This PDF file contains the front matter associated with SPIE Proceedings Volume 8285, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

The modern era of interior-point methods (IPM) dates to 1984, when Karmarkar proposed his algorithm for linear programming. In the years since then, interior-point algorithm and software for linear problems have become quite sophisticated and gained extraordinary interest as an alternative to the sparse simplex based methods. In this paper, we introduce the infeasible primal-dual algorithm which is widely considered the most efficient general purpose IPM. Then many issues that are crucial for the efficient implementation of an interior-point algorithm are addressed in detail. To facilitate the understanding of different implementation strategies, some illustrative numerical results on a subset of problems from the Netlib collection are presented.

In this article we described methods of link spam detection with using of machine learning. We analyzed main factors of link spam, which helps to find them. There is algorithm of link spam detection, based on support vector machines. The methods of link spam detection shows good results

Increased organizational dependence on data warehouse (DW) systems drives management attention towards improving DW systems success. However, the successful implementation rate of DW systems is low and many firms did not achieve intended goals. A recent studies show that improves and evaluates DW success is one of the top concerns facing IT/DW executives. Existing information system (IS) research has studied DW success more from information quality and system quality. Researchers argue in this study that we should also take the relationship quality, which has significant research and practical implications in that it connects to IS success directly. As our first attempt, this study, referring to both IS and marketing literature, examines how communication, coordination, cooperation, commitment, and trust can be achieved to some degrees by high quality relationships between DW parties.

Power of a class reflects the relative importance of the class in an object-oriented software system, and has many implications for resource allocating. And how to measure it is still a problem we face. They paper presents to measure it based the connection structure of a network constructed from the software. To support this, we first use a network model to describe the connection structure. Then some centrality metrics used in social network analysis are introduced to quantify the power of classes. The commons and differences among these centrality metrics are revealed. We think these metrics can be used together to have a more systematic view of the importance of each class.

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The aim of the research presented here is in twofold. First stage of research involves machine learning techniques, which segments and extracts features from the mass of digital mammograms. Second level is on problem solving approach which includes classification of mass by performance based case base classifier. In this paper we build a case-based Classifier in order to diagnose mammographic images. We explain different methods and behaviors that have been added to the classifier to improve the performance of the classifier. Currently the initial Performance base Classifier with Bagging is proposed in the paper and it's been implemented and it shows an improvement in specificity and sensitivity.

This paper contains the case description of reviewing of different research papers for scientific conference and Models related to the chosen case description. The evaluation of the business models is based on the Sequal quality framework. This paper is based on the case description as provided in later sections of this paper; however the case is extended and focused as necessary to explain the business models.

This paper describes about real-time moving pedestrian object detection and tracking. In this work, a new pedestrian object tracking approach is proposed, which uses a new texture model for better representing the pedestrian object. This approach based primarily on a step of object detection for automatic pedestrian object area extraction, features extraction step for pedestrian object representation and finally tracking step based on region matching method. We propose a new texture model for discriminant pedestrian object representation. This model based on a set of Haralick indexes computed from co-occurrence matrix. The presented approach can handle orientation, object scale, illumination changes, and clutter and complex occlusions.

One of the main drawbacks of OFDM systems is the high Peak-to-Average Power ratio, which could limit transmission efficiency and efficient use of HPA. In this paper we present a modified tone reservation scheme for PAPR reduction using FFT iterations to generate the tones. In this Scheme, the reserve tones are designed to both cancel peaks and slightly increase the average power to induce a better PAPR reduction..The tones are generated by means of 2 FFT operations and the process is sometimes iterated to achieve better PAPR reductions. This scheme achieves a significant PAPR reduction of at least 4.6dB when about 4% of the carriers are used as reserve tones and with even lesser iterations when simulated in an OFDM system.

Remote sensing image have been known to be an important part for environmental analysis. Drought early warning system is one of the few example of remote sensing image applications. One of the oldest tool in remote sensing studies, NDVI is often used for drought detection. Although very essential, remote sensing image requires large storage requirement. A large image data may cause network congestion which certainly affects the aptitude of the drought detection system. An image compression may be used as an approach to this issue. However, this would lead to another issue, image quality. This article emphasize on the effect of image compression through transformation towards remote sensing image. Analysis is conducted through NDVI pixel threshold as well as other complimentary error metric method. Hybrid methods of transformation are presented here for the image transformation process. The experiments performed on test images shows that hybrid transformation is capable of reducing image data and preserving sufficient quality.

Graph Drawing is a branch of computer graphics that addresses the problem of constructing geometric representations of conceptual structures that are modeled by graphs. Crossing reduction is one of the crucial problems in drawing networks that are presented in a hierarchical manner which is solved by repeated layer-by-layer techniques or by global ones. In this paper, we introduce a new efficient algorithm for drawing hierarchical graphs based on the well-known barycenter algorithm. Our algorithm improves the existing layer-by-layer techniques by 15-25% and recent global algorithms by 5% further reduction in the number of crossings.

