2011.
How to learn an effective dictionary for a sparse representation of signal is an important topic in machine learning, sparse coding, blind source separation, etc. Recently, dictionary selection (DS) method has been developed for learning a dictionary from a mass of candidates of atoms, such as discrete cosine transform (DCT), Wavelets, etc. It is an interesting topic to make a dictionary by selecting atoms from candidate atoms composing original signal. However, the resultant dictionary tends to be too redundant among the atoms. For solving this problem, in this paper, we propose making a sparse decomposition of the dictionary obtained by DS method by a nonnegative matrix factorization (NMF) in order to reduce redundancy. Experiment results show that the proposed method can yield effective dictionary and the resulting image representation is better than that of the DCT dictionary and very close to that of the ksingular value decomposition (KSVD) dictionary.
Z. Yang, G. Zhou, S. Xie, S. Ding, J. Yang, and J. Zhang. Blind Spectral Unmixing Based on Sparse Nonnegative Matrix Factorization. IEEE Transactions on Image Processing, 20(4):11121125, 2011.
Nonnegative matrix factorization (NMF) is a widely used method for blind spectral unmixing (SU), which aims at obtaining the endmembers and corresponding fractional abundances, knowing only the collected mixing spectral data. It is noted that the abundance may be sparse (i.e., the endmembers may be with sparse distributions) and sparse NMF tends to lead to a unique result, so it is intuitive and meaningful to constrain NMF with sparseness for solving SU. However, due to the abundance sumtoone constraint in SU, the traditional sparseness measured by L0/L1norm is not an effective constraint any more. A novel measure (termed as Smeasure) of sparseness using higher order norms of the signal vector is proposed in this paper. It features the physical significance. By using the Smeasure constraint (SMC), a gradientbased sparse NMF algorithm (termed as NMFSMC) is proposed for solving the SU problem, where the learning rate is adaptively selected, and the endmembers and abundances are simultaneously estimated. In the proposed NMFSMC, there is no pure index assumption and no need to know the exact sparseness degree of the abundance in prior. Yet, it does not require the preprocessing of dimension reduction in which some useful information may be lost. Experiments based on synthetic mixtures and realworld images collected by AVIRIS and HYDICE sensors are performed to evaluate the validity of the proposed method.
Takao Maeda and Takafumi Hayashi. Parameterization of Perfect Sequences of Real Numbers. IEICE Trans. Fundamentals, E94A(7):1401 1407, july 2011.
A perfect sequence is a sequence having an impulsive autocorrelation function. Perfect sequences have several applications, such as CDMA, ultrasonic imaging, and position control. A parameterization of a perfect sequence is presented in the present paper. We treat a set of perfect sequences as a zero set of quadratic equations and prove a decomposition law of perfect sequences. The decomposition law reduces the problem of the parameterization of perfect sequences to the problem of the parameterization of quasiperfect sequences and the parameterization of perfect sequences of short length. The parameterization of perfect sequences for simple cases and quasiperfect sequences should be helpful in obtaining a parameterization of perfect sequences of arbitrary length. According to our theorem, perfect sequences can be represented by a sum of trigonometric functions.
Takafumi Hayashi, Takao Maeda, and Satoshi Okawa. A Generalized Construction of ZeroCorrelation Zone Sequence Set with Sequence Subsets. IEICE Trans. Fundamentals, E94A(7):15971602, Jul. 2011.
A perfect array is an array for which the autocorrelation function is impulsive. A parameterization of perfect arrays of real numbers is presented. Perfect arrays are represented by trigonometric functions. Three formulae are obtained according to the parities of the size of the array. Examples corresponding to each formula are shown. In the case of 66 arrays, the existence of a set of perfect arrays having integer components is shown.
Takafumi Hayashi, Takao Maeda, Shinya Matsufuji, and Satoshi Okawa. A Ternary ZeroCorrelation Zone Sequence Set Having Wide InterSubset ZeroCorrelation Zone. IEICE Trans. Fundamentals, E94A(11):22302235, nov 2011.
The present paper introduces a novel construction of ternary sequences having a zerocorrelation zone. The crosscorrelation function and the sidelobe of the autocorrelation function of the proposed sequence set is zero for the phase shifts within the zerocorrelation zone. The proposed sequence set consists of more than one subset having the same member size. The correlation function of the sequences of a pair of different subsets, referred to as the intersubset correlation function, has a wider zerocorrelation zone than that of the correlation function of sequences of the same subset (intrasubset correlation function). The wide intersubset zerocorrelation enables performance improvement during application of the proposed sequence set. The proposed sequence set has a zerocorrelation zone for periodic, aperiodic, and odd correlation functions.
