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Random equivalent sampling (RES) can composite a waveform with high equivalent sampling rate from multiple low speed sampling sequences. In practical application, the performance of RES signal reconstruction would be degraded by the non-uniform distribution of sampling time. Compressed sensing (CS) theory is adopted to reconstruct RES samples, which could mitigate the inherent coherence of sampling time. However, the CS reconstruction algorithm is sensitive to the signal sparsity level that is unknown in the reconstruction stage. In this paper, we propose a redundancy reduction algorithm for CS base RES signal reconstruction that can ensure reconstruction accuracy while reducing the number of random samples. The experimental results are reported to evaluate the performance of the proposed algorithm.

In this work, we develop a new method of setting the input to reservoir and reservoir to reservoir weights in echo state machines. We use a clustering technique which we have previously developed as a pre-processing stage to set the reservoir parameters which at this stage are prototypes. We then use these prototypes as weights in the standard architecture while setting the reservoir to output weights in a standard manner. We show results on a variety of data sets in the literature which show that this method out-performs a standard random echo state machine.

A multi-spectral image fusion algorithm based on red-black wavelet (RBW) and principal component analysis (PCA) is proposed in this paper to enhance the image performance. The PCA algorithm was used to extract the diverse features and details of the multi-spectral image, and, then, these features were decomposed by RBW and fused by the improved diverse algorithms using low-frequency and high-frequency coefficients at different scales, frequency domains, decomposition layers, and frequency bands. Finally, these fused features and multi-spectral images were reconstructed by RBW and PCA inversion. The results of our experiments showed that the proposed algorithm provided higher spatial resolution and more excellent spectral information. In addition, it improved the processing speed, took less memory, and offers the potential for real-time processing.

In this paper, we establish virtual physical model of PUMA560 industrial robot according to kinematic model and configuration parameter of PUMA560 industrial robot under VisualNastran condition. Simulating the moving track and position from one point to another along curves and straight line when the robot clamps are attached to the load of m and simulating the changing curves of force and moment of each joint when adding dynamic load under VisualNastran condition.

In the past few decades, Free Space Optics (FSO) has gained importance over the RF/microwave transmission systems due to the availability of license free spectrum and long range operations. Free Space Optical communication system has many advantages such as compact size, large bandwidth, lower cost and lease for implementation. Free Space Optical communication system also suffers from few limitations such as beam dispersion, atmospheric attenuation, and scintillation. One of the most significant limitations of FSO is the dependence of the performance of FSO system on the weather conditions. In this paper, a 10 Gbps FSO system has been designed and analyzed for different weather conditions and modulation formats at varying distances. From the results, it can be seen that as we move from clear weather conditions to heavy fog weather conditions, Q factor of the received signal and maximum transmission distance both decreases. Also, NRZ modulation format performs better as compared to other modulation formats in an FSO communication link.

Signal generator is a kind of electronic devices that generate repeating or nonrepeating electronic signals which are generally used in designing, testing and so on. Under a certain reference frequency, it is able to control the output main signal frequency by selecting the suitable mixed signal at the output with bandwidth filter. This paper introduces a design and implementation of a high-frequency signal generator based on the DDS mixing principle. The generator uses the characteristics of D/A convertor as DDS output frequency spectrum. Experiments show that using the bandwidth filter, most of the spurious signals are removed and the main frequency is confined, thus, signal of 101MHz could be achieved significantly. Additionally, it is observed that the Sine wave is finally obtained from the figure and the filter circuit is able to confine the signal bigger than 30dB for the frequency over 101MHz.

In this paper, we will present a dataset of handwritten mathematical expressions outcome of several mathematical disciplines (logic, analysis, algebra and probability). This paper also describes all the details concerning the necessary steps of our approach for handwritten mathematical expressions recognition. At the end of this paper, we will present all the results obtained by this system.

It is very important to design a feedback loop which takes the output into consideration so that the system is able to adjust its performance. Reliability analysis in the control system is able to examine the system stability and make the control system is more compatible and reliable. This paper introduces a spherical coordinate-based reliability analysis model to examine the feedback in a control system for improving the control signal between encoder and decoder of the controller. Under the feedback closed-loop system with erasing operations and limited coding ratio, the proposed spherical coordinated-based approach is able to ensure the average asymptotic reliability. To this end, the design methodology of controller, encoder, decoder, and quantifier are introduced. From the experiments analysis, it could be observed that the proposed approach is able to achieve high system reliability. It is observed that, under the bigger erasing possibility, the designed quantifier and controller are able to ensure the system average asymptotic reliability.