Video based pupil detection techniques are useful for Perceptual user interfaces and human monitoring. Pupil detection plays a vital role in identification systems. This paper presents an algorithm for pupil detection in video images under active infrared (IR) illumination. At first, using a low pass filter the eyelashes are removed. Following, the thresholding process takes place. Using the Otsu's thresholding method, the proper thresholding value is estimated. Then, using the genetic algorithm, filter band width and threshold value are estimated accurately. After the pupil segmentation process, pupil boundary is modelled. The modeling process is based on circular Hough transform. Experimental results show promising performances on eye video images captured in ideal and non-ideal conditions.

Enterprise Resource Planning systems are the combination of different business IS (Information System) applications that are designed according to the organization requirements. Generally ERP systems are suffering from complex user interface issues. Recent research shows that there is a need for improvement concerning, the user interface from their perspectives. In order to design the software applications that are easy to use, memorize and apply to new problems, we must know the users philosophy and something about learning, reminiscence and problems solving. The Usability engineering is the only way to study the deeds of users while interacting with ERP (Enterprise Resource & Planning). This paper will focus on the users' experiences view of financial module in ERP system.

Image transmission takes place as an important research branch in multimedia broadcasting communication systems in the last decade. Our paper presents image transmission over a FFT-OFDM (Fast Fourier Transform Orthogonal Frequency Division Multiplexing). The need for encryption techniques increase with the appearance of the expression which said that our world became small village, and the use of image application such as conference and World Wide Web which increase rapidly in recent years. Encryption is an effective method for protecting the transmitted data by converting it into a form being invisible over transmission path and visible in receiver side. This paper presents a new hybrid encryption technique based on combination of Backer maps and logistic map. This proposed technique aims to increase PSNR and reduce the noise in the received image. The encryption is done by shuffling the positions of a pixel image using two dimensional Baker maps then encrypt using XOR operation with logistic map to generate cipher image over orthogonal frequency multiplexing (OFDM). The encryption approach adopted in this paper is based on chaotic Baker maps because the encoding and decoding steps in this approach are simple and fast enough for HDTV applications. The experimental results reveal the superiority of the proposed chaotic based image encryption technique using two logistic maps and two dimensional Backer map over normal Backer map.

The paper studied the sub-pixel positioning theory and algorithms of image processing and measurement. we derived the formular and had the boundary points located to Sub-pixel level.

New technologies, especially, the Internet have made a huge impact on knowledge management and information dissemination in education. The web portal as a knowledge management system is very popular topics in many organizations including universities. Generally, a web portal defines as a gateway to online network accessible resources through the intranet, extranet or Internet. This study develops a knowledge portal for the students in the Faculty of Computer Science and Information Technology (FCSIT), University of Malaya (UM). The goals of this portal are to provide information for the students to help them to choose the right courses and major that are relevant to their intended future jobs or career in IT. A quantitative approach used as the selected method for this research. Quantitative method provides an easy and useful way to collect data from a large sample population.

A unified framework for integrating PC cluster based parallel rendering with distributed virtual environments (DVEs) is presented in this paper. While various scene graphs have been proposed in DVEs, it is difficult to enable collaboration of different scene graphs. This paper proposes a technique for non-distributed scene graphs with the capability of object and event distribution. With the increase of graphics data, DVEs require more powerful rendering ability. But general scene graphs are inefficient in parallel rendering. The paper also proposes a technique to connect a DVE and a PC cluster based parallel rendering environment. A distributed multi-player video game is developed to show the interaction of different scene graphs and the parallel rendering performance on a large tiled display wall.

In most practical blind source separation (BSS) applications, the observations contain additive source noise that limits the performances of most existing BSS algorithms. In this paper, we propose a new BSS approach exploiting the difference in the time-frequency (t-f) signatures of these sources to be separated. The approach uses smooth pseudo Wigner-Ville distribution (SPWVD) to obtain t-f distribution, then localizes the signal energy by Hough transform and selects a set of spatial t-f points based on the dominant eigenvalue of SPWVD of observations. Finally, numerical performance simulations are provided highlighting its effectiveness.

