Sharif Digital Repository

An improved wavelet-based 2-D ECG data compression method is presented which employs a double stage compression. In the first stage the set partitioning in hierarchical trees (SPIHT) algorithm is used to compress the 2-D data array formed by cutting and beat-aligning the heartbeat data sequence. In the second stage vector quantization is applied to the residual image obtained from the previous stage. The 2-D approach utilizes both inter-beat and inter-sample redundancies in the ECG signal The proposed algorithm is applied to several records in the MIT-BIH arrhythmia database. Results show lower Percent Root mean square Difference (PRD) than 1-D methods and several 2-D methods for the same... 

Denoising of corrupted images has been a classical problem in image processing. In this paper we propose a new approach for image noise reduction using wavelet transform. In this method an improved version of thresholding neural networks (TNN) is used to find the optimum threshold values in the sense of minimum mean square error (MMSE). Based on these optimum thresholds a novel cycle-spinning based method is used to reduce image artifacts. In this method, we utilize two thresholding schemes as the thresholding operator of cycle-spinning. A neighbor dependent thresholding scheme is employed as its first shrinkage step and a simple wavelet thresholding with the optimum derived threshold values... 

In assembly level programming we need to know the mnemonic of each assembly instruction. But it is not so easy to remember mnemonics of all instructions, while the instruction set of Pentium processor includes 280 instructions. In this paper we introduce a new programming approach to assembly level programming in which most important assembly instructions (about 100 instructions) can be substituted for simple equivalents. These equivalent instructions are based on simple familiar operators and make a large simplification in assembly programming. This new language has a free format structure and hence is called Free Format Assembly Language. Evaluating the performance of this language using a... 

In this paper, a novel scaling based information hiding approach robust against noise and gain attack is presented. The host signal is assumed to be stationary Gaussian modeled with a first-order autoregressive process. For data embedding, the host signal is divided into two parts. One part is manipulated while the other part is kept unchanged for parameter estimation. The decoding scheme using the ratio of samples is suitable for highly correlated signals in which the decoding process is difficult. By calculating the distribution of the ratio, the performance of the maximum likelihood decoder is analytically studied. The proposed algorithm is applied to several artificial Gaussian... 

Up to now, different methods have been developed for estimation of buildup factor (BF). However, either expensive estimation or time-consuming estimation are major restrictions/challenges of these methods. In this study a new technique utilizing combination of Monte Carlo method and the Bayesian regularization (BR) learning algorithm of multilayer feed-forward neural network (FFNN) is employed for estimation of BFs. First, the BFs of the different elements (i.e. Al, Cu, and Fe) at different energies and different mean free paths (MFPs) are modeled by the MCNP code. The results show that the calculated BFs by MCNP code are in good agreement with the reported values of American nuclear society... 

In this paper we propose a new method for image noise reduction based on wavelet transform. In this method we: introduce an improved version of thresholding neural networks. (TNN) by utilizing a new class of smooth nonlinear thresholding functions as the activation function. Using this approach we will find the best thresholds in the sense of minimum mean square error (MMSE). Then using, TNN with obtained thresholds, we employ a cycle-spinningbased technique to reduce image artifacts. Experimental results indicate that the proposed method outperforms several other established wavelet denoising techniques, in terms of Peak-Signal-to-Noise-Ratio (PSNR) and visual quality. © 2007 IEEE  

Up to now, different methods have been developed for estimation of buildup factor (BF). However, either expensive estimation or time-consuming estimation are major restrictions/challenges of these methods. In this study a new technique utilizing combination of Monte Carlo method and the Bayesian regularization (BR) learning algorithm of multilayer feed-forward neural network (FFNN) is employed for estimation of BFs. First, the BFs of the different elements (i.e. Al, Cu, and Fe) at different energies and different mean free paths (MFPs) are modeled by the MCNP code. The results show that the calculated BFs by MCNP code are in good agreement with the reported values of American nuclear society... 

This paper present a simple watermarking approach based on the rotation of low frequency components of image blocks. The rotation process is performed with less distortion by projection of the samples on specific lines according to message bit. To have optimal detection Maximum Likelihood criteria has been used. Thus, by computing the distribution of rotated noisy samples the optimum decoder is presented and its performance is analytically investigated. The privilege of this proposed algorithm is its inherent robustness against gain attack as well as its simplicity. Experimental results confirm the validity of the analytical derivations and also high robustness against common attacks. ©... 

In this paper, a new scaling based information hiding approach with high robustness against noise and gain attack is presented. The host signal is assumed to be stationary Gaussian with first-order autoregressive model. For data embedding, the host signal is divided into two parts, and just one patch is manipulated while the other one is kept unchanged for parameter estimation. A maximum likelihood (ML) decoder is proposed which uses the ratio of samples for decoding the watermarked data. Due to the decorrelating property of the proposed decoder, it is very efficient for watermarking highly correlated signals for which the decoding process is not straightforward. By calculating the... 

In this paper, a new blind scaling based watermarking approach is presented. The host signal is assumed to be stationary Gaussian with first-order autoregressive model. Partitioning the host signal into two separate parts, the data is embedded in one part and the other is kept unchanged for blind parameter estimation. Driving the distribution of the decision variable we have suggested a maximum likelihood decoding algorithm which is independent of the host signal distribution and can be applied for any transform domains. The proposed algorithm is applied to both artificial Gaussian autoregressive signals as well as various test images. Experimental results confirm the independence of the... 

