Sharif Digital Repository

The single-reference/multiple-output active noise control (ANC) of the accurate physical model of an acoustic duct system, developed earlier by the authors [1] is investigated. An adaptive feedforward algorithm is developed that minimizes a generic cost function consisting of the p-norm of the error vector. A computer model of a multi-channel ANC system, with the tonal and sweep sine input signals, is established, and the results for different single-input/single-output (SISO) configurations (error microphone and secondary source locations) of the ANC system are compared. The dynamic response of the single-reference/multiple-output ANC systems, using the Minimax and MEFXLMS algorithms, is... 

Based on the closed-form solution of a one-dimensional wave equation, the primary, secondary and acoustic feedback paths for the active control of sound in an acoustic duct have been investigated. Accurate models for the condenser microphone and loudspeaker, which include both the electro-mechanical and mechano-acoustical couplings as well as acoustical damping, have been considered. A generalized form of the filtered-x least mean square (FXLMS) algorithm that uses a more general recursive adaptive weight update equation to improve the performance of the FXLMS algorithm has been developed. Computer simulations were carried out to investigate the performance of acoustical feedback and... 

In this work, neural networks are used for estimation of flow stress of AA5083 with regard to dynamic strain ageing that occurs in certain deformation conditions and varies flow stress behavior of the metal being deformed. The input variables are selected to be strain rate, temperature and strain and the output value is the flow stress. In the first stage, the appearance and terminal of dynamic strain aging are determined with the aid of tensile testing at various temperatures and strain rates and subsequently for the serrated flow and the smooth yielding domains different neural networks are constructed based on the achieved results. While a feed-forward backpropagation algorithm is... 

A method for noise reduction in active-$RC$ circuits is introduced. It is shown that the output noise in an active-$RC$ circuit can be considerably reduced, without disturbing the circuit transfer function by inserting appropriate passive or active components in the circuit. The inserted components introduce new signal paths in the circuit for noise reduction while the original circuit transfer function is kept unchanged. The procedure to define the proper paths in the circuit and their transfer functions is given. The effectiveness of the presented method is demonstrated using a second-order active-RC filter fabricated in a 0.18-$ {m}$ CMOS technology  

A novel frequency synthesizer architecture for reducing spur level is presented. By using a feedforward path a new zero in the transfer function is generated which enables us to increase the capacitor tied to the control voltage line and thus reducing spur level. A fast settling technique is also used to compensate the effect of spur reduction technique on settling time. Different blocks of frequency synthesizer are implemented in MATLAB/Simulink. Simulations show 13 dB improvement in reference spur level compared to conventional architecture for a 2.4 GHz frequency synthesizer  

In this paper, a wideband full-360o phase shifter with 6 bits of accuracy has been designed and simulated with minimal root mean square (RMS) phase error. The proposed phase shifter deployed a feed forward path including a frequency-to-voltage converter (FVC) to minimize the mismatch in quadrature generation to eventually reduce the RMS phase error for S-band (2-4GHz) applications. The designed phase shifter in 180nm CMOS technology achieves an RMS phase error in the range of 0.607-1.18? with -50dBm input signal over 2-4GHz frequency band. With lower input signal of -75dBm, the RMS phase error is 0.621-1.34? for 2-4GHz input frequency. The proposed phase shifter shows an RMS amplitude error... 

In this paper, a feedforward proportional-integral-derivative (PID) controller is developed to effectively control the clad height in laser solid freeform fabrication (LSFF). The scanning velocity is selected as the input control variable and the clad height is chosen as the output. A novel knowledge-based Hammerstein model, including a linear dynamic and a nonlinear memoryless block, is developed, and its parameters are identified offline using experimental data. The architecture of the controller consists of a PID and a feedforward module, which is the inverse of the identified model. The advantage of adding a feedforward path to the PID controller is evaluated experimentally, in which the... 

In this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. CGAM problem, a benchmark in cogeneration systems, is chosen as a case study. Thermodynamic model includes precise modeling of the whole plant. For simulation of the steady sate behavior, the static neural network is applied. Then using dynamic neural network, plant is optimized thermodynamically. Multi layer feed forward neural networks is chosen as static net and recurrent neural networks as dynamic net. The steady state behavior of CGAM problem is simulated by MFNN. Subsequently, it is optimized by dynamic net. Results of static net have excellence agreement... 

The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. In this paper, an adaptive algorithm for tracking maneuvering targets based on neural networks is proposed. This algorithm is implemented with two filters based on the current statistical model and a multilayer feedforward neural network. The two filters track the same maneuvering target in parallel and the neural network automatically considers all the state information of the two filters and adaptively adjusts the process variance of one of them to achieve better performance in different target maneuver tracking. Simulations results show that the proposed adaptive... 

Crude oil density is an important thermodynamic property in simulation processes and design of equipment. Using laboratory methods to measure crude oil density is costly and time consuming; thus, predicting the density of crude oil using modeling is cost-effective. In this article, we develop a neural network-based model to predict the density of undersaturated crude oil. We compare our results with previous works and show that our method outperforms them  

In this study, we have proposed a novel approach to investigate the features of four subbands of 2-D wavelet transform in magnetic resonance images (MRIs) for normal and abnormal brains which defected by Multiple Sclerosis (MS). Concurrently, another method extracts different kinds of features in spatial domain. Totally, 116 features have been extracted. Before applying the algorithm, we have to use a registration method because of variety in size of brain images. All extracted features have been passed over the Principal Component Analysis (PCA) and have been pushed to an Artificial Neural Network (ANN) that is a feed-forward type. According to changing in position of defected parts of... 

In the last decades, much attention has been paid to the design of multi-speaker voice conversion. In this work, a new method for voice conversion (VC) using nonlinear principal component analysis (NLPCA) is presented. The principal components are extracted and transformed by a feed-forward neural network which is trained by combination of Genetic Algorithm (GA) and Back-Propagation (BP). Common pre- and post-processing approaches are applied to increase the quality of the synthesized speech. The results indicate that the proposed method can be considered as a step towards multi-speaker Voice conversion. © 2007 IEEE  

In this paper the combined optimal feedback-adaptive feedforward controller proposed to attain better performance of active vibration suppression of flexible structures subjected to different type of disturbances. The structure considered here is a cantilever beam actuated with a PZT patch actuator. The proposed controller consists of two individual parts, a filtered-x controller as a feedforward part and an optimal linear controller as a feedback part. Recursive Least Square algorithm (RLS) is used for the adaptive filtering scheme in Filtered-x adaptive feedforward controller. LQG optimal controller is also used in the feedback part of the controller. This research investigates the... 

Blur identification is one important part of image restoration process. Linear motion blur is one of the most common degradation functions that corrupts images. Since 1976, many researchers tried to estimate motion blur parameters and this problem is solved in noise free images but in noisy images improvement can be done when image SNR is low. In this paper we have proposed a method to estimate motion blur parameters such as direction and length using Radon transform and Feed-Forward back propagation neural network for noisy images. To design the desired neural network, we used Weierstrass approximation theorem and Steifel reference Sets. The experimental results showed algorithm precision... 

The aim of this work is to introduce a new method based on time frequency distribution for classifying the EEG signals. Some parameters are extracted using time-frequency distribution as inputs to a feed-forward backpropagation neural networks (FBNN). The proposed method had better results with 98.25% accuracy compared to previously studied methods such as wavelet transform, entropy, logistic regression and Lyapunov exponent