Super Resolution Blind Reconstruction of Low Resolution Images using Wavelets based Fusion

Crucial information barely visible to the human eye is often embedded in a series of low resolution images taken of the same scene. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. The ideal algorithm should be fast, and should add sharpness and details, both at edges and in regions without adding artifacts. In this paper we propose a super resolution blind reconstruction technique for linearly degraded images. In our proposed technique the algorithm is divided into three parts an image registration, wavelets based fusion and an image restoration. In this paper three low resolution images are considered which may sub pixels shifted, rotated, blurred or noisy, the sub pixel shifted images are registered using affine transformation model; A wavelet based fusion is performed and the noise is removed using soft thresolding. Our proposed technique reduces blocking artifacts and also smoothens the edges and it is also able to restore high frequency details in an image. Our technique is efficient and computationally fast having clear perspective of real time implementation.

The Performance Analysis of Valveless Micropump with Contoured Nozzle/Diffuser

The operation performance of a valveless micro-pump is strongly dependent on the shape of connected nozzle/diffuser and Reynolds number. The aims of present work are to compare the performance curves of micropump with the original straight nozzle/diffuser and contoured nozzle/diffuser under different back pressure conditions. The tested valveless micropumps are assembled of five pieces of patterned PMMA plates with hot-embracing technique. The structures of central chamber, the inlet/outlet reservoirs and the connected nozzle/diffuser are fabricated with laser cutting machine. The micropump is actuated with circular-type PZT film embraced on the bottom of central chamber. The deformation of PZT membrane with various input voltages is measured with a displacement laser probe. A simple testing facility is also constructed to evaluate the performance curves for comparison. In order to observe the evaluation of low Reynolds number multiple vortex flow patterns within the micropump during suction and pumping modes, the unsteady, incompressible laminar three-dimensional Reynolds-averaged Navier-Stokes equations are solved. The working fluid is DI water with constant thermo-physical properties. The oscillating behavior of PZT film is modeled with the moving boundary wall in way of UDF program. With the dynamic mesh method, the instants pressure and velocity fields are obtained and discussed.Results indicated that the volume flow rate is not monotony increased with the oscillating frequency of PZT film, regardless of the shapes of nozzle/diffuser. The present micropump can generate the maximum volume flow rate of 13.53 ml/min when the operation frequency is 64Hz and the input voltage is 140 volts. The micropump with contoured nozzle/diffuser can provide 7ml/min flow rate even when the back pressure is up to 400 mm-H2O. CFD results revealed that the flow central chamber was occupied with multiple pairs of counter-rotating vortices during suction and pumping modes. The net volume flow rate over a complete oscillating periodic of PZT

Human Body Configuration using Bayesian Model

In this paper we present a novel approach for human Body configuration based on the Silhouette. We propose to address this problem under the Bayesian framework. We use an effective Model based MCMC (Markov Chain Monte Carlo) method to solve the configuration problem, in which the best configuration could be defined as MAP (maximize a posteriori probability) in Bayesian model. This model based MCMC utilizes the human body model to drive the MCMC sampling from the solution space. It converses the original high dimension space into a restricted sub-space constructed by the human model and uses a hybrid sampling algorithm. We choose an explicit human model and carefully select the likelihood functions to represent the best configuration solution. The experiments show that this method could get an accurate configuration and timesaving for different human from multi-views.

Correlations between Cleaning Frequency of Reservoir and Water Tower and Parameters of Water Quality

This study was investigated on sampling and analyzing water quality in water reservoir & water tower installed in two kind of residential buildings and school facilities. Data of water quality was collected for correlation analysis with frequency of sanitization of water reservoir through questioning managers of building about the inspection charts recorded on equipment for water reservoir. Statistical software packages (SPSS) were applied to the data of two groups (cleaning frequency and water quality) for regression analysis to determine the optimal cleaning frequency of sanitization. The correlation coefficient (R) in this paper represented the degree of correlation, with values of R ranging from +1 to -1.After investigating three categories of drinking water users; this study found that the frequency of sanitization of water reservoir significantly influenced the water quality of drinking water. A higher frequency of sanitization (more than four times per 1 year) implied a higher quality of drinking water. Results indicated that sanitizing water reservoir & water tower should at least twice annually for achieving the aim of safety of drinking water.

