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Advances in heuristic signal processing and applications
, Book ; Chatterjee, Amitava ; Nobahari, Hadi ; Siarry, Patrick
Springer
2013
Abstract
There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and multidimensional signal processing, namely image and video processing, with the recent focus changing from a step-by-step procedure of designing the algorithm first and following up with in-depth analysis and performance improvement to instead applying heuristic-based methods to solve signal-processing problems. In this book the contributing authors demonstrate both general-purpose algorithms and those aimed at solving specialized application problems, with a special emphasis on heuristic iterative optimization methods employing modern evolutionary and swarm...
A Deep Learning Approach to Classify Motor Imagery Based on The Combination of Discrete Wavelet Transform and Convolutional Neural Network for Brain Computer Interface System
, M.Sc. Thesis Sharif University of Technology ; Selk Ghafari, Ali (Supervisor) ; Zabihollah, Abolghssem (Supervisor)
Abstract
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...
EMG Feature Extraction to Control the Prosthetic Hand
, M.Sc. Thesis Sharif University of Technology ; Vossoughi Vahdat, Bijan (Supervisor)
Abstract
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.
EEG Signal Processing in BCI Applications
, M.Sc. Thesis Sharif University of Technology ; Haj Sadeghi, Khosrow (Supervisor)
Abstract
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...
Feasibility Study of TMS (DSP-Core Base)and Xilinx FPGA for Speech Algorithm
, M.Sc. Thesis Sharif University of Technology ; Mortazavi, Mohammad (Supervisor) ; Ghorshi, Mohammad Ali (Supervisor)
Abstract
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...
An Investigation of Signal Processing Techniques for Monitoring of the Heart Abnormalities
, M.Sc. Thesis Sharif University of Technology ; Ghorshi, Alireza (Supervisor)
Abstract
In this thesis we have investigated and improved the signal processing techniques which are used for monitoring the heart abnormalities in terms of ECG (ElectroCardioGram) signals in order to detect heart attacks before they occur. De-noising ECG signals are one of the most important research topics in computer and electrical engineering fields. There are many different algorithms for de-noising signals in various domains. It usually is needed to propose a suitable algorithm for each specific system. In some cases instead of developing a new algorithm, we could modify the available ones for de-noising in our system. ECG signals are output from an electrocardiograph which measures electrical...
Designing a System for Pulse Pile-Up Processing in Gamma Ray Spectroscopy Based on Digital Method
, M.Sc. Thesis Sharif University of Technology ; Hosseini, Abolfazl (Supervisor)
Abstract
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...
Active Control of Structural Non-stationary Response Using Improved Hilbert Huang Method
, Ph.D. Dissertation Sharif University of Technology ; Hosseini Kordkheili, Ali (Supervisor) ; Mohammad Navazi, Hossein (Co-Supervisor)
Abstract
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...
Pronunciation Scoring in Computer-Assisted Language Learning
, M.Sc. Thesis Sharif University of Technology ; Sameti, Hossein (Supervisor)
Abstract
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...
Unsupervised Command Detection in EEG-based Brain-computer Interface
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
A Brain–Computer Interface is a system that provides a direct pathway for communication between a brain and a computer device by processing signals from sensors measuring brain activity (here Electroencephalography signals). Brain signals are known to be stochastic, non-stationary, non-linear and highly noisy, Therfore Brain–Computer Interface Systems rely on signal preprocessing, feature extraction and use of machine learning methods in order to detect mental state of Brain–Computer Interface user. Current approaches addressing the problem are mainly based on supervised learning methods. In this Thesis, first some of freely obtainable datasets with motor or motor-imagery paradigms are...
Effect of Subsequent Drying and Wetting on Small Strain Shear Modulus of Unsaturated Silty Soils Using Bender Element
, M.Sc. Thesis Sharif University of Technology ; Khosravi, Ali (Supervisor)
Abstract
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...
Detection of Abrupt Changes in Structural Properties Through Vibration Signal Processing
, Ph.D. Dissertation Sharif University of Technology ; Kazemi, Mohammad Taghi (Supervisor)
Abstract
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...
WT-SOBI Method Towards Blind System Identification of Structures
, M.Sc. Thesis Sharif University of Technology ; Kazemi , Mohammad Taghi (Supervisor)
Abstract
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...
Studying Site Effects Through Seismic Signal Processing
, M.Sc. Thesis Sharif University of Technology ; Ghannad, Mohammad Ali (Supervisor)
Abstract
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...
Improving the Accuracy of a Microparticle Biosensor by Artificial Intelligence
, M.Sc. Thesis Sharif University of Technology ; Taghipoor, Mojtaba (Supervisor)
Abstract
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...
Conceptual Design and Performance Simulation of a Vibration Measurement Device for Modal Analysis of Rotating Cylindrical Shells
, M.Sc. Thesis Sharif University of Technology ; Saadat Foumani, Mahmoud (Supervisor)
Abstract
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...
Comparison of the Performance of Accelerometer and Non-Contact Displacement Sensor in Fault Detection of Ball Bearing
, M.Sc. Thesis Sharif University of Technology ; Behzad, Mahdi (Supervisor)
Abstract
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...
Experimental and Numerical Study of Pulse Shaper Geometry Effects in Split Hopkinson Pressure Bar Test
,
M.Sc. Thesis
Sharif University of Technology
;
Naghdabadi, Reza
(Supervisor)
;
Sohrabpour, Saeed
(Supervisor)
Abstract
Split Hopkinson pressure bar (SHPB) test is one of the tests used for characterizing the mechanical behavior of materials at high strain rates between and s -1. A SHPB test consists of a gas gun, a striker bar, an incident bar, a transmission bar and a specimen which is sandwiched between the incident and transmission bars. In order to achieve constant strain rate during the test which is necessary in SHPB test, the incident pulse should obey the stress-strain curve of the specimen. For shaping the incident pulse usually a thin deformable disk -called pulse shaper- is placed between the striker and incident bars. In this thesis, first we designed, implemented and calibrated the measuring...
Improving CCA Based Methods for SSVEP Classification using Graph Signal Processing
, M.Sc. Thesis Sharif University of Technology ; Hajipour Sardouie, Sepideh (Supervisor) ; Einizadeh, Aref (Co-Supervisor)
Abstract
The Brain Computer Interface (BCI) translates brain signals into a series of commands, enabling individuals to fulfill many of their basic needs without physical activity. Electroencephalogram (EEG) signals are commonly used as input for BCI systems, because the recording of this signal is non-invasive, inexpensive, and also have an acceptable time resolution. One of the most prevalent methods in BCI systems is the brain-computer interface based on Steady State Visual Evoked Potentials (SSVEP). These systems provide high response speed and Information Transfer Rate (ITR) as well as a good signal-to-noise ratio (SNR). The main purpose of these systems is to detect the frequency of SSVEP in...
Improving the Performance of Graph Filters and Learnable Graph Filters in Graph Neural Networks
, M.Sc. Thesis Sharif University of Technology ; Babaiezadeh, Masoud (Supervisor)
Abstract
Graph signals are sets of values residing on sets of nodes that are connected via edges. Graph Neural Networks (GNNs) are a type of machine learning model for working with graph-structured data, such as graph signals. GNNs have applications in graph classification, node classification, and link prediction. They can be thought of as learnable filters. In this thesis, our focus is on graph filters and enhancing the performance of GNNs. In the first part, we aim to reduce computational costs in graph signal processing, particularly in graph filters. We explore methods to transform signals to the frequency domain with lower computational cost. In the latter part, we examine regulations in...