Non-negative matrix factorization (NMF) is widely used for monaural musical sound source separation because of its efficiency and good performance. However, an additional clustering process is required because the musical sound mixture is separated into more signals than the number of musical tracks during NMF separation. In the conventional method, manual clustering or training-based clustering is performed with an additional learning process. Recently, a clustering algorithm based on the mel-frequency cepstrum coefficient (MFCC) was proposed for unsupervised clustering. However, MFCC clustering supplies limited information for clustering. In this paper, we propose various timbre features for unsupervised clustering and a clustering algorithm with these features. Simulation experiments are carried out using various musical sound mixtures. The results indicate that the proposed method improves clustering performance, as compared to conventional MFCC-based clustering.

Network structure has a great impact both on hazard spread and network immunization. The vulnerability of the network node is associated with each other, assortative or disassortative. Firstly, an algorithm for vulnerability relevance clustering is proposed to show that the vulnerability community phenomenon is obviously existent in complex networks. On this basis, next, a new indicator called network “hyper-betweenness” is given for evaluating the vulnerability of network node. Network hyper-betweenness can reflect the importance of network node in hazard spread better. Finally, the dynamic stochastic process of hazard spread is simulated based on Monte-Carlo sampling method and a two-player, non-cooperative, constant-sum game model is designed to obtain an equilibrated network immunization strategy.

This letter reveals that whole link reciprocity does not exist in general amplify-and-forward (AF) time division duplex (TDD) relay systems due to the gain matrix. To resolve this problem, a novel gain matrix design method is proposed. Any existing gain matrix design criterion can be adopted in the downlink (uplink) to ensure optimal performance, and the proposed scheme is used in the uplink (downlink), with small adjustment, to keep whole link reciprocity. Simulation results show that, the proposed method can maintain whole link reciprocity without performance loss.

The present paper introduces a novel construction of ternary sequences having a zero-correlation zone. The cross-correlation function and the side-lobe of the auto-correlation function of the proposed sequence set is zero for the phase shifts within the zero-correlation 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 inter-subset correlation function, has a wider zero-correlation zone than that of the correlation function of sequences of the same subset (intra-subset correlation function). The wide inter-subset zero-correlation enables performance improvement during application of the proposed sequence set. The proposed sequence set has a zero-correlation zone for periodic, aperiodic, and odd correlation functions.

In this paper, a geometric source separation system using nonnegative matrix factorization (NMF) is proposed. The adaptive beamformer is the best method for geometric source separation, but it suffers from a “target signal cancellation” problem in multi-path situations. We modified the HALS-NMF algorithm for decomposition into bases, and developed an interference suppression module in order to cancel the interference bases. A performance comparison between the proposed and subband GSC-RLS algorithm using a MATLAB® simulation was executed; the results show that the proposed system is robust in multi-path situations.

This letter considers the problem of detecting an offset quadrature phase shift keying (O-QPSK) modulated signal in colored Gaussian noise. The generalized likelihood ratio test (GLRT) is employed for detection. By deriving the GLRT, it is shown that the assumption of colored Gaussian noise results in a more complicated problem than with the white noise assumption that was previously examined in the literature. An efficient solution for the detection maximization problem is proposed, based on which the GLRT is implemented. Performance results are presented to illustrate the detector performance.

Histogram-based image features such as HoG, SIFT and histogram of visual words are generally represented as high-dimensional, non-negative vectors. We propose a supervised method of reducing the dimensionality of histogram-based features by using non-negative matrix factorization (NMF). We define a cost function for supervised NMF that consists of two terms. The first term is the generalized divergence term between an input matrix and a product of factorized matrices. The second term is the penalty term that reflects prior knowledge on a training set by assigning predefined constants to cannot-links and must-links in pairs of training data. A multiplicative update rule for minimizing the newly-defined cost function is also proposed. We tested our method on a task of scene classification using histograms of visual words. The experimental results revealed that each of the low-dimensional basis vectors obtained from the proposed method only appeared in a single specific category in most cases. This interesting characteristic not only makes it easy to interpret the meaning of each basis but also improves the power of classification.

In this paper, a novel algorithm, Cross Low-dimension Pursuit, based on a new structured sparse matrix, Permuted Block Diagonal (PBD) matrix, is proposed in order to recover sparse signals from incomplete linear measurements. The main idea of the proposed method is using the PBD matrix to convert a high-dimension sparse recovery problem into two (or more) groups of highly low-dimension problems and crossly recover the entries of the original signal from them in an iterative way. By sampling a sufficiently sparse signal with a PBD matrix, the proposed algorithm can recover it efficiently. It has the following advantages over conventional algorithms: (1) low complexity, i.e., the algorithm has linear complexity, which is much lower than that of existing algorithms including greedy algorithms such as Orthogonal Matching Pursuit and (2) high recovery ability, i.e., the proposed algorithm can recover much less sparse signals than even 1-norm minimization algorithms. Moreover, we demonstrate both theoretically and empirically that the proposed algorithm can reliably recover a sparse signal from highly incomplete measurements.