Advanced Neural Network Learning Applied to Pulping Modeling

This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.

Experimental Investigation of Heat Transfer and Flow of Nano Fluids in Horizontal Circular Tube

We have measured the pressure drop and convective heat transfer coefficient of water – based AL(25nm),AL2O3(30nm) and CuO(50nm) Nanofluids flowing through a uniform heated circular tube in the fully developed laminar flow regime. The experimental results show that the data for Nanofluids friction factor show a good agreement with analytical prediction from the Darcy's equation for single-phase flow. After reducing the experimental results to the form of Reynolds, Rayleigh and Nusselt numbers. The results show the local Nusselt number and temperature have distribution with the non-dimensional axial distance from the tube entry. Study decided that thenNanofluid as Newtonian fluids through the design of the linear relationship between shear stress and the rate of stress has been the study of three chains of the Nanofluid with different concentrations and where the AL, AL2O3 and CuO – water ranging from (0.25 - 2.5 vol %). In addition to measuring the four properties of the Nanofluid in practice so as to ensure the validity of equations of properties developed by the researchers in this area and these properties is viscosity, specific heat, and density and found that the difference does not exceed 3.5% for the experimental equations between them and the practical. The study also demonstrated that the amount of the increase in heat transfer coefficient for three types of Nano fluid is AL, AL2O3, and CuO – Water and these ratios are respectively (45%, 32%, 25%) with insulation and without insulation (36%, 23%, 19%), and the statement of any of the cases the best increase in heat transfer has been proven that using insulation is better than not using it. I have been using three types of Nano particles and one metallic Nanoparticle and two oxide Nanoparticle and a statement, whichever gives the best increase in heat transfer.

Advance in Monitoring and Process Control of Surface Roughness

This paper presents an advance in monitoring and process control of surface roughness in CNC machine for the turning and milling processes. An integration of the in-process monitoring and process control of the surface roughness is proposed and developed during the machining process by using the cutting force ratio. The previously developed surface roughness models for turning and milling processes of the author are adopted to predict the inprocess surface roughness, which consist of the cutting speed, the feed rate, the tool nose radius, the depth of cut, the rake angle, and the cutting force ratio. The cutting force ratios obtained from the turning and the milling are utilized to estimate the in-process surface roughness. The dynamometers are installed on the tool turret of CNC turning machine and the table of 5-axis machining center to monitor the cutting forces. The in-process control of the surface roughness has been developed and proposed to control the predicted surface roughness. It has been proved by the cutting tests that the proposed integration system of the in-process monitoring and the process control can be used to check the surface roughness during the cutting by utilizing the cutting force ratio.

Describing Learning Features of Reusable Resources: A Proposal

One of the main advantages of the LO paradigm is to allow the availability of good quality, shareable learning material through the Web. The effectiveness of the retrieval process requires a formal description of the resources (metadata) that closely fits the user-s search criteria; in spite of the huge international efforts in this field, educational metadata schemata often fail to fulfil this requirement. This work aims to improve the situation, by the definition of a metadata model capturing specific didactic features of shareable learning resources. It classifies LOs into “teacher-oriented" and “student-oriented" categories, in order to describe the role a LO is to play when it is integrated into the educational process. This article describes the model and a first experimental validation process that has been carried out in a controlled environment.

Improved Robust Stability and Stabilization Conditions of Discrete-time Delayed System

The problem of robust stability and robust stabilization for a class of discrete-time uncertain systems with time delay is investigated. Based on Tchebychev inequality, by constructing a new augmented Lyapunov function, some improved sufficient conditions ensuring exponential stability and stabilization are established. These conditions are expressed in the forms of linear matrix inequalities (LMIs), whose feasibility can be easily checked by using Matlab LMI Toolbox. Compared with some previous results derived in the literature, the new obtained criteria have less conservatism. Two numerical examples are provided to demonstrate the improvement and effectiveness of the proposed method.

