Sharif Digital Repository

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... 

The present study aims to evaluate the important issue of elimination of direct path interference and clutter in ambiguity function and detection of passive radars with adaptive filters. The efficiency and performance of each of them were investigated. Finally, by simulation and their learning curve, the best algorithm was proposed for these radars  

Group-testing refers to the problem of identifying (with high probability) a (small) subset of D defectives from a (large) set of N items via a 'small' number of 'pooled' tests (i.e., tests have a positive outcome if even one of the items being tested in the pool is defective, else they have a negative outcome). For ease of presentation in this work we focus the regime when the number of defectives is sublinear, i.e., D = O (N1-δ) for some δ > 0. The tests may be noiseless or noisy, and the testing procedure may be adaptive (the pool defining a test may depend on the outcome of a previous test), or non-Adaptive (each test is performed independent of the outcome of other tests). A rich body... 

In this article, we present a cross-layer adaptive algorithm that dynamically maximizes the average utility function. A per stage utility function is defined for each link of a carrier sense multiple access-based wireless network as a weighted concave function of energy consumption, smoothed rate, and smoothed queue size. Hence, by selecting weights we can control the trade-off among them. Using dynamic programming, the utility function is maximized by dynamically adapting channel access, modulation, and coding according to the queue size and quality of the time-varying channel. We show that the optimal transmission policy has a threshold structure versus the channel state where the optimal... 

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 purpose of this paper is to deal with a novel intelligent predictive control scheme using the multiple models strategy with its application to an industrial tubular heat exchanger system. The main idea of the strategy proposed here is to represent the operating environments of the system, which have a wide range of variation in the span of time by several local explicit linear models. In line with this strategy, the well-known linear generalized predictive control (LGPC) schemes are initially designed corresponding to each one of the linear models of the system. After that, the best model of the system and the LGPC control action are precisely identified, at each instant of time, by an... 

Recently announcement of a physical realization of a fundamental circuit element called memristor by researchers at Hewlett Packard (HP) has attracted so much interest worldwide. Combination of this newly found element with crossbar interconnect technology, opened a new field in designing configurable or programmable electronic systems which can have applications in signal processing and artificial intelligence. In this paper, based on the simple memristor crossbar structure, we will propose a new mixed analog-digital circuit as a hardware implementation of the sign-sign least mean square (LMS) adaptive filter algorithm. In this proposed hardware, any multiplication and addition is performed... 

Incorporating the finite-element method for the modeling of the SQUID NDE response to a predefined defect pattern, an adaptive algorithm has been developed for the reconstruction of unknown defects using an optimization algorithm for updating of the forward problem. The defect reconstruction algorithm starts with an initial estimation for the defect pattern. Then the forward problem is solved and the obtained field pattern is compared with the measured signal from the SQUID NDE system. The result is used by an optimization algorithm to update the defect structure to be incorporated in the FEM forward problem for the next iteration. Since the mentioned model based inverse algorithm normally... 

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... 

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... 

In this paper, we propose an adaptive call admission algorithm based on learning automata. The proposed algorithm uses a learning automaton to specify the acceptance/rejection of incoming new calls. It is shown that the given adaptive algorithm converges to an equilibrium point which is also optimal for uniform fractional channel policy. To study the performance of the proposed call admission policy, the computer simulations are conducted. The simulation results show that the level of QoS is satisfied by the proposed algorithm and the performance of given algorithm is very close to the performance of uniform fractional guard channel policy which needs to know all parameters of input traffic.... 

This work deals with the problem of controlling the outlet temperature of a tubular heat exchanger system by means of flow pressure. The usual industrial case is to try to control the outlet temperature by either the temperature or the flow of the fluid, which flows through the shell tube. But, in some situations, this is not possible, due to the fact that the whole of system coefficients variation cannot quite be covered by control action. In this case, the system behavior must precisely be modeled and appropriate control action needs to be obtained based on novel techniques. A new multiple models control strategy using the well-known linear generalized predictive control (LGPC) scheme has... 

Low-complexity error concealment techniques for missing macroblock (MB) recovery based on the boundary matching principle are extensively studied and evaluated. In this paper, an improved boundary matching algorithm (BMA) using adaptive search is presented to conceal channel errors in inter-frames of video images. The proposed scheme adaptively selects proper candidate regions to conceal the artifact of a lost block. The candidate regions are examined based on analyzing motion activity of the neighboring MBs. Simulations show that the proposed scheme outperforms both on PSNR and visual quality obviously of about 1-4dB compared to existing methods  

In this paper a new least mean square (LMS) based adaptive weighting algorithm is proposed. It is appropriate for antenna array systems with moving targets and mobile applications. The essential goal of this algorithm is to reduce the complexity of weighting process and to decrease the time needed for adjusting the antenna radiation pattern. The main lobe of antenna will be adjusted in the direction of desired signal (main signal) and nulls pointed in the direction of undesired signals (interference signals). By predicting the relative velocity of source, the next location of the source will be estimated and the array weights will be determined using LMS algorithm before arriving to the new... 

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... 

A new adaptive steganography method based on image contrast to improve the embedding capacity and imperceptibility of the stego images is presented. The method exploits the average difference between the gray level values of the pixels in 2x2 blocks of non-overlapping spatially and their mean gray level in order to select valid blocks for embedding. The method was tested on different gray scale images. Results show that our proposed approach provides larger embedding capacity, while being less detectable by steganalysis methods such as χ2 attack and machine learning steganalysis systems, as compared to some well-known adaptive and non-adaptive steganography algorithms