Segmentation of an image plays an important role in image processing applications. In this paper segmentation of pomegranate magnetic resonance (MR) images has been explored. Pomegranate has healthy nutritional and medicinal properties for which the maturity indices and quality of internal tissues play an important role in the sorting process in which the admissible determination of features mentioned above cannot be easily achieved by human operator. Seeds and soft tissues are the main internal components of pomegranate. For research purposes, such as non-destructive investigation, in order to determine the ripening index and the percentage of seeds in growth period, segmentation of the internal structures should be performed as exactly as possible. In this paper, we present an automatic algorithm to segment the internal structure of pomegranate. Since its intensity of stem and calyx is close to the internal tissues, the stem and calyx pixels are usually labeled to the internal tissues by segmentation algorithm. To solve this problem, first, the fruit shape is extracted from its background using active contour model (ACM). Then stem and calyx are removed using morphological filters. Finally the image is segmented by fuzzy c-means (FCM). The experimental results represent an accuracy of 95.91% in the presence of stem and calyx, while the accuracy of segmentation increases to 97.53% when stem and calyx are first removed by morphological filters.

A new class of sequences with three-valued zero correlation zone (TVZCZ) is presented on the basis of mutually orthogonal sets of ZCZ sequences. By means of the interleaving operation, the proposed TVZCZ sequence set can be divided into multiple sequence groups with each sequence group containing several sequence subgroups, which assures that the presented TVZCZ sequences can obtain three different ZCZ values, namely inter-group zero cross-correlation zone (ZCCZ), inter-subgroup ZCCZ, and intra-subgroup ZCCZ having the same value with zero auto-correlation zone (ZACZ). Compared with bi-valued zero correlation zone (BVZCZ) sequences, the constructed TVZCZ sequences have greater flexibility with regard to the tradeoff between correlation properties and set size, and hence are more suitable for multi-carrier code division multiple access (MC-CDMA) systems.

The increasing popularity of online video share and video-on-demand systems makes video similarity search become a hot research field in content-based video retrieval. There is still no satisfying fast scalable video similarity search method in large database. To solve two challenging problems: similarity measurement and search method, a novel efficient video similarity search approach is proposed in this paper. The video features are represented by image characteristic code based on the statistics of spatial-temporal distribution of video frame sequences. The video similarity is measured based on the calculation of the number of video components. For the scalable computing requirement, an efficient search method via clustering index table was presented by index clustering. The experimental results from the query tests in large database show this method is highly efficient and effective for similar video search.

In this paper, we present a novel image reconstruction method based on penalized least squares (PLS) objective function for positron emission tomography (PET). Unlike usual PLS algorithm, the proposed method, which is called NL-PLS, combines a novel nonlocal quadratic prior with the classical least squares algorithm. The novel prior can not only solve the unfavorable oversmoothing effect produced by the simple quadratic membrane (QM) smoothing prior, but also partly eliminate blocky piecewise regions or so-called staircase artifacts produced by edge-preserving nonquadratic priors. What's more, we can easily confirm the convergence of the NL-PLS as the objective function' quadratic characteristic. The performance of the proposed NL-PLS method is evaluated in experiments using simulated data. The results show that the method is advantageous, compared with the Filter Back Projection (FBP) reconstruction and Maximum Likelihood (MLEM) reconstruction, and Bayesian constructions using the normal local priors.

This paper proposes a high-efficiency low bit-rate image compression algorithm that is based on weighted classified vector quantization (WCVQ) of discrete wavelet transform (DWT) coefficients. The algorithm first does multi-level wavelet decomposition to the original image, and then constructs band-cross vectors along resolution-level-cross high frequency sub-bands respectively in the horizontal, vertical and diagonal three directions that makes the most of the correlation of DWT coefficients among those sub-bands, and then classifies these band-cross vectors into important clustering and unimportant clustering by zero-tree vector and vector energy, and last merges several adjacent unimportant vectors into an unimportant vector block and codes them uniformly, while, applies weighted vector quantization consistent with HVS to the important vectors by progressive constructive clustering (PCC) algorithm that improves the coding efficiency and the reconstructed image quality.

Demodulator is a vital part for deep space telemetry system. On the condition of low signal-to-noise ratio, lower bit rate and more sorts of modulation types are used in deep space communication. Comparing with the conventional telemetry demodulator, some improved technologies are added to satisfy the requirements of deep space telemetry. An overview of the demodulation technology is provided here and key technologies applied in deep space demodulation are analyzed. The testing results of demodulator show that the demodulation loss can be lower than 0.3dB, bit synchronization and the Es/N0 estimation can be achieved correctly at less than -5dB.

Discrete Wavelet Transform (DWT) is widely used in signal processing applications. In this paper, we describe hardware implementation of a lifting-based DWT, which is used in image compression. The CDF(2,2) lifting-based wavelet transform is modeled and simulated using MATLAB. Based on DSP methodologies, the signal flow graph and dependence graph are derived. The dependence graph is optimized and used to implement the hardware description of the circuit in Verilog. We have synthesized and implemented the circuit using both Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) design approaches. To confirm the circuit operation, post-synthesis and post-layout simulations were done for FPGA and ASIC designs, respectively.