Effects of the Second Entrant in GSM Telecommunication Market in MENA Region

For the first incumbent operator it is very important to understand how to react when the second operator comes to the market. In this paper which is prepared for preliminary study of GSM market in Iran, we have studied five MENA markets according to the similarity point of view. This paper aims at analyzing the impact of second entrants in selected markets on certain marketing key performance indicators (KPI) such as: Market shares (by operator), prepaid share, minutes of use (MoU), Price and average revenue per user (ARPU) (for total market each).

Forecasting Fraudulent Financial Statements using Data Mining

This paper explores the effectiveness of machine learning techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. To this end, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms in the recent period 2001-2002. The decision of which particular method to choose is a complicated problem. A good alternative to choosing only one method is to create a hybrid forecasting system incorporating a number of possible solution methods as components (an ensemble of classifiers). For this purpose, we have implemented a hybrid decision support system that combines the representative algorithms using a stacking variant methodology and achieves better performance than any examined simple and ensemble method. To sum up, this study indicates that the investigation of financial information can be used in the identification of FFS and underline the importance of financial ratios.

Extracting Single Trial Visual Evoked Potentials using Selective Eigen-Rate Principal Components

In single trial analysis, when using Principal Component Analysis (PCA) to extract Visual Evoked Potential (VEP) signals, the selection of principal components (PCs) is an important issue. We propose a new method here that selects only the appropriate PCs. We denote the method as selective eigen-rate (SER). In the method, the VEP is reconstructed based on the rate of the eigen-values of the PCs. When this technique is applied on emulated VEP signals added with background electroencephalogram (EEG), with a focus on extracting the evoked P3 parameter, it is found to be feasible. The improvement in signal to noise ratio (SNR) is superior to two other existing methods of PC selection: Kaiser (KSR) and Residual Power (RP). Though another PC selection method, Spectral Power Ratio (SPR) gives a comparable SNR with high noise factors (i.e. EEGs), SER give more impressive results in such cases. Next, we applied SER method to real VEP signals to analyse the P3 responses for matched and non-matched stimuli. The P3 parameters extracted through our proposed SER method showed higher P3 response for matched stimulus, which confirms to the existing neuroscience knowledge. Single trial PCA using KSR and RP methods failed to indicate any difference for the stimuli.

A New Edit Distance Method for Finding Similarity in Dna Sequence

The P-Bigram method is a string comparison methods base on an internal two characters-based similarity measure. The edit distance between two strings is the minimal number of elementary editing operations required to transform one string into the other. The elementary editing operations include deletion, insertion, substitution two characters. In this paper, we address the P-Bigram method to sole the similarity problem in DNA sequence. This method provided an efficient algorithm that locates all minimum operation in a string. We have been implemented algorithm and found that our program calculated that smaller distance than one string. We develop PBigram edit distance and show that edit distance or the similarity and implementation using dynamic programming. The performance of the proposed approach is evaluated using number edit and percentage similarity measures.

Health Care Ethics in Vulnerable Populations: Clinical Research through the Patient's Eyes

Chronic conditions carry with them strong emotions and often lead to charged relationships between patients and their health providers and, by extension, patients and health researchers. Persons are both autonomous and relational and a purely cognitive model of autonomy neglects the social and relational basis of chronic illness. Ensuring genuine informed consent in research requires a thorough understanding of how participants perceive a study and their reasons for participation. Surveys may not capture the complexities of reasoning that underlies study participation. Contradictory reasons for participation, for instance an initial claim of altruism as rationale and a subsequent claim of personal benefit (therapeutic misconception), affect the quality of informed consent. Individuals apply principles through the filter of personal values and lived experience. Authentic autonomy, and hence authentic consent to research, occurs within the context of patients- unique life narratives and illness experiences.

