Earticle

Image encryption algorithm usually features high iteration and low confidentiality since the key space of low-dimensional discrete chaotic encryption is small. In order to address these challenges when carrying out image encryption, this paper proposes an innovative method which uses Cat mapping to realize the image discretization. The proposed approach uses the periodic changes to achieve the encryption of images. Images with different sizes may use different cycles to encrypt. The experiments show that the encryption approach is able to fulfill the image encryption effectively through drawing the best parameters to achieve the best image encryption effect. The sensitivity analysis implies that, this method is capable of performing well on the image pixel scrambling and replacement. For encrypted security, this proposed method has strong sensitivity to the plaintext which may attribute to handle the plaintext attack under difference situations.

The robust measurement of the intima media thickness (IMT) of longitudinal common carotid artery (CCA) has an important clinical value because clinicians often use it as an important predictor to assess the possibility of potential cardiovascular events. The purpose of this study was to develop a fully automated algorithm to measure the IMT in the longitudinal ultrasound B-mode images. A completely automated algorithm for identification and calculation of IMT is proposed in this paper. Based on signal analysis, the algorithm can be divided into four steps. The first step is to automatically identify the lumen-intima (LI) interface points of posterior wall. Starting from the detected LI interface points, the second step uses the gradient-based method to locate the candidate media-adventitia (MA) interface points. The third step applies the canny edge detector to remove the outliers from the candidate points. The last step is to calculate the IMT from the final available points. On 35 ultrasound video sequences of the common carotid artery (CCA) taken from 13 healthy subjects, the results generated by the proposed method were compared to the manual annotated data. The proposed method yielded an IMT of 0.61 mm ± 0.085 (mean ± standard deviation) whereas the corresponding result yielded by the manual annotated ground truth data is 0.60 mm ± 0.1. The proposed method eliminates the need of manual initialization, and measures the IMT of the longitudinal CCA with high precision similar to the ones observed in the manual segmentations. It has the potential to be a suitable replacement for manual segmentation and measurement of the IMT.

Aiming at the complexity and limitations of traditional character recognition design method, an algorithm combined with genetic algorithms and neural network is proposed. Using this method, the advantages genetic algorithm which global optimal solution or a very good performance suboptimal solutions can easily be obtained is fully utilized. The shortcomings of neural network model such as slow convergence speed, entrapment in local optimum, unstable network structure etc are solved. Combined neural network and genetic algorithm is to make full use of the advantages of both, so that the new algorithms both neural network learning capability and robustness, but also a strong genetic algorithms global random search capability, the neural network has self-evolutionary, adaptive capacity, so as to construct evolutionary neural network. The actual application in character recognition results show that, compared with the traditional method, this model has a strong feasibility and effectiveness.

With the increasing rate of unauthorized access and attacks, security of confidential data is of utmost importance. While Cryptography only encrypts the data, but as the communication takes place in presence of third parties, so the encrypted text can be decrypted and can easily be destroyed. Steganography, on the other hand, hides the confidential data in some cover source such that the existence of the data is also hidden which do not arouse suspicion regarding the communication taking place between two parties. In this paper, we have proposed a new steganographic approach for hiding data in digital images based on calculating the modulus of RGB values with the modfactor.

During the image acquisition and communication the image is corrupted by noise. This is a classical problem in the field of signal or image processing. A new compromise threshold method that improves performance is presented, based on the standard compromise threshold method, yet more flexible and easier to treat mathematically. The improved adaptive wavelet compromise method avoids the discontinuity of the hard-threshold method, at the same time increase the correlation judgment process, improve image details loss problem. Simulation results show that the improved method is better at the adaptability, at the same time increase the correlation judgment process, improve image details loss problem.

Signal source is an important part of modern electronic system and has a wide range of applications in the fields of communication, measuring & control, navigation and medical care. This project developed a new dual-channel sine wave generator with Tunable Phase Difference on the basis of the direct digital frequency synthesis (DDS) technology. Design was accomplished using STC89C52 MCU and FPGA as the control center, integrating basic operation circuit and employing low-pass filter and the necessary software algorithm. The experimental results showed that the system can produce dual sine waves with tunable phase difference in 0~359° range. The system is stable in work and it is easy to adjust parameters.

This paper presents a general framework for seamlessly combining multiple low cost and inaccurate estimated segmentation maps (with an arbitrary number of regions) of the same scene to achieve a final improved segmentation. The proposed fusion model is derived from the well-known precision-recall criterion, specially dedicated to the specific clustering problem of any spatially indexed data and which is also efficient and widely used in the vision community for evaluating both a region-based segmentation and the quality of contours produced by this segmentation map compared to one or multiple ground-truth segmentations of the same image. The proposed combination framework is here specifically designed to be robust with respect to outlier segmentations (that appear to be inconsistent with the remainder of the segmentation ensemble) and includes an explicit internal regularization factor reflecting the inherent ill-posed nature of the segmentation problem. We propose also a hierarchical and efficient way to optimize the consensus energy function related to this fusion model that exploits a simple and deterministic iterative relaxation strategy combining the different segments or individual regions belonging to the segmentation ensemble in the final solution. The experimental results on the Berkeley database with manual ground truth segmentations show the effectiveness of our combination model.

The key problem of computer vision reconstruction technology is to improve accuracy of shape reconstruction and authenticity of color reconstruction. First, color edge Gray code is presented, which encodes with red and blue Gray code stripes, and decodes with sub-pixel located stripe edges. So quantization error and decoding error were eliminated and accuracy of shape reconstruction was improved. Second, color correction method is presented. The coupling phenomena of RGB primary colours existed in the encode and decode process was analyzed, and then Color coupler Correction scheme based on Caspi model Hardware Calibration was designed. Accordingly, a color imbalance caused by the surface curvature in the reconstruction process was analyzed, and color imbalance correction scheme used surface geometric information was also proposed. Shape reconstruction experimental results show that relative error of reconstructed plane is 0.05%, and reconstructed complex surface has same visual effects as real surface. Color reconstruction experimental results show that color of reconstructed surface is uniform after correction, which has same color as real surface. And moreover, This method has strong anti-interference ability and can help to improve the accuracy of shape and color reconstruction.

Voltage sag is a prominent problem in power quality. It is key to identify voltage sag exactly for choosing scheme of managing power quality. Hilbert-Huang transform (HHT) is applied for the voltage sag recognize. Using the empirical mode decomposition (EMD) for voltage sag, the intrinsic mode function (IMF) can be obtained, then perform the Hilbert transform in each IMF, after that the instantaneous frequency spectrum can be obtained. And then determine start and end. Simulation results show the method has better effect on detecting voltage sag.

In computer vision related applications, video analysis of human walking motion is currently one of the most active research topics. The task of analyzing human walking can be divided into three distinct subtasks – human detection or segmentation, motion tracking and walking pose analysis. Typically, the analysis of the human walking starts with the extraction of motion information, detection of the presence of humans in the sequences of frames and then followed by analysis of events related to walking. This paper presents a survey of different methodologies used for human walking motion analysis, approaches used for human detection or segmentation, various tracking methods, approaches for pose estimation and pose analysis. The common data sets available for building robust, automatic and intelligent systems to understand “walking” motion are also included. Finally, uses of unsupervised techniques for analysing human walking are highlighted. Human walking motion is a subset of a broad topic of human motion analysis.

Head pose can indicate the eye-gaze direction and face toward which is an important part of human motion estimation and understanding. Due to physical factors of the camera, shooting environment, as well as the appearance change of humanity, the head pose estimation becomes a challenging task. Synchronization sub manifold embedding can find the internal structure of nonlinear data for nonlinear dimensionality reduction and random regression forests can make the nonlinear function mapping for getting the right head pose. In this paper, the advantages of these two algorithms are combined with a method for solving the head pose estimation. Data collection step, the depth data come from the 3D sensor; and training data step, the data is using the local linear structure for label and using a statistical model for synchronization pose samples. Meanwhile the experimental results on a publicly available database prove that the proposed algorithm can achieve state-of-the-art performance while the current estimate has a faster speed and higher robustness when large range of pose changes and outperforms existing.

This paper compares various defogging techniques such as Dark Channel Prior (DCP), Improved Dark Channel Prior (IDCP), IDCP with guided filter, IDCP with histogram specification, Tarel method, Anisotropic diffusion and Adaptive defogging in HSV color plane. For the purpose of comparison these techniques were implemented on MATLAB-09. The result shows that Adaptive defogging technique has highest value of PSNR among all the techniques having lowest MSE, NCD and MAE value. The computation time for removal of fog is found to follow the trend from low to high is: Anisotropic diffusion, Tarel method, DCP, IDCP, IDCP with guided filter, IDCP with histogram specification and Adaptive defogging.

Deep learning is an emerging approach for finding concise, slightly higher level representations of the inputs, and has been successfully applied to many practical learning problems, where the goal is to use large data to help on a given learning task. We present an algorithm for image denoising task defined by this model, and show that by training on large image databases we are able to outperform the current state-of-the-art image denoising methods.

When the cytological smear method is used to check cervical lesions, the irregularity of the cells contour is essential for smear image interpretation and has the vital significance for research on computer aided diagnosis of cervical lesions. Aimed at measuring the regularity of cells outlines, based on the characteristic that the normal cell smears is generally circular, oval, convex polygon, we put forward a cell contour irregularity feature extraction and measurement method based on linear geometric heat flow curve evolution, in which the curve of cells outline is dealt with linear geometric heat flow curve evolution to totally convex (i.e. no curvature zero crossing) and compared with the original contour to extract indexes which can describe irregularity features of cell contour. Experimental results show that the method proposed in this paper can better describe the irregularity of cell contour and distinguish cells of different lesions.

The fuzziness and randomness of decision table affect hugely on the performance of knowledge acquisition in rough set. In order to reduce their influence, a novel reduction algorithm based on grey relational analysis is proposed. In the algorithm, every value of decision table is converted to the same domain. Moreover, on the basis of grey relational analysis, the grey relational matrix for the converted decision table is constructed to describe the equivalence relations between samples of decision table. Finally, the samples with the same similar level are adopted as the coarser granularity. The experiments fully show that the reduction decision table achieved almost the same recognition rate with less than one tenth of the former conditions. It fully shows the effectiveness of the algorithm.

A robust watermarking technique using wavelet transform/hybrid wavelet transform and SVD is proposed in the paper. Singular values of watermark are embedded in mid-frequency transform coefficients of host located in a single row. Selection of single row makes only 256 transform coefficients available where watermark singular values can be embedded. By using the compression property of Singular Value Decomposition, only few singular values are embedded in the host row. Transform coefficients of host are sorted in descending order and their index positions are used to embed singular values. Scaling factor is also adaptively selected based on highest transform coefficient in the selected host row and highest singular value of watermark. Selection of single row makes it difficult to predict the location where watermark is embedded thus making it more robust over selecting multiple rows for embedding. Adaptive scaling factor adds to the robustness by adjusting the strength of watermark according to the highest singular value of watermark and host row selected for embedding. This technique proves better in terms of robustness against various attacks than embedding singular values of watermark in multiple rows of host proposed in our previous work.

In this paper, we propose a new finger multi-biometric cryptosystem using feature-level fusion to simultaneously protect multiple templates of fingerprint, finger vein, finger knuckle print and finger shape traits as a single secure sketch. We theoretically analyze the feature-level fusion for finger multi-biometric cryptosystem with respect to their impact on security and recognition accuracy. The comparative experimental results ascertain that finger multi-biometric cryptosystem outperforms the uni-biometric counterparts in terms of verification performance and template security.

Ship detection is an important application of optical remote sensing image processing. Sea-land segmentation is the key step in ship detection. Traditional sea-land segment methods only based on the gray-level information of an image to choose a gray threshold to segment the image; however, it is very difficult to establish a self-adapting mechanism to select a suitable threshold for different images. Thus, the segmentation result is greatly influenced by the threshold chosen for sea-land segmentation. In this paper, we are integrating the LBP feature information to propose a novel sea-land segmentation algorithm. Moreover, a new ship detection method based on our sea-land segmentation algorithm is proposed for optical remote sensing images. The performance of ship detection is measured in terms of precision and false-alarm-rate. Experimental results show that, as compared to minimum error meth-od, the proposed algorithm can decrease the false-alarm-rate from 23.2% to 9.24%. And compared to Otsu method, the proposed algorithm improve the precision from 82.9% to 90.2%.

A computer simulation study based on Drift-Diffusion model has been carried out to explore and analyze the DC and high frequency properties of Zinc-Blende (β-phase) Gallium Nitride based p+pnn+ DDR IMPATT. The simulation study based on bias current optimization is performed at Ka-band window frequency of 35 GHz. The results portray the strong potentiality of β-GaN IMPATT as a powerful millimetre wave source with maximum conversion efficiency of 15% at an optimum bias current density of 3.2×109A/m2. The design results presented in the paper will be very helpful in realization of these diodes for millimetre wave communication systems.

In this paper, a quick table look-up optimization algorithm is presented to solve the problems of long table look-up time for CAVLC decoding in H.264/AVC. The achievement of the new algorithm rests that we make full use of the hash table query and index technology to improve the table look-up speed for CAVLC decoding. The basic idea of the new algorithm is that we take the number of zero in code prefix calculated from input bit-stream as index key of the first level, the value of codeword suffix as index key of the second level, then through index key of the one and second index key above, we can quickly get the decoded codeword located in the third level in a hash table built, which can reduce a lot of table look-up time for CAVLC decoding in H.264/AVC. The simulation results show that our proposed schemes based on hash-index method can reduce about 40% table look up time for CAVLC decoding compared with TLSS method, without degrading video quality.

In this paper a novel face recognition approach based on Adaptive Principal Component Analysis (APCA) and de-noised database is produced. The aim of our approach is to overcome PCA disadvantages especially the two limitations of discriminatory power poverty and the computational load complexity, by producing a new adaptive PCA based on single level 2-D discrete wavelet transform using Daubachies filter mode. All face images in ORL database are transformed to JPG file format and are de-noised by Haar wavelet at level 10 of decomposition; the goal is to exhibit the advantage of wavelet over compressed JPG files instead of using origin PGM file format. As a result , our adaptive approach produced good performance in raising the accuracy ratio and reducing both the time and the computation complexities when compared with four other methods represented by standard statistical PCA, Kernel PCA, Gabor PCA and PCA with Back propagation Neural Network (BPNN).

The aim of this paper is to reduce the main problems in biomedical data processing like electrocardiography is the separation of the wanted signal from noises caused by power line interference, external electromagnetic fields, random body movements and respiration. Different types of digital filters are used to remove signal components from unwanted frequency ranges. It is difficult to apply filters with fixed coefficients to reduce Biomedical Signal noises, because human behavior is not exact known depending on the time. Adaptive filter technique is required to overcome this problem. In this paper different types of adaptive filters are considered to reduce the ECG signal noises like Base Line Interference, EM noise and muscle artifact, results of simulations in MATLAB are presented. While the LMS algorithm and its normalized version (NLMS), have been thoroughly used and studied. Connections between the Kalman filter and the RLS algorithm have been established however, the connection between the Kalman filter and the LMS algorithm has not received much attention. By linking these two algorithms, a new normalized Kalman based LMS (KLMS) algorithm can be derived that has some advantages to the classical one. Their stability is guaranteed since they are a special case of the Kalman filter. More, they suggests a new way to control the step size, that results in good convergence properties for a large range of input signal powers, that occur in many applications. In this paper, different algorithms based on the correlation form, information form (IKLMS) and simplified versions (SIKLMS) of these are presented. The simplified form maintains the good convergence properties of the KLMS with slightly lower computational complexity.

Today Global Positioning System (GPS) is the most important system of positioning in the world and is used in different industries. Carrier phase has a substantial accuracy, but its main problem is that it is an indirect measurement which only computes the displacement. In order to realize precision positioning, this paper proposes an improved satellite positioning algorithm based on carrier phase difference and Kalman filter. The second-difference positioning mathematical model aiming at satellite navigation signal carrier phase on a single fiducial station is established. For the integer ambiguity influence of carrier phase, it presents the mathematical model to eliminate. The Kalman filter is constructed, so that it is more improvement of system stability. Then, it realizes positioning by computer simulation, and the results show that the accuracy of the algorithm is higher.

We propose a new lossless compression algorithm for hyper spectral images based on the third -order interband predictor and the backward pixel search scheme (IP3-BPS). Specifically, we propose an adaptive search threshold algorithm and a bi-directional pixel search scheme. The resulting algorithm takes the bi-directional pixel search and the backward pixel search with adaptive search threshold as the last two predictors; its performance evaluation on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 1997 images shows that it outperforms the original IP3-BPS algorithm, at lower computational complexity level.

In this paper Time-varying Auto regressive model (TVAR) based approach for instantaneous frequency (IF) estimation of the nonstationary signal is presented. Time-varying parameters are expressed as a linear combination of constants multiplied by basis functions. Then, the time-varying frequencies are extracted from the time-varying parameters by calculating the angles of the estimation error filter polynomial roots. Since there were many existing basis functions that could be used as basis for the TVAR parameter expansion, one might be interested in knowing how to choose them and what difference they may cause. The performance of different basis functions in TVAR modeling approach is tested with synthetic signals. Our objective is to find an efficient basis for all testing signals in the sense that, for a small number of basis (or) expansion dimension, the basis yields the least error in frequency. In this paper, the optimal basis function of TVAR Model for the instantaneous frequency (IF) estimation of the test signals was obtained by comparing IF estimation precise and anti-noise performance of several types basis functions through simulation.

A FBG wavelength demodulation system based on the continuous dynamic scanning of tunable distributed feedback (DFB) laser is put forward and implemented. First, this paper introduces the basic principle of FBG wavelength demodulation system; second, the demodulation algorithm as the key of whole system is realized and compiled into DLL files by using the C programming language, then the Labview program calls for DLL files through a dynamic link library to establish a data processing program, including a interactive data processing interface. Not only it improves the accuracy of FBG wavelength demodulation system, also it helps the subsequent development on the system even though the hardware equipments keep invariant. The results show that the demodulation system has a fine resolution and stability, which can meet the practical requirements and be suited to produce in business and put into use in the engineering.

In this paper usefulness of time series based Auto Regressive (AR) modelling technique has been explored for identification of a person. For this purpose, time series is obtained from the contour coordinates of the ear. AR model is fitted to this time series. AR coefficients thus obtained serve as a feature vector. Recognition Rate (RR) has been found by a classifier that is based on Euclidian distance between feature vector of test samples with training samples within itself (intraclass) and with respect to others (interclass). Model has been found invariant to posture, rotation and illumination. RR up to 99% has been obtained. Results have been compared with existing techniques. The results demonstrate the effectiveness of technique for human identification.

Multi-focus image fusion means to fuse multiple source images with different focus settings into one image, so that the resulting image appears sharper. In order to extract the focused regions of the fused image efficiently, a novel pulse coupled neural network (PCNN) method for multi-focus image fusion is proposed. The registered source images are decomposed into principal components and sparse components by robust principal component analysis (RPCA) decomposition, and the important features of the sparse components are used to motivate the PCNN neurons, whose outputs detect the focused regions of the source images and integrate them to construct the final fused image. Experimental results show that the proposed scheme works better in extracting the focused regions and improving the fusion quality compared to the other existing fusion methods in terms of mutual information (MI) and QAB/F .

The technology of carrier synchronization is an essential unit in the demodulation technology. This article introduced the related theory of FPGA development, has produced the overall system design and each module function of launches and the carrier synchronization subsystem as well as giving the realization details on the QUARTUSII. And has carried on the function simulation and the succession simulation, has produced the simulation result. After the system simulation, the system design basically reached the target which prearranged.