Abstract:With the proposed network coding technol ogy and research on wireless interference channels, the physical layer network c oding for wireless multi source information is proposed, which can effectively m ix interference and improve the spectrum efficiency of wireless networks. This article introduces the physical layer network coding for wireless cooperative net works from the research background and advancement of physical layer ne twork coding. Specifically, some typical analog network co ding and digital network coding of multi source relay systems are introduced starting from rel a y technologies. Some of the authors′ work is also included. The paper provides a reference for the research in the areas of relay communications and wireless network coding.

Abstract:The future wireless mobile communication demands for the increased transmission rate, spectrum efficiency, and energy efficiency by orders of magnitude, while it also targets at providing much uniform coverage and shorter time delay. Amorphous cellular network, which is based on flexible networking, can fully exploit the wireless resources and significantly enhance user experience, and is then deemed as the most promising research area for the next generation of broadband green communications. Despite of all these advantages, the related research remains in its initial stage, while lots of challenges still reside in the fundamental theory and key technologies which ask for further investigation. The article intends to carry out the analysis of channel characteristic and information theory under the frame of amorphous cellular networks. The transmission technologies of amorphous cellular networks are studied, the comprehensive environment sensing is performed, and dynamic resource allocation is designed. The core theoretical issues are focused on, such as the overlapped coverage of multiple layer cells, the separation of control planes and user planes, as well as the decoupling of up/downlink access mode. We expect to formulate complete communication theory and methodology for amorphous cellular networks, tremendously improve the system throughput and region capacity, achieve the uniform coverage with full dimensionally, and ultimately meet the growing demand of future mobile communication traffic flow with high quality user experience.

Abstract:W ith the wide application of wireless communication, Mobile Internet business nee ds rapid growth, and the static spectrum planning policy makes available spectru m resources become more and more scarce. Cognitive radio can change the working parameters in real time by sensing the spectrum environment, so as to realize the dy namic spectrum allocation. It provides theoretical and technical support to solv e the problem of spectrum scarcity，and has drawn significant attention in wirel ess communication fields. First， the research status of cognitive radio system and standard are introduced, then three key technologies of s pectrum sensing, spectrum sharing and power control are in depth analyzed, and f inally the application prospect of cognitive radio is summarized, including the t hree aspects of TV white space, energy harvesting and 5G network, and th e research on wireless resource management, seamless handoff and load balance is discussed ba sed on cognitive radio technology in the heterogeneous ubiquitous network enviro nment in the future.

Abstract:Nyquist Shannon sa mpling theorem was presented about 70 years ago, which contributed a lot to the transform from analog mode to digital one. In the process of 70 years′ development, focusing on how to reduce the sampling rate and amount of data without informa tion loss, many novel sampling theories are pr oposed and developed, including random sampling, compressive sensi ng based sampling, finite rate of innovation sampling and Xampling. Based on the overview of the present sparse sampling theories and frameworks, the features and application fields are summarized, the lat est developments are introduced, several open problems are reviewed and the existing difficul ties of these new sampling methods with personal opinions are discussed. Finally, t he prospect of sparse sampling technology is drafted to provide a refer for improving the sampling technology and expanding the application fields.

Abstract:Security and privacy are critical for cloud storage because extensive privacy information, such as person al d a ta, is involved in services. The important progress has been focused on the are a of secure cloud storage recently, including data deduplication , oblivious storage, data encryption and ciphertext searching, and data integrit y audit. First, the secure data deduplication is discussed for cloud sto rage to ad dress multiple types of attacks, for instance, identifying files attacks, which reduces the overhead to cloud servers and brings benefit to end users. The recent prog ress is also investigated on the oblivious storage, which introduces the partiti o n based framework and asynchronicity to reduce the computation overhead, pro vidi n g privacy protection and scalability for cloud storage. Then, inspired by tradit ional encryption techniques, the cloud encryption and ciphertext sea rching are considered, such as the ciphertext policy attribute based encryptio n that provides s earch services for encrypted data and avoids privacy leakage. With key exposure resistance, data integrity audit system provides security, efficiency and verifi ability for cloud storage. In summary, the advanced research of above security techniques are discussed, and the future research on improvin g the security and privacy of commercial cloud storage systems is explored.