Blind Non-Minimum Phase Channel Identification Using 3rd and 4th Order Cumulants

In this paper we propose a family of algorithms based on 3rd and 4th order cumulants for blind single-input single-output (SISO) Non-Minimum Phase (NMP) Finite Impulse Response (FIR) channel estimation driven by non-Gaussian signal. The input signal represents the signal used in 10GBASE-T (or IEEE 802.3an-2006) as a Tomlinson-Harashima Precoded (THP) version of random Pulse-Amplitude Modulation with 16 discrete levels (PAM-16). The proposed algorithms are tested using three non-minimum phase channel for different Signal-to-Noise Ratios (SNR) and for different data input length. Numerical simulation results are presented to illustrate the performance of the proposed algorithms.

Numerical and Experimental Stress Analysis of Stiffened Cylindrical Composite Shell under Transverse end Load

Grid composite structures have many applications in aerospace industry in which deal with transverse loadings abundantly. In present paper a stiffened composite cylindrical shell with clamped-free boundary condition under transverse end load experimentally and numerically was studied. Some electrical strain gauges were employed to measure the strains. Also a finite element analysis was done for validation of experimental result. The FEM software used was ANSYS11. In addition, the results between stiffened composite shell and unstiffened composite shell were compared. It was observed that intersection of two stiffeners has an important effect in decrease of stress in the shell. Fairly good agreements were observed between the numerical and the measured results. According to recent studies about grid composite structures, it should be noted that any investigation like this research has not been reported.

Time Comparative Simulator for Distributed Process Scheduling Algorithms

In any distributed systems, process scheduling plays a vital role in determining the efficiency of the system. Process scheduling algorithms are used to ensure that the components of the system would be able to maximize its utilization and able to complete all the processes assigned in a specified period of time. This paper focuses on the development of comparative simulator for distributed process scheduling algorithms. The objectives of the works that have been carried out include the development of the comparative simulator, as well as to implement a comparative study between three distributed process scheduling algorithms; senderinitiated, receiver-initiated and hybrid sender-receiver-initiated algorithms. The comparative study was done based on the Average Waiting Time (AWT) and Average Turnaround Time (ATT) of the processes involved. The simulation results show that the performance of the algorithms depends on the number of nodes in the system.

A Self Configuring System for Object Recognition in Color Images

System MEMORI automatically detects and recognizes rotated and/or rescaled versions of the objects of a database within digital color images with cluttered background. This task is accomplished by means of a region grouping algorithm guided by heuristic rules, whose parameters concern some geometrical properties and the recognition score of the database objects. This paper focuses on the strategies implemented in MEMORI for the estimation of the heuristic rule parameters. This estimation, being automatic, makes the system a highly user-friendly tool.

Control Technology for a Daily Load-following Operation in a Nuclear Power Plant

In Korea, the technology of a load fo nuclear power plant has been being developed. automatic controller which is able to control temperature and axial power distribution was developed. identification algorithm and a model predictive contact former transforms the nuclear reactor status into numerically. And the latter uses them and ge manipulated values such as two kinds of control ro this automatic controller, the performance of a coperation was evaluated. As a result, the automatic generated model parameters of a nuclear react to nuclear reactor average temperature and axial power the desired targets during a daily load follow.

Mounting Time Reduction using Content-Based Block Management for NAND Flash File System

The flash memory has many advantages such as low power consumption, strong shock resistance, fast I/O and non-volatility. And it is increasingly used in the mobile storage device. The YAFFS, one of the NAND flash file system, is widely used in the embedded device. However, the existing YAFFS takes long time to mount the file system because it scans whole spare areas in all pages of NAND flash memory. In order to solve this problem, we propose a new content-based flash file system using a mounting time reduction technique. The proposed method only scans partial spare areas of some special pages by using content-based block management. The experimental results show that the proposed method reduces the average mounting time by 87.2% comparing with JFFS2 and 69.9% comparing with YAFFS.