Sharif Digital Repository

By generalising dictionary learning (DL) algorithms to multidimensional (MD) mode and using them in applications where signals are inherently multidimensional, such as in three-dimensional (3D) inverse synthetic aperture radar (ISAR) imaging, it is possible to achieve much higher speed and less computational complexity. In this study, the formulation of the multidimensional dictionary learning (MDDL) problem is expressed and two algorithms are proposed to solve it. The first one is based on the method of optimum directions (MOD) algorithm for 1D dictionary learning (1DDL), which uses alternating minimisation and gradient projection approach. As the MDDL problem is non-convex, the second... 

Aim of study: Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans. Methodology/Approach: The proposed method finds the effectiveness of previous and current treatment plans, hierarchically determining the best decision for... 

Although Unmanned Aerial Vehicles (UAVs) are capable of significantly improving the information security by detecting the eavesdropper's location, their limited energy motivates our research to propose a secure and energy efficient scheme. Thanks to the common-message philosophy introduced by Rate-Splitting (RS), we no longer have to allocate a portion of the transmit power to radiate Artificial Noise (AN), and yet both the Energy Efficiency (EE) and secrecy can be improved. Hence we define and study the Secrecy Energy Efficiency (SEE) of a multi-carrier multi-UAV network, in which Cooperative Rate-Splitting (CRS) is employed by each multi-antenna UAV Base-Station (UAV-BS) for protecting... 

Localization of radio-frequency (RF) transmitters using time difference of arrival (TDOA)-based methods is one of the conventional passive techniques that admits noncooperative source position finding, but suffers from challenging requirements such as precise intersensor synchronization and high-throughput transmission data links. A tradeoff governing the TDOA systems is in the sensor placement configuration. The more distance the sensors are placed, the more accurate localization is carried out, while the cost for the synchronization and data link increases, at the same time. In this work, a novel reconfigurable intelligent surface (RIS)-aided localization system is proposed that enjoys... 

In this letter, we examine the positioning in wireless networks in the presence of IRSs. We show that IRSs can be used to localize the user when there are not enough transmitters (Base Station). To this end, we consider a network of one transmitter, few IRSs, and one receiver, whose location should be determined. Our results indicate that we can achieve the accuracy required in the localization by controlling the number of elements of IRSs and properly selecting the location of IRSs for a given region. © 2012 IEEE  

Single-walled carbon nanotubes (SWCNTs) emit near-infrared (NIR) fluorescence that is ideal for optical sensing. However, the low quantum yields diminish the sensor's signal-to-noise ratio and limits the penetration depths for in vivo measurements. In this study, we perform a systematic investigation of the plasmonic effects of Ag and Au nanoparticles of various geometries to tune and even enhance the fluorescence intensity of single-stranded DNA-wrapped SWCNTs (ssDNA-SWCNTs). We observe a chirality-dependent NIR fluorescence enhancement that varies with both nanoparticle shape and material, with Au nanorods increasing (7, 5) and (7, 6) chirality emissions by 80% and 60% and Ag nanotriangles... 

We consider the K-user Multiple Input Multiple Output (MIMO) Gaussian interference channel with M antennas at each transmitter and N antennas at each receiver. It is assumed that channel coefficients are constant real numbers and are available at all transmitters and at all receivers. The main objective of this paper is to characterize the number of Degrees of Freedom (DoF) of this channel. Using the real interference alignment technique introduced in Motahari et al., 2014, we show that MN M+N K degrees of freedom can be achieved for almost all channel realizations. Also, a new upper-bound on the DoF of this channel is provided. This upper-bound coincides with our achievable DoF for K K u... 

Event-related potential (ERP) extraction from ongoing electroencephalograph (EEG) and its enhancement is one of the long-established problems in EEG signal processing. Most of the previous studies have focused mainly on the ERP enhancement without considering the multi-dimentional structure of the signal. In order to take advantage of this property, we propose a tensor-based solution with trial-by-trial concatenated ERP data. Then we develop an algorithm based on low-rank Tucker decomposition to detect single trial ERP component with maximized signal to noise ratio (SNR). In other words, by using tensor algebra we consider both self-similarity in intratrials and global correlation in spatial... 

Co-channel interference (CCI) is a performance limiting factor in molecular communication (MC) systems with shared medium. Interference alignment (IA) is a promising scheme to mitigate CCI in traditional communication systems. Due to the signal-dependent noise in MC systems, the traditional IA schemes are less useful in MC systems. In this paper, we propose a novel IA scheme in molecular interference channels (IFCs), based on the choice of releasing/sampling times. To cancel the aligned interference signals and reduce the signal dependent noise, we use molecular reaction in the proposed IA scheme. We obtain the feasible region for the releasing/sampling times in the proposed scheme. Further,... 

In this paper, we propose an accurate two-phase millimeter-Wave (mmWave) beamspace channel tracking mechanism. Particularly in the first phase, we train an extreme reservoir machine (ERM) for tracking the historical features of the mmWave beamspace channel and predicting them in upcoming time steps. Towards a more accurate prediction, we further fine-tune the ERM by means of Xavier initializer technique, whereby the input weights in ERM are initially derived from a zero mean and finite variance Gaussian distribution, leading to 49% degradation in prediction variance of the conventional ERM. The proposed method numerically improves the achievable spectral efficiency (SE) of the existing... 