This topic focuses on the underdetermined sparse blinding source separation from the mixtures. A novel algorithm is put forwards based on the linear direction detection and short-time Fourier transformation (STFT). This algorithm includes two stages: (1) the mixing matrix is estimated in the frequency domain by the STFT and a stochastic gradient algorithm; and (2) the linear programming method is applied to separate the source from the mixtures in the frequency and then the frequency domain source is transformed into the time domain source by the inverse STFT. Finally, some experimental results show that the algorithm is effective.

Cosmic Movement Detection is concerned with the difficult task of taking the images of sky through high powered telescope or satellite transmitted images and performing image processing in order to discover new galaxies, stars and other cosmic objects or to describe already known galaxies, stars and other cosmic objects. Description meant to describe the type of cosmic object under consideration, whether it's already been recognized previously or not, whether it's moving close to Earth or moving away from Earth. It has several applications astrophysics, astronomy and astroscience. Automating this process to a computer requires the use of various image processing techniques. The method which the present paper describes is based on Doppler Effect of light i.e. red and blue shifting property. Several factors like poor illumination, noise disturbance, viewpoint-dependence, Climate factors, Transmission and Imaging conditions can affect the algorithm working. This paper reports an algorithm for Cosmic Movement Detection using image processing and Doppler Effect of light. The present paper suggests Cosmic Movement can be detected checking the color of the galaxy and it can help in determining whether it's moving towards Earth or its moving away.

This paper primarily exploits some of the more obscure, but inherent properties of camera and image to propose a simpler and more efficient way of perceiving depth. The proposed method involves the use of a single stationary camera at an unknown perspective and an unknown height to determine depth of an object on unknown terrain. In achieving so a direct correlation between a pixel in an image and the corresponding location in real space has to be formulated. First, a calibration step is undertaken whereby the equation of the plane visible in the field of view is calculated along with the relative distance between camera and plane by using a set of derived spatial geometrical relations coupled with a few intrinsic properties of the system. The depth of an unknown object is then perceived by first extracting the object under observation using a series of image processing steps followed by exploiting the aforementioned mapping of pixel and real space coordinate. The performance of the algorithm is greatly enhanced by the introduction of reinforced learning making the system independent of hardware and environment. Furthermore the depth calculation function is modified with a supervised learning algorithm giving consistent improvement in results. Thus, the system uses the experience in past and optimizes the current run successively. Using the above procedure a series of experiments and trials are carried out to prove the concept and its efficacy.

Dual tree complex wavelet transform(DTCWT) is a form of discrete wavelet transform, which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. The purposes of de-noising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal or image. This paper proposes a method for removing white Gaussian noise from ECG signals and biomedical images. The discrete wavelet transform (DWT) is very valuable in a large scope of de-noising problems. However, it has limitations such as oscillations of the coefficients at a singularity, lack of directional selectivity in higher dimensions, aliasing and consequent shift variance. The complex wavelet transform CWT strategy that we focus on in this paper is Kingsbury's and Selesnick's dual tree CWT (DTCWT) which outperforms the critically decimated DWT in a range of applications, such as de-noising. Each complex wavelet is oriented along one of six possible directions, and the magnitude of each complex wavelet has a smooth bell-shape. In the final part of this paper, we present biomedical image and signal de-noising by the means of thresholding magnitude of the wavelet coefficients.

This paper present an affine projection algorithm (APA) using grouping selection of input vectors. To improve the performance of conventional APA, the proposed algorithm adjusts the number of the input vectors using two procedures: grouping procedure and selection procedure. In grouping procedure, the some input vectors that have overlapping information for update is grouped using normalized inner product. Then, few input vectors that have enough information for for coefficient update is selected using steady-state mean square error (MSE) in selection procedure. Finally, the filter coefficients update using selected input vectors. The experimental results show that the proposed algorithm has small steady-state estimation errors comparing with the existing algorithms.

In this paper, a hybrid table look-up method for H.264 Coeff_Token Decoding is presented. In the proposed method the probabilities of the codewords with various lengths are analyzed, and based on the statistics a hybrid look-up table is constructed. In the coeff_token decoding process, firstly, a few bits are read from the bit-stream, if a matched codeword is found in the first look-up table, the further look-up steps will be skipped. Otherwise, more bits need to be read and looked up in the second table, which is built upon the number of leading 0's before the first number one. Experimental results on the RTSM Emulation Baseboard ARM926 of RealView show that the proposed method speeds up CAVLD of H.264 by about 8% with more efficient memory utilization, when compared to the prefix-based decoding method. And compared with the pattern-search method based on hashing algorithms adopted in the newest version of FFMPEG, the proposed method reduces memory space by about 77%.

