Code construction for digital fingerprinting, which is a copyright protection technique for multimedia, is considered. Digital fingerprinting should deter collusion attacks, where several fingerprinted copies of the same content are mixed to disturb their fingerprints. In this paper, we consider the averaging attack, which is known to be effective for multimedia fingerprinting with the spread spectrum technique. We propose new methods for constructing fingerprinting codes to increase the coding rate of conventional fingerprinting codes, while they guarantee to identify the same number of colluders. Due to the new fingerprinting codes, the system can deal with a larger number of users to supply digital contents.

A class of zero-correlation zone (ZCZ) sequences constructed by the recursive procedure from a perfect sequence and a unitary matrix was proposed by Torii, Nakamura, and Suehiro [1] . In the reference [1] , three parameters, s.t., the sequence length, the family size and the length of the ZCZ, were evaluated for a general estimate of the performance of the ZCZ sequences. In this letter, we give more detailed distributions of that correlation values are zero on their ZCZ sequence sets.

This paper proposes a DCT-based steganographic method named StegErmelc in the JPEG domain. Three strategies are proposed, namely (i) edge-like block selection, (ii) recursive matrix encoding, and (iii) largest coefficient serving, to form a novel steganographic method for achieving scalable carrier capacity, low detectability by universal blind steganalyzer, and high image quality, simultaneously. For a given message length, StegErmelc flexibly scales its carrier capacity to accommodate the message while trading off with stego detectability. At full capacity, StegErmelc has comparable carrier capacity relative to the existing methods. When embedding the same amount of information, StegErmelc remarkably reduces the stego detection rate to about 0.3-0.5 lower than that of the existing methods considered, and consequently StegErmelc can withstand blind steganalyzer when embedding up to 0.10 bpc. Under the same condition, StegErmelc produces stego image with quality higher than that of the existing methods considered. Graphical comparison with three additional evaluation metrics is also presented to show the relative performance of StegErmelc with respect to the existing methods considered.

In multiple input multiple output (MIMO) communication systems, eigenvalues of channel correlation matrices play an essential role for the performance analysis, and particularly the investigation about their behavior under time-variant environment ruled by a certain statistics is an important problem. This paper first gives the theoretical expressions for the marginal distributions of all the ordered eigenvalues of MIMO correlation matrices under i.i.d. (independent and identically distributed) Rayleigh fading environment. Then, an approximation method of those marginal distributions is presented: We show that the theory of SIMO space diversity using maximal ratio combining (MRC) is applicable to the approximation of statistical distributions of all eigenvalues in MIMO systems with the same number of diversity branches. The derived approximation has a monomial form suitable for the calculation of various performance measures utilized in MIMO systems. Through computer simulations, the effectiveness of the proposed method is demonstrated.

This paper describes the matrix order reduction method by the nodal analysis formulation and the application of relaxation-based simulation technique to interconnect and plane networks. First, the characteristics of the power/ground plane networks are considered. Next, the formulation of the plane network by nodal analysis (NA) method is suggested. Furthermore, application and estimation results of the relaxation-based numerical analyses are shown. Finally, it is confirmed that the relaxation-based methods improved by the suggested formulation are much more efficient than the conventional direct-based methods.

In this letter, we propose a new H2 smoother (H2S) with a finite impulse response (FIR) structure for discrete-time state-space signal models. This smoother is called an H2 FIR smoother (H2FS). Constraints such as linearity, quasi-deadbeat property, FIR structure, and independence of the initial state information are required in advance to design H2FS that is optimal in the sense of H2 performance criterion. It is shown that H2FS design problem can be converted into the convex programming problem written in terms of a linear matrix inequality (LMI) with a linear equality constraint. Simulation study illustrates that the proposed H2FS is more robust against uncertainties and faster in convergence than the conventional H2S.

This paper presents an integration architecture of content addressable memory (CAM) and a massive-parallel memory-embedded SIMD matrix for constructing a versatile multimedia processor. The massive-parallel memory-embedded SIMD matrix has 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. The SIMD matrix architecture is verified to be a better way for processing the repeated arithmetic operation types in multimedia applications. The proposed architecture, reported in this paper, exploits in addition CAM technology and enables therefore fast pipelined table-lookup coding operations. Since both arithmetic and table-lookup operations execute extremely fast, the proposed novel architecture can realize consequently efficient and versatile multimedia data processing. Evaluation results of the proposed CAM-enhanced massive-parallel SIMD matrix processor for the example of the frequently used JPEG image-compression application show that the necessary clock cycle number can be reduced by 86% in comparison to a conventional mobile DSP architecture. The determined performances in Mpixel/mm2 are factors 3.3 and 4.4 better than with a CAM-less massive-parallel memory-embedded SIMD matrix processor and a conventional mobile DSP, respectively.