Deep learning architectures employ various regularization terms to handle different types of priors. Non-smooth regularization terms have shown promising performance in the deep learning architectures and a learning framework has recently been proposed to train autoencoders with such regularization terms. While this framework efficiently manages the non-smooth term during training through proximal operators, it is limited to autoencoders and suffers from low convergence speed due to several optimization sub-problems that must be solved in a row. In this paper, we address these issues by extending the framework to general feed-forward neural networks and introducing variable extrapolation... 

This paper introduces an algorithm for beamforming systems by the aid of multidimensional harmonic retrieval (MHR). This algorithm resolves problems, removes limitations of sampling and provides a more robust beamformer. A new sample space is created that can be used for estimating weights of a new beamforming called spatial-harmonics retrieval beamformer (SHRB). Simulation results show that SHRB has a better performance, accuracy, and applicability and more powerful eigenvalues than conventional beamformers. A simple mathematical proof is provided. By changing the number of harmonics, as a degree of freedom that is missing in conventional beamformers, SHRB can achieve more optimal outputs... 

Centralized and cloud computing-based network architectures are the promising tracks of future communication systems where a large scale compute power can be virtualized for various algorithms. These architectures rely on high-performance communication links between the base stations and the central computing systems. On the other hand, massive Multiple-Input Multiple-Output (MIMO) technology is a promising solution for base stations toward higher spectral efficiency. To reduce system complexity and energy consumption, 1-bit analog-to-digital converters (ADCs) are leveraged with the cost of lowering the signal quality. To recover the lost information, more sophisticated algorithms, like... 

Improving detection efficiency in small animal PET scanners without degrading spatial resolution is one of the main problems of these scanners. Commercial small animal PET scanners use different methods to achieve desirable levels of sensitivity and spatial resolution. GE Healthcare eXplore VISTA PET scanner uses double layer (LYSO-GSO) depth-of-interaction (DOI) capable cuboid detector modules. In this work, the design of GE Healthcare eXplore VISTA PET scanner is improved using tapered detector geometry instead of cuboid geometry. Using tapered detector geometry, the gaps between adjacent modules are filled and the sensitive volume has increased about 11.5%. The new designed PET scanner... 

We consider the problem of recovering channel code parameters over a candidate set by merely analyzing the received encoded signals. We propose a deep learning-based solution that I) is capable of identifying the channel code parameters for several coding scheme (such as LDPC, Convolutional, Turbo, and Polar codes), II) is robust against channel impairments like multi-path fading, III) does not require any previous knowledge or estimation of channel state or signal-to-noise ratio (SNR), and IV) outperforms related works in terms of probability of detecting the correct code parameters. © 2012 IEEE  

In recent studies, several methods have been suggested to decrease noise of magnetic resonance image (MRI) in order to raise the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM). In this paper, we propose a novel method based on a minimization problem in Ripplet domain that uses singular value decomposition (SVD) in low rank learning to eliminate the noise of MRI images. We reschedule the weighted nuclear norm minimization (WNNM) problem in any edges of Ripplet domain transform and using an adaptive weighting structure to denoise the patches of Ripplet component matrix. The parameters of the proposed method are divided into two groups, some of them are calculated... 

We consider the K-user Multiple Input Multiple Output (MIMO) Gaussian interference channel with M antennas at each transmitter and N antennas at each receiver. It is assumed that channel coefficients are constant real numbers and are available at all transmitters and at all receivers. The main objective of this paper is to characterize the number of Degrees of Freedom (DoF) of this channel. Using the real interference alignment technique introduced in [20], we show that MN/M+NK degrees of freedom can be achieved for almost all channel realizations. Also, a new upper-bound on the DoF of this channel is provided. This upper-bound coincides with our achievable DoF for K ≥ Ku≜M+N/gcd(M;N), where... 

Cooperative relaying is utilized as an efficient method for data communication in wireless sensor networks and the Internet of Things. However, sometimes due to the necessity of multi-hop relaying in such communication networks, it is challenging to guarantee the secrecy of cooperative transmissions when the relays may themselves be eavesdroppers, i.e., we may face with the untrusted relaying scenario where the relays are both necessary helpers and potential adversary. To obviate this issue, a new cooperative jamming scheme is proposed in this paper, in which the data can be confidentially communicated from the source to the destination through multiple untrusted relays. In our proposed... 

The primary aim in pre-surgical evaluations in patients with neurological disorders such as epilepsy is determining the precise location of the cortical region responsible for the malfunctions which is called source localization. Different modalities unravel different views of brain activity. Combining these complementary aspects of the brain yields more accurate source localization. In this paper, a method is proposed for combining localization methods in different modalities based on the theory of evidence, the result of some localization methods in modalities are integrated using weights in accordance to their relative performance and are combined using Dempster's rule of combination and... 

A novel photoacoustic microscopy (PAM) structure, based on Haar wavelet patterns, is proposed in this paper. Its main goal is to mitigate the PAM imaging resolution and thus the time of its sampling process without compromising the image quality. Owing to the intrinsic nature of wavelet transform, this structure collects spatial and spectral components simultaneously, and this feature speeds up the sampling process by 33%. The selection of these patterns helps in better control of required conditions, such as multi-resolution imaging, to guarantee adequate image quality in comparison to previous microscopic structures. Simulation results prove the superior quality of the proposed approach...