Sharif Digital Repository

A new robust image adaptive digital watermarking algorithm in wavelet transform domain is proposed in this paper. The proposed method exploits Human Visual System (HVS) characteristics and entropy masking concept to determine image adaptive thresholds for selection of perceptually significant coefficients. The mark is embedded in the coefficients of all subbands including the LL subband. Experimental results show that the proposed method significantly improves watermarking performance over conventional methods, in the terms of invisibility and robustness  

In this paper, a low order recursive linear prediction method and recursive least square as an adaptive filter (LP-RLS) are introduced to predict the speech and the excitation signals. In real-time packet-based communication systems, one major problem is misrouted or delayed packets which results in degraded perceived voice quality. If packets are not available on time, the packet is known as lost packet. The easiest task of a network terminal receiver is to replace silence for the duration of lost speech segments. In a high quality communication system, to avoid quality reduction due to packet loss, a suitable method and/or algorithm is needed to replace the missing segments of speech. The... 

The existing learning methods usually assume that training data and test data follow the same distribution, while this is not always true. Thus, in many cases the performance of these methods on the test data will be severely degraded. In this paper, we study the problem of unsupervised domain adaptation, where no labeled data in the target domain is available. The proposed method first finds a new representation for both the source and the target domain and then learns a prediction function for the classifier by optimizing an objective function which simultaneously tries to minimize the loss function on the source domain while also maximizes the consistency of manifold (which is based on... 

We consider the minimization of power consumption in target tracking wireless sensor networks (WSNs) using dynamic modification of tracking interval. In this context, we first analyze the performance of such networks, using a quantitative mathematical analysis. Then we calculate an upper bound for the achievable improvement in total power consumption, when using an adaptive time interval modification algorithm for tracking moving objects with acceleration. Towards this optimum functionality, we propose a novel adaptive algorithm (AHC) to adapt the tracking interval such that it minimizes power consumption while keeping an acceptable accuracy. Simulation results show that using the proposed... 

One of the essential usages of adaptive filters is in sparse system identification on which the performance of classic adaptive filters is not acceptable. There are several algorithms that designed especially for sparse systems; we call them sparsity aware algorithms. In this paper we studied the performance of two newly presented adaptive algorithms in which P-norm constraint is considered in defining cost function. The general title of these algorithms is continuous mixed P-norm (CMPN). The performances of these algorithms are considered for the first time in sparse system identification. Also the performance of l0 norm LMS algorithm is analysed and compared with our proposed algorithms.... 

New heuristic filters are proposed for state estimation of nonlinear dynamic systems based on particle swarm optimization (PSO) and differential evolution (DE). The methodology converts state estimation problem into dynamic optimization to find the best estimate recursively. In the proposed strategy the particle number is adaptively set based on the weighted variance of the particles. To have a filter with minimal parameter settings, PSO with exponential distribution (PSO-E) is selected in conjunction with jDE to self-adapt the other control parameters. The performance of the proposed adaptive evolutionary algorithms i.e. adaptive PSO-E, adaptive DE and adaptive jDE is studied through a... 

In this paper an iterative method for recovery of the missing samples of signals is investigated in detail, and some novel linear, nonlinear, and adaptive extrapolation techniques are proposed to be used along with it to increase the convergence rate of the recovery system. The proposed methods would remarkably speed up the convergence rate, save processing power, and reduce the delay of the system compared to some well known accelerated versions of the aforementioned iterative algorithm. © 2007 Elsevier B.V. All rights reserved  

A routing algorithm defines a route which packet traverses to get to destination. In this research we study some kind of routing algorithms that are used in internal connections networks of multi-processor and multi-computers systems. Then we discuss about some routing algorithms which have been implemented network on chip architecture. First, we present a group of routing algorithms based on various criterions, and review so-called category. Afterwards, we study adaptive and deterministic routing algorithms and express circular model applying in internal connections networks and its governing rules in order to prevent dead lock. Then we survey adaptive algorithms such as Deflection routing,... 

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. Even the performance of the most of the existing robust adaptive beamformers is degraded when the signal-to-noise ratio (SNR) is increased. In this study, a high converging rate robust adaptive beamformer is proposed. This method is a promoted eigenspace-based beamformer. In this paper, a new signal-plus-interferences (SPI) covariance matrix estimator is proposed. The subspace of the ideal SPI covariance matrices is exploited and the estimated covariance matrix is projected into this subspace. This projection... 

Equivariant adaptive separation via independence (EASI) is one of the most successful algorithms for blind source separation (BSS). However, the user has to choose non-linearities, and usually simple (but non-optimal) cubic polynomials are applied. In this paper, the optimal choice of these non-linearities is addressed. We show that this optimal non-linearity is the output score function difference (SFD). Contrary to simple non-linearities usually used in EASI (such as cubic polynomials), the optimal choice is neither component-wise nor fixed: it is a multivariate function which depends on the output distributions. Finally, we derive three adaptive algorithms for estimating the SFD and... 

The necklace hypercube has recently been introduced as an attractive alternative to the well-known hypercube. Previous research on this network topology has mainly focused on topological properties, VLSI and algorithmic aspects of this network Several analytical models have been proposed in the literature for different interconnection networks, as the most cost-effective tools to evaluate the performance merits of such systems. This paper proposes an analytical performance model to predict message latency in wormhole-switched necklace hypercube interconnection networks with fully adaptive routing. The analysis focuses on a fully adaptive routing algorithm which has been shown to be the most... 

Partially adaptive routing algorithms are a useful category of routing algorithms due to their simple router logic and restricted adaptivity in selecting the next output channel towards the destination. Several partially adaptive routing algorithms on mesh and hypercube networks have been presented in the literature. But there is no study on evaluating the performance of these algorithms. This paper tries to compare the most important partially adaptive routing algorithms on the mesh and hypercube networks as the most popular topologies for multicomputers. The evaluation has been performed by the use of event driven simulator coded by C++ compiler. © 2006 IEEE  

In this paper a decentralized control scheme for multiple cooperative manipulators is developed to achieve the desired performance in motion and force tracking, in the presence of uncertainties in dynamic equations of the robots. To eliminate the effects of uncertainties in the closed-loop performance, a new adaptive control algorithm is proposed. Based on the Lyapunov stability method, it is proved that all the error signals in the closed-loop system which is compose of a robot, an observer and a controller asymptotically converge to zero. Also to avoid the difficulties of using velocity sensors within the cooperative system, an output feedback control scheme with a linear observer is used.... 

In this paper, adaptive model predictive control is applied to schedule differentiated buffers in routers. The proposed algorithm, adaptive model predictive control scheduler (AMPCS), dynamically regulates the service rates of aggregated traffic classes. This algorithm guarantees some required constraints on proportional or absolute delay. The control parameters and the way they are adjusted as well as the problems of implementing the controller at high data rates are investigated. Theoretical analysis and numerical simulations demonstrate stability of AMPCS and its acceptable quality of service differentiations at core routers while maintaining end to end delay constraints. © 2007 Elsevier... 

Nature has always been a source of great inspiration for engineers and mathematicians. Evolutionary Algorithms are the latest in a line of natural-based innovations which have had a profound effect on the application of optimization in science and engineering. Although based on nature, Evolutionary Algorithms are nonetheless distinctly different from natural evolution in several areas. This paper outlines early and recent developments of Evolutionary Algorithms while covering those areas of difference. Practical issues related to the use of Evolutionary Algorithms, key parameters that affect the quality of the search and impact of user choices in problem formulation are also covered in this... 

This paper presents a new algorithm for detecting user-selected objects in a sequence of images based on a new weighted adaptive lifting scheme transform. In our algorithm, we first select a set of coefficients as object features in the wavelet transform domain and then build an adaptive transform considering the selected features. The goal of the designed adaptive transform is to "vanish" the selected features as much as possible in the transform domain. After applying both non-adaptive and adaptive transforms to a given test image, the corresponding transform domain coefficients are compared for detecting the object of interest. We have verified our claim with experimental results on 1-D...