Sharif Digital Repository

Recent studies have demonstrated that high-level semantics in data can be captured using sparse representation. In this paper, we propose an approach to human body pose estimation in static images based on sparse representation. Given a visual input, the objective is to estimate 3D human body pose using feature space information and geometrical information of the pose space. On the assumption that each data point and its neighbors are likely to reside on a locally linear patch of the underlying manifold, our method learns the sparse representation of the new input using both feature and pose space information and then estimates the corresponding 3D pose by a linear combination of the bases... 

This letter proposes a sparse diffusion algorithm for 1-bit compressed sensing (CS) in wireless sensor networks, and the algorithm is inherently robust against impulsive noise. The approach exploits the diffusion strategy from distributed learning in the 1-bit CS framework. To estimate a common sparse vector cooperatively from only the sign of measurements, a steepest descent method that minimizes the suitable global and local convex cost functions is used. A diffusion strategy is suggested for distributive learning of the sparse vector. A new application of the proposed algorithm to sparse channel estimation is also introduced. The proposed sparse diffusion algorithm is compared with both... 

In this paper, we propose an algorithm for recovery of sparse and low-rank components of matrices using an iterative method with adaptive thresholding. In each iteration of the algorithm, the low-rank and sparse components are obtained using a thresholding operator. The proposed algorithm is fast and can be implemented easily. We compare it with the state-of-the-art algorithms. We also apply it to some applications such as background modeling in video sequences, removing shadows and specularities from face images, and image restoration. The simulation results show that the proposed algorithm has a suitable performance with low run-time. © 2017 Elsevier Inc  

In this paper, we propose two methods for signal denoising where both signal and noise are sparse but in different domains. First, an optimization problem is proposed which is non-convex and NP-hard due to the existence of ℓ 0 norm in its cost function. Then, we propose two approaches to approximate and solve it. We also provide the proof of convergence for the proposed methods. The problem addressed in this paper arises in some applications for example in image denoising where the noise is sparse, signal reconstruction in the case of random sampling where the random mask is unknown, and error detection and error correction in the case of missing samples. The experimental results indicate... 

Data stream classification is one of the most challenging areas in the machine learning. In this paper, we focus on three major challenges namely infinite length, concept-drift and concept-evolution. Infinite length causes the inability to store all instances. Concept-drift is the change in the underlying concept and occurs in almost every data stream. Concept-evolution, in fact, is the arrival of novel classes and is an undeniable phenomenon in most real world data streams. There are lots of researches about data stream classification, but most of them focus on the first two challenges and ignore the last one. In this paper, we propose new method based on ensembles whose classifiers use... 

Most of the supervised classification algorithms are proposed to classify newly seen instances based on their learned label space. However, in the case of data streams, conceptevolution is inevitable. In this paper we propose a support vector based approach for classification beyond the learned label space in data streams with regard to other challenges in data streams like concept-drift and infinite-length. We maintain the boundaries of observed classes through the stream by utilizing a support vector based method (SVDD). Newly arrived instances located outside these boundaries will be analyzed by constructing neighborhood graph to detect the emergence of a class beyond the learned label... 

In this letter, we consider the problem of blind source separation under certain nonlinear mixing conditions using a deep learning approach. Conventionally, the separation of sources within linear mixtures is achieved by applying the independence property of the sources. In the nonlinear regime, however, this property is no longer sufficient. In this letter, we consider nonlinear mixing operators where the non-linearity could be fairly approximated using a Taylor series. Next, for solving the nonlinear BSS problem, we design an end-to-end recurrent neural network (RNN) that learns the inverse of the system, and ultimately separates the sources. For training the RNN, we employ a set of... 

We consider a basic caching system, where a single server with a database of N files (e.g. movies) is connected to a set of K users through a shared bottleneck link. Each user has a local cache memory with a size of M files. The system operates in two phases. a placement phase, where each cache memory is populated up to its size from the database, and a following delivery phase, where each user requests a file from the database, and the server is responsible for delivering the requested contents. The objective is to design the two phases to minimize the load (peak or average) of the bottleneck link. We characterize the rate-memory tradeoff of the above caching system within a factor of... 

We consider a basic caching system, where a single server with a database of N files (e.g., movies) is connected to a set of K users through a shared bottleneck link. Each user has a local cache memory with a size of M files. The system operates in two phases: A placement phase, where each cache memory is populated up to its size from the database, and a following delivery phase, where each user requests a file from the database, and the server is responsible for delivering the requested contents. The objective is to design the two phases to minimize the load (peak or average) of the bottleneck link. We characterize the rate-memory tradeoff of the above caching system within a factor of... 

The problem of motion detection has received considerable attention due to the explosive growth of its applications in video analysis and surveillance systems. While the previous approaches can produce good results, the accurate detection of motion remains a challenging task due to the difficulties raised by illumination variations, occlusion, camouflage, sudden motions appearing in burst, dynamic texture, and environmental changes such as weather conditions, sunlight changes during a day, and so on. In this paper, a novel per-pixel motion descriptor is proposed for motion detection in video sequences which outperforms the current methods in the literature particularly in severe scenarios.... 

