Sharif Digital Repository

A dictionary learning algorithm learns a set of atoms from some training signals in such a way that each signal can be approximated as a linear combination of only a few atoms. Most dictionary learning algorithms use a two-stage iterative procedure. The first stage is to sparsely approximate the training signals over the current dictionary. The second stage is the update of the dictionary. In this paper we develop some atom-by-atom dictionary learning algorithms, which update the atoms sequentially. Specifically, we propose an efficient alternative to the well-known K-SVD algorithm, and show by various experiments that the proposed algorithm is much faster than K-SVD while its results are... 

In this letter, we study the Gaussian two-way relay channel (GTWRC) with a direct link in which two nodes want to exchange information with each other with the help of a relay node in the presence of a direct link between two nodes. Specially, we focus on a protocol with four phases, which is called the hybrid broadcast (HBC) protocol. In the HBC protocol, sequential transmissions from both users are followed by a transmission from the relay. Using nested lattice codes, we obtain a new achievable rate region for this protocol. In fact, utilizing a four-stage lattice partition chain, we introduce an intermediate lattice in our lattice-based coding scheme, and by using it, we split the... 

A generalized coordinates molecular Hamiltonian for polyatomic molecules has been obtained. Using this Hamiltonian in Radau coordinates as the orthogonal coordinates, the molecular Hamiltonian is derived for tri-atomic molecules. An exact derivation is presented of the molecular Hamiltonian in different axis embedding such as bond embedding, bisector embedding, and the linear combination of Radau vector coordinates. In addition, the symmetrized Radau coordinates are considered as an example for this general formulation. This new approach is greatly simplified. Also presented are simple and explicit expressions without cross-terms in Radau coordinates as vibrational variables  

Compute-and-forward is a promising strategy to tackle interference and obtain high rates between the transmitting users in a wireless network. However, the quality of the wireless channels between the users substantially limits the achievable computation rate in such systems. In this paper, we introduce the idea of using intelligent reflecting surfaces (IRSs) to enhance the computing capability of the compute-and-forward systems. For this purpose, we consider a multiple access channel (MAC) where a number of users aim to send data to a base station (BS) in a wireless network, where the BS is interested in decoding a linear combination of the data from different users in the corresponding... 

We study the degrees of freedom of the three-user Gaussian interference channel under the circumstance where one or more transmitters are non-causally cognitive, meaning they are aware of the messages of one or more transmitters in a non-causal manner. Our focus is on the total degrees of freedom of the three-user cognitive interference channel. Our first case is when only one of the transmitters is fully cognitive and knows the messages of the other users. We prove that using interference alignment and interference cancellation, the achievable total degrees of freedom is 11/6 and does not coincide with the calculated total degrees of freedom upper bound which is 2. In the second and third... 

In this paper, we present a spatio-temporal event-based approach to video signal analysis and abstraction employing wavelet transform features. The video signal is assumed to be a sequence of overlapping independent visual components called events, which typically are temporally overlapping compact functions that describe temporal evolution of a given set of the spatial parameters of the video signal. We utilize event-based temporal decomposition technique to resolve the overlapping arrangement of the video signal that is known to be one of the main concerns in video analysis via conventional frame-based schemes. In our method, a set of spatial parameters, extracted from the video, is... 

This paper considers two different due date assignment and sequencing problems in single machine where the processing times of jobs are random variables. The first problem is to minimise the maximum due date so that all jobs are stochastically on time. It is shown that sequencing the jobs in decreasing service level (DSL) order optimally solves the problem. The results are then extended for two special cases of flow shop problem. The other problem is to minimise a total cost function which is a linear combination of three penalties: penalty on job earliness, penalty on job tardiness, and penalty associated with long due date assignment. The assignment of a common due date and distinct due... 

In this work, we consider the problem of communicating linear functions of correlated Gaussian sources over a two-user additive Gaussian noise multiple access channel (MAC) in the presence of known interference at the transmitter. The goal is to estimate a linear combination of two sources at the receiver with minimum average mean squared error (MSE) distortion. Here, a lattice-based coding scheme is presented and its achievable distortion is analyzed. In addition, two lower bounds on the achievable distortion are derived. The proposed lattice coding scheme can cancel channel interference completely  

We investigate the potentials of applying the coded caching paradigm in wireless networks. In order to do this, we investigate physical layer schemes for downlink transmission from a multiantenna transmitter to several cache-enabled users. As the baseline scheme, we consider employing coded caching on the top of max-min fair multicasting, which is shown to be far from optimal at high-SNR values. Our first proposed scheme, which is near-optimal in terms of DoF, is the natural extension of multiserver coded caching to Gaussian channels. As we demonstrate, its finite SNR performance is not satisfactory, and thus we propose a new scheme in which the linear combination of messages is implemented... 