L. Sendur and I. W. Selesnick suggest four jointly non-Gaussian bivariate models to characterize the dependency between a coefficient and its parent, and respectively derive the corresponding MAP estimators based on noisy wavelet coefficients in detail in [6]. Among the four models, the second is a mixture model and it is quite complicated to evaluate parameters, so L. Sendur and I.W. Selesnick didn't give a concrete method. In this letter, a concrete mixture bivariate model will be described by drawing inspiration from Model 2. Expectation maximization (EM) algorithm is employed to find the parameters of new model. The simulation results show that the values of PSNR have a bit improvement compared with Model 1. The results can be viewed as a supplementary of model 2 in [6].

Remote Sensing (RS) images or satellite images include information about earth. Compression of RS images is important in the field of satellite transmission systems and mass storage purposes. Because of importance of information and existent of large amount of details, lossless compression preferred. Real time compression technique is applied on satellite and aerial transmission systems [1]. A simple algorithm accelerates the whole process in real time purposes. Lossless JPEG, JPEG-LS and JPEG2000 are some famous lossless compression methods. Transformation is the first step of these methods. In this paper, a simple and efficient method of lossless image transformation has been introduced by improving prediction ability which leads to more energy compaction. After mathematical proof for efficiency of new method, it compared with previous transformations of JPEG and JPEG2000 respectively by comparing their entropy value. Finally, we conclude that, our new method is cost effective for real time applications.

In this paper, a new Doppler shift estimator is proposed for Time Division-Synchronous Code Division Multiple Access (TD-SCDMA) over Rician channels. Different from other existing methods, the proposed estimator is developed based on the training sequence, i.e., "midamble" and the statistical properties of demodulation signal. By the help of a training sequence, the received line-of-sight (LOS) signal is first demodulated. Afterwards, its second-order and fourth-order statistics are calculated. Then, by exploiting these statistical results, the Doppler shift estimator is obtained. The proposed estimator employs a smoothing function with corrective factor. By optimizing the corrective factor, an accuracy solution is acquired within the Doppler shift range of possibilities. Simulation results indicate that the proposed estimator is greatly reliable and effective to estimate Doppler shift up to 400 Hz.

Voice conversion (VC) is a process which modifies the speech signal produced by one source speaker so that it sounds like another target speaker. In this paper the transformation is determined by using equal Arabic utterances from source and target speakers; these utterances are time-aligned using dynamic time warping algorithm. A conversion function based on Gaussian mixture model (GMM) is used for transforming the spectral envelope described by line spectral frequencies (LSF) and the residuals are converted using three residual prediction techniques. We also compare between these techniques in the conversion of some Arabic utterances. The quality of the transformed utterances is measured using subjective and objective evaluations.

In this paper, a tight wavelet frame algorithm based on Wiener filtering is proposed, which can avoid the amplification of noise during the processing. First, wavelet decomposition on the first level is done, the wavelet coefficients will be denoised by soft thresholding to eliminate some noise and pretreated image with noise is obtained; Next, the pretreated noise image is further denoised by the frame algorithm which is inserted Wiener filter on wavelet domain; Finally, the algorithm is used to restore the degraded DICOM with noise and numerical simulation shows that our algorithm is better than soft thresholding iterative algorithm.

Orthogonal frequency division multiplexing (OFDM) is sensitive to carrier frequency offset (CFO) that causes inter-carrier interference (ICI). In this paper, we present two schemes for the CFO estimation, which are based on rejection sampling (RS) and a form of particle filtering (PF) called kernel smoothing technique, respectively. The first scheme is offline estimation, where the observations contained in the OFDM training symbol are treated in the batch mode. The second scheme is online estimation, where the observations in the OFDM training symbol are treated in the sequential manner. Simulations are provided to illustrate the performances of the schemes. Performance comparisons of the two schemes and with other Bayesian methods are provided. Simulation results show that the two schemes are effective when estimating the CFO and can effectively combat the effect of ICI in OFDM systems.

A sign language (also signed language) is a language which, instead of acoustically conveyed sound patterns, uses visually transmitted sign patterns to convey meaning- "simultaneously combining hand shapes, orientation and movement of the hands". Sign languages commonly develop in deaf communities, which can include interpreters, friends and families of deaf people as well as people who are deaf or hard of hearing themselves. In this paper, we proposed a novel system for recognition of static hand gestures from a video, based on Kohonen neural network. We proposed algorithm to separate out key frames, which include correct gestures from a video sequence. We segment, hand images from complex and non uniform background. Features are extracted by applying Kohonen on key frames and recognition is done.

Multi-rate transmission may lead to performance anomaly in an IEEE 802.11 network. It will decrease the throughputs of all the higher rate stations. This paper proposes an adaptive rate selection scheme for video service when performance anomaly occurs. Considering that video has the characteristic of tolerance to packet loss, we actively drop several packets so as to select the rates as high as possible for transmitting packets. Experiment shows our algorithm can decrease the delay and jitter of video, and improve the system throughput as well.

