We present a scenario-aware bus functional modeling method which improves the accuracy of traditional methods without sacrificing the simulation run time. Existing methods focused on the behavior of individual IP (Intellectual Property) components and neglected the interplay effects among them, resulting in accuracy degradation from the system perspective. On the other hand, our method thoroughly considers such effects and increases the analysis accuracy by adopting control signal modeling and hierarchical stochastic modeling. Furthermore, our method minimizes the additional design time by reusing the simulation results of each IP component and an automated design flow. The experimental results show that the accuracy of our method is over 90% of RTL simulation in a multimedia SoC (System-on-Chip) design.

Business Processes are considered a crucial issue by many enterprises because they are the key to maintain competitiveness. Moreover, business processes are important for software developers, since they can capture from them the necessary requirements for software design and creation. Besides, business process modeling is the center for conducting and improving how the business is operated. Security is important for business performance, but traditionally, it is considered after the business processes definition. Empirical studies show that, at the business process level, customers, end users, and business analysts are able to express their security needs. In this work, we will present a proposal aimed at integrating security requirements through business process modeling. We will summarize our Business Process Modeling Notation extension for modeling secure business process through Business Process Diagrams, and we will apply this approach to a typical health-care business process.

This letter presents a novel implementation for Bus-Invert Coding called No Invert-Line Bus-Invert Coding (NIL-BIC) architecture. It not only removes the invert-lines used in previous BIC implementations, but sends the coding information without additional bus-transitions. NIL-BIC can save about 50% more bus-power than the implementations using invert-line.

We propose a gradient-limited affine projection algorithm (GL-APA), which can achieve fast and double-talk-robust convergence in acoustic echo cancellation. GL-APA is derived from the M-estimation-based nonlinear cost function extended for evaluating multiple error signals dealt with in the affine projection algorithm (APA). By considering the nonlinearity of the gradient, we carefully formulate an update equation consistent with multiple input-output relationships, which the conventional APA inherently satisfies to achieve fast convergence. We also newly introduce a scaling rule for the nonlinearity, so we can easily implement GL-APA by using a predetermined primary function as a basis of scaling with any projection order. This guarantees a linkage between GL-APA and the gradient-limited normalized least-mean-squares algorithm (GL-NLMS), which is a conventional algorithm that corresponds to the GL-APA of the first order. The performance of GL-APA is demonstrated with simulation results.

We present a latency-aware bus arbitration scheme for real-time embedded systems. Only a few works have addressed the quality of service (QoS) issue for traditional busses or interconnection network. They mostly aimed at minimizing the latencies of several master blocks, resulting in decreasing overall bandwidth and/or increasing the latencies of other master blocks. In our method, the optimization goal is different in that the latency of a master should be as close as a given latency constraint. This is achieved by introducing the concept of "slack". In this method, masters effectively share the given communication architecture so that they all observe expected latencies and the degradation of overall bandwidth is marginal. The experimental results show that our method greatly reduces the number of constraint violations compared to other conventional arbitration schemes while minimizing the bandwidth degradation.

The relationship between the topology and collective function of a nonlinear oscillator network was investigated using nonlinear electrochemical oscillators. The constitutive experiments showed that the physiological robustness in the living system is due to their topological redundancy and asymmetry in the nonlinear network.

This paper describes the design and evaluation of a massively parallel processor base on Matrix architecture which is suitable for portable multimedia applications. The proposed architecture in this paper achieves 40 GOPS of 16-bit fixed-point additions at 200 MHz clock frequency and 250 mW power dissipation. In addition, 1 M-bit SRAM for data registers and 2,048 2-bit processing elements connected by a flexible switching network are integrated in 3.1 mm2 in 90 nm low-power CMOS technology. The energy-efficient Matrix architecture supports 2,048-way parallel operations and the programmable functions required for multimedia SoCs.

Stability analysis is one of the most important problems in analysis of hybrid dynamical systems. In this paper, a computational method of Lyapunov functions is proposed for stability analysis of hybrid automata that have set-valued vector fields. For this purpose, a formulation of matrix-valued sums of squares is provided and applied to derive an LMI/LME problem whose solution yields a Lyapunov function.

A highly collision-resistive RFID system multiplexes communications between thousands of transponders and a single reader using TH-CDMA based anti-collision scheme. This paper focuses on the back-end design consideration of such an RFID system with the deployment of high-level modeling techniques, accompanying a technical comparison of physical-level description, hardware-based emulation, and software-based simulation. A new rapid-prototyping simulation system was constructed to evaluate the robustness of a multiplexed RFID link system with more than 1,000 channels in the presence of field disturbances, and the design parameters of the back-end digital signal processing that dominated anti-collision performance were explored. Finally, the derived optimum parameters were applied to the design of a back-end digital integrated circuit to be installed in collision-resistive transponder circuitry.

A novel pitch-synchronous auditory-based feature extraction method for robust automatic speech recognition (ASR) is proposed. A pitch-synchronous zero-crossing peak-amplitude (PS-ZCPA)-based feature extraction method was proposed previously and it showed improved performances except when modulation enhancement was integrated with Wiener filter (WF)-based noise reduction and auditory masking. However, since zero-crossing is not an auditory event, we propose a new pitch-synchronous peak-amplitude (PS-PA)-based method to render the feature extractor of ASR more auditory-like. We also examine the effects of WF-based noise reduction, modulation enhancement, and auditory masking in the proposed PS-PA method using the Aurora-2J database. The experimental results show superiority of the proposed method over the PS-ZCPA and other conventional methods. Furthermore, the problem due to the reconstruction of zero-crossings from a modulated envelope is eliminated. The experimental results also show the superiority of PS over PA in terms of the robustness of ASR, though PS and PA lead to significant improvement when applied together.