Vein-based biometrics is a newly developed technology for personal recognition, and it is widely used in practice and intensively studied. This paper proposes a method for palm vein recognition based on the directional information derived from local binary patters. In the proposed method, palm vein images are firstly enhanced using a multi-scale Gaussian matched filter to emphasize vein patterns before feature extraction. After that, local binary patterns are extracted from the enhanced palm vein images. Considering that the direction is the most discriminative feature of veins, the directional information is then computed from the local binary patterns. The computed palm vein features are represented as binary series, therefore, similarities can be computed efficiently by binary operation. Experiments carried out over the near infer-red band of the PolyU multispectral database shows the superiority of the proposed method on verification accuracy to some state-of-the-art literatures.

This paper presents an efficient algorithm based on SIFT (Scale Invariant Feature Transform), SURF (Speeded up Robust Features) and PCA. The method applies the SIFT and SURF algorithm in the detection and description for image features for a particular image and then applying PCA to the image for the better performance in matching in terms of rotation, expression and pose. The SURF technique is fast and robust interest point detector which is used in many computer vision applications. The basic process of face recognition system and improvement in matching the invariant faces is described in this paper. Performance can be seen on the basis of Recognition rate and Computation time. Image Processing Toolbox under MATLAB Software is used for the implementation of this proposed work.

To improve the Features from Accelerated Segment Test (FAST) algorithm such as artificial threshold setting and the choice and ordering of continuous points on circle, a novel compass detection algorithm is proposed. The algorithm uses four compasses as the test mask and calculates threshold value based on image intensity feature. Four compasses are used to pre-detect corners. In order to improve the location accuracy of corners, pseudo response on skewed edges is eliminated. Finally, multiple adjacent responses are tackled by keeping only points which have extremal value of Laplacian. Comparative experiments between FAST and the proposed algorithm are carried out with respect to validity, robustness and efficiency. The results show that the detected corners by the proposed algorithm are accurately located and well-distributed.

The structural vibration generated by building mounted small wind turbine was fully investigated by author. In order to achieve this, the real vibration condition of wind turbine in field was accurately simulated in Lab for research. There are some difficulty during the whole simulation process for further quantification of vibration levels. One of the main concerns was the correct estimation of the excitation or contact force at the interface between vibration source (turbine) and receiver (building or walls). However, the relevant simulation method in Lab is not available so far. In this paper, the direct measurement of force was investigated and found unsuitable. A new simulation method was proposed including solving the problem of power supply (DC electric motor used), automatic control of rotational speed of motor and indirect interface force determination. At last, the new established finite element models (FEM) was validated by experimental data with a good initial agreement.

In this paper, a new ear anatomy feature edge extraction method based on Hessian matrix is proposed. Stable edge is obtained from principal curvature image across scale space. Firstly, the side face image that includes an ear is filtered and forms Gaussian pyramid. Secondly, the 2D gray image in the pyramid was regarded as a surface, maximum and minimum principal curvature and their direction were calculated by using Hessian matrix, and principal curvature image was formed. The characteristic of surface is that gray level changes in edge area is sharp and the curvature is larger compared to that of the smooth area. In accordance with this characteristic, automatic hysteresis thresholding based on curvature direction flow is used to segment curvature images. Lastly, combine different scale threshold images to get the feature edge image. The experiments demonstrate that extracted feature edge is smooth and connected. New method is robust to noise, and is sensitive to the weak edge, using Hausdorff distance as similarity measurement of two edge images can obtain above 96% recognition rate.

Spectrum sensing is a key functionalities of Cognitive radio networks (CRN). There are several methods to improve sensing techniques. In this paper, we present a robust detector using signal to noise ratio (SNR) with adaptive threshold (ADT) scheme in Cognitive Radio Networks (CRN). This technique has two detectors, energy detector (ED)&EDwithADT detector, out of these two detectors only one will perform sensing operation at a time. Selection of detector depends on condition between estimated SNR value (Se) and threshold (γ). Numerical results show that proposed ESNR_ADT scheme optimizes detection performance and outperforms the cyclostationary based sensing method and adaptive spectrum sensing (SS) by 30.5 % and 30 % at – 10 dB signal to noise ratio (SNR) respectively. It is also shown that the proposed scheme yields lesser sensing time than cyclostationary detection and adaptive SS scheme in the order of 5.2 ms and 1.0 ms at - 20 dB SNR respectively.

In order to suppress electromagnetic interference of PWM driving system, electromagnetic interference characteristics ,system distribution and main influence factor in this system must be accurately mastered. Therefore this paper used conduction separation network to meter common mode interference of PWM inverter system ,and through comparison results of different conditions ,the author obtained the distribution of common mode interference on power side and load side . Then the team made analysis and improvement to the common-mode interference model, at last through theory analysis and calculation some suppression method to CM interference was found , which may provide certain theoretical guidance for EMI filter design.

