Sharif Digital Repository

Blind source separation methods such as independent component analysis (ICA) and second order blind identification (SOBI) have shown considerable potential in the area of ambient vibration system identification. The objective of these methods is to separate the modal responses, or sources, from the measured output responses, without the knowledge of excitation. Several frequency domain and time domain methods have been proposed and successfully implemented in the literature. Whereas frequency-domain methods pose several challenges typical of dealing with signals in the frequency-domain, popular time domain methods such as NExT/ERA and SSI pose limitations in dealing with noise, low sensor... 

Blind Source Separation (BSS) is a technique for estimating individual source components from their mixtures at multiple sensors, where the mixing model is unknown. Although it has been mathematically shown that for linear mixtures, under mild conditions, mutually independent sources can be reconstructed up to accepted ambiguities, there is not such theoretical basis for general nonlinear models. This is why there are relatively few resultsin the literature in this regard in the recent decades, which are focused on specific structured nonlinearities.In the present study, the problem is tackled using a novel approach utilizing temporal information of the signals. The original idea followed in... 

The objective of this project is to consider the various methods employed in processing the EMG signal to control an artificial hand. The current presented methods of EMG processing do not benefit from the fact that sequential movements are closely correlated. Using this time correlation we are going to improve the correctness of the prediction of movements. To find the application of this project in commercial artificial hand we focus on one DOF hand for a high level of accuracy. Using this processing method combined with an artificial hand having Geometric Adaptability may present an acceptable product for commercial use.

 

Orthogonal Frequency Division Multiplexing (OFDM) is a digital transmission method developed to meet the increasing demand for higher data rates in communications which can be used in both wired and wireless environments. This thesis describes the issue of the Peak to Average Power Ratio (PAPR) in OFDM system, based on compressed sensing at the receiver of a peak-reducing sparse clipper applied to an OFDM signal at the transmitter. By choosing proper clipping threshold, clipping signal will be sparse so locations, magnitudes, and phases of the clipping signal can be recovered by compressed sensing method. We demonstrate that in the absence of optimization algorithms at the transmitter,... 

Brain-inspired methods are now widely used to process the data generated by the brain with the aim of improving our understanding of how the brain functions and produces the remarkable intelligence exhibited by humans, which is the source of all realizations, perception and actions. Therefore brain-computer interface (BCI) is one of the most challenging scientific problems which focuses scientists attention, in most cases these systems are based on EEG signals recorded from the surface of the scalp because this method of the brain activity monitoring is noninvasive, easy to use and quit inexpensive. Brain computer interface (BCI) systems analyse the EEG signals and translate person’s... 

Although up to now different processing algorithms have been proposed for Synthetic Aperture Radar (SAR) raw data, all of them suffer from one common problem and that is huge amount of data to be processed. So because of current system limitations, efficient compression algorithms for processing, saving, or transmitting data are needed. Up to now many algorithms have been proposed for SAR raw data compression, but each of them has some defects that should be payed attention to. The most important reason of these defects is the special characteristics of SAR images. With the aid of “Compressed Sensing (CS)”, the new field which has emerged recently, a special characteristic of the scene... 

Employing array of antennas in amny signal processing application has received considerable attention in recent years due to major advances in design and implementation of large dimentional antennas. In many applications we deal with such large dimentional antennas which challenge the traditional signal processing algorithms. Since most of traditional signal processing algorithms assume that the number of samples is much more than the number of array elements while it is not possible to collect so many samples due to hardware and time constraints.
In this thesis we exploit new results in random matrix theory to charachterize and describe the properties of Sample Covariance Matrices... 

Evaluation of the seismic-induced settlement of an unsaturated soil layer depends on several variables, among which the small strain shear modulus, Gmax, and soil’s state of stress have been demonstrated to be of particular significance. Recent interpretation of trends in Gmax revealed considerable effects of the degree of saturation and hydraulic hysteresis on the shear stiffness of soils in unsaturated states. Accordingly, the soil layer is expected to experience different settlement behaviors depending on the soil saturation and seasonal weathering conditions. In this study, a semi-empirical formulation was adapted to extend an existing Gmax model to infer hysteretic effects along... 

Structural system identification from vibration data is one of the most interesting research topics in the structural health monitoring area. Recently, realization and detection of the effects of damage when a structure is subjected to strong ground motion has become a great concern in earthquake and structural engineering communities. Seismic signal processing is one of the most reliable methods of detecting the structural damage during earthquakes. The structural responses during earthquakes are nonstationary with respect to both amplitude and frequency. The state-of-the-art time-frequency distributions when applied to vibration records were studied. Different methods of analysis for... 

Sparse representation and consequently, dictionary learning have been two of the great importance topics in signal processing problems for the last two decades. In sparse representation, each signal has to be represented as a linear combination of some basic signals, which are called atoms, and their collection is called a dictionary. To put it in other words, if complete dictionaries such as Fourier or Wavelet dictionaries are used for the representation of signals, the representation will be unique, but not sparse. On the other hand, if overcomplete dictionaries are used, we will confront with too many representations, and the goal of sparse representation is to find the sparsest one. ... 