Matrix sensing refers to recovering a low-rank matrix from a few linear combinations of its entries. This problem naturally arises in many applications including recommendation systems, collaborative filtering, seismic data interpolation and wireless sensor networks. Recently, in these applications, it has been noted that exploiting additional subspace information might yield significant improvements in practical scenarios. This information is reflected by two subspaces forming angles with column and row spaces of the ground-truth matrix. Despite the importance of exploiting this information, there is limited theoretical guarantee for this feature. In this work, we aim to address this issue... 

Classification is the most important part of brain-computer interface (BCI) systems. Because the neural activities of different individuals are not identical, using the ordinary methods of subject-dependent classification, does not lead to high accuracy in betweensubject classification problems. As a result, in this study, we propose a novel method for classification that performs well in between-subject classification. In the proposed method, at first, the subject-dependent classifiers obtained from the train subjects are applied to the test trials to obtain a set of scores and labels for the trials. Using these scores and the real labels of the labeled test trials, linear regression is... 

In this paper the dual synchronization of chaotic systems via output feedback strategy is investigated. The slave chaotic systems are fed by a scalar signal generated by a linear combination of the master systems state variables. The sufficient condition and design procedure for dual synchronization are presented. The proposed method is applied for dual synchronization of the Lorenz-Rossler, Rossler-Chen and Duffing-Van der Pol chaotic systems through computer simulation. The results show the effectiveness and feasibility of the proposed algorithm. © 2008 Elsevier Ltd. All rights reserved  

People use trust to cope with uncertainty which is a result of the free will of others. Previous approaches for inferring trust have focused on homogeneous relationships and attempted to infer missing information based on existing information in a single relationship. In this paper we propose using methods of social network analysis to infer trust in a heterogeneous social network. We have extended the problem of relation extraction and allowed using any type of binary operator on matrixes, whereas previous work have focused on linear combination of base matrixes (the only allowed operator was summation of two matrixes). We present two genetic algorithms which use ordinary numerical and... 

A coding scheme for transmission of a bit maps a given bit to a sequence of channel inputs (called the codeword associated with the transmitted bit). In this paper, we study the problem of designing the best code for a discrete Poisson channel with memory (under peak-power and total-power constraints). The outputs of a discrete Poisson channel with memory are Poisson distributed random variables with a mean comprising of a fixed additive noise and a linear combination of past input symbols. Assuming a maximum-likelihood (ML) decoder, we search for a codebook that has the smallest possible error probability. This problem is challenging because error probability of a code does not have a... 

A coding scheme for transmission of a bit maps a given bit to a sequence of channel inputs (called the codeword associated to the transmitted bit). In this paper, we study the problem of designing the best code for a discrete Poisson channel with memory (under peak-power and total-power constraints). The outputs of a discrete Poisson channel with memory are Poisson distributed random variables with a mean comprising a fixed additive noise and a linear combination of past input symbols. Assuming a maximum-likelihood (ML) decoder, we find the best codebook design by minimizing the error probability of the decoder over all codebooks. For the case of having only a total-power constraint, the... 

A coding scheme for transmission of a bit maps a given bit to a sequence of channel inputs (called the codeword associated with the transmitted bit). In this paper, we study the problem of designing the best code for a discrete Poisson channel with memory (under peak-power and total-power constraints). The outputs of a discrete Poisson channel with memory are Poisson distributed random variables with a mean comprising of a fixed additive noise and a linear combination of past input symbols. Assuming a maximum-likelihood (ML) decoder, we search for a codebook that has the smallest possible error probability. This problem is challenging because error probability of a code does not have a... 

A coding scheme for transmission of a bit maps a given bit to a sequence of channel inputs (called the codeword associated to the transmitted bit). In this paper, we study the problem of designing the best code for a discrete Poisson channel with memory (under peak-power and total-power constraints). The outputs of a discrete Poisson channel with memory are Poisson distributed random variables with a mean comprising a fixed additive noise and a linear combination of past input symbols. Assuming a maximum-likelihood (ML) decoder, we find the best codebook design by minimizing the error probability of the decoder over all codebooks. For the case of having only a total-power constraint, the... 

The authors present an improved achievable rate region for two-pair bidirectional Gaussian relay networks based on successive compute-and-forward method. In these networks, one relay helps in the communication between two pairs of users. In their proposed scheme, the authors use nested lattice codes for encoding the messages at the users, and Gaussian random codes for the encoding at the relay. They use the successive compute-and-forward strategy to decode two integer linear combinations of the lattice codewords in the uplink, and successive interference cancellation for decoding the Gaussian codewords in the downlink. The downlink channel can be considered as a broadcast channel with two... 

We propose an event based approach for locating keyframes in natural video through detection of locally correlated spectral targets. Temporal Decomposition (TD) is used to describe a set of spectral parameters of the video as a linear combination of a set of temporally overlapping event functions. This process provides preliminary information about keyframes, by selecting the frames located at event centroids as the keyframe candidates. No shot or shot cluster boundary detection is needed and keyframes are extracted directly from among event centroids that are much smaller in number than the number of frames. Generalized Gaussian Density (GGD) parameters, extracted from 2D wavelet transform...