In this paper, we propose a method to estimate affine motion parameters from consecutive images with the assumption that the motion in progress can be characterized by an affine model. The motion may be caused either by a moving camera or moving object. The proposed method first extracts motion vectors from a sequence of images and then processes them by adaptive robust estimation to obtain affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a flexible weight function based on a sigmoid function. During the estimation process, we tune the sigmoid function gradually to its hard-limit as the errors between the input data and the estimation model are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. The experimental results show that the suggested approach is very effective in estimating affine parameters.

In this paper, we propose to employ an extension to the natural gradient algorithm for robust Independent Component Analysis against outliers. The standard natural gradient algorithm does not exhibit this property since it employs nonrobust sample estimates for computing higher order moments. In order to overcome this drawback, we propose to use robust alternatives to higher order moments, which are comparatively less sensitive to outliers in the observed data. Some computer simulations are presented to show that the proposed method, as compared to the standard natural gradient algorithm, gives better performance in the presence of outlying data.

A deep null algorithm for adaptive narrowband beamforming in the presence of array gain errors is proposed. This new algorithm not only preserves the desired signal, but also yields superior performance. Simulations confirm this new approach.

Design of optimal controllers satisfying performance criteria of minimum tracking error and disturbance level for an interval system using a multi-objective evolutionary approach is proposed in this paper. Based on a worst-case design philosophy, the design problem is formulated as a minimax optimization problem, subsequently solved by a proposed two-phase multi-objective genetic algorithm (MOGA). By using two sets of interactive genetic algorithms where the first one determines the maximum (worst-case) cost function values for a given set of controller parameters while the other one minimizes the maximum cost function values passed from the first genetic algorithm, the proposed approach evolutionarily derives the optimal controllers for the interval system. To suitably assess chromosomes for their fitness in a population, root locations of the 32 generalized Kharitonov polynomials will be used to establish a constraints handling mechanism, based on which a fitness function can be constructed for effective evaluation of the chromosomes. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature of minimax optimization, a parallel computation scheme for the evolutionary approach in the MATLAB-based working environment is also proposed to accelerate the design process.

A novel approach of robust adaptive beamforming (RABF) is presented in this paper, aiming at robustness against both finite-sample effects and steering vector mismatches. It belongs to the class of diagonal loading approaches with the loading level determined based on worst-case performance optimization. The proposed approach, however, is distinguished by two points. (1) It takes finite-sample effects into account and applies worst-case performance optimization to not only the constraints, but also the objective of the constrained quadratic equation, for which it is referred to as joint worst-case RABF (JW-RABF). (2) It suggests a simple closed-form solution to the optimal loading after some approximations, revealing how different factors affect the loading. Compared with many existing methods in this field, the proposed one achieves better robustness in the case of small sample data size as well as steering vector mismatches. Moreover, it is less computationally demanding for presenting a simple closed-form solution to the optimal loading. Numerical examples confirm the effectiveness of the proposed approach.

This letter describes a robust speech recognition system for recognizing fast speech by stretching the length of the utterance in the cepstrum domain. The degree of stretching for an utterance is determined by its rate of speech (ROS), which is based on a maximum likelihood (ML) criterion. The proposed method was evaluated on 10-digits mobile phone numbers. The results of the simulation show that the overall error rate was reduced by 17.8% when the proposed method was employed.

This paper presents a robust object tracking method under pose variation and partial occlusion. In practical environment, the appearance of objects is changed dynamically by pose variation or partial occlusion. Therefore, the robustness to them is required for practical applications. However, it is difficult to be robust to various changes by only one tracking model. Therefore, slight robustness to variations and the easiness of model update are required. For this purpose, Kernel Principal Component Analysis (KPCA) of local parts is used. KPCA of local parts is proposed originally for the purpose of pose independent object recognition. Training of this method is performed by using local parts cropped from only one or two object images. This is good property for tracking because only one target image is given in practical applications. In addition, the model (subspace) of this method can be updated easily by solving a eigen value problem. Performance of the proposed method is evaluated by using the test face sequence captured under pose, partial occlusion, scaling and illumination variations. Effectiveness and robustness of the proposed method are demonstrated by the comparison with template matching based tracker. In addition, adaptive update rule using similarity with current subspace is also proposed. Effectiveness of adaptive update rule is shown by experiment.

This paper studies the problem of the relations between existence conditions of common quadratic and those of common infinity-norm Lyapunov functions for sets of discrete-time linear time-invariant (LTI) systems. Based on the equivalence between the robust stability of a class of time-varying systems and the existence of a common infinity-norm Lyapunov function for the corresponding set of LTI systems, the relations are determined. It turns out that although the relation is an equivalent one for single stable systems, the existence condition of common infinity-norm type is strictly implied by that of common quadratic type for the set of systems. Several existence conditions of a common infinity-norm Lyapunov functions are also presented for the purpose of easy checking.

In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective.

A novel robust watermarking scheme based on image connectivity is proposed. Having obtained the connected objects according to the selected connectivity pattern, the gravity centers are calculated in several larger objects as the reference points for watermark embedding. Based on these reference points and the center of the whole image, several sectors are formed, and the same version watermarks are embedded into these sectors. Thanks to the very stable gravity center of the connected objects, watermark detection is synchronized successfully. Simulation results show that our scheme can survive under both local and global geometrical distortions.