The research in this study aims to analyze data in healthcare, especially the diagnosis of several diseases caused by heart failure. Analyzing and analyzing this data can lead to the discovery of relationships and patterns that can play an important role in the decision-making process of relevant officials in any field. Today, medical data around the world is stored in large volumes for future research. Various infrastructures and software have been set up in many health centers and research centers affiliated with those organizations.In this research, the general process of work is such that the data related to the heart sounds, which are in the four broad categories of S1 to S4, are... 

Sparse representation and compressed sensing have been widely used in various fields, one of the most popular of which is ISAR imaging. Inverse Synthetic Aperture Radar (ISAR) provides an electromagnetic image of the target, which is mainly used to identify and classify targets. In some applications, recognizing targets from a 2D image can be difficult and error-prone. One idea to deal with this problem is 3D ISAR imaging. The most widely used method of ISAR imaging is direct method based on Fourier transform. This method requires the measurement of radar data with high measurement density in 3 directions, which increases the data collection time and volume, which is the main problem of... 

The problem of inferring a graph from a set of graph signals over it plays a crucial role in the field of Graph Signal Processing (GSP). When provided with a graph that best models the structure of data, the GSP algorithms can offer high data processing capability. However, a meaningful graph of data is not always available, hence in some applications, the graph needs to be learned from the data itself. When the data is corrupted and missing, this task becomes even more challenging. In this paper, we present a graph learning algorithm that is capable of learning the underlying structure of data from an incomplete and noisy dataset of graph signals. We propose an algorithm that jointly... 

Blind separation of mixed graph signals is one of the new topics in the field of graph signal processing. However, similar to the most proposed methods for separating traditional signals, it is assumed that the number of observed signals is equal to or greater than the number of sources. In this thesis, we show that a signal can be uniquely decomposed into the summation of a set of smooth graph signals, up to the indeterminacy of their DC values. From the blind source separation point of view, this is like the separation of a set of graph signals from a single mixture, contrary to traditional blind source separation in which at least two observed mixtures are required. Moreover, we... 

Due to the increase in the number of people interested in learning new languages, in recent years, multiple systems have been developed to teach new languages to those who are interested. These systems are called Computer Assisted Language Learning (CALL). However, the most credible CALL systems, like Duolingo, do not support Persian. So the of this study is to design and implement one of the technical parts of CALL systems, the Computer Assisted Pronunciation Training(CAPT), which is the part responsible for evaluating the learners' input voice's pronunciation and generating appropriate score and feedback.In this study, good pronunciation means correct expression of words, correct... 

In recent years, due to the structural need of most medical data for graphic models such as the graphic model of patients and the loss of data correlation in previous methods, graphic methods have been designed and developed. On the other hand, with the growing presence of magnetic resonance imaging devices in various medical centers, a large amount of functional magnetic resonance images of healthy and sick people have become available to researchers. In this study, our goal is to use a new method in the field of graphic modeling so that we can extract functional connectivity graphs from functional magnetic resonance images and measure the performance of these graphs in different groups of... 

Cylindrical shells play an important role in a wide range of engineering applications such as mechanics, aerospace, marine engineering, and nuclear applications due to their high load-carrying capacity, light structure, and economic advantages. Because these structures are often made of high lengths and thin sheets, they are very prone to vibration, especially under the conditions that they play a role in the rotating state, which can cause failure. For the reasons mentioned, the vibration analysis of rotating cylindrical shells will be essential in engineering designs. In the present study, after introducing general concepts regarding the analysis of structural vibrations, previous research... 

EEG brain signals have gained particular attention among researchers in the field of brain signal processing due to their easy and cheap recording, high temporal resolution, and non-invasiveness. On the other hand, defects such as high vulnerability to various types of noise and artifacts have caused the main challenge before processing them to improve the signal-to-noise ratio and the interpretability of brain connectivity obtained from them. In order to solve these challenges, two important problems of "separation of desired and undesired signal subspace" and "functional and effective connectivity analysis" have been raised, respectively. In solving both problems, EEG signals are usually... 

Multistatic radar has several advantages over monostatic radar (such as better detection), which are due to the use of different viewing angles and the difference in their clutter characteristics. Clutter in many applications (such as marine applications) has the property of being sparse in certain dictionaries. Therefore, the investigation of sparse clutter (such as sea clutter) is of particular importance. It is worth noting that the detection of targets in the vicinity of the sea faces difficulties due to the dynamics of the sea, which causes the Doppler spectrum to change with time and change in space. Considering the fact that the sea clutter is sparse clutter, one of the powerful... 

In this research, the ability of artificial intelligence to improve the biosensor performance of a microfluidic system has been investigated. Coulter counter is a microfluidic system that measures the concentration of particles in a fluid using signals obtained from a biosensor. The method of this system is called " Resistive Pulse Sensing (RPS)" method. The pulses in the Coulter counter signals are affected by the number, shape, size and speed of particles passing through the orifice. The pulse of two particles of the same size and different shape in these systems are very similar and this makes it difficult for an operator to distinguish the type of particle. Another disadvantage of...