Takao Maeda and Takafumi Hayashi. Parameterization of Perfect Arrays of Real Numbers. IEICE Trans. Fundamentals, E94A(11):2178 2187, july 2011.
A perfect array is an array for which the autocorrelation function is impulsive. A parameterization of perfect arrays of real numbers is presented. Perfect arrays are represented by trigonometric functions. Three formulae are obtained according to the parities of the size of the array. Examples corresponding to each formula are shown. In the case of 66 arrays, the existence of a set of perfect arrays having integer components is shown.
X. Guo, Y. Toyoda, H. Li, J. Huang, S. Ding, and Y. Liu. Environmental Sound Recognition Using TimeFrequency Intersection Patterns. In Proc. 3rd International Conference on Awareness Science and Technology (iCAST 2011), pages 244247. iCAST, IEEE, Sep. 2011.
Environmental sound recognition is an important function of robots and intelligent computer systems. In this research, we tried to use a multistage perceptron type neural network system for environmental sound recognition. The input data is the onedimensional combination of instantaneous spectrum at power peak and the power pattern in time domain. Since for almost environmental sounds, their spectrum changes are not remarkable compared with speech or voice, the combination of power and frequency pattern will preserve the major features of environmental sounds but with drastically reduced data. Two experiments were conducted using an original database and a database created by the RWCP. The recognition rate for about 45 data kinds of environmental sound was about 92combines the power pattern and the instantaneous spectrum of sound data. Comparing with the method using only instantaneous spectrum, the new method are sufficient for larger sound database and the recognition rate was increased about 12while those methods require 2dimensional spectrum Lime series data and more complicated computation.
Y. Zhao, H. Zhang, W. Liu, and S. Ding. A Snoring Detector for OSAHS Based on the Patient's Individual Personality. In Proc. 3rd International Conference on Awareness Science and Technology (iCAST 2011), pages 24 27. iCAST, IEEE, Sep. 2011.
A conventional diagnostic tool for assessing Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is polysomnography (PSG), which is expensive and uncomfortable for patients. It is an important and urgent topic to find a noninvasive and lowcost diagnostic approach for OSAHS detection. Recently, the snore signal analysis receives much attention due to its potential capability for OSAHS detection. In this paper, we propose a novel method for diagnosing OSAHS based on patient’s individual personality. First, the first formant frequencies of each snorer are classified into two clusters by Kmeans clustering. And then, using the first cluster center of each snorer, we set a personalized threshold to distinguish the hypopneic snores from the normal ones. Since the proposed threshold varies with each individual, the patient‚s individual personality can be overcome effectively. Experimental results show the validity of the proposed detector. In the experiments, the sensitivity of our method can achieve 90
Q. Piao, Z. Tang, and S. Ding. Blind Source Separation Based on the Support Recovery of PilotSignals. In Proc. 3rd International Conference on Awareness Science and Technology (iCAST 2011), pages 496501. iCAST, IEEE, Sep. 2011.
Blind source separation (BSS) has been widely discussed since it has many real applications. Recently, under the assumption that mixing matrix is orthogonal and source signals are sparse, Mishali et al. developed an amazing BSS method by using the support recovery of sources and the singular value decomposition (SVD). However, the performance of the algorithm is not as good as expected. In this paper, we present a novel BSS method that is performed by an identification of the mixing matrix by introducing the socalled pilotsignals. The pilotsignals are not required to be known, rather, they are required to have a known extent of sparsity. The method includes two phases, the mixing matrix estimation and the separation phases. The estimation phase is constructed with iterating of the three parts, support recovery, mixing matrix identification and pilotsignals recovery. The numerical experiments show that proposed method can efficiently converge and can recover the unknown source signals efficiently.
Takafumi Hayashi, Takao Maeda, and Shigeru Kanemoto. ZeroCorrelation Zone Sequence Sets having Subsets and Its Application to Instrumentation. In Proc. SICE 2011, pages 2528. SICE, Sept. 2011.