It takes an important role to get the disease information from melancholia electroencephalograph (EEG). Firstly, A common spatial subspace decomposition (CSSD) method was used to extract features from 16-channel EEG of melancholia and normal healthy persons. Then based on support vector machines (SVM), a classifier was designed to train and test its classification capability between Melancholia and healthy persons. The results indicated that the proposed method can reach a higher accuracy as 95% in EEG classification, while the accuracy of the method based on wavelet is only 88%.That is, the proposed method is feasible for the melancholia diagnosis and research.

Signal is a non-linear and non- stationary signal, the empirical mode decomposition (EMD) can decompose the signal into several intrinsic mode functions(IMF), the IMF can reflect the characteristic in frequency changing of logging data along depth, IMF curves of different steps might reveal cycle characteristics of different scale, which are in correspondence with geological cycles o different scales, so that different scale sequences might be detected clearly from IMF curves. Empirical mode decomposition method is used to demarcat stratigraphic sequence and stratigraphic correlation in this paper ,the result show that EMD method can better show the internal vibration of logging signal and reflect the characteristics of the stratigraphic sequence ,and EMD method can provide technical support for stratigraphic correlation.

It is universally admitted that propagable attacks pose a more serious threat to network than a single attack. And the propagation of attacks mainly depends on vulnerabilities. Popular vulnerabilities assessment tools are able to detect hosts' vulnerabilities in detail and produce the related reports. However they always ignore the threats led by predecessor attacks from other hosts in the network, because they do not take the network conditions into consideration. In this paper, digraph is used to generate the attack paths by virtue of network and vulnerabilities information. And according to the attacks relationship of hosts, the risk of predecessor attack is considered for the general risk calculation of target hosts. A method is presented to assess the risk of hosts in network environment qualitatively and quantitatively.

Non-invasive temperature monitoring is one of the key techniques in the ontological thermal therapy since there is a need for quantitatively determining the progress of therapy and its endpoint. However, the conventional temperature estimation method can not meet the required precision in the clinical application. In order to realize a reliable and effective temperature estimation method, a non-invasive monitoring method based on ultrasonic backscattered energy (UBE) was proposed in this paper. Firstly, the changes of UBE was studied, which was in entire liver tissue, ablation centre and tissue surface during therapy process performed by microwave thermal ablation experiment in vitro porcine liver. Then a new tissue characterization method was proposed, which avoided the problem encountered in quantitatively tracking the rising temperature and reduced the impact of individual differences. The experimental results indicated that the UBE is a suitable parameter for non-invasive temperature estimation in the tumour ablation therapy. The parameter of UBE can presented increase trend in tissue surface and decrease trend in ablation centre regularly. Furthermore, those two trend curves crossed at the temperature ranging from 60°C to 70°C, which is just the temperature of coagulation threshold. That is, it provides a good guidance to control the thermal therapy by tracking the cross point at over 60°C.

It is difficult to deal with the auto-focus of aerial push-broom remote sensing camera (APRC) based on image processing. First of all, this paper recommended the Splicing structure of CCD in the APRC Based on this, auto-focus method which is made use of the overlapping area of CCD mosaic structure was proposed, combined with the characteristic of the imaging mode. After analyzing of experiments about all kinds of focus-examine functions based on image processing, the gradient square function has been chosen. The experimental results show that the proposed auto-focus method for the APRC is proved to be feasible and real-time.

This paper aims at locating base station (BS) rationally to satisfy the most customs by using the least BSs. An ameliorated GA is proposed to search for the optimum solution. In the algorithm, we mesh the area to be planned according to least overlap length derived from coverage radius, bring into isometric grid encoding method to represent BS distribution as well as its number and develop select, crossover and mutation operators to serve our unique necessity. We also construct our comprehensive object function after synthesizing coverage ratio, overlap ratio, population and geographical conditions. Finally, after importing an electronic map of the area to be planned, a recommended strategy draft would be exported correspondingly. We eventually import HongKong, China to simulate and yield a satisfactory solution.

In this paper, an image resolution enhancement algorithm using dual-tree complex wavelet transform and neural network is proposed. The low resolution image is used as the input signal of the neural network and the network estimates all the 16 wavelet subbands of the corresponding high resolution image. The high resolution image is then obtained using the inverse dual-tree complex wavelet transform. Directional selectivity of the DT-CWT increases the accuracy of subband estimation done by the neural network. Subjective comparisons and objective quality assessments indicate additional improvement over the conventional bicubic and bilinear interpolation techniques and even new complex counterpart methods.

Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of image processing in MRI segmentation of pomegranate is explored. Pomegranate is a fruit with pharmacological properties such as being anti-viral and anti-cancer. Having a high quality product in hand would be critical factor in its marketing. The internal quality of the product is comprehensively important in the sorting process. The determination of qualitative features cannot be manually made. Therefore, the segmentation of the internal structures of the fruit needs to be performed as accurately as possible in presence of noise. Fuzzy c-means (FCM) algorithm is noise-sensitive and pixels with noise are classified inversely. As a solution, in this paper, the spatial FCM algorithm in pomegranate MR images' segmentation is proposed. The algorithm is performed with setting the spatial neighborhood information in FCM and modification of fuzzy membership function for each class. The segmentation algorithm results on the original and the corrupted Pomegranate MR images by Gaussian, Salt Pepper and Speckle noises show that the SFCM algorithm operates much more significantly than FCM algorithm. Also, after diverse steps of qualitative and quantitative analysis, we have concluded that the SFCM algorithm with 5×5 window size is better than the other windows.

The system was based on the need of micro-portable and integrated spectrometer, on the basis of analysis in the domestic and international micro-spectrometer development status and trend, embedded system applied in the micro-spectrometer forth need of special environment was proposed, such as wild, battlefield. After overall structure of embedded micro-spectrometer was made, signal acquisition circuit and processing circuit based on ARM were designed for spectrometer, hardware of embedded micro-spectrometer was finished, operating systems and file system were established for providing advantages for exploitation and running of soft acquisition.

Building scheme and developing procedures of a measuring system for radar targets were introduced. Theory of time domain measurement was studied. Sampling Oscilloscope was used for measurement in compact range for the first time. Structure of the system and operating flow for the measurement were presented. Programs for controlling of the oscilloscope and turntable as well as data processing were developed, based on which entire measuring software of UWB compact range was achieved, including measurement of radar cross-section, polarization scattering matrix and microwave imaging. FFT and windowing technique as well as digital filtering were used to transform data from time-domain to frequency-domain and to improve the accuracy. Idea of virtual instrument was used in the program. Finally, some data measured and processed by the system were presented, which proves performance of the system.

Design procedures of waveguide aperture antennas are presented. Determination and calculation methods for number of waveguides, number, width and location of radiation apertures, as well as location and slanting angles of feeding waveguides are expatiated. Wide-arm coupling resonant aperture summer-subtracter with half-height waveguide is designed. Influence of aperture mutual coupling on radiation performance is studied. Antenna computation and design is finished based on the theory. Simulation results reveal that gain of the sum lobe is 29dB and the first side lobe is -20.8dB at 12GHz, and the subtract zero depth arrives at -30dB. Level of the first side lobe of sum can reach -27dB after optimization of genetic algorithm. With compact structure, the designed antenna can attain good sum and subtract azimuth patterns, voltage standing wave ration and gain.

This paper proposes a novel kernel parameter optimization method based on Gaussian mass, which aims to overcome the current brute force parameter optimization method in a heuristic way. Generally speaking, the choice of kernel parameter should be tightly related to the target objects while the variance between the samples, the most commonly used kernel parameter, doesn't possess much features of the target, which gives birth to Gaussian mass. Gaussian mass defined in this paper has the property of the invariance of rotation and translation and is capable of depicting the edge, topology and shape information. Simulation results show that Gaussian mass leads a promising heuristic optimization boost up for kernel method. In MNIST handwriting database, the recognition rate improves by 1.6% compared with common kernel method without Gaussian mass optimization. Several promising other directions which Gaussian mass might help are also proposed at the end of the paper.

The paper presents a new design method and power lines communication circuit consolidates their power and data in a single bus. This method is based on power electronic topology and can export new application circuit. Our-the bus's principle are briefly introduced. Buck-by considering the circuit transmission line, the characteristics of the steady-state circuit influence and dynamic switch characteristics are analyzed. The validity of this method is verified by the experiment results.

Tinnitus is the perception of phantom sounds in the ears or in the head without external sound sources even in the completely silent environment. There is no known effective medical treatment for tinnitus and acoustical stimulation has provided patients with some measure of relief. In this paper treatment method with acoustical stimulation has been investigated and simulated by neural oscillator model, simulation results are confirmed by clinical and physiological reports.

This paper analyzes the problems existing in the operation of the interference in the process of network communication electronic equipment. According to the typical problems in the electromagnetic compatibility and the anti-interference general network engineering practice, such as grounding technical problems, the application of the circuit separation technology, cable interference suppression ability, improve the quality of power supply, etc. The new technology and the application of new technology are introduced in this paper.

