Earticle

Because stiff tissue deforms less than soft tissue under the same external compression, elastography can provide relative stiffness information of biological tissue. However, elastography suffers from artifact noise which may come from two dominant sources: de-correlation error and amplitude modulation error. In order to reduce artifacts and improve the quality of ultrasonic elastography, this paper proposes a centralized displacement operation based on an adaptive anisotropic diffusion filtering. We have applied the median and the mean of displacement axial gradient to differentiate edges from artifacts and categorize the whole displacement image into two different patterns; followed by the adjustable anisotropic diffusion filtering. The proposed algorithm can reduce artifact noise and, at the same time, maintain the tissue structure. Phantom testing shows that the proposed method can improve the quality of ultrasonic elastography in terms of tissue SNRe and CNRe values.

Motion blur of image is caused by relative motion between camera and photographed object during protographing. Motion-blurred image restoration is an important part of the image restoration. Image restoration is the process of recovering original image from its degraded version. This paper focuses on image restoration blurred by uniform linear motion. Principle of image restoration technique is introduced. Degradation model and blurred model of horizontal motion are analyzed. Two methods to solve image restoration caused by uniform linear motion are described in detail: Wiener filter in frequency domain and difference method in spatial domain. Comparisons are made between spatial method and the method in frequency domain. Experimental results on test image show that the spatial difference technique has better performance than the method based on Wiener filter.

In this paper, we propose a robust tracking method with a novel appearance model based on fragments-based PCA sparse representation. It samples non-overlapped local image patches within the templates in PCA subspace. Then, the candidate local image patches are sparse represented by the local template patches in PCA subspace. Finally, tracking is continued using the particle filter for propagating sample distributions over time. In addition, the templates are updated online based on incremental subspace learning .Using the fragments-based PCA templates rather than the image templates facilitates the tracker to handle significant illumination and pose change as well as occlusion. Experimental results on challenging videos show that our method can track accurately and robustly, and outperform many other state-of-the- art trackers.

In order to make efficient obstacle detection or to create a mapping, SLAM simultaneous localization and mapping being a special case, a good estimation of the sensor's move is needed. Adding dedicated extra sensors to the carrier is the most common solution but not without several limits: sensors have their own accuracy and range of use. Moreover, extra sensors are not often at the same place and so have different moves from one to another. In our outdoor application, the use of a panoramic radar as the main sensor offers another solution: the properties of the Radon's transform and the use of high resolution analysis ESPRIT allows the estimation of the motion's parameters without extra sensors, by the comparisons of successive panoramic maps. The method have been tested in both natural and urban environments and compared to motion estimated by a gyrometer and an odometer.

Electrical capacitance tomography (ECT) image reconstruction is a typical ill-posed problem. Tikhonov regularization algorithm is one of the popular methods to solve ill-posed problem. Because functional for the standard Tikhonov regularization algorithm is over-smoothness. The reconstruction image could miss edge information and the quality is not ideal. This paper proposes an image fusion algorithm based on Tikhonov regularization algorithm and wavelet transform, which can correct the edge of the reconstruction image. The simulation proves that it can enhance edges and improve the quality of the reconstruction image.

This paper presents a SVM classification method based on cluster boundary sampling and sample pruning. We actively explore an effective solution to solve the difficult problem of imbalanced data set classification from data re-sampling and algorithm improving. Firstly, we creatively propose the method of cluster boundary sampling, using the clustering density threshold and the boundary density threshold to determine the cluster boundaries, in order to guide the process of re-sampling more scientifically and accurately. Secondly, we put forward a new sample pruning algorithm based on dynamic threshold KNN to deal with the complexity and overlapping problem of imbalanced data set. The phenomenon of data complexity and overlapping will reduce the classification performance and generalization ability of SVM classifier. Experiments show that our method acquires obviously promotion effect in various different imbalanced data sets and it can prove the validity and st

Based on microcontroller, an audio source switcher which can be used for medium wave transmitter is proposed in this paper. According to the default priority, the proposed switcher can correctly realize the function of switch among other signal channels when the current audio source is in transmission fault. In order to improve the stability and practical value, the switcher can correctly distinguish the statuses of speech pause and transmission fault according to default time. Moreover, the trap filter is also used in switcher to inhabit the high frequency interference of transmitting station. It is expected that this switcher has potential application in future.

In this paper a low pass finite impulse response (FIR) filter has been designed using artifi-cial neural network. The optimization of the network has been done using generalized regres-sion algorithm. The proposed approach has been compared with rectangular window method. The accuracy of this algorithm is about 98%, that is much higher than multi layer perceptron (MLP) back propagation algorithm.

Blurry images are the bane of many photographers. Although sometimes these images could be retaken in the hope that the next exposure will not be blurred, but frequently they are of some unique event that could only be captured once. The most common cause of blurry images is camera shake. Camera shake means that during the exposure the camera moved. This movement may be very small but still creates blurry images. In this paper, a novel blind-deblurring approach for removing the effect of camera shake from blurry image is proposed. Starting with an image that has been blurred by camera shake, we recover the unknown shape image in two phases: (i) a kernel estimation phase using Radon transform method, and (ii) the shape image recovery based on EM algorithm. Comprehensive comparisons on a number of blurry images show that our approach is not only substantially faster, but it also leads to better deblurring results. Our experimental results are also shown for comparisons. Visually, we find that the restored images are better than those given by the algorithm in other methods from previous works.

B-mode measurement of the contour of the common carotid artery (CCA) has an important clinical value. The purpose of this study was to develop a fully automated ultrasound common carotid artery segmentation method using active shape model (ASM). An image database with 90 images was used to train the ASM model during the offline training phase of ASM. When it came to the online segmentation phase, a knowledge-based seed point detection method was first used to locate the centroid of the CCA. Then the trained ASM model automatically produced an exact contour of the CCA. The proposed method yielded a Dice Metric of 90.5% ± 4.35% and a Hausdorff Distance of 9.28 ± 5.2 pixels in a database of 40 ultrasound images. The segmentation result of upper and bottom part of the CCA was better than that of lateral part of the CCA. The proposed method eliminates the need of manual initialization, and identifies the contour of the CCA with high precision. It has the potential to be a suitable replacement for manual segmentation of the CCA.

Compressed sensing is a revolutionary technology in the research eld of signal processing, which can reconstruct the sparse signal using fewer number of compressive measurements compared with conventional reconstruction methods. Compressed sensing can also be utilized to detect the sparse signal. However, the exact reconstruction operation is not necessary when the system aims to detect such sparse signal. Based on compressed sensing, a new compressive signal detection scheme using the sparsity order of the sparse signal is proposed in this paper. Compared with similar detection scheme using the supports of the sparse signal, the newly proposed scheme requires much fewer number of compressive samples. In particular, the proposed scheme does not require the support prior-information of the sparse signal. Simulation results verify the advantages of the proposed scheme and indicate that the new scheme can achieve better detection performance.

The goal of data mining is to discover hidden useful information in large databases. Mining frequent patterns from transaction databases is an important problem in data mining. As the database size increases, the computation time and required memory also increase. Base on this, we use the MapReduce programming mode which has parallel processing ability to analysis the large-scale network. All the experiments were taken under hadoop, deployed on a cluster which consists of commodity servers. Through empirical evaluations in various simulation conditions, the proposed algorithms are shown to deliver excellent performance with respect to scalability and execution time.

In this paper, a comprehensive study has been made on performance assessment of a single relayed cooperative wireless communication system under implementation of OFDM aided MIMO technology. This paper provides a new insight on system performance with proposed STBC encoded MIMO-OFDM radio interface technology, four channel equalization techniques (ZF, MMSE, ZF-SIC and MMSE-SIC) and three digital modulation schemes (BPSK, QPSK and QAM). Simulation results show that the STBC encoded single relayed MIMO-OFDM cooperative wireless communication system with BPSK and MMSE-SIC outperforms as compared to other digital modulations and channel equalization schemes on scrambled voice frequency signal transmission.

In the peer to peer (p2p) VoD system, there are many factors to influence the fluency of video, such as watching behavior, distribution of segments, download rates, data transmission, and so on. Most research concentrates on single factor without considering the interrelation of all the factors. In the paper, factors would be researched separately to find the relation with fluency of video playing. The mechanism can improve the download ratio with condition of keep watching smoothly. The model proposes a precision method to understand users’ watching behavior and can help to optimize design in other kinds of p2p application.

There are two disadvantages in variational regularization based image restoration model. Firstly, the restored image is susceptible to noise because the diffusion coefficient depends on image gradient. Secondly, in the process of energy minimization, the selection of Lagrange multiplier λ which is used to balance the regular term and the fidelity term can directly affects the quality of the restored image. To solve the above problems, the multi-resolution feature of multi-scale wavelet is introduced into the energy minimization model and a wavelet based image restoration model is proposed. In the proposed model, Lagrange multiplier λ is replaced by an adaptive weighting function λj which is constructed by the image wavelet transform coefficients. Theoretical analysis and experiment results show that, comparing with other methods, the proposed model reduces iterations in the energy minimization process, overcomes the cartoon effects in variational model and pseudo-Gibbs effect in traditional wavelet threshold methods, and can well protect the detail features while denoising.

Designed and fabricated a monolithically integrated high-speed optical receiver module optocoupler chip, which contains “Nwell-Pepitaxial-Psub” model for the photoelectric detector (PD), the transimpedance amplifier (TIA) and the subsequent processing circuits. Clarify the circuit structure of the TIA and the adjustable gain theory. Detailed analysis the frequency response of the PD models and simulated the PD theoretical analysis. Simulation results showed that when the temperature is constant and the photocurrent change from 5uA to 80uA, the response speed of the PD and TIA module change quickly. When the photocurrent (Iph) unchanged and the test temperature (T) change from -25℃ to 125℃, the delay time does not exceed 10ns. Tape-out with the 1P3M 0.35um BCD process and the test results showed when the T is 25℃ and the Iph is 20uA, the delay time of the whole chip are 155ns and 205ns. When the T is125℃ and the Iphh kept constant, the delay time of the whole chip are 153ns and 220ns.The results show that the photodetector design with high-speed response capability, combined with the TIA gain adjusted in real time, the high-speed monolithic integration optocouplers can be established.

This paper analyzes the characteristics of the state-of-the-art technologys with regard to content-aware image retargeting in detail, contrasts the merits and demerits between diverse technologys, and lists the performance and effect about each method. The primary processes of content-aware image retargeting are divided into two steps: first step is the recognition of image importance-map, which is generally generated by the combination of image gradient and visual significance, indicates that human eyes generates diverse sensitivity of watching different area in image; Current papers with respect to content-aware image retargeting primarily centers on the resizing method in the second step. In the light of the traits of different ways, this article subdivided the technologys into three categories: on account of seam-carving, image-deformation and multi-operator. At last, we give the final qualitative analysis about the three algorithms mentioned above.

This paper focus on template matching approach for tracking object for remote sensing images. Such types of image are source of information which is largely used for information extraction. In this paper, two approaches SAD (sum of absolute differences) and SSD (sum of squared differences), template matching for finding the object. Template matching has provided basics and advance functionality for the image processing, object recognition, features based template matching and many more in a variety of remotely sensed Images. A variety of algorithms exist for comparison of images and making the template matching process fast and reliable. This comparative study with implementation approach focus on the core basics of the template matching in remote sensing images. The contribution of this paper is that, help to find the GCP (ground control point). These GCP can be generated in the form of template and these templates can run on the image and temporal image to find the correct location.

The aim of this paper is twofold. First, we define an ECG feature parameter set (32 features) which could represent ECG signal as adequately as possible for diagnosing requirements. Second, we design an automatic classification framework. After benchmark point detection, feature parameter will be extracted. And then the classifier methods and its comparison based on SVM and QNN are presented. The long-term objective is to design a thorough system to realize the recognition of real-time ECG signal and enhance medical treatment.

Trip-line detection is one of the very important research topics of intelligent monitoring category. This paper will present a trip-line detection system. In our trip line detection algorithm, Gauss model is used to model background, background differencing is used to detect object. Scanning the set of points on the line, if object is cross the line, for double-direction trip-line detection system directly alerts, but for single-direction trip-line detection, the moving direction of the object must be analyzed first. If the moving direction of the object violate passing rule, system directly alerts.

Formal model of spatial directional relation is one of the most important parts in the area of spatial relation, and it is necessary to develop a new model with high generality and practicality. Firstly this paper analyzes the characteristics and disadvantages of the present models. Secondly it introduces in detail the basic notion and construction method of quadtree histogram。Thirdly a new approach to judging basic and special spatial directional relations based on quadtree histogram is proposed. Finally in order to prove the correctness of this model, several examples are given. The experimental results show that this model is feasible.

Because of its excellent image quality, magnetic resonance imaging (MRI), as a new means of image guidance for breast biopsy robot, is used widely in clinical diagnostics and advanced breast research. However, the robot for MRI-guided biopsy is challenging due to the compatibility caused by strong magnetic field and very limited available space inside MR scanner. In this paper, a five degrees of freedom (DOF) robot is presented to perform biopsy in the breast with MRI guidance for the early detection of breast cancer. In an MR scanner (1.5T), the compatibility of copper, A4 non-magnetic stainless steel and POM, nylon, teflon is tested, and analyze the influence on the image of water model. Based on the analysis of available space, a 5 DOF robot which consists of 3-DOF Cartesian motions, 1-DOF rotation and 1-DOF insertion is designed, and the statics analysis of the robot is simulated using ANSYS. The results of statics analysis demonstrate that the robot meet the requirement of stiffness and precision.

Forest height is one of the most important vegetation vertical structure for many forest management activities. It is closely related with forest biomass and absorption of carbon. PolInSAR is a promising remote sensing technique that has been frequently used for extracting forest height. Recently, there have been plenty of research on the retrieval of vegetation parameters by single frequency single baseline PolInSAR such as the ESPRIT method, three-stage inversion. However, these methods tend to underestimate the forest height due to attenuations of the electromagnetic waves in the ground medium. This paper proposes a method to improve the accuracy of forest height estimation. This method is based on a fusion of hybrid method extracting forest parameters and the coherence amplitude method. The proposed algorithm performance is evaluated with simulated data from PolSARProSim software and ALOS PALSAR L-band polarimetric interferometric SAR data.

In order to improve the quality of diamond and other super-hard materials, fast and accurate detection of its strength is significant. The System use air pressure, broken automatic detection, automatic samples, testing is completed automatically returned. By resistance strain gauges to the pressure value is converted to voltage signal, after a high-precision A / D converter chip into a numerical quantity, given to high-performance MCU processing. Sample test system can be set according to sample size, data communications, data statistics and report printing, with a large-screen LCD display, dynamic display of strength parameters of the distribution curve. System is reliable test data stability, good repeatability and high reliability.

Analysis and several common edge detection algorithm is improved, and put them in the original experiment results were compared with the results in this paper, we defined the new threshold, fully explained in this paper, we define the rationality and validity of the threshold value. Kirsch of traditional algorithm was improved, and improve the efficiency of the algorithm, and verify the rationality of the defined threshold and improve the ability to suppress noise.

The study of gesture is still in its early childhood, gesture is present early in evolution, and is used to communicate before a child has the ability to enunciate. This paper describes the use of fast Fourier transforms both for preprocessing and feature extraction of hand gestures images. First the input gesture images are padded as the padding is necessary before applying Fourier transform to the image. Directional derivatives are then applied to the real part of the filtered images. Derivative angles are divided into the bins. These bins provide the final feature vectors. Distance based techniques were used for gesture classification. The real time simulation software WEBOTS is used for performing the classified gesture.

As the digital information must be stored and recovered in an effective ways, it is necessary to compress data in image coding, wavelet transform and lattice vector quantization are adopted in this paper to improve the compression effective of image coding. We first propose the two dimensional spatial wavelet decomposition of image, and then we present lattice Vector Quantization coding scheme for each image subband, lattice vector quantization can make full use of the correlation between the image wavelet coefficients to remove the information redundancy, and the coding method of lattice quantization can be effective because of applying the symmetries of the lattice. The establishment of rate distortion (RD) model suitable for lattice vector quantization of wavelet image coder is also important for image compression, study shows that the RD performance for the spatial subbands are fitted by an exponential form theoretical model, this yields an analytical solution to the bit rate distribution issue, we explore an effective rate control scheme by using the lagrangian optimization method to distribute the bit rate for the spatial subbands. The experimental results show that the proposed algorithm can achieve better compressing effect with minimum loss.

A detection method for transient power disturbances by complex wavelet transform is proposed in this paper. The core of it is that, the compactly supported dyadic orthogonal linear- phase complex wavelet (CSDOLCW, compactly supported dyadic orthogonal linear-phase complex wavelet) is constructed in terms of lifting scheme. Amplitude feature, phase feature and frequence feature can be obtained by complex wavelet coefficient, which also indicate characteristic of time domain, such as start-end time of disturbances occur, the amplitude of disturbance, and then realizing the correct detection and classification. Simulation results show that complex wavelet transform can distinguish various voltage transient disturbances and reflect disturbance’s each characteristic, in the condition of lower SNR, classification results are also acceptable.

In this paper, three different segmentation techniques are used, namely, region growing segmentation technique, watershed segmentation and texture segmentation, to accurately extract the shapes of membranes and nuclei from the inverted microscopic images, taken throughout the monolayer formation of BGM-70 (Baby Grevit Monkey) cell culture. This strategy is a prerequisite for an accurate quantitative analysis of cell shape and morphodynamics during organogenesis and is the basis for an integrated understanding of biological processes. The segmentation of cellular structures is achieved by first normalizing the microscopic images by performing CLAHE operation followed by de-noising by morphological opening and filling of the holes and then applying segmentation algorithm for cell shape reconstruction.

The existing recognition methods of radiation source signal are extremely sensitive to the change of signal-to-noise ratio (SNR). In order to solve this problem, in this paper, it proposes a radar signal recognition method based on fractal box dimension and neural network under the condition of unstable SNR. Firstly, it extracts box counting dimension features of four different radiation source signals, and then uses the characteristic values of stable box counting dimension as the inputs of the neural network. Finally, it can recognize the four different kinds of radiation source signals. Simulation results show that, taking the box counting dimension characteristics as the input of the neural network to train, test and classify, it has good recognition rate in a certain changed range of SNR, and it has more widely application in the application environment.