In this paper, a new multiplicative image watermarking system is presented. As human visual system is less sensitive to the image edges, watermarking is applied in the contourlet domain, which represents image edges sparsely. In the presented scheme, watermark data is embedded in the most energetic directional subband. By modeling General Gaussian Distribution (GGD) for the contourlet coefficients, the distribution of watermarked noisy coefficients is analytically calculated. At the receiver, based on the Maximum Likelihood (ML) decision rule, the optimal detector is proposed. Experimental results show the imperceptibility and high robustness of the proposed method against Additive White... 

In this paper, a new multiplicative semi-blind image-adaptive watermarking system has been presented, which exploits human visual model for adapting the watermark data to local properties of the host image. To have a better robustness, the proposed algorithm is implemented on the low-frequency coefficients of the wavelet transform. Moreover, regarding invisibility of the algorithm, the strength factor which controls the watermark power is optimally selected. To extract the watermark data, the Maximum Likelihood (ML) estimator is used. Experimental results confirm the imperceptibility of the proposed method and its high robustness against various attacks such as JPEG compression, noise... 

In the present study, the effect of the number of stages of Tesla Micro-Valve (TMV), as well as the dependency of Reynolds number, Re, on the valve performance has been analyzed. For this purpose, different layouts include one to four-stage with different sizes are investigated numerically. The main criterion for evaluation of valves performance is diodicity, Di. Unsteady and steady flow in valve have been simulated and compared. It is shown that although there are some difference but the trend is similar for both responses. Finally, 2-D and steady state computations of the fluid flow have been utilized that reveal a strong dependence of Di on Re and pressure drop, ΔP. The results showed... 

In this study, the performance of Tesla-type microvalves, used in micropumps for low frequency driving force, is examined. Three-dimensional and unsteady numerical analysis of fluid flow inside a valveless reciprocating micropump in the range of low working frequencies is carried out. Reciprocating movement of flow actuator provides the actuation and pumping of the working fluid. Ferrofluidic valveless micropump is one practical sample of this kind of micropumps that operate in the range of low working frequencies. To model the reciprocating movement of flow actuator, two time varying functions that includes sinusoidal and step excitation are employed. Also, the performance of... 

In nanometer-scale process technologies, the effects of process variations are observed in Multiprocessor System-on-Chips (MPSoC) in terms of variations in frequencies and leakage powers among the processors on the same chip as well as across different chips of the same design. Traditionally, worst-case values are assumed for these parameters and then a deterministic optimization technique is applied to the MPSoC application under design. We show that such worst-case-based approaches are not optimal with the increasing variation observed at system-level, and instead, statistical approaches should be employed. We consider the problem of simultaneously choosing MPSoC architecture and task... 

Colloidal gas aphron (CGA) based fluids have recently been introduced to develop depleted hydrocarbon reservoirs due to their ability in controlling fluid losses. Bubbles size of CGAs plays an important role in pore blockage ability as the controlling mechanism in fluid invasion reduction. However, fundamental understanding of how bubble size distribution is controlled by polymer and surfactant concentrations is not well discussed in the available literature. Almost all reported experiences on CGAs sizing were conducted on single bubble behavior, and little attention has been given to the variation of bubble size distribution (BSD) of CGAs at different levels of polymer/surfactant... 

Recently, Colloidal Gas Aphron (CGA) based fluids have been introduced to further develop depleted hydrocarbon reservoirs. This fluid system has been employed in an attempt to control drilling fluid invasion and, thus, reducing formation damage occurred during drilling operations. Understanding the mechanisms of fluid invasion control is of great importance for successful design and application of CGA-based fluids in drilling operations. Although fluid flow of conventional foams has been studied extensively in the available literature, little attention has been paid to CGA fluids flow, especially in heterogeneous fractured porous media. Here, an experimental study was conducted to achieve... 

Recently, colloidal gas aphron (CGA) fluids technology has been employed to drill depleted oil and gas reservoirs. Almost all reported experience on CGA fluids were conducted at ambient conditions, and little attention has been paid on the behavior of CGAs at high pressures which is more close to real conditions. In this study, high pressure experiments were conducted by using High Pressure Microscope cell to visualize/monitor the behavior of CGAs at elevated pressures. Single bubble behavior and bubble size distribution (BSD) of CGAs were investigated under different scenarios of pressure change. Results of experiments revealed that BSD of CGAs is controlled by the path of pressure changes,... 

Application of light-weight drilling fluids is essential to develop depleted hydrocarbon reservoirs. Recently, colloidal gas aphron (CGA)-based fluids have been introduced for such applications due to their ability in controlling fluid losses. In this work, a comprehensive experimental study was performed to choose the best formulation for CGA fluids by implementing static stability tests, rheological behavior measurements, and bubble size analyses of CGAs. Xanthan gum polymer and sodium dodecyl benzene sulfonate (SDBS), an anionic surfactant, and cetyl trimethyl ammonium bromide (CTAB), a cationic surfactant, were utilized to prepare CGAs. For the range of experiments conducted, the...