Image inpainting is a technique used to automatically recover the damaged or missing regions in digital images and is used to remove the unwanted objects from images. This paper presents a novel approach for recovering the damaged regions of digital images using splines. The proposed technique is divided into two steps. In the first step structure of the object in the damaged region is recovered using splines. The animator marks a reasonable number of points on the structure of the object around the damaged region. Using these points a spline is constructed and the isophote lines arriving at the contours of the damaged region are interpolated into the damaged regions using that spline. After recovering the structure of the object in the damaged or missing region, in the second step, a fill in process is done to fill in the color information in the damaged region. Any interpolation technique can be used in the second step to flow the color information into the damaged region. The proposed algorithm is tested over a large variety of images and has sown excellent results to restore the small damaged regions and satisfactory results for large damaged regions

Content-based image retrieval using low-level features such as color, texture and shape is one of the major challenges in image processing and computer vision. Color as the most important low-level feature, has very wide applications in image retrieval systems. In this paper a new content-based image retrieval method using color feature of image regions is expressed. To this end, at the first step, images are partitioned to five fixed regions include center, top, bottom, left and right. Then the color autocorrelogram for each region is computed separately and kept as a feature vector to compare different images similarities. Because of the importance of the images center, weight of the center region is doubled when comparing similarity of images. For comparing other regions, difference of most similar regions is computed. This comparison makes the algorithm more invariant to rotation and to somehow changing the viewing angle, than the similar works. By combining the output of this method with the output of global color histogram-based retrieval method, system performance and accuracy of the results are more improved.

As we all know, the origin of modern logistics was in the United States, developed in Japan, became mature in Europe, and expanded in China. This is a historical development of the modern logistics recognized track. Due to China's economic and technological development, and with the construction of Shanghai International Shipping Center and Shanghai Yangshan International Deepwater development, China's modern logistics industry will attain a leap-forward development of a strong pace, and will also catch up with developed countries in the Western modern logistics level. In this paper, the author explores the flexibility of China's modern logistics management techniques to extend the use, and has certain practical and guidance significances.

In order to improve the performance of spectrum sensing in cognitive radio networks, certain parameters of cooperative spectrum sensing need to be optimized. Aiming at maximizing the capacity of cognitive radio network, a target function for determining the optimal sensing parameters is established. A novel algorithm of adopting a numerical searching method to optimize this established target function is proposed to achieve the optimization of the sensing time and the fusion rule of hard decision. The simulation results show that the cognitive radio network capacity can be maximized when the parameters of the sensing time and the fusion rule are jointly optimized by the proposed algorithm.

As the size of the Internet is growing enormously the illegal manipulation of digital multimedia data become very easy with the advancement in technology tools. In order to protect those multimedia data from unauthorized access the digital watermarking system is used. In this paper a new Discrete walsh Hadamard Transform based visible watermarking system is proposed. As the watermark is embedded in transform domain, the system is robust to many signal processing attacks. Moreover in this proposed method the watermark is embedded in tiling manner in all the range of frequencies to make it robust to compression and cropping attack. The robustness of the algorithm is tested against noise addition, cropping, compression, Histogram equalization and resizing attacks. The experimental results show that the algorithm is robust to common signal processing attacks and the observed peak signal to noise ratio (PSNR) of watermarked image is varying from 20 to 30 db depends on the size of the watermark.

An enhanced filtering method in Contourlet transform domain is proposed in this paper. According to local properties, SAR image will be firstly divided into homogeneous area, non-homogeneous area and edge area. Then average filtering method is applied to the homogeneous area, Contourlet transform de-noising method applied to the non-homogeneous area and edge area retained directly. It's proved that not only can we effectively eliminate the noise but also maintain edge details through this method. Compared with other classical filters, the capability to keep the edge details is stronger.

In this paper, we present a new approach to recognize Tibetan words by analyzing the structure and geometric features. The printed Tibetan words, including consonant and vowel symbols, are firstly generated by FontLister tool and through pre-processing, such as skeletonization and deburring. Then the words are splitted into the single characters, which can easily recognized by feature extraction and pattern matching. We adopt six features according to the geometry and stroke structure. Finally, the Tibetan words are automatically "understood" and converted into Latin alphabet transcription, which make more people read. The experiments show this approach has a recognization accuracy over 89%.

In real time applications signal characteristic and signal noise cannot be determined and predicted which makes very hard while designing digital filters for noise suppression. To outstrip these problem adaptive filters are preferable in which filter coefficients are designed based on the situation by using adaptive algorithms. In this paper a simulator containing adaptive algorithms(Least Mean Square(LMS), Normalized Least Mean Square(NLMS), Recursive Least Square(RLS) , Signed Least Mean Square(SLMS), Signed Normalized Least Mean Square(SNLMS)) using different applications was developed in MATLAB using Graphical User Interphone(GUI). Performance of all algorithms had been observed and some of the results obtained for different applications had been depicted by using real time noise corrupted voice signal. This proposed simulator helps the end user for depicting and comparing adaptive algorithms for real time applications. Pros and cons of each algorithm are observed manually without any motive of mathematical and paper results.

In wireless communication, channel equalization is one of the most challenging tasks because the broadcast channels are often subjected to multipath fading and several bandwidth limitations. Our project is mainly concerned with the Single Carrier Long Term Ev