Abstract:The overlapped deployment of heterogeneous cells brings new challenges into inter cell interference coordination (ICIC), while the enhanced ICIC techniques, such as cell range expansion (CRE) and almost blank subframe (ABS), are considered as effective techniques for solving related issues, but their performance is not extensively explored. Uplink CRE scheme and downlink ABS based CRE scheme are focused on, and it is found that the CRE technique could distinctly improve uplink performance. However, downlink performance may be severely impacted if the downlink of a picocell is identically expanded as its uplink. To solve the problem, the ABS based CRE scheme is investigated for downlink and it is found that the joint optimization of CRE and ABS could effectively improve the downlink performance. Results show that Max Min fairness can be obtained by appropriate settings on CRE bias and ABS ratio.

Abstract:A community partition al gorithm is designed based on theme and connection. Both theme and connection sim ilarity of nodes are integrated in the algorithm, which also adopts a localized way to avoid the searching of good initial nodes. In the proposed algorithm, loc al modularity is accepted as a terminating condition of community partition. The algorithm is applied to a set of Twitter users who had joined into the topics r elated to Haiti earthquake. Three baseline community partition algorithms, i.e., an algorithm simply based on link, an algorithm simply based on topic, and an a lgorithm based on both topic and link, are also applied to the same data set. Experiment results show that the proposed algorithm is [JP2]more advantageous than the three baseline algorithms according to the measurement of purity and entropy.

Abstract:With the development of computer vision technology, it is possible to inspect the inst ruments regularly through the inspection robot in some complex industrial environm ents. However, the realization of the inspection depends on the precise localization of instruments . Here, a method based on locally adaptive regression kernels (LARK) is introduced to achieve quick localiza tion of instruments. LARK can quickly search the visual object without training or too much preprocessing, which greatly improve the efficiency of the instruments localization. Firstly, the salient features are extracted and analogous objects are searched and with the target image to find al l possible similar matches. Then nonmaxima suppression is employed to localize th e target object. The instruments images with different scaling taken from different angles are used in experiments. Experimental results suggest that the a lgorithm is accurate and can meet the requirements of instrument localiza tion in industrial environments.

Abstract:To overcome the shortcomings of entropy bas ed multilevel segmentation methods, such as high computational complexity and po or segmentation performance, a fast multilevel thresholding for image segmentation method based o n generalized probability Tsallis entropy is proposed. First the t raditional gray probability is modified into generalized probability to form gene ralized probability Tsallis entropy (GPTE). Then the parameters of GPTE are cho sen adaptively through the image histogram average value. Thirdly， a multilevel t hresholding method based on GPTE is formulated to get more effective segmentation. Finally the differential evolution(DE) and the recursive algorithm are combined and introduced into the multilevel thresholding method to find the best threshold vector quickly. Experi mental results of image segmentation show that the propos method can obtain bett er segmentation results and adaptability with less computation time compared with the tra ditional entropy based multilevel segmentation methods.

Abstract:The state of the art algorithms for voice conversion are computationally expensive and time consuming, thus they cannot be run in the embedded systems efficiently. An voice conversion method bas ed on mixture mapping of codebooks is proposed. In the training stage, different codebook mapping relationships are built according to the training speech amount, which saves training time and improves conver sion accuracy. In the transformation stage, the system converts the vocal tract parameters of voiced frames according to the corresponding codebook mapping buil t in the training stage. In addition, to improve the quality of the con verted speech, the system converts the feature parameters of unvoiced frames as well as correcting the formant frequency to overcome the formant jitters between frames. Both objective and subjective experiments show that the proposed method reduc es computational complexity and saves training time without degrading or deterio rating the quality of the converted speech.

