Sharif Digital Repository

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... 

A Brain-Computer Interface (BCI) is a communication system that does not need any peripheral muscular activity. The huge goal of BCI is to translate brain activity into a command for a computer. One of the most important topics in the brain-computer interface is motor imagery (MI), which shows the reconstruction of subjects. The electrical activities of the brain are measured as electroencephalogram (EEG). EEG signals behave as low to noise ratio also show the dynamic behaviors.In the present work, a novel approach has been employed which is based on feature extraction with discretion wavelet transform (DWT), support vector machine (SVM), Artificial Neural Network (ANN) and Convolutional... 

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. ... 

Adaptive vibration control of a structure under different condition of exciting forces or structural response is the main scope of this research.Using a combination of the pole placement and online Empirical Mode Decomposition (EMD) methods, a new algorithm is proposed for adaptive active control of structural vibration. For this purpose, by structural response which is evaluated from Hilbert-Huang Transform (HHT) and using prior knowledge for corresponding conditions, proper and optimum control forces are applied to structure. Hence, error sources of EMD method in the HHT such as end effects error, mode mixing problem and decomposition resolution are being studied. A modified method based... 

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... 

Reservoir Computing is a novel computing paradigm that uses a nonlinear recurrent dynamical system to carry out information processing. Recent electronic and optoelectronic Reservoir Computers based on an architecture with a single nonlinear node and a delay loop have shown performance on standardized tasks comparable to state-of-the-art digital implementations. Here we report an all-optical implementation of a Reservoir Computer, made of off-the-shelf components for optical telecommunications. It uses a semiconductor optical amplifier as nonlinearity, and a Fabry-Perot Resonator as a key element to establish the virtual nodes, connecting them and consequently, build the virtual neural... 

Earthquakes are one of the greatest natural hazards. Hazard mitigation requires studies in many areas including geotechnical aspects of earthquake engineering. Characteristics of seismic waves change significantly as they pass through soft soil layers near the earth's surface. This phenomenon, commonly known as site effects or site response, is a major factor influencing the extent of damage on structures. Processing of seismic data recorded during earthquakes is one of the most reliable methods to study site effects. The ground motions generated during earthquakes are nonstationary with respect to both amplitude and frequency. The state-of-the-art time-frequency distributions when applied... 

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... 

Audio signal processing is a greatly useful approach to the Internet of Things since analyzing prominent audio signals can provide valuable information about environmental activities. Environmental sound processing is used in applications such as mechanical systems diagnosis, industrial maintenance, security systems, etc. This approach requires the design and development of sound classification and detection systems. In this thesis, we have achieved 84.5% accuracy on optimizing the features (by feature engineering and feature learning) and exploiting different types of machine learning algorithms. Well-known databases such as ESC-50 have been used to test and evaluate the whole system. Among... 

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... 

The main purpose of the thesis is Digital image Processing using the sparsity of image. The issue could be described in two ways. First is reconstruction of missed blocks of an image based on the sparsity of image in the transform domain and second is impulsive noise removal using the sparsity of noise in the spatial domain. In the first approach we will review Guleryuz method and simulate and analyze it. In the second approach, two new methods named RDE and Knockout will be introduced. These methods try two remove the impulsive noise of an image using its sparseness. RDE method is a development of conventional methods, but Knockout has a completely new idea. We will show that Knockout is... 

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 use of multiple sensors that produce different physical parameters of the measured system for its health monitoring raises the reliability of the diagnosis. At the moment, the fault detection capacities of ball bearings by means of proximity probe are detected by exploiting the advantages and reducing its defects by appropriate signal processing of the raw data in the time domain. Also, the application of numerical derivation and integration to achieve to the desired frequency spectrum in defect detection is investigated. A set of experiments with different sizes of internal ring, outer ring and ball defects, four levels of speed and two load levels have been performed. Data acquisition... 

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,... 

Digital Signal Processing (DSP) is used in a wide range of applications such as high-definition TV, digital audio, multimedia, digital cameras, radar, sonar detectors, biomedical imaging, global positioning, digital radio, speech recognition and etc. These applications can be implemented by either DSP processors or FPGA technology. Digital Signal Processors are microprocessors specifically designed to handle Digital Signal Processing tasks. These devices have seen a tremendous growth in the last decade, finding use in everything from cellular telephones to advanced scientific instruments. On the other hand, the rise of FPGA in the signal processing realm could be assigned to hardware to... 

Image compression is a noteworthy problem in image processing field. Transform coding provides a scheme to confront the image compression problem. The discrete cosine transform (DCT) is used in the majority of image compression standards by transform coding. The DCT can efficiently represent smooth signals, but it becomes inefficient when the image contains arbitrary-shaped discontinuities. As an example, piecewise-smooth images (i.e., an image that contains multiple smooth areas separated with arbitrary-shaped boundaries), which are widely used in 3-dimensional image representation, cannot well represent by the DCT. Therefore, replacing the DCT with an adaptive transform can improve image... 

The brain-computer interface is an advanced technology in human-machine interaction. The Speller system is a typical use of BCI, in which the target stimulation is detected by the induced signal in the brain. The most commonly speller system, the matrix Speller, has a major disadvantage, and it is Gaze-dependent. Research has proven that target-character selection in the matrix Speller is dependent on eye movement, or as referred to in technical terminology, it is gaze dependent. Therefore, the Speller matrix is not usable for users suffering from unimpaired oculomotor control. Many researchers attempted to overcome this issue, and their results led to two solutions; 1) changing the type 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... 

In Radiation Detection Systems applied in Spectroscopy and Imaging, if the rate of particle incidence with detector is high, the output pulses being stacked up on each other will be expected, which is known as “Pulse Pile-up”. Pulse pile-up might result in a serious disorder of energy and timing data expected from the pulses. In recent decades and after successful implementation of pile-up rejection methods on analog circuits, considering development and progression in the field of digital, different algorithms based on pileup correction methods have been developed to obtain all spectrum information, whether energy, rise time and recorded pulses. Such methods can be compared in different... 

Adaptive beamforming performance is very sensitive to any mismatch between actual and presumed steering vectors of desired signal. In addition to this sensitivity, presence of desired signal in training snapshots dramatically reduces the convergence rate, as compared to the case that signal-free training snapshots are available.
The present work is aimed at proposing a new adaptive beamformer that is robust against direction-of-arrival (DOA) mismatch and has high convergence rate. This method is based on desired signal elimination from training snapshots and sub-array beamforming technique. To this end, a Blocking matrix that converts primary data to desired signal free data is...