Data aggregation is a key, yet time-consuming functionality introduced to conserve energy in wireless sensor networks (WSNs). In this paper, to minimize time latency, we focus on aggregation scheduling problem and propose an efficient distributed algorithm that generates a collision-free schedule with the least number of time slots. In contrast to others, our approach named FAST mainly contributes to both tree construction, where the former studies employ Connected 2-hop Dominating Sets, and aggregation scheduling that was previously addressed through the Competitor Sets computation. We prove that the latency of FAST under the protocol interference model is upper-bounded by 12R+Δ-2, where R... 

Increasing power densities in future technology nodes is a crucial issue in multicore platforms. As the number of cores increases in them, power budget constraints may prevent powering all cores simultaneously at full performance level. Therefore, chip manufacturers introduce a power budget constraint as Thermal Design Power (TDP) for chips. Meanwhile, multicore platforms are suitable for implementation of fault-tolerance techniques to achieve high reliability. Task Replication is a known technique to tolerate transient faults. However, careless task replication may lead to significant peak power consumption. In this paper, we consider the problem of achieving a given reliability target... 

Increasing power densities in future technology nodes is a crucial issue in multicore platforms. As the number of cores increases in them, power budget constraints may prevent powering all cores simultaneously at full performance level. Therefore, chip manufacturers introduce a power budget constraint as Thermal Design Power (TDP) for chips. Meanwhile, multicore platforms are suitable for the implementation of fault-tolerance techniques to achieve high reliability. Task Replication is a well-known technique to tolerate transient faults. However, careless task replication may lead to significant peak power consumption. In this article, we consider the problem of achieving a given reliability... 

This study presents a method based on minimum description length criterion to enumerate the incident waves impinging on a large array using a relatively small number of samples. The proposed scheme exploits the statistical properties of eigenvalues of the sample covariance matrix (SCM) of Gaussian processes. The authors use a number of moments of noise eigenvalues of the SCM in order to separate noise and signal subspaces more accurately. In particular, the authors assume a Marcenko-Pastur probability density function (pdf) for the eigenvalues of SCM associated with the noise subspace. We also use an enhanced noise variance estimator to reduce the bias leakage between the subspaces.... 

Recently, the problem of integrating side information about classes has emerged in the learning settings like zero-shot learning. Although using multiple sources of information about the input space has been investigated in the last decade and many multi-view and multi-modal learning methods have already been introduced, the attribute learning for classes (output space) is a new problem that has been attended in the last few years. In this paper, we propose an attribute learning method that can use different sources of descriptions for classes to find new attributes that are more proper to be used as class signatures. Experimental results show that the learned attributes by the proposed... 

All current highest efficiency perovskite solar cells (PSCs) use highly toxic, halogenated solvents, such as chlorobenzene (CB) or toluene (TLN), in an antisolvent step or as solvent for the hole transporter material (HTM). A more environmentally friendly antisolvent is highly desirable for decreasing chronic health risk. Here, the efficacy of anisole (ANS), as a greener antisolvent for highest efficiency PSCs, is investigated. The fabrication inside and outside of the glovebox showing high power conversion efficiencies of 19.9% and 15.5%, respectively. Importantly, a fully nonhalogenated solvent system is demonstrated where ANS is used as both the antisolvent and the solvent for the HTM.... 

A single cell downlink scenario is considered where a multiple-antenna base station delivers contents to cache-enabled user terminals. Using the ideas from multi-server coded caching (CC) scheme developed for wired networks, a joint design of CC and general multicast beamforming is considered to benefit from spatial multiplexing gain, improved interference management and the global CC gain, simultaneously. The proposed multicast beamforming strategies utilize the multiantenna multicasting opportunities provided by the CC technique and optimally balance the detrimental impact of both noise and inter-stream interference from coded messages transmitted in parallel. The proposed scheme is shown... 

A multi-antenna broadcast channel scenario is considered where a base station delivers contents to cache-enabled user terminals. A joint design of coded caching (CC) and multigroup multicast beamforming is proposed to benefit from spatial multiplexing gain, improved interference management and the global CC gain, simultaneously. The developed general content delivery strategies utilize the multiantenna multicasting opportunities provided by the CC technique while optimally balancing the detrimental impact of both noise and inter-stream interference from coded messages transmitted in parallel. Flexible resource allocation schemes for CC are introduced where the multicast beamformer design and... 

Although the read disturb STT-RAM approximation technique improves performance, it consists of an approximate read plus an approximate write both at the same time. So it may degrade the application Quality of Result (QoR) considerably. On the other hand, the incorrect read decision approximation technique improves power without corrupting the stored data. We adopt an opportunity study for instruction-based distinction of read implementation to take advantage of both of the approximation techniques, while enhancing application’s QoR. We evaluated the proposed method using a set of state of the art benchmarks. The experimental results show that our method allows to increase application’s QoR... 

Although the read disturb spin-transfer torque RAM approximation technique improves performance, it may consist of an approximate read plus an approximate write both at the same time. So it may degrade the application quality of result (QoR) considerably. On the other hand, the incorrect read decision approximation technique improves power without corrupting the stored data. We adopt an opportunity study for instruction-based distinction of read implementation to take advantage of both of the approximation techniques, while enhancing application's QoR. We evaluated the proposed method using a set of state-of-the-art benchmarks. The experimental results show that our method allows to increase...