This work presents a novel scalable and systolic Montgomery's algorithm in GF(2m). The proposed algorithm is based on the Toeplitz matrix-vector representation, which obtains the scalable and systolic Montgomery multiplier in a flexible manner, and can adapt to the required precision. Analytical results indicate that the proposed multiplier over the generic field of GF(2m) has a latency of d+n(2n+1), where n = m / d , and d denotes the selected digital size. The latency is reduced to d+n(n+1) clock cycles when the field is constructed from generalized equally-spaced polynomials. Since the selected digital size is d ≥5 bits, the proposed architectures have lower time-space complexity than traditional digit-serial multipliers. Moreover, the proposed architectures have regularity, modularity and local interconnect ability, making them very suitable for VLSI implementation.

This paper addresses the problem of view invariant action recognition using 2D trajectories of landmark points on human body. It is a challenging task since for a specific action category, the 2D observations of different instances might be extremely different due to varying viewpoint and changes in speed. By assuming that the execution of an action can be approximated by dynamic linear combination of a set of basis shapes, a novel view invariant human action recognition method is proposed based on non-rigid matrix factorization and Hidden Markov Models (HMMs). We show that the low dimensional weight coefficients of basis shapes by measurement matrix non-rigid factorization contain the key information for action recognition regardless of the viewpoint changing. Based on the extracted discriminative features, the HMMs is used for temporal dynamic modeling and robust action classification. The proposed method is tested using real life sequences and promising performance is achieved.

Multiple-input multiple-output (MIMO) maximum-SNR (max-SNR) system employs the maximum ratio combiner (MRC) at the receiver side and the maximum ratio transmitter (MRT) at the transmitter side. Its performances highly depend on MIMO channel characteristics, which vary according to both the number of antennas and their distribution between the transmitter and receiver sides. By using the decomposition of the ordered Wishart distribution in the uncorrelated Rayleigh case, we derived a closed-form expression of the largest eigenvalue probability density function (PDF). The final result yields to an expression form of the PDF where polynomials are multiplied by exponentials; it is worth underlining that, though this form had been previously observed for given couples of antennas, to date no formally-written closed-form was available in the literature for an arbitrary couple. Then, this new expression permits one to quickly and easily get the well known largest eigenvalue PDF and use it to determine the binary error probability (BEP) of the max-SNR.

We investigate the problem of joint transmitter and receiver power allocation with the minimax mean square error (MSE) criterion for uplink transmissions in a multi-carrier code division multiple access (MC-CDMA) system. The objective of power allocation is to minimize the maximum MSE among all users each of which has limited transmit power. This problem is a nonlinear optimization problem. Using the Lagrange multiplier method, we derive the Karush-Kuhn-Tucker (KKT) conditions which are necessary for a power allocation to be optimal. Numerical results indicate that, compared to the minimum total MSE criterion, the minimax MSE criterion yields a higher total MSE but provides a fairer treatment across the users. The advantages of the minimax MSE criterion are more evident when we consider the bit error rate (BER) estimates. Numerical results show that the minimax MSE criterion yields a lower maximum BER and a lower average BER. We also observe that, with the minimax MSE criterion, some users do not transmit at full power. For comparison, with the minimum total MSE criterion, all users transmit at full power. In addition, we investigate robust joint transmitter and receiver power allocation where the channel state information (CSI) is not perfect. The CSI error is assumed to be unknown but bounded by a deterministic value. This problem is formulated as a semidefinite programming (SDP) problem with bilinear matrix inequality (BMI) constraints. Numerical results show that, with imperfect CSI, the minimax MSE criterion also outperforms the minimum total MSE criterion in terms of the maximum and average BERs.

In this paper, a generalized Montgomery multiplication algorithm in GF(2m) using the Toeplitz matrix-vector representation is presented. The hardware architectures derived from this algorithm provide low-complexity bit-parallel systolic multipliers with trinomials and pentanomials. The results reveal that our proposed multipliers reduce the space complexity of approximately 15% compared with an existing systolic Montgomery multiplier for trinomials. Moreover, the proposed architectures have the features of regularity, modularity, and local interconnection. Accordingly, they are well suited to VLSI implementation.

Open circuit voltage (OCV) of electrical devices is an issue in various fields, whose numerical evaluation needs careful treatment. The open-circuited structure is ill-conditioned because of the singular electric field at the corners, and the TEM component of the electric field has to be extracted before integrated to give the voltage in the direct method of obtaining the OCV. This paper introduces the indirect methods to calculate the OCV, the admittance matrix method and the Norton theorem method. Both methods are based on the short-circuited structure which is well-conditioned. The explicit expressions of the OCV are derived in terms of the admittance matrix elements in the admittance matrix method, and in terms of the short circuit current and the antenna impedance of the electrical device under consideration in the Norton theorem method. These two methods are equivalent in theory, but the admittance matrix method is suitable for the nearby transmitter cases while the Norton theorem method is suitable for the distant transmitter cases. Several examples are given to show the usefulness of the present theory.