The present paper introduces the construction of a class of sequence sets with zerocorrelation zones called zerocorrelation zone sequence sets. The proposed zerocorrelation zone sequence set can be generated from an arbitrary perfect sequence of length L_{p} = k(2n+1)+1, and a Hadamard matrix of order L_{h}, where L_{p} and L_{h} are comprime to each other. In an ultrasonic synthetic aperture imaging system, the proposed sequence set can improve the signaltonoise ratio of the acquired image. The constructed sequence set consists of 2nL_{h} ternary sequences, each of length 2^{m+1}L_{p}L_{h}, for a nonnegative integer m. The zerocorrelation zone of the proposed sequences is γ ≤ 2^{m+1}k − 1, where γ is the phase shift. The intersubset zerocorrelation zone of the proposed sequences is γ ≤ 2^{m+2}k, where t is the phase shift. The proposed scheme can improve radars using the zerocorrelation property of the sequence set.
Takafumi Hayashi, Hideyuki Fukuhara, Masayuki Hisada, Kazunori Suzuki, Takuto Yamada, Yodai Watanabe, Junya Terazono, Taro Suzuki, Toshiaki Miyazaki, Senro Saito, Isamu Koseda, and Jiro Iwase. A NetworkCentric Approach to Sensordata and Service Integration. In Proc. SICE 2011, pages 2528. SICE, Sept. 2011.
Sensor net require the information integration of various sensors and related contents and services. The present paper describes an approach to the construction of a sensor network using a contentaware network so called messaging network. A messaging network can be constructed as a structured overlay network. The proposed scheme enables looselycoupled integration of sensor data and related services. The proposed scheme can be realized by an overlay network over an ordinary IP network. The paper also introduces a policy mediation, which is a kind of message mediation, for overlay networks having each own policies enables secure overlay networks interoperation. This paper also introduces a secure datastore grids for sensor network. The datastore grids helps to construct and manage as secure, flexible, elastic, and sustainable loosely coupled integration of sensor data and related services. The application of the proposed scheme to SmartGrid and healthcare system are be discussed.
Takao Maeda, Shigeru Kanemoto and T. Hayashi. Pulse compression for radar using zerocorrelation zone sequence sets. In Proc. IEEE IGARSS 2011, pages 34403443, Jul. 2011.
The present paper introduces a new approach to the application of sequence set with a zerocorrelation zone to the pulse compression of radar. The proposed sequence set has a zerocorrelation zone for both periodic and aperiodic correlation functions. The sequences of the proposed scheme can be constructed from a pair of Hadamard matrices of the orders n_{0} and n_{1} . The constructed sequence set consists of n_{0}n_{1} ternary sequences, each of length n_{0}^{(m+2)} (n_{1} + Δ;), for a nonnegative integer m and Δ ≥ 2. The zerocorrelation zone of the proposed sequences is γ ≤ Δn_{0}^{m+1} − 1, where γ is the phase shift. The proposed scheme can improve radars using the zerocorrelation property of the sequence set.
Takafumi Hayashi, Takao Maeda, Shigeru Kanemoto, and Shinya Matsufuji. A novel Construction of ZeroCorrelation Zone Sequence Set with Wide InterSubset ZeroCorrelation Zone. In Proc. IWSDA 2011, pages 2528. IEEE, nov. 2011.
The present paper introduces a new approach to the construction of a sequence set with a zerocorrelation zone (ZCZ). The proposed sequence construction generates a ZCZ sequence set from a perfect sequence pair or a single perfect sequence. The member size of the proposed sequence set approaches the theoretical bound. The proposed sequence set consists of L_{g} subsets, where a Hadamard matrix of order L_{g} is used in the sequence construction. The correlation function of the sequences of a pair of different subsets, intersubset correlation function, has a ZCZ with a width that is (Λ + 1) times that of the intrasubset correlation function for a positive integer Λ ≥ 1. The wide intersubset zerocorrelation improves the performance of the applications of the proposed sequence set.
S. Ding and A. Cichocki. Special Issue on Aware Science and Technology, International Journal of Innovative Computing, Information and Control (ISSN: 13494198, SCI Journal List), Vol. 7, No. 5(B), Guest editors. ICIC International, 2011.
Yodai Watanabe. GrantinAid for Encouragement of Young Scientists (B) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan, 20062008.
Yodai Watanabe. GrantinAid for Encouragement of Young Scientists (B) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan, 20032005.
Yodai Watanabe. GrantinAid for Encouragement of Young Scientists (B) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan, 20092011.
S. Ding, 2011.
IEICE, Membership
S. Ding, 2011.
General CoChair of 2011 IEEE International Conference on Awareness Science and Technology (iCAST 2011)
S. Ding, 2011.
Program and Steering Committee member of The Sixth International Conference on Innovative Computing, Information and Control (ICICIC2011)
S. Ding, 2011.
Program Committee member of Fourth International Symposium on Intelligent Informatics (ISII2011)
S. Ding, 2011.
Institute of Electrical and Electronics Engineers (IEEE), Membership.