Abstract:To sol ve the deployment optimization problem for detecting stealth target based on net ted radar, two level anti stealth optimization indexes are presented according t o the simplified model of detecting stealth target using single radar when the o bject trajectory is fixed. Since netted radar deployment is a mult i objective optimization problem with multiple solutions, the hierarchical sear c h algorithm integrated particle swarm optimization (PSO) with Powell is propose d. Firstly PSO algorithm is used to obtain global and local optimal solution. Th en the Powell algorithm is used to search the final deployment solution. Si mul ation results demonstrate that the proposed algorithm combines the advantages o f PSO′s global search ability and Powell′s local search ability. The deploymen t solution using the proposed algorithm compared with the PSO algorithm i mproves the detection probability of stealth target effectively, and increases t he warning distance, under the premise that responsible cover area does not decr ease.

Abstract:The ordinal discrete labels are usually obtained from continuous labels, and the se regres sor seldom use the mutual membership information between ordinal discret e labels, which can be further improved . Therefore, quantitive representation is character ized for the membership information, and then a kernel discriminant learning for ordinal regression using label membershi p (LM KDLOR) is established by combining the representation with typical off t he shelf KDLOR. Experimental results with the standard ordinal regression data sets verify the e ffectiveness of the proposed strategy.

Abstract:Usually in multi label text classification, the relationship of labels is obscure and the dimension of features is too high. To solve these problem s, a multi label text categorization algorithm called multi label algorithm of hypothe sis reuse based on probabilistic latent semantic analysis (PLSA) is proposed. Fi r stly, the training samples are mapped to a hidden semantic space by PLSA model, using the theme distribution to represent a piece of text, which remov e the noise interference and reduce the data dimension significantly. Then, the m ulti label algorithm of hypothesis reuse (MAHR) is utilized to classify samples . The features obtained from PLSA dimension reduction have the semantic informat ion. Therefore, the relationship of labels can be obtained accurately to train t he ba se classifier, and the artificial defect is thus avoided. Experimental results d em onstrate that the proposed method can make full use of the semantic information by PLSA dimension reduction and improve the performance of multi label text cl assification.

Abstract:Compared with traditional BPSK modulation, binary offset carrier(BOC) modulation is widely used owing to its outstanding advantages, such as good correlation, anti jamming property, band sharing and spectral separability, etc. However, due to the existence of multi peaks in BOC correlation curves, it is liable to cause receiver false locked during acquisition. To resolve this problem, based on the characteristics of TMBOC signal and the traditional discriminator, a new design method for TMBOC code tracking based on autocorrelation side peak cancellation technique(ASPeCT) is presented. It is shown from the simulation results and test data that, compared with traditional discriminator, the new design method can solve the problem of fuzzy tracking and it is available and effective when used in TMBOC software receiver. Furthermore, the new design method can also be a useful reference and guidance for BOC software design.

Abstract:The echoes of surface movement radar (SMR) are real aperture images with high resolution, which is difficult for traditional radar target de tection method to dea l with. Firstly, the problem in SMR target detection is analyzed. Then a new SMR target detection algorithm according to the characteristics of SMR echo is proposed , based on the digital image processing. The flow chart of th e algorithm and the realization of the main components are described in detail s eparately. The processing result of SMR return signal collected from certain air port is presented to reveal the validity of the proposed algorithm. The target c an be detected effectively. A mass of isolated false alarm points are removed an d th e cavities inside the target region are filled by target cluster aggregating pro cessing.

Abstract:To tackle the staircase effect pro blem of classical total variational models(TV) in SAR image d enoising, a high order variational model based on Euler′s elasti c a model is proposed, taking advantage of Euler′s elastica and impainting regula rized term. A numerical additive operating splitting (AOS) scheme is us ed to discre tize the image, which overcomes the time step size restriction for explicit sche m e. Experimental results show that the proposed model performs well in simulation, while leaving isolated particles on SA R imag e. To solve the problem, the proposed method is complemented through Gauss filtering in conjunction with the proposed model. Results verify that the pr oposed method can eliminate the large particles effectively and suppress the staircase effect with a high equivalent numbe r of looks (ENL).