This research considers an efficient method for calculating the transition matrix in an MPL (Max-Plus Linear) state-space representation. This matrix can be generated by applying the Kleene star operator to an adjacency matrix. The proposed method, based on the idea of a topological sort in graph theory and block splitting, is able to calculate the transition matrix efficiently.

This paper proposes an efficient systolic array construction method for optimal planar systolic design of the matrix multiplication. By connection network adjustment among systolic array processing element (PE), the input/output data are jumping in the systolic array for multiplication operation requirements. Various 2-D systolic array topologies, such as square topology and hexagonal topology, have been studied to construct appropriate systolic array configuration and realize high performance matrix multiplication. Based on traditional Kung-Leiserson systolic architecture, the proposed "Jumping Systolic Array (JSA)" algorithm can increase the matrix multiplication speed with less processing elements and few data registers attachment. New systolic arrays, such as square jumping array, redundant dummy latency jumping hexagonal array, and compact parallel flow jumping hexagonal array, are also proposed to improve the concurrent system operation efficiency. Experimental results prove that the JSA algorithm can realize fully concurrent operation and dominate other systolic architectures in the specific systolic array system characteristics, such as band width, matrix complexity, or expansion capability.

We propose the construction of an appearance manifold with embedded view-dependent covariance matrix to recognize 3D objects which are influenced by geometric distortions and quality degradation effects. The appearance manifold is used to capture the pose variability, while the covariance matrix is used to learn the distribution of samples for gaining noise-invariance. However, since the appearance of an object in the captured image is different for every different pose, the covariance matrix value is also different for every pose position. Therefore, it is important to embed view-dependent covariance matrices in the manifold of an object. We propose two models of constructing an appearance manifold with view-dependent covariance matrix, called the View-dependent Covariance matrix by training-Point Interpolation (VCPI) and View-dependent Covariance matrix by Eigenvector Interpolation (VCEI) methods. Here, the embedded view-dependent covariance matrix of the VCPI method is obtained by interpolating every training-points from one pose to other training-points in a consecutive pose. Meanwhile, in the VCEI method, the embedded view-dependent covariance matrix is obtained by interpolating only the eigenvectors and eigenvalues without considering the correspondences of each training image. As it embeds the covariance matrix in manifold, our view-dependent covariance matrix methods are robust to any pose changes and are also noise invariant. Our main goal is to construct a robust and efficient manifold with embedded view-dependent covariance matrix for recognizing objects from images which are influenced with various degradation effects.

We describe an extension of the wideband direction-of-arrival (DOA) estimation method using a frequency-domain frequency-invariant beamformer (FDFIB). The technique uses the Matrix Pencil Method (MPM) instead of conventional methods based on the eigen-structure of the input covariance matrix. MPM offers excellent resolution compared to conventional methods.

Ultra fast switching speed of superconducting digital circuits enable realization of Digital Signal Processors with performance unattainable by any other technology. Based on rapid-single-flux technology (RSFQ) logic, these integrated circuits are capable of delivering high computation capacity up to 30 GOPS on a single processor and very short latency of 0.1 ns. There are two main applications of such hardware for practical telecommunication systems: filters for superconducting ADCs operating with digital RF data and recursive filters at baseband. The later of these allows functions such as multiuser detection for 3G WCDMA, equalization and channel precoding for 4G OFDM MIMO, and general blind detection. The performance gain is an increase in the cell capacity, quality of service, and transmitted data rate. The current status of the development of the RSFQ baseband DSP is discussed. Major components with operating speed of 30 GHz have been developed. Designs, test results, and future development of the complete systems including cryopackaging and CMOS interface are reviewed.

This letter propose a new H∞ smoother (HIS) with a finite impulse response (FIR) structure for discrete-time state-space models. This smoother is called an H∞ FIR smoother (HIFS). Constraints such as linearity, quasi-deadbeat property, FIR structure, and independence of the initial state information are required in advance. Among smoothers with these requirements, we choose the HIFS to optimize H∞ performance criterion. The HIFS is obtained by solving the linear matrix inequality (LMI) problem with a parametrization of a linear equality constraint. It is shown through simulation that the proposed HIFS is more robust against uncertainties and faster in convergence than the conventional HIS.

This letter presents new delayed perturbation bounds (DPBs) for stabilizing receding horizon H∞ control (RHHC). The linear matrix inequality (LMI) approach to determination of DPBs for the RHHC is proposed. We show through a numerical example that the RHHC can guarantee an H∞ norm bound for a larger class of systems with delayed perturbations than conventional infinite horizon H∞ control (IHHC).