S. Ding, 2011.
IEEE Signal Processing Society, Membership. Reviewers for the IEEE Transactions on Signal Processing and IEEE Signal Processing Letters
S. Ding, 2011.
The Association for Computing Machinery (ACM), Membership
S. Ding, 2011.
The Acoustical Society of America (ASA), Membership
S. Ding, 2011.
Review committee member candidate for GrantsInAid for Scientific Research projects, JSPS
Takafumi Hayashi, 2010.
Reviewer of Eletctronics Letters, IET
Takafumi Hayashi, 2010.
Reviewer of OE Magazine, SPIE
Takafumi Hayashi, 2010.
Program Chair of CIT2010, IEEE
Takafumi Hayashi, 2010.
Program Chair of CIT2010, IEEE
Takafumi Hayashi, 2010.
Reviewer of IEEE Signal Processing Letters
Takafumi Hayashi, 2010.
Reviewer of IEEE Communication Letters
Takafumi Hayashi, 2010.
Reviewer of IEICE Transactions
Takafumi Hayashi, 2010.
Program Chair of CIT2010
Takafumi Hayashi, 2010.
Reviewer of ICC, IEEE
Wataru Matsumoto and Yodai Watanabe. Quantum key delivery method and communication device Registered United States Patent 7,461,323, December 2008.
Yodai Watanabe. Quantum Key Distribution Method, Communication System, and Communication Device Registered Japanese Patent 4231926, December 2008.
Wataru Matsumoto and Yodai Watanabe. Quantum key distribution method and communication device Registered United States Patent 7,609,839, October 2009.
Wataru Matsumoto and Yodai Watanabe. Quantum key distribution method and communication device Registered Japanese Patent 4346929, July 2009.
Yodai Watanabe. Quantum Key Distribution Method, Communication System, and Communication Device Registered Japanese Patent 4862159, November 2011.
Wataru Matsumoto and Yodai Watanabe. Quantum key delivery method and communication device Registered Japanese Patent 4290401, April 2009.
Hiroyuki Abe. Computer Simulation of the 3+1dimensional Time Reversal Acoustics. Master thesis, Graduate School of Computer Science and Engineering, 2012.
Thesis Adviser: S. Ding
Keiji Muto. Sparse Representation of NonSparse Signal and an Advanced Recovery Method of the NonSparse from the Representation. Master thesis, Graduate School of Computer Science and Engineering, 2012.
Thesis Adviser: S. Ding
Yusuke Baba. Independent Component Analysis of Sparse Signal by Maximizing Negentropy Approximated with Two NonLinear Functions. Graduation thesis, School of Computer Science and Engineering, 2012.
Thesis Adviser: S. Ding
Youichi Maeda. Independent Component Analysis Based on Generalized Autocorrelation. Graduation thesis, School of Computer Science and Engineering, 2012.
Thesis Adviser: S. Ding
Yusei Yabuki. n Intelligent Infrastructure for Loosely Coupled Integration of Heterogeneous Services. Master thesis, Graduate School of Computer Science and Engineering, March 2012.
Thesis Adviser: T. Hayashi
Yoshinobu Tanno. Optimization of Information System Resources Based on Information of System User Behaviors. Master thesis, Graduate School of Computer Science and Engineering, 2012.
Thesis Adviser: T. Hayashi
Tomohiro Warashina. A Scalable Intelligent Infrastructure for Largescale Heterogeneous Data Analysis. Graduation thesis, School of Computer Science and Engineering, 2012.
Thesis Adviser: T. Hayashi
Marina Watanabe. Electronic prescription management system with basic resident registration card. Graduation thesis, School of Computer Science and Engineering, March 2012.
Thesis Adviser: T. Hayashi
Satoshi Takahashi. Intelligent Infrastructure for Allocating Knowledge Sharing on the Internet. Graduation thesis, School of Computer Science and Engineering, 2012.
Thesis Adviser: T. Hayashi
Yohei Sato. An Intelligent Infrastructure for Disaster Management. Graduation thesis, School of Computer Science and Engineering, 2012.
Thesis Adviser: T. Hayashi
Yuta Takahashi. Information Infrastructure for Integration in Smart Grid. Graduation thesis, University of Aizu, 2012.
Thesis Adviser: T. Hayashi