Abstract:A blind recognition method of synchronous scrambler paramet er based on the statistical correlation is proposed. Firstly, the polynomial scr ambler order is determined using the autocorrelation characteristics of the s ynchronous scrambler. Then， the correlation between the linear feedback shift register (LFSR ) sequence and the scrambler sequence is defined. Moreover, it is prov ed that the correlation of correct LFSR sequence and the imbalance of information sequence is linearly dependent, while that of wrong LFSR seq uence and the imbalance of information sequences is linearly independent. Fina lly, the polynomial and initial state of the scrambler is recognized with the ma ximu m correlation value after searching for all possible polynomial and initial stat e of the scrambler. Experiment and simulation results verify the correctness of t he theoretical analysis and the effectiveness of the proposed method.

Abstract:novel method is presented for image feature repres entation. The underlying color information of images is extracted to obtain colo r feature values based on the presented method. With the edge detection of objects in images, e dge orientation values of pixels in images are calculated. Then color feature values and edge orientation values are quantized. Based on quantizatio n result analysis of neighboring pixels, an eight dimensional feature vector is constructed for each pixel. Different weights are given to different positions based on the relationship between a central pixel and neighboring pixels. According to feature vectors of pixels, characteristic values are calculated. Fi nally, the pixels number with the same characteristic value is counted to f orm a histogram, which is the basis for image retrieval. Experimental result s demonstrate that the proposed method can effectively describe color distributi on and spatial structure of objects in images. Moreover, more image details can be saved t o enhance the discrimination power. The method is proved to be much more effective than other methods.

Abstract:For the wireless propagation environment with channel fading and jamming, a received signal theor etical model of unmanned aerial vehicles (UAV) data link system under Nakagami f ading channel is proposed. Then, for two jamming types which is single tone jamm ing and continuous noise jamming, the probability density function (PDF) expressions of instantaneous jamming to signal ratio (JSR), signal to noise ratio (SNR) and signal to interference plus noise ratio (SINR) are derived separately. On this b asis, the average error probability, as well as the outrage probability, is also derived. Numerical simulations verify the analyzed results and showed tha t the continuous noise jamming outperforms the single tone jamming about 3—5 dB, which are helpful for studying on jamming and anti jamming technologies of UAV data link.

Abstract:To solve the problems of tracking failure caused by blocke d target and low tracking precision in the tracking learning detection (TLD) a lg orithm, an improved TLD algorithm based on multi innovation Kalman filter is pr o posed. The improved algorithm models the target before tracking, u ses the results of TLD tracking algorithm as the current observations and then c ombines with the predicted values of the multi innovation Kalman filter to opti m ize the tracking results. The experiments show that the improved algorithm of TL D has higher precision than the original TLD algorithm, and it is able to predict the position of the target when the target is occluded.

Abstract:The K Means clustering algorithm can not determine the initial cluster ing centers, which results in low accuracy and inability to reflect the interest ing hotspots. Here, algorithm based on clustering is proposed through applying Min M ax active learning strategy to ask the user for identifying the seed points. Several points are provided in small quantities of query for users to confirm the initial centers, and the weight is set in the recalculation of K Means centers, which reduces the number of iterations and improves the accu racy of clustering results. Moreover, the hot topics are obtained by applying th is algorithm to the micro blog clustering analysis.

Abstract:A compressive autocorrelation detection al gorithm is proposed for overcoming the detection problem of unknown high bandwid th sparse signals. Firstly, the compressive sensing technology is unutilized to acquire the signals at a sampling rate which is far lower than Nyquist samplin g rate. Then based on researching autocorrelation matrix theories of signal dete ction, a sparse coefficients compressive autocorrelation detection algorithm usi ng statistical distribution is deduced through the restricted is ometry property of the sensing matrix and the compressive samplings are dealt wi th d irectly. The connection is subsequently obtained between the decision threshold and the f alse a larm probability theoretically. Moreover, the computational complexity of the algori thm is analyzed. Therefore, the method can improve the dete ction timeliness efficiently through few compressive samