Earticle

The capacity of modern cellular wireless networks is mainly limited by inter-cell interference (ICI). Coordinated multi-point (CoMP) transmission and reception were adopted as key techniques for Long Term Evolution-Advanced (LTE-A) as they are capable of mitigating ICI through cooperation between several cell sites. Therefore, CoMP techniques provide a significant improvement in cell-edge and average throughput. For downlink CoMP, most research work mainly focuses on joint processing/transmission (CoMP-JP) mode. However this mode needs the access points (AP) to share the UEs' data and channel state information (CSI) simultaneously, resulting in a high overhead on the network. Coordinated beamforming (CoMP-CBF), which can avoid the data information exchange is a better solution to reduce the overhead. In this paper, an improved cooperative beamforming schemes for downlink Coordinated Multi-point System is described. The proposed improved beamforming schemes rely on the principal eigen-vector feedback schemes (e.g. codebook based). Although the description of the beamforming schemes is not a part of specification, the detailed information on the used coordinated beamforming approaches is important for performance evaluation and calibration of the results during CoMP phase. System-level simulation results demonstrate that the proposed scheme can significantly reduce the ICI, and it performs well even with imperfect channel-state information (CSI) at the transmitter.

Tracking multiple objects in real-time videos represents a challenging area in the era of computer vision. This paper proposes a new method to track the multiple objects under different environment conditions such as rotation, illumination, blurred, occlusion, and many others. In addition, the kinect color depth image processing is used to estimate the distance of the objects. The tracking of multiple objects is formulated as classification task which competitively use the object features in the different video frames of the video sequences. To obtain the optimal configuration of feature classification, a neural network based framework is presented to make a global influence based on winner pixel estimation between the video frames. The objects are tracked efficiently in less time as compared with SIFT techniques and distance of objects is calculated with kinect based depth image processing. Experimental results are given for real-time scenes, and many experiments are conducted to examine the performance of the proposed approach. The proposed method resulted into efficient tracking of multiple objects in various conditions including rotation, scaling, occlusion, etc. The distance of multiple tracked objects is estimated using the kinect depth processing.

The main research object in this paper is cotton-PET blended fabric. The study hopes that the content of cotton can be accurately obtained from the data of only a few near-infrared spectral point. In the research we choose spectra data within 1400nm-2000nm to establish the calibration model. At the time of modeling, we use the method which combine stepwise regression and normalization. The result indicates that the method can not only reduce the influence of fabric structural difference on quantitative analysis, but also achieve a relative accurate analysis of cotton content with the presence of a small amount of wavelength data.

In order to increase the effect of matching for local stereo matching method and decrease the amount of computation, a new adaptive weight based local stereo matching method is proposed in this paper. In this method, two methods are mainly employed to construct weight model: (1) Neural Network is used to establish the spatial weight model, which makes good use of the pixels in support window; (2) An edge hold off Mean-Shift method is proposed to distribute the intensity weight accurately. For decreasing the matching cost error, the census transform is introduced to calculate the matching cost. The influence of the parameters on the performance of our method is also discussed at last. Simulation results indicate that the performance of our method is better than that of Yoon’s method under low support window.

Cognitive radio network is one of the prime solutions of band width crises problem. To resolve band width crises firstly we have to sense primary user (PU) licensed signal. To sense PU signal there are various sensing techniques which have been proposed by researchers, but each of them are having some limitations. In this paper we present a two stage detector for spectrum sensing (SS) in cognitive radio (CR). It comprises of a weighted-energy detector (W-ED) and a correlated-generalized likelihood ratio test (C-GLRT). The first stage detects the energy and then if required, the C-GLRT makes the final decision in second stage. Performance of the proposed two stage detector is compared with the existing energy detector (ED), generalized likelihood ratio test (GLRT), and adaptive spectrum Sensing (ASS) detectors. The numerical results show that proposed sensing technique has better detection performance and require less sensing time.

Current coins often get damaged in the current environment, such as smudginess, discoloration, corrosion, mutilation and attrition. When the damage comes to a certain degree, the continuous use of coins will be impacted. In this paper, relevant identification technologies to mutilation and corrosion which are two common defects in circulation are investigated. At first, an auto identification and quantization algorithm about mutilation area of coin surface, based on Freeman boundary tracing is designed. On the basis of binarization, the algorithm accomplishes boundary tracing and extraction and area calculation automatically after the seed point is determined which is an arbitrary point in extracting mutilation area. Secondly, a clustering method based on HIS color model is developed for auto identification of corrosion area and calculation of corrosion area and average corrosion degree. At last, the validity and practicability of the algorithms are proved by a large number of experimental results.

Color constancy allows visitors to recognize color in the diversity of conditions, and to see some regularity in the color. Generally, color constancy is a method of which calculates the particular impact involving discrete light sources using a digital image. The particular image documented with a camera is resolute by several components: the actual physical information with the picture, the particular illumination occurrence within the scene, as well as the features with the digital camera. Color based images have several applications. Retrieval based on object colors must take into an explanation the features that manipulate the pattern of color images: illumination conditions, sensor spectral sensibilities and surface reflectance. Tracking objects in environments under non-uniform lighting condition is mainly challenging as the practical appearance may alter in space and time [1]. We create an integrated effort of fuzzy membership with edge preserving filtering. As fuzzy membership exploits two classes because we make use of dual membership functions in the research work. In this paper, improved results have extracted for color constancy from optimal method based on fuzzy membership with edge preservation filtering. An evaluation of the proposed technique is also drawn with existing techniques, the comparisons have obviously shown that the fuzzy based color constancy outperforms over the available techniques.

This paper addresses the estimation of carrier frequency for cyclostationary AM, BPSK, and QPSK signals in the presence of interfering signals and α-stable impulsive noise. The performance of conventional DFT algorithms suffers from severe degradation in impulsive noise environments. By fusing cyclostationarity and fractional lower-order statistics, we introduce a signal selective carrier frequency estimation algorithm for AM, BPSK, and QPSK signals. The new method exploits p th-order cyclostationarity property of signals in impulsive noise. Compared with the existing DFT algorithm, the new method is highly tolerant to interference, Gaussian and non-Gaussian impulsive noises. The performance of the new algorithm is studied using simulations in a variety of interference and noise conditions. Simulation results indicate that the proposed algorithm outperforms the conventional DFT method in impulsive noise.

With the extensive application of automotive electrical and electronic equipment, issues of electromagnetic radiation and electromagnetic compatibility have become increasingly prominent, human body in complex automotive electromagnetic environment is bound to be some damage of electromagnetic radiation. This paper established a relatively sophisticated car simulation model,through electromagnetic characteristics of antenna model, we got automotive electric field distribution and simulation results of human body SAR value, so conclusion can be drawn that automotive electromagnetic environment is relatively safe to passengers.

In this paper, a technique for processing data produced from an EEG though the use of an FPGA architecture is proposed. The proposed FPGA architecture should efficiently determine the wavelet transform of the inputted data. The transformed signal would then be used for diagnosis and monitoring of patients. The application of the wavelet transformed signal for the diagnosis of an epileptic patient is also proposed.

A Physical Internet-enabled Manufacturing Executive System (PIMES) is proposed for intelligent workshop production. PIMES uses Radio Frequency Identification (RFID), 433MHZ wireless communication, and Physical Internet concept to create a ubiquitous production environment where real-time data could be collected and then converted into dynamic feedback factors sent back to the system. PIMES includes several services such as Production Decision Service, Communication Control Service, Visibility Service, and Interface Service which are designed and developed by making full use of the service-oriented architecture (SOA) and Cloud technology. With the assistance of these services, PIMES is able to achieve several outperformances upon traditional production systems like paperless operations, real-time decision-making, automatic statistics and reporting, as well as visualized manufacturing control and management. After the implementation of PIMES, workshop production of a case enterprise is streamlined and optimized by making full use of the collected data to support the workshop production and logistics.

Facial expression recognition performs a critical role in the human-machine interaction area. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, Offices, University, ATM, Bank and in any locations with a security system. Facial Expression Recognition system is used in security. During the past years recognition of face has received most important attention as one of the most significant image application understanding and analysis. Many algorithms have been implemented on different static and non static conditions (uncontrolled conditions).Static conditions include static and uniform background, identical poses, similar illumination, neutral frontal face .Non static conditions include position, partial occlusion orientation; varying lighting conditions and facial hair, which makes recognition process a complex problem. Facial expression recognition is a example where computer and humans underperforms. It has most importance for the video retrieval and video based management classification. It may be used in behavior psychology and science.

Feature-points matching is an important concept in binocular stereo vision. The procession of multi-scale feature-points matching in classical Harris-SIFT algorithm is time-consuming and has high complexity when describing the feature-points. This paper proposed a new improved Harris-SIFT algorithm based on rotation-invariant LBP (Local binary patterns) operator. Firstly, the Harris operator is used to extract feature points from DOG (Difference of Gaussian) scale space. Then, the dominant direction of feature point is calculated and 81-dimensional rotation-invariant LBP descriptors are extracted when the rotation matching window is coordinated to this direction. At last, Best-Bin-First (BBF) algorithm is used to search the matching points between the two sets of feature points. Experimental results show that the proposed algorithm is lower time-consuming than classical Harris-SIFT algorithm and remains the similar matching correct rate.

It is survey over various approaches applied on brain tumor extraction & detection from magnetic resonance image (MRI). Some of them are completely elaborated below with their strength & weaknesses. Magnetic resonance brain image are segmented & synthetically colored to represent original data through modified fuzzy c-means algorithm, supervised computational neural network approach (SCNNA), knowledge based technique (KBT), fractal-based brain tumor detection (FBTD), automatic segmentation of brain tumor (ASBT).

Up to now, the non-convex ℓp (0 < p < 1) norm regularization function has shown good performance for sparse signal processing. Indeed, it benefits from a significantly heavier-tailed hyper-Laplacian model, which is desirable in the context of image gradient distributions. Both ℓ1/2 and ℓ2/3 regularization methods have been given analytic solutions and fast closed-form thresholding formulae in recent image deconvolution methods. However, the methods with the other p-value norm penalty term still suffer difficulties in getting the analytic solution and fast closed-form thresholding algorithm. In this paper, to deal with these issues, we propose an approximation of ℓp regularization terms with 0.5 p < 1 using a linear combination of two ℓp terms (that is 1 and 1 / 2 ) with closed form thresholding formulae. We develop an alternating minimization method to solve the image deconvolution problems involving the constructed approximating function. We derive theoretical analytic solutions and fast closed-form thresholding formulae. We perform extensive numerical experiments to demonstrate the versatility and effectiveness of the proposed method, through a comparison with the recent non-convex ℓp regularization dealing with the special p-value term, with an application to image deconvolution.

Feature-based registration is an effective and most widely used image registration method currently. It includes three critical steps, feature extraction, feature matching and transformation parameters estimation. This paper mainly explores the first two steps. In one of Chahira Serief’s paper about image registration, feature points extraction based on nonsubsampled contourlet transform (NSCT) was proposed and feature points matching based on Zernike moments was adopted. The registration accuracy and robustness of his algorithm are acceptable, but it can still be improved. In this paper, an improved scheme of this registration algorithm is proposed. The rotation invariance of NSCT-based feature points extraction is improved, which is beneficial to extract homologous feature points. And the reliability and effectiveness of Zernike moments-based feature points matching are improved, which can improve the matching accuracy. The improved registration algorithm can realize registration of images related by larger scaling, rotation and translation transformation. The simulation results show that the registration robustness is further improved, and the registration accuracy is still high.

In this work, Rectangular Micro strip Patch Antenna (RMPA) along with Meta material which has design of “Slotted Square Connected with Rectangular shaped cover structure” is proposed at height of 3.2 mm from the ground plane. The RMPA with proposed Meta material structure is designed to resonate at 2.6 GHz frequency. This work is mainly focused on increasing the potential parameters of micro strip patch antenna. Proposed Meta material structure is significantly reduced the return loss and increased the bandwidth and directivity of the antenna with compare to RMPA alone. The bandwidth is increased up to 22MHz in comparison to RMPA alone. The return loss of proposed antenna is reduced by 41.88dB by incorporating the proposed Meta material structure. For simulation purpose CST-MWS Software has been used.

Poynting vector is used to decompose source and receiver wavefields into different angle, then the partial images of different incident and scatter angle are computed and local imaging matrix(LIM) can be constructed. LIM composes all the angle information which is related with the geological structure, and it can be used as the base of angle-domain imaging analysis. We used LIM to reverse time migration image in angle domain and geological dip estimate. The numerical example is tested to demonstrate the computation method.

Aiming at complicated characteristic analysis and implementation of the randomization space vector pulse width modulation (SVPWM) scheme, a new random zero-vector SVPWM scheme with a fixed randomization range is proposed. The fixed range is a linear function of the modulation index. Firstly, the principle of the new scheme is given. In addition, the implicit modulating voltages, the derivation procedure of the micro and macro harmonic distortion factor (HDF) are presented in detail, and the Monte Carlo method is proposed to efficiently analyze the HDF. Finally, the harmonic spectrum of the new scheme is analyzed compared with the commonly used symmetrical 7-segment SVPWM scheme through an example, and the result verifies its excellent performance on suppressing the cluster harmonic magnitude around the integer multiple switching frequency.

In this paper, the 16 channel and 32 channel DWDM systems at 20 Gbps have been investigated for hybrid amplifiers. The performance has been analyzed on the basis of transmission distance. The comparison of BER and Q-factor for two hybrid amplifiers EDFA+RAMAN and RAMAN+EDFA at different transmission distance is done. It is observed that for 16 × 20 Gbps and 32×20Gbps DWDM system using EDFA+RAMAN provides better results than the DWDM system using RAMAN+EDFA. The analysis is done using OptiSystem 7.0 simulator.

In order to accurately, fast and efficiently forecast the short-term load of power system, an improved particle swarm optimization algorithm is proposed to optimize the parameters of fuzzy radial basis function fuzzy neural network(FRBFNN) model in order to train the FRBFNN model for obtaining the optimized FRBFNN(IWPSRFN) method. In the proposed IWPSRFN method, the linear decreasing weight method is used to adjust the inertia weight of PSO algorithm. The global optimization ability of improved PSO algorithm is used to adjust the parameters of FRBFNN model by putting these parameters in the particle encoding, then the optimal values are found in the large number of viable solutions by continuous iteration of improved PSO algorithm. The found optimal values are regarded as the parameters of FRBFNN model to obtain the final IWPSRFN method for forecasting short-term load of power system. Finally, a certain region is selected to test the effectiveness of IWPSRFN method, the experiment results show that the improved PSO algorithm can effectively optimize the weights of FRBFNN and solve the slow convergence speed, and the IWPSRFN method can obtain the higher prediction accuracy and is an effective method for forecasting short-term load.

The mixture model is a commonly used approach for image segmentation. However, it doesn’t consider the spatial information. In order to overcome this disadvantage, several spatially constrained mixture models have been proposed. In this paper, these spatially constrained mixture models and their experimental results on synthetic and real world images are presented. These experimental results demonstrate that the spatially constrained mixture models can achieve competitive performance compared to the standard mixture model.

The knowledge involved in digital image processing is very wide, and there are many kinds of methods. Traditional image processing technology is mainly focused on the acquisition, transformation, enhancement, restoration, compression encoding, segmentation, edge extraction and so on. With the emergence of new tools and new methods, the image processing technology has been updated and developed. In this paper, an effective method for edge detection and image de-noising is proposed. In this article, the impulse noise detector is composed of a BP neural network (BPNN) and a decision switch. BPNN requires four input values, which are the current pixel value, grey median value, energy value, and contrast. To take these four values as the input values, the impulse noise detector can show good performance. The output of the BPNN is transferred to the decision switch, and the output value is converted to 0 or 1, which is used to distinguish whether the pixels are polluted. At this point, we introduce an additional impulse term and establish the improved BPNN model. The additional impulse term can effectively speed up the convergence of the network, avoid the emergence of the local minimum problem, and ensure the stability of the training process. In this way, the IBPNN filter of this paper only uses the information of the non polluted pixels to filter the noise pixels, which avoids the secondary pollution, and obtains a better performance. This algorithm has high PSNR value and strong detail information and edge preserving ability. Finally, the improved BPNN algorithm is applied to the image edge detection, and we use the improved neural network model to detect the edge of the image. Because the method can be used to include the prior knowledge, the IBPNN method is better than the traditional method in image edge detection.

In this paper, adjust the system parameters back-propagation algorithm based on fuzzy similarity interval type proposed by the fuzzy rule base to streamline redundant fuzzy sets, we can also merge with the means to reduce the number of redundant fuzzy rules, then singular value decomposition method is preferred fuzzy rules. The algorithm can effectively eliminate the adverse effects caused by redundant fuzzy rule, which improve the interpretability of fuzzy rules to reduce the computational complexity of the fuzzy reasoning process, and to improve the approximation accuracy of the system. Based on the long-term and short-term load power load characteristics analysis, to identify the influence of the load itself changes and related factors, gray system theory, neural network model and chaotic time series methods, models and methods for forecasting power load range were research. Examples verified, interval prediction has better precision, demonstrate the effectiveness of the interval prediction algorithm, the research results can be used in power market analysis and forecasting systems, power system operation and provide scientific basis for management decisions.

The improvement in propagation characteristics of WBAN is required to develop the high quality WBAN system. In this paper, the error rate performance of channel model CM4 of WBAN is evaluated for different modulation schemes for distinct body directions for UWB. The effect of rake receiver structure is also assessed. The bit error rate has been obtained by using BPSK (Binary Phase Shift Keying), M-ary PAM (Pulse Amplitude Modulation) and M-ary BOK (Bi- orthogonal Keying). The evaluation is done by calculating the value of Signal to noise ratio for targeted Bit Error Rate for different body directions using different modulation scheme. The obtained results give an assessment to better understand the effect of modulation schemes selection on error rate performance of the WBAN channel.

As the rapid increasing of Internet users from all over the world, it is necessary to enhance the reliability and compatibility of network when large number of data transferred frequently. One of the most important services is routing method since enormous data are transferred and exchanged within the network. This paper introduces an improved OSPF routing algorithm which uses the quotient and granularity space theory. Based on the simulation and experiments, several key findings are observed. Firstly, using the improved algorithm, the convergence time is much less than the no hierarchical network and the performance is better in terms of OSPF response. Secondly, it is found that the bandwidth utilization reaches 30Kbit/s, comparing with the network without hierarchy, the improvement is 40% that the bandwidth utilization increased from 18Kbit/s to 30 Kbit/s. The hierarchical network outperforms the one without layers.

Traffic congestion is on the rise due to the continuing growth of urban areas, the increasing number of vehicles, and the increasing cost of building new roads. Therefore, it is necessary to explore creative and viable solutions to traffic problems at traffic intersections. On the basis of on-site traffic data collection, the traffic model was established，by taking both vehicle and pedestrian into consideration of intersection near Songjiang University Town Station transportation hub, using VISSIM. Simulation results showed that traffic congestion was higher in Meijiabang Rd (East to West) road section, which also had higher vehicle flow disorder and lane change pattern. Correspondently, intersection optimization were done in four aspects, including traffic signal green and red light timing adjusting, adding conflict area between straight and left-turn lanes, and road section movement. After optimization, traffic congestion was reduced in this intersection, which indicated the effectiveness of optimization on traffic management in this intersection.

Synchronization of chaotic systems can be used in the secure communication so it greatly attracted interests of many researchers. The bounded characteristics of chaotic system was used and an uniformed adaptive law was designed to solve system uncertainties which make the whole design very simple. And two kinds of functions was used to take place of traditional sign function, then the chattering problem was greatly improved. A Lyapunov function was chosen to guarantee the stability of the whole design. At last, detailed simulation was done to show the rightness and effectiveness of the proposed method.

In distributed sensor networks, track association and track fusion become difficult due to the existence of various uncertainties in multiple target tracking (MTT). In an actual tracking system, state estimates of a local track are usually transmitted from local nodes to the global node by message, and each message generally contains single state estimate. Based on this fact, one can define two state estimates of a local track in continuous times as a tracklet. Then, local track-global track association can be divided into tracklet-global track (T2GT) association in real time. Hence, a T2GT association method based on Hough transform (HT-T2GT) is proposed. By Hough transform, all the tracklets in the same interval can be mapped into a set of points in Hough space, and the track association problem can be transformed as one of point clustering in Hough space. The maximum entropy fuzzy c-mean (ME-FCM) clustering method is used to realize T2GT association. In addition, a T2GT fusion method based on the support degree function (SDF-T2GT) is developed for track fusion. The experimental results illustrate that the proposed methods can respectively realize T2GT association and track fusion in the situations with multiple local nodes, reduce the average time of updating global tracks and satisfy the requirement of real-time processing in the global node. It achieves higher association processing rate than other two track association methods.