Sharif Digital Repository

This paper presents a new implementation of adaptive beamforming algorithm that can be fully implemented on chip. It does not require the knowledge of the incoming signal direction or phase shifter characteristics. Besides, it eliminates the need for the ADC to convert the analog output signal to digital values for the microprocessor and the DAC to apply the calculated values to the control voltages of the analog phase shifters. Thus, it exhibits better convergence speed. In addition, the need for the complex and power-hungry processor is eliminated. Therefore, this implementation consumes less power. Analytical equations and constraints on system design parameters are derived, and the... 

This paper presents a new implementation of adaptive beamforming algorithm that can be fully implemented on chip. It does not require the knowledge of the incoming signal direction or phase shifter characteristics. Besides, it eliminates the need for the ADC to convert the analog output signal to digital values for the microprocessor and the DAC to apply the calculated values to the control voltages of the analog phase shifters. Thus, it exhibits better convergence speed. In addition, the need for the complex and power-hungry processor is eliminated. Therefore, this implementation consumes less power. Analytical equations and constraints on system design parameters are derived, and the... 

It has been recently shown that guided modes in two-dimensional photonic crystal based structures can be fast and efficiently extracted by using the Galerkin's method with Hermite-Gauss basis functions. Although quite efficient and reliable for photonic crystal line defect waveguides, difficulties are likely to arise for more complicated geometries, e.g., for coupled resonator optical waveguides. First, unwanted numerical instability may well occur if a large number of basis functions are retained in the calculation. Second, the method could have a slow convergence rate with respect to the truncation order of the electromagnetic field expansion. Third, spurious solutions are not unlikely to... 

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.... 

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  

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.... 

According to the fact that humans and animals show marvelous abilities in walking on irregular terrain, there is a strong need for adaptive algorithms in walking of biped robots to behave like them. Since the stance leg can easily rise from the ground and it can easily rotate about the toe or the heel, the problem of controlling the biped robots is difficult. In this paper, according to the adaptive locomotion patterns of animals, coordination and control of body links have been done with Central Pattern Generator (CPG) in spinal cord and feedback network from musculoskeletal system. A one layer feedforward neural network that its inputs are the scaled joint variables and the touch sensors... 

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.... 

This paper investigates the problem of maneuvering target tracking by using hybrid (intelligent/classical) methods. The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. The proposed algorithm is implemented with two second-order Gaussian filters based on the current statistical model and a multilayer feedforward neural network. The two filters, which use the noise corrupted measurements of the target line of sight (LOS) angle, track the same maneuvering target in parallel. The neural network automatically considers all the state information of the two filters and adaptively adjusts the process variance of one of them to... 

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, a target parameter estimation problem is addressed for the recently emerging frequency diverse array multiple-input multiple-output (FDA-MIMO) radar system, utilizing sparse learning. The scene is modeled as a two dimensional (2D) angle-incremental range grid. To solve the resulting sparse problem, the recently proposed user-parameter free algorithms including block sparse learning via iterative minimization (BSLIM), iterative adaptive approach (IAA), sparse iterative covariance-based estimation (SPICE), likelihood-based estimation of sparse parameters (LIKES), and orthogonal matching pursuit (OMP) are applied which achieve excellent parameter estimation performance. However,... 

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...