Device-to-Device (D2D) communications potentially can improve spectrum efficiency because of resource spatial reuse. There are two possibilities for D2D resource allocation: contention-based resource allocation and scheduling-based resource allocation. In this paper, these resource allocation schemes are analyzed. Then, we propose a scheduling-based resource allocation that has the advantage of low scheduling overhead in addition to providing a common design for under the in-network coverage and out-of-network coverage. Simulation results show that the proposed algorithm can increase system throughput by exploring spatial reuse gain and maintain good proportional fairness among D2D communication pairs.

Segmentation refers to a technique in which an image in digital form is partitioned into multiple segments (basically groups of pixels, also termed as Super pixels). This paper is a survey on Image Segmentation with its clustering techniques. Image Segmentation is the procedure of apportioning a picture into numerous segments, to change the exemplification of a picture into another which is more useful and easy to segment. A few universally useful calculations and approaches have been developed for picture division. It separates a digital picture into numerous locales to investigate them. It is likewise used to recognize segment items in the picture. A few picture segmentation procedures have been developed by the specialists with a specific end goal to make pictures smooth and simple to access. This paper describes segmentation techniques, advantages and disadvantages of the clustering methods and a comparison of the techniques.

Aiming at the problem of ground penetrating radar image denoising, a new adaptive image denoising algorithm based on nonsubsampled Contourlet transform is proposed. The algorithm firstly performs nonsubsampled Contourlet transform to the noise image, to obtain the coefficients of each directional sub band and each scale, then, according to the energy of the coefficient, the denoising threshold value is adjusted adaptively. Simulation results show that, compared with the wavelet threshold denoising algorithm, the proposed algorithm can effectively remove the Gauss white noise in the image, improve the peak signal to noise ratio (PNSR), while preserving the edge details of the image, it can improve the PSNR value and reduce the Gibbs phenomenon.

Signal processing takes an important role in genomic with enormous data available in public domain. Generally digital filters are applied to predict the protein and genes, but it needs to be redesigned when the characteristic frequency and periodic behavior is changed. In this paper proposed the novel adaptive algorithm which can identify the genes and proteins effectively from unified framework. First using the electron ion potential method the symbolic DNA sequences are converted in to digital signal. Secondly the filtering scheme for genomic signal processing with periodic behavior in biological sequence is introduced, which can predict and analyze the biological region that are interested in. finally the proposed adaptive filtering method is applied to recognize the exons of protein coding regions according to periodic-3 property. The exons prediction curves are obtained with Discrete Fourier Transform (DFT), Least mean square (LMS), and proposed Fast Recursive least Mean Square (F-RLS) algorithms. It is shown that proposed method shows efficiency in convergence of identification and precise prediction of exons regions compared to existed methods.

In this paper, the method ofextract event from newspaper is proposed. First, the candidate sentences are select by using trigger word. Then, statistic models in three levels are consisted of extract model, which is sentences level, news level and topic level is used to extract event from candidate sentences and the probability of extracted events is calculated by three levels model. Finally, the method proposed in this paper is used to extract 4 type events. The experiment result shows this method have ideal effect on event extraction.

In the present work, a computer aided classification system has been proposed for classification of mammogram images into normal, benign and cancer classes. The work has been carried out on thirty Digital Database for Screeningmammography(DDSM) cases consisting of 10 normal, 10 benign and 10 cancer images. The regions of interest (ROI) have been extracted from the right Medio Lateral Oblique (RMLO) part of the mammogram. We extracted 256×256 pixel size ROI from each case. Texture descriptors based on gray level co-occurrence method by varying the value of inter pixel distance ‘d’ from 1 to 8 have been used. The SVM classifier has been used for the classification task. The result of the study indicates that GLCM mean and range features computed at d=1 yield the maximum overall classification accuracy of 75% and 65 % respectively.

In this paper, the design and analysis of a hexagonal left-handed material is presented, which is based on the analysis of the structural parameters. The effective permittivity and the effective permeability of the designed hexagonal left-handed material are simultaneously negative in the range from 7.4GHz to 7.9GHz. According to the empirical formula of the microstrip antenna, a conventional microstrip antenna is designed. The different combinations of the hexagonal left-handed materials and the conventional microstrip antenna are analyzed in HFSS software. The novel antenna is proposed based on the analysis of the combinations. The simulation results show that the gain of the antenna increased by 0.51dB, and the half power beam width decreased by 3°. Besides, the bandwidth of the novel antenna is 140MHz wider than that of the conventional microstrip antenna. It is concluded that the performance of microstrip antenna can be improved by using left-handed materials.

Digital image tamper detection by analyzing the statistical properties of the image content of the authenticity of the digital image, the scene authenticity and integrity of authentication methods, namely determine whether the image is the original image, whether the image is real and whether it contains other secret information image, these techniques is the digital image forensics. Digital image forensics technology is dependent on the basis of the original carrier may be divided into active and passive two technologies forensics evidence. This paper generated image detection research for digital image forensics scene authenticity certification in natural images and computer. Combining the results of recent research, based on systematic analysis of the differences between two types of image generation, high-end features for wavelet conducted a detailed study of the effectiveness, from the validity of the characteristic data, proposed several general-purpose computer generating an image detection algorithm. Compared with the previous detection algorithm, strong stability of the proposed algorithm, computational complexity is low, and has easy implementation.

A compact planar monopole antenna with band-notched characteristics suitable for Ultra Wideband (UWB) applications is presented. The proposed UWB microstrip-fed antenna consisting of U-shaped slots in both patch and the microstrip-feed line with a defected ground structure. This antenna is designed to cover the Federal Communication Commission (FCC) bandwidth for UWB applications (2.04-14.6 GHz), while showing tri band-notched characteristics in the frequency bands of: (3-4.3 GHz) used for WiMAX, (6.12-6.8 GHz) used for C-band uplink frequency and (8.7-9.2 GHz) used for aeronautical radio navigation. The proposed antenna is suitable for various narrow band wireless applications, satellite applications and radar applications. The proposed UWB antenna is simulated by using High Frequency Structural Simulator (HFSS).

A novel objective quality assessment method is proposed for super-resolution images in this manuscript. We not only estimate the preserved information of each spatial location in the super-resolution image by structural similarity, but also compute the local phase coherence (LPC) with which we can detect the image blur in the super-resolution image. After the preserved structural information and blur information is obtained, an overall evaluation of visual quality of the super-resolution image can be computed. Experimental results show that the proposed objective quality assessment method can be used in the real applications with the original high-resolution images unavailable.

Feature extraction is the most crucial part of face recognition system, which has important role in the field of pattern recognition. There are a lot of classic algorithms about feature extraction at present, such as the method based on linear and nonlinear. An effective classification and dimension reduction method, local embedded graph attribute selection algorithm with maximal region is to generate the inherent graph and penalty graph, to overcome the low efficiency problem of local linear embedding (LLE) method and maximum margin criterion (MMC) method. With inherent picture, the structured nonlinear can be found on the high-dimensioned space by using local geometry of the restructured linear, which leads to the same instances gathering together as more as possible. Meanwhile, different class instances are as far as possible from each other in penalty picture. Since LLE is an unsupervised method, not enhance visual clustering classification ability, so compact figure within the class to consider the sample class information, can sample the same category as compact. In this method, the smallest size instance issue was tackled by the employment of MMC and the neighborhood relationship can be better described by an adequate improvement of the adjacency matrix. The effectiveness of algorithm proposed by this paper is present by a large amount of experiences in the standard face databases of Yale, AR and ORL facial data.

To ease the concept of diagnosing human health a strong and viable biomedical signal is of importance. Biomedical signal measurement and processing of signal cause the probability of artifacts which can obstruct the features of interest and quality of information available in the signal. So elimination of artifacts from physiological signals is an essential step. The single channel measurement is important when instrumentation complexity is needed to be minimized, in spite of many multichannel signals recording methods available.In this paper, an enhanced empirical approach to remove the artifacts of single channel signal is described followed by filtering mechanism using ICA and DWT. The input EEG is a single channel and is converted into multichannel for ICA operations. The multi-channel EEG signal is filtered with fast ICA algorithmand DWT is employed to reject any traces of artifacts left in the signal. This technique is tested against currently available wavelet de-noising and EMD-ICA technique using functional near infrared spectroscopy (EEG) signal. Artifact removal technique has been evaluated by DSNR, Lambda (λ), Autocorrelation and PSD. The results pronounce the eligibility of proposed algorithm to stand on top of currently deployed algorithms with 12% improvement in DSNR and alsoa significant improvement in different parameters too.

The traditional method of heterodyne detection is improved in this paper based on the principle of laser heterodyne detection. The laser heterodyne detection system is designed with all-fiber laser radar in all fiber system, and the laser, the optical system, the receiving device and so on are connected via the passive component. The system can mix the local oscillation light with signal light of measured object information in the photodetector surface, and the difference frequency signal is obtained. Then the signal is filtered and amplified by the signal processing technique to eliminate the DC component, and the desired difference frequency signal is obtained. Through the analysis of the performance of all fiber laser heterodyne detection system, the system has a strong ability to detect weak signal, high conversion gain and signal to noise ratio, and has a good filtering effect. The system is very suitable for the application of laser ranging and so on.