Sharif Digital Repository

Several researches have been done to identify visual system characteristics. Some of them are based on the processing of the brain signal recordings. Visual evoked potentials (VEPs) are electrical signals which are produced in response to the visual stimuli and recorded by means of electrodes placed on the head. These signals are usually characterized by the amplitude and latency of their peaks. Different types of visual stimuli and visual system characteristics can affect the shape and hence the characteristics of VEPs. In this paper, proper visual stimuli were used and VEPs were recorded in order to classify visual acuity. To achieve this goal, visual evoked potentials were recorded and... 

This paper presents the potential of acoustic emission (AE) technique to detect valve damage in internal combustion engines. The cylinder head of a spark-ignited engine was used as the experimental setup. The effect of three types of valve damage (clearance, semicrack, and notch) on valve leakage was investigated. The experimental results showed that AE is an effective method to detect damage and the type of damagein valves in both of the time and frequency domains. An artificial neural network was trained based on time domain analysis using AE parametric features (AErms, count, absolute AE energy, maximum signal amplitude, and average signal level). The network consisted of five, six, and... 

Due to flexibility, relatively small weight and low energy-dissipative characteristics of cables, they are vulnerable to external excitations such as wind, wind-rain, earthquake and traffic loadings. Among them, galloping phenomenon is one of the most important sources of electrical/mechanical failures in power transmission lines. In this paper, tuneable vibration absorbers (TVAs) are used to suppress galloping forced vibrations (as a semi-active control approach). Using mode summation technique, mathematical model of the hybrid problem including the transmission line and an arbitrary number of absorbers is presented. Developing a sophisticated multi-loops optimization algorithm, best values... 

In recent years, different studies have proposed new methods for DNN-based feature extraction and joint acoustic model training and feature learning from raw waveform for large vocabulary speech recognition. However, conventional pre-processed methods such as MFCC and PLP are still preferred in the state-of-the-art speech recognition systems as they are perceived to be more robust. Besides, the raw waveform methods - most of which are based on the time-domain signal - do not significantly outperform the conventional methods. In this paper, we propose a frequency-domain feature-learning layer which can allow acoustic model training directly from the waveform. The main distinctions from... 

Peripheral milling is extensively used in manufacturing processes, especially in aerospace industries where end mills are used for milling of wing parts and engine components. The generation of complex shapes with high quality for various types of materials is the main advantage of milling in contrast to other machining processes. During the milling process, the occurrence of self-excited vibrations or chatter may cause reduction in material removal rate (MRR), damage to the tool and spindle bearing or may result in poor dimensional accuracy and surface finish of the work-piece. In this paper, milling process is modeled as two degrees of freedom (2DOF) system in which the tool wear and... 

In this paper we use a feature vector consisting of the Mel Frequency Discrete Wavelet Coefficients to recognize spoken phonemes in the Persian language. The purpose of using wavelet in feature extraction is to benefit from its multi resolution analysis and localization property in time and frequency domains. The MFDWCs are obtained by applying the Discrete Wavelet Transform (DWT) to the Mel-scaled log filter bank energies of a speech frame. Feature vectors are used for the HMM-based phoneme recognition on a portion of the FarsDat Persian language database consisting of 35 hour recorded data for training and 15 hour for testing. We evaluate the performance of new features for clean speech... 

A systematic procedure of simulating charge pump based delay locked loops (DLLs) represents in this paper. The presented procedure is based on the systematic modeling of the DLL components in Matlab Simulink simulator. The system has been designed for 1Hz input clock signal that by changing the whole system scale, it can be used for every other input frequencies. The simulation results in Matlab and design considerations for DLL based frequency multiplier are presented  

Finding leakage in valves is important to troubleshoot performance of internal combustion engines. Leakage can lead to a reduction in engine power and an increase in emissions. The main objective of the present study is to investigate relationship between valve leakage and the acoustic emission generated from the steady flow in the cylinder head of the internal combustion engine. The test rig is the cylinder head for a spark-ignited engine. The test rig simulates the valve leakage due to valve clearance. The valve clearance fault was artificially simulated by a very small lift in valve. The acoustic emission method was used to measure acoustic emission signals generated by valve flow.... 

