Sharif Digital Repository

Compressed Sensing (CS), as a new rapidly growing research field, promises to effectively recover a sparse signal at the rate of below Nyquist rate. This revolutionary technology strongly relies on the sparsity of the signal and incoherency between sensing basis and representation basis. Exact recovery of a sparse signal will be occurred in a situation that the signal of interest sensed randomly and the measurements are also taken based on sparsity level and log factor of the signal dimension. In this paper, compressed sensing method is proposed to reduce the noise and reconstruct the image signal. Noise reduction and image reconstruction are formulated in the theoretical framework of... 

As long as the size of the embedded scatterer in comparison to the internal length scale of its surrounding elastic matrix is large, then the linear sampling method (LSM) and singular sources method (SSM) can be used in conjunction with classical theory of elasticity to reconstruct the size of the scatterer with reasonable accuracy. On the other hand, for the micro-/nano-size scatterer this treatment ceases to hold due to the shortcomings of classical theory of elasticity. Moreover, in the realm of this theory, wave propagation through a homogeneous medium is nondispersive on the macro-scale even for high frequency waves. This outcome is incompatible with the practical observations. A remedy... 

In this paper, an iterative method for block-sparse recovery is suggested for target parameters estimation in a distributed MIMO radar system. The random sampling has been used as the sensing scheme in the receivers. The simulation results prove that the proposed method is superior to the other state-of-the-art techniques in the accuracy of the target estimation task. © 2016 IEEE  

The theory of compressive sampling involves making random linear projections of a signal. Provided signal is sparse in some basis, small number of such measurements preserves the information in the signal, with high probability. Following the success in signal reconstruction, compressive framework has recently proved useful in classification. In this paper, conventional random projection scheme is first extended to the image domain and the key notion of concentration of measure is studied. Findings are then employed to develop a 2D compressive classifier (2DCC) for sparse images. Finally, theoretical results are validated within a realistic experimental framework. ©2009 IEEE  

Digital music is now widely used and a great number of digital music files are available on the Internet. Designing systems that can automatically index, search and retrieve the digital music is one of the active research fields. One of the requirements for design and evaluation of such systems is corpuses of digital music samples. A good corpus is expected to have a number of features such as including a great number of samples, have samples of different artists and also samples with different quality. Additionally, the regional music of different parts of the world uses local musical instruments which results in music that are acoustically different. So, corpuses of regional music are... 

The authors propose asynchronous level crossing (LC) A/D converters for low redundancy voice sampling. They propose to utilise the family of iterative methods with adaptive thresholding (IMAT) for reconstructing voice from non-uniform LC and adaptive LC (ALC) samples thereby promoting sparsity. The authors modify the basic IMAT algorithm and propose the iterative method with adaptive thresholding for level crossing (IMATLC) algorithm for improved reconstruction performance. To this end, the authors analytically derive the basic IMAT algorithm by applying the gradient descent and gradient projection optimisation techniques to the problem of square error minimisation subjected to sparsity. The... 

This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted for visually enhanced quality of reconstruction of image signals. Although, the popular Mean Square Error (MSE) criterion is convex and simple, it may not be entirely consistent with Human Visual System (HVS). Thus, instead of ℓ2-norm or MSE, a new perceptual quality measure is used as the similarity criterion between the original and the reconstructed images. The proposed criterion called Convex SIMilarity (CSIM) index is a modified version of the Structural SIMilarity (SSIM) index, which despite its predecessor, is convex and uni-modal. We derive mathematical properties... 

Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal... 

In this paper, a multi-product inventory problem is investigated in which a retailer buys items from different suppliers based on their purchasing costs and defective rates. Due to the warehouse and staff constraints involved, the inventory cycle consists of two parts. The first part corresponds to a screening period in which a destructive testing acceptance-sampling plan is used to accept or reject a lot. The other part is for selling the items. In the screening period, a lot that is rejected is returned to the suppliers where another lot is claimed for substitution at no cost. Shortage occurs during the screening period and the defective items are sold at a lower price at the end of the... 

This paper introduces "switched-resistor" circuits as a new family of current-mode sampled-data circuits with improved accuracy and linearity. Advantages of the switched-resistor circuits for high-speed and low-voltage applications are demonstrated. A switched-resistor biquad band-pass filter with a quality factor of 10 and clock frequency of 100 MHz, fabricated in a 0.18 μ m CMOS process, is also presented. The filter consumes 9 mW from 1.8 V supply. © 2008 Elsevier GmbH. All rights reserved  

In this paper, a multi-product inventory problem is investigated in which a retailer buys items from different suppliers based on their purchasing costs and defective rates. Due to the warehouse and staff constraints involved, the inventory cycle consists of two parts. The first part corresponds to a screening period in which a destructive testing acceptance-sampling plan is used to accept or reject a lot. The other part is for selling the items. In the screening period, a lot that is rejected is returned to the suppliers where another lot is claimed for substitution at no cost. Shortage occurs during the screening period and the defective items are sold at a lower price at the end of the... 

Hand gesture recognition (HGR) from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of the human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of keyframes of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatiotemporal... 

In this paper the structure of Maximum Likelihood (ML) CFAR for lognormal clutter is derived assuming that some reference samples are censored to avoid destructive effect of interfering targets. It is shown that under some acceptable approximations we will achieve in a simple structure for this detector which is suitable for real time processing. The proposed detector is compared with non censored ML CFAR (i.e. logt CFAR) via simulation. It is shown that while the proposed detector is a little weaker than logt CFAR in homogeneous clutter, in non homogeneous clutter it outperforms the logt CFAR  

This paper studies an iterative stochastic algorithm for solving the stochastic shortest path problem. This algorithm, which uses a distributed learning automata, tries to find the shortest path by taking a sufficient number of samples from the edges of the graph. In this algorithm, which edges to be sampled are determined dynamically as the algorithm proceeds. At each iteration of this algorithm, a distributed learning automata used to determine which edges to be sampled. This sampling method, which uses distributed learning automata, reduces the number of samplings from those edges, which may not be along the shortest path, and resulting in a reduction in the number of the edges to be... 

A modular method was suggested before to recover a band limited signal from the sample and hold and linearly interpolated (or, in general, an nth-order-hold) version of the regular samples. In this paper a novel approach for compensating the distortion of any interpolation based on modular method has been proposed. In this method the performance of the modular method is optimized by adding only some simply calculated coefficients. This approach causes drastic improvement in terms of signal-to-noise ratios with fewer modules compared to the classical modular method. Simulation results clearly confirm the improvement of the proposed method and also its superior robustness against additive...