Thursday, June 7, 2012

MATLAB IEEE PROJECT ABSTRACTS


MATLAB IEEE PROJECT ABSTRACTS


AN ALGORITHM FOR INTELLIGIBILITY PREDICTION OF TIME-FREQUENCY WEIGHTED NOISY SPEECH
Audio, Speech, and Language Processing, IEEE Transactions on
ABSTRACT
In the development process of noise-reduction algorithms, an objective machine-driven intelligibility measure which shows high correlation with speech intelligibility is of great interest. Besides reducing time and costs compared to real listening experiments, an objective intelligibility measure could also help provide answers on how to improve the intelligibility of noisy unprocessed speech.
In this paper, a short-time objective intelligibility measure (STOI) is presented, which shows high correlation with the intelligibility of noisy and time–frequency weighted noisy speech (e.g., resulting from noise reduction) of three different listening experiments.
In general, STOI showed better correlation with speech intelligibility compared to five other reference objective intelligibility models. In contrast to other conventional intelligibility models which tend to rely on global statistics across entire sentences, STOI is based on shorter time segments (386 ms).
Experiments indeed show that it is beneficial to take segment lengths of this order into account. In addition, a free Matlab implementation is provided.

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ADAPTIVE MULTISCALE COMPLEXITY ANALYSIS OF FETAL HEART RATE

Biomedical Engineering, IEEE Transactions on
ABSTRACT
Per partum fetal asphyxia is a major cause of neonatal morbidity and mortality. Fetal heart rate monitoring plays an important role in early detection of acidosis, an indicator for asphyxia.
This problem is addressed in this paper by introducing a novel complexity analysis of fetal heart rate data, based on producing a collection of piecewise linear approximations of varying dimensions from which a measure of complexity is extracted.
This procedure specifically accounts for the highly non-stationary context of labor by being adaptive and multiscale. Using a reference dataset, made of real per partum fetal heart rate data, collected in situ and carefully constituted by obstetricians, the behavior of the proposed approach is analyzed and illustrated.
Its performance is evaluated in terms of the rate of correct acidosis detection versus the rate of false detection, as well as how early the detection is made. Computational cost is also discussed. The results are shown to be extremely promising and further potential uses of the tool are discussed

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TISSUE-SPECIFIC COMPARTMENTAL ANALYSIS FOR DYNAMIC CONTRAST-ENHANCED MR IMAGING OF COMPLEX TUMORS
Medical Imaging, IEEE Transactions on
ABSTRACT
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides a noninvasive method for evaluating tumor vasculature patterns based on contrast accumulation and washout. However, due to limited imaging resolution and tumor tissue heterogeneity, tracer concentrations at many pixels often represent a mixture of more than one distinct compartment.
This pixel-wise partial volume effect (PVE) would have profound impact on the accuracy of pharmacokinetics studies using existing compartmental modeling (CM) methods. We therefore propose a convex analysis of mixtures (CAM) algorithm to explicitly mitigate PVE by expressing the kinetics in each pixel as a nonnegative combination of underlying compartments and subsequently identifying pure volume pixels at the corners of the clustered pixel time series scatter plot simplex.
The algorithm is supported theoretically by a well-grounded mathematical framework and practically by plug-in noise filtering and normalization preprocessing. We demonstrate the principle and feasibility of the CAM-CM approach on realistic synthetic data involving two functional tissue compartments, and compare the accuracy of parameter estimates obtained with and without PVE elimination using CAM or other relevant techniques.
Experimental results show that CAM-CM achieves a significant improvement in the accuracy of kinetic parameter estimation.
We apply the algorithm to real DCE-MRI breast cancer data and observe improved pharmacokinetics parameter estimation, separating tumor tissue into regions with differential tracer kinetics on a pixel-by-pixel basis and revealing biologically plausible tumor tissue heterogeneity patterns.
This method combines the advantages of multivariate clustering, convex geometry analysis, and compartmental modeling approaches. The open-source MATLAB software of CAM-CM is publicly available from the Web.