In this paper, we investigate the performance of orthogonal frequency division multiplexed quantum key distribution (OFDM-QKD) in an integrated quantum-classical wavelength-division-multiplexing system. The presence of an intense classical signal alongside the quantum one generates Raman background noise. Noise reduction techniques should, then, be carried out at the receiver to suppress this crosstalk noise. In this work, we show that OFDM-QKD enables efficient filtering, in time and frequency domains, making it an attractive solution for the high-rate links at the core of quantum-classical networks  

Partial discharge (PD) measurement is one of the best methods for condition monitoring of transformers. In this paper, we use 5 different types of defects as follows: scratch on winding insulation, bubble in oil, moisture in insulation paper, a very small free metal particle in the transformer tank, and a fixed sharp metal point on the transformer tank, for our PD-related studies. Each type of defect is implemented into 1 of the 5 identical transformer models, which had been developed in the authors' recent work. The continuous wavelet transform is applied to each related measured time-domain PD signals. This process results in an image, for each PD pulse in the time-frequency domain. Using... 

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. The EMD method is a time-frequency domain analysis method that can be used for nonstationary and nonlinear problems. Combining the EMD method and Hilbert transform, which is called Hilbert-Huang transform, achieves a method that can be implemented to extract instantaneous properties of signals such as structural response dominant instantaneous frequencies. In the proposed algorithm, these estimated instantaneous properties are utilized to improve the pole-placement method as an adaptive active control technique. The... 

Attention deficit hyperactivity disorder (ADHD) is a common brain disorder among children. It presents various symptoms, hence, utilizing the information obtained from functional magnetic resonance imaging (fMRI) time-series data can be useful. Finding functional connections in typically developed control (TDC) and ADHD patients can be helpful in classification. The aim of this paper is to present a multifold method for the study of fMRI data to diagnose ADHD patients. In the proposed method, first, by applying the Stockwell transform (ST), we obtain detailed information about the time-series of the region of interests (ROIs) in the time and frequency domains. ST provides information about... 

Abnormal operating conditions for the timing belt can lead to cracks, fatigue, sudden rupture and damage to engines. In this study, an intelligent system was developed to detect and classify high-load operating conditions and high-temperature operating conditions for timing belts. To achieve this, vibration signals in normal operating conditions, high-load operating conditions and high-temperature operating conditions were collected. Time-domain signals were transformed to the frequency domain and the time-frequency domain using the fast Fourier transform method and the wavelet transform method respectively. In the data-mining stage, 25 statistical features were extracted from different... 

Nonlinear ultrasound modeling is finding an increasing number of applications in both medical and industrial areas where due to high pressure amplitudes the effects of nonlinear propagation are no longer negligible. Taking nonlinear effects into account makes the ultrasound beam analysis more accurate in these applications. One of the most widely used nonlinear models for propagation of 3D diffractive sound beams in dissipative media is the KZK (Khokhlov, Kuznetsov, Zabolotskaya) parabolic nonlinear wave equation. Various numerical algorithms have been developed to solve the KZK equation. Generally, these algorithms fall into one of three main categories: frequency domain, time domain and... 

Electromyogram signal (EMG) is an electrical manifestation of contractions of muscles. Surface EMG (sEMG) signal collected from the surface of skin has been used in diverse applications. One of its usages is in pattern recognition of hand prosthesis movements. The ability of current prosthesis devices has been generally limited to simple opening and closing tasks, minimizing their efficacy compared to natural hand capabilities. In order to extend the abilities and accuracy of prosthesis arm movements and performance, a novel sEMG pattern recognizing system is proposed. To extract more pertinent information we extracted sEMGs for selected hand movements. These features constitute our main... 

Electromyogram (EMG) signal is an electrical manifestation of muscle contractions. EMG signal collected from surface of the skin, a non-invasive bioelectric signal, can be used in different rehabilitation applications and artificial extremities control. This study has proposed to utilize the surface EMG (SEMG) signal to recognize patterns of hand prosthesis movements. It suggests using an adaptive neuro-fuzzy inference system (ANFIS) to identify motion commands for the control of a prosthetic hand. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP) and least mean square (LMS) is utilized. Also in order to optimize the number of fuzzy rules, a...