In this letter, we propose an appealing class of nonbinary quasi-cyclic low-density parity-check (QC-LDPC) cycle codes. The parity-check matrix is carefully designed such that the corresponding generator matrix has some nice properties: 1) systematic, 2) quasi-cyclic, and 3) sparse, which allows a parallel encoding with low complexity. Simulation results show that the performance of the proposed encoding-aware LDPC codes is comparable to that of the progressive-edge-growth (PEG) constructed nonbinary LDPC cycle codes.

This paper presents a software-based parallel cryptographic solution with a massive-parallel memory-embedded SIMD matrix (MTX) for data-storage systems. MTX can have up to 2,048 2-bit processing elements, which are connected by a flexible switching network, and supports 2-bit 2,048-way bit-serial and word-parallel operations with a single command. Furthermore, a next-generation SIMD matrix called MX-2 has been developed by expanding processing-element capability of MTX from 2-bit to 4-bit processing. These SIMD matrix architectures are verified to be a better alternative for processing repeated-arithmetic and logical-operations in multimedia applications with low power consumption. Moreover, we have proposed combining Content Addressable Memory (CAM) technology with the massive-parallel memory-embedded SIMD matrix architecture to enable fast pipelined table-lookup coding. Since both arithmetic logical operation and table-lookup coding execute extremely fast on these architectures, efficient execution of encryption and decryption algorithms can be realized. Evaluation results of the CAM-less and CAM-enhanced massive-parallel SIMD matrix processor for the example of the Advanced Encryption Standard (AES), which is a widely-used cryptographic algorithm, show that a throughput of up to 2.19 Gbps becomes possible. This means that several standard data-storage transfer specifications, such as SD, CF (Compact Flash), USB (Universal Serial Bus) and SATA (Serial Advanced Technology Attachment) can be covered. Consequently, the massive-parallel SIMD matrix architecture is very suitable for private information protection in several data-storage media. A further advantage of the software based solution is the flexible update possibility of the implemented-cryptographic algorithm to a safer future algorithm. The massive-parallel memory-embedded SIMD matrix architecture (MTX and MX-2) is therefore a promising solution for integrated realization of real-time cryptographic algorithms with low power dissipation and small Si-area consumption.

Most document clustering methods are a challenging issue for improving clustering performance. Document clustering based on semantic features is highly efficient. However, the method sometimes did not successfully cluster some documents, such as highly articulated documents. In order to improve the clustering success of complex documents using semantic features, this paper proposes a document clustering method that uses terms of the condensing document clusters and fuzzy association to efficiently cluster specific documents into meaningful topics based on the document set. The proposed method improves the quality of document clustering because it can extract documents from the perspective of the terms of the cluster topics using semantic features and synonyms, which can also better represent the inherent structure of the document in connection with the document cluster topics. The experimental results demonstrate that the proposed method can achieve better document clustering performance than other methods.

A lot of improvements and optimizations for the hardware implementation of SubBytes of Rijndael, in detail inversion in F28 have been reported. Instead of the Rijndael original F28, it is known that its isomorphic tower field F((22)2)2 has a more efficient inversion. Then, some conversion matrices are also needed for connecting these isomorphic binary fields. According to the previous works, it is said that the number of 1's in the conversion matrices is preferred to be small; however, they have not focused on the Hamming weights of the row vectors of the matrices. It plays an important role for the calculation architecture, in detail critical path delays. This paper shows the existence of efficient conversion matrices whose row vectors all have the Hamming weights less than or equal to 4. They are introduced as quite rare cases. Then, it is pointed out that such efficient conversion matrices can connect the Rijndael original F28 to some less efficient inversions in F((22)2)2 but not to the most efficient ones. In order to overcome these inconveniences, this paper next proposes a technique called mixed bases. For the towerings, most of previous works have used several kinds of bases such as polynomial and normal bases in mixture. Different from them, this paper proposes another mixture of bases that contributes to the reduction of the critical path delay of SubBytes. Then, it is shown that the proposed mixture contributes to the efficiencies of not only inversion in F((22)2)2 but also conversion matrices between the isomorphic fields F28 and F((22)2)2.

This paper presents a novel technique to realize Karnin et al.'s (k,n)-threshold schemes over binary field extensions as a software. Our realization uses the matrix representation of finite fields and matrix-vector multiplications, and enables rapid operations in software implementation. The theoretical evaluation and computer simulation reveal that our realization of Karnin et al.'s scheme achieves much faster processing time than the ordinary symbol oriented realization of the scheme. Further, we show that our realization has comparable performance to the existing exclusive-OR-based fast schemes of Fujii et al. and Kurihara et al.

We study nn random symmetric matrices whose entries above the diagonal are iid random variables each of which takes 1 with probability p and 0 with probability 1-p, for a given density parameter p=α/n for sufficiently large α. For a given such matrix A, we consider a matrix A ' that is obtained by removing some rows and corresponding columns with too many value 1 entries. Then for this A', we show that the largest eigenvalue is asymptotically close to α+1 and its eigenvector is almost parallel to all one vector (1,...,1).

State Transition Matrix (STM) is a table-based modeling language that has been frequently used in industry for specifying behaviors of systems. Functional correctness of a STM design (i.e., a design developed with STM) could often be expressed as invariant properties. In this paper, we first present a formalization of the static and dynamic aspects of STM designs. Consequentially, based on this formalization, we investigate a symbolic encoding approach, through which a STM design could be bounded model checked w.r.t. invariant properties by using Satisfiability Modulo Theories (SMT) solving technique. We have built a prototype implementation of the proposed encoding and the state-of-the-art SMT solver - Yices, is used in our experiments to evaluate the effectiveness of our approach. Two attempts for accelerating SMT solving are also reported.

An advanced interest point detector is proposed to improve the Hessian-Matrix based detector of the SURF algorithm. Round-like shapes are utilized as the filter shape to calculate of the Hessian determinant. Dxy can be acquired from approximate round areas, while the regions for computing Dyy or Dxx are designed with the consideration to symmetry and a balance of pixel number. Experimental results indicate that the proposed method has higher repeatability than the one used in SURF, especially in the aspects of rotation and viewpoint, due to the centrosymmetry of the proposed filter shapes. The results of image matching also show that more precision can be gained with the application of proposed detector.

This research aims to accelerate the computation module in max-plus algebra using CUDA technology on graphics processing units (GPUs) designed for high-performance computing. Our target is the Kleene star of a weighted adjacency matrix for directed acyclic graphs (DAGs). Using a inexpensive GPU card for our experiments, we obtained more than a 16-fold speedup compared with an Athlon 64 X2.

We edit in this paper several archives on the research and development in the field of microwave circuit technology in Japan, that originated with the invention of Yagi-Uda antenna in 1925, together with generally unknown historical topics in the period from the 1920s up until the end of World War II. As the main subject, we investigate the origin and evolution of the Multiply Split-Anode Magnetron, and clarify that the basic magnetron technology had been established until 1939 under the direction of Yoji Ito in cooperation of expert engineers between the Naval Technical Institute (NTI) and the Nihon Musen Co., while the Cavity Magnetron was invented by Shigeru Nakajima of the Nihon Musen Co. in May 1939, and further that physical theory of the Multiply Split-Anode Cavity Magnetron Oscillation and the design theory of the Cavity Magnetron were established in collaboration between the world-known physicists and the expert engineers at the NTI Shimada Laboratory in the wartime. In addition, we clarify that Sin-itiro Tomonaga presented the Scattering Matrix representation of Microwave Circuits, and others. The development mentioned above was carried out, in strict secrecy, in an unusual wartime situation up until 1945.

In this letter, an eigenspace of network topology is introduced to increase a spatial capacity. The network topology is represented as an adjacency matrix. By an eigenvector of adjacency matrix, efficient two way transmission can be realized in wireless distributed networks. It is confirmed by numerical analysis that the scheme with an eigenvector of adjacency matrix supplies higher spatial capacity and reliability than that of conventional scheme.

We investigate binary sequence pairs with two-level correlation in terms of their corresponding cyclic difference pairs (CDPs). We define multipliers of a cyclic difference pair and present an existence theorem for multipliers, which could be applied to check the existence/nonexistence of certain hypothetical cyclic difference pairs. Then, we focus on the ideal case where all the out-of-phase correlation coefficients are zero. It is known that such an ideal binary sequence pair exists for length υ = 4u for every u ≥ 1. Using the techniques developed here on the theory of multipliers of a CDP and some exhaustive search, we are able to determine that, for lengths υ ≤ 30, (1) there does not exist "any other" ideal/ binary sequence pair and (2) every example in this range is equivalent to the one of length υ = 4u above. We conjecture that if there is a binary sequence pair with an ideal two-level correlation then its in-phase correlation must be 4. This implies so called the circulant Hadamard matrix conjecture.