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CELLULAR NEURAL NETWORKS, NAVIER-STOKES EQUATION AND MICROARRAY IMAGE RECONSTRUCTION
Image Processing, IEEE Transactions on
ABSTRACT
Despite the latest improvements in the microarray technology, many developments are needed particularly in the image processing stage. Some hardware implementations of microarray image processing have been proposed and proved to be a promising alternative to the currently available software systems. However, the main drawback is the unsuitable addressing of the quantification of the gene spots which depend on many assumptions.
It is our aim in this paper to present a new Image Reconstruction algorithm using Cellular Neural Network, which solves the Navier-Stokes equation. This algorithm offers a robust method to estimate the background signal within the gene spot region.
Quantitative comparisons are carried out, between our approach and some available methods in terms of objective standpoint. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner, and also, in a remarkably efficient time.

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MEMORY-EFFICIENT ARCHITECTURE FOR HYSTERESIS THRESHOLDING AND OBJECT FEATURE EXTRACTION
Image Processing, IEEE Transactions on
ABSTRACT
Hysteresis thresholding is a method that offers enhanced object detection. Due to its recursive nature, it is time consuming and requires a lot of memory resources. This makes it avoided in streaming processors with limited memory.
We propose two versions of a memory-efficient and fast architecture for hysteresis thresholding: a high-accuracy pixel-based architecture and a faster block-based one at the expense of some loss in the accuracy. Both designs couple thresholding with connected component analysis and feature extraction in a single pass over the image.
Unlike queue-based techniques, the proposed scheme treats candidate pixels almost as foreground until objects complete; a decision is then made to keep or discard these pixels. This allows processing on the fly, thus avoiding additional passes for handling candidate pixels and extracting object features.
Moreover, labels are reused so only one row of compact labels is buffered. Both architectures are implemented in MATLAB and VHDL. Simulation results on a set of real and synthetic images show that the execution speed can attain an average increase up to 24× for the pixel-based and 52× for the block-based when compared to s

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A CLOSED-FORM APPROXIMATION OF THE EXACT UNBIASED INVERSE OF THE ANSCOMBE VARIANCE-STABILIZING TRANSFORMATION
Image Processing, IEEE Transactions on

ABSTRACT
We presented an exact unbiased inverse of the Anscombe variance-stabilizing transformation and showed that when applied to Poisson image denoising, the combination of variance stabilization and state-of-the-art Gaussian denoising algorithms is competitive with some of the best Poisson denoising algorithms.
We also provided a Matlab implementation of our method, where the exact unbiased inverse transformation appears in non-analytical form. Here we propose a closed-form approximation of the exact unbiased inverse, in order to facilitate the use of this inverse.
The proposed approximation produces results equivalent to those obtained with the accurate (non-analytical) exact unbiased inverse, and thus notably better than one would get with the asymptotically unbiased inverse transformation, which is commonly used in applications.

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IMPLEMENTATION OF NEURAL NETWORK CONTROLLED THREE-LEG VSC AND A TRANSFORMER AS THREE-PHASE FOUR-WIRE DSTATCOM
Industry Applications, IEEE Transactions on
ABSTRACT
In this paper, a neural-network (NN)-controlled distribution static compensator (DSTATCOM) using a dSPACE processor is implemented for power quality improvement in a three-phase four-wire distribution system.
A three-leg voltage-source-converter (VSC)-based DSTATCOM with a zig-zag transformer is used for the compensation of reactive power for voltage regulation or for power factor correction along with load balancing, elimination of harmonic currents, and neutral current compensation at the point of common coupling.
The Adaline (adaptive linear element)-based NN is used to implement the control scheme of the VSC. This technique gives similar performance as that of other control techniques, but it is simple to implement and has a fast response and gives nearly zero phase shift.
The zig-zag transformer is used for providing a path to the zero-sequence current in a three-phase four-wire distribution system. This reduces the complexity and also the cost of the DSTATCOM system.
The performance of the proposed DSTATCOM system is validated through simulations using MATLAB software with its Simulink and Power System Blockset toolboxes and hardware implementation.

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POSTURE CONTROL OF ELECTROMECHANICAL ACTUATOR-BASED THRUST VECTOR SYSTEM FOR AIRCRAFT ENGINE
Industrial Electronics, IEEE Transactions on
ABSTRACT
This paper deals with the dynamical modeling and posture control of the electromechanical actuator (EMA)-based thrust vector control (TVC) system for aircraft engine. Addressing the issues of the large inertia and low stiffness existed in the TVC system driven by EMA, this paper established a 2-DOF mathematical model to describe EMA dynamic characteristics.
In order to overcome the influence of the motion coupling of the TVC-EMA existed in the pitching and yawing channels, we presented a kind of dual-channel coordinated-control method which realizes the trust vector control for the swung aircraft engine based on the inverse kinematics.
This control strategy uses the command Eulers angles transformation to solve the desired actuator linear lengths, and tracks the desired lengths via the compound control law composed of robust PID with the lead compensation and Bang-Bang control in the two actuators.
The hybrid experimental simulation system based on dSPACE was set up, the control parameters of the compound control methods were confirmed by off-line simulation based on Matlab, and the load experiments of circular motion and step response were implemented on the test system. The simulation and test results show that the designed thrust vector controller can achieve the satisfactory control performances.

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MODELING, CONTROL AND MONITORING OF S3RS BASED HYDROGEN COOLING SYSTEM IN THERMAL POWER PLANT
Industrial Electronics, IEEE Transactions on
ABSTRACT
The faster heat dissipation of generators in power plant call for hydrogen cooling, and water is used as coolant to cool down the hot hydrogen which comes out from the hydrogen cooling system (HCS) at generating end. Therefore, in large generating plants the process of cooling and coolant becomes an integral part of the Heat Exchangers. Hence, requirement of a reliable hydrogen cooling system is a must.
This paper presents development and implementation of supervisory control and data acquisition (SCADA) based process control and monitoring system. A novel method of Six Stage Standby Redundant Structured (S3RS) HCS is proposed for the cooling of large generators in thermal power plant(s).
This proposed system is equally reliable for steam turbine based generating plants and Integrated Gasification Combined Cycle (IGCC) plants. The entire process control and monitoring, popularly known as human machine interface (HMI) of HCS has been developed and simulated on RSViewSE, a real-time automation platform by Rockwell Automation. And, the system reliability of the proposed S3RS process model is implemented using MATLAB

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POWER LOSS COMPARISON OF SINGLE- AND TWO-STAGE GRID-CONNECTED PHOTOVOLTAIC SYSTEMS
Energy Conversion, IEEE Transactions on
ABSTRACT
This paper presents power loss comparison of single- and two-stage grid-connected photovoltaic (PV) systems based on the loss factors of double line-frequency voltage ripple (DLFVR), fast irradiance variation + DLFVR, fast dc load variation + DLFVR, limited operating voltage range + DLFVR, and overall loss factor combination.
These loss factors will result in power deviation from the maximum power points. In this paper, both single-stage and two-stage grid-connected PV systems are considered. All of the effects on a two-stage system are insignificant due to an additional maximum power point tracker, but the tracker will reduce the system efficiency typically about 2.5%.
The power loss caused by these loss factors in a single-stage grid-connected PV system is also around 2.5%; that is, a single-stage system has the merits of saving components and reducing cost, and does not penalize overall system efficiency under certain operating voltage ranges. Simulation results with the MATLAB software package and experimental results have confirmed the analysis.

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SIMPLE ANALYTICAL METHOD FOR DETERMINING PARAMETERS OF DISCHARGING BATTERIES
Energy Conversion, IEEE Transactions on
ABSTRACT
This paper derives simple and explicit formulas for computing the parameters of Thevenin's equivalent circuit model for a discharging battery. The general Thevenin's equivalent circuit model has $n$ pairs of parallel resistors and capacitors (nth-order model).
The main idea behind the new method is to transform the problem of solving a system of high-order polynomial equations into one of solving several linear equations and a single-variable $n$th-order polynomial equation, via some change of variables. The computation can be implemented with a simple MATLAB code less than half-page long.
Experimental and computational results are obtained for three types of batteries: Li-polymer, lead--acid, and nickel metal hydride. For all the tested batteries, the first-order models are not able to generate voltage responses that closely match the measured responses, while second-order models can generate well-matched responses. For some of the batteries, a third-order model can do a better job matching the voltage responses.

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BOOSTING COLOR FEATURE SELECTION FOR COLOR FACE RECOGNITION
Image Processing, IEEE Transactions on

ABSTRACT
This paper introduces the new color face recognition (FR) method that makes effective use of boosting learning as color-component feature selection framework. The proposed boosting color-component feature selection framework is designed for finding the best set of color-component features from various color spaces (or models), aiming to achieve the best FR performance for a given FR task.
In addition, to facilitate the complementary effect of the selected color-component features for the purpose of color FR, they are combined using the proposed weighted feature fusion scheme.
The effectiveness of our color FR method has been successfully evaluated on the following five public face databases (DBs): CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0.
Experimental results show that the results of the proposed method are impressively better than the results of other state-of-the-art color FR methods over different FR challenges including highly uncontrolled illumination, moderate pose variation, and small resolution face images.

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AUTOMATIC EXACT HISTOGRAM SPECIFICATION FOR CONTRAST ENHANCEMENT AND VISUAL SYSTEM BASED QUANTITATIVE EVALUATION
Image Processing, IEEE Transactions on

ABSTRACT
Histogram equalization, which aims at information maximization, is widely used in different ways to perform contrast enhancement in images. In this paper, an automatic exact histogram specification technique is proposed and used for global and local contrast enhancement of images.
The desired histogram is obtained by first subjecting the image histogram to a modification process and then by maximizing a measure that represents increase in information and decrease in ambiguity. A new method of measuring image contrast based upon local band-limited approach and center-surround retinal receptive field model is also devised in this paper.
This method works at multiple scales (frequency bands) and combines the contrast measures obtained at different scales using Lp-norm. In comparison to a few existing methods, the effectiveness of the proposed automatic exact histogram specification technique in enhancing contrasts of images is demonstrated through qualitative analysis and the proposed image contrast measure based quantitative analysis.

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HIGH DYNAMIC RANGE IMAGE DISPLAY WITH HALO AND CLIPPING PREVENTION
Image Processing, IEEE Transactions on

ABSTRACT
The dynamic range of an image is defined as the ratio between the highest and the lowest luminance level. In a high dynamic range (HDR) image, this value exceeds the capabilities of conventional display devices; as a consequence, dedicated visualization techniques are required.
In particular, it is possible to process an HDR image in order to reduce its dynamic range without producing a significant change in the visual sensation experienced by the observer. In this paper, we propose a dynamic range reduction algorithm that produces high-quality results with a low computational cost and a limited number of parameters.
The algorithm belongs to the category of methods based upon the Retinex theory of vision and was specifically designed in order to prevent the formation of common artifacts, such as halos around the sharp edges and clipping of the highlights, that often affect methods of this kind.
After a detailed analysis of the state of the art, we shall describe the method and compare the results and performance with those of two techniques recently proposed in the literature and one commercial software.

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GRADIENT PROFILE PRIOR AND ITS APPLICATIONS IN IMAGE SUPER-RESOLUTION AND ENHANCEMENT
Image Processing, IEEE Transactions on

ABSTRACT
In this paper, we propose a novel generic image prior-gradient profile prior, which implies the prior knowledge of natural image gradients. In this prior, the image gradients are represented by gradient profiles, which are 1-D profiles of gradient magnitudes perpendicular to image structures.
We model the gradient profiles by a parametric gradient profile model. Using this model, the prior knowledge of the gradient profiles are learned from a large collection of natural images, which are called gradient profile prior.
Based on this prior, we propose a gradient field transformation to constrain the gradient fields of the high resolution image and the enhanced image when performing single image super-resolution and sharpness enhancement. With this simple but very effective approach, we are able to produce state-of-the-art results.
The reconstructed high resolution images or the enhanced images are sharp while have rare ringing or jaggy artifacts

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EXPLORING DUPLICATED REGIONS IN NATURAL IMAGES
Image Processing, IEEE Transactions on

ABSTRACT
Duplication of image regions is a common method for manipulating original images, using typical software like Adobe Photoshop, 3DS MAX, etc. In this study, we propose a duplication detection approach that can adopt two robust features based on discrete wavelet transform (DWT) and kernel principal component analysis (KPCA). Both schemes provide excellent representations of the image data for robust block matching.
Multiresolution wavelet coefficients and KPCA-based projected vectors corresponding to image-blocks are arranged into a matrix for lexicographic sorting. Sorted blocks are used for making a list of similar point-pairs and for computing their offset frequencies. Duplicated regions are then segmented by an automatic technique that refines the list of corresponding point-pairs and eliminates the minimum offset-frequency threshold parameter in the usual detection method.
A new technique that extends the basic algorithm for detecting Flip and Rotation types of forgeries is also proposed. This method uses global geometric transformation and the labeling technique to indentify the mentioned forgeries.
Experiments with a good number of natural images show very promising results, when compared with the conventional PCA-based approach. A quantitative analysis indicate that the wavelet-based feature outperforms PCA- or KPCA-based features in terms of average precision and recall in the noiseless, or uncompressed domain, while KPCA-based feature obtains excellent performance in the additive noise and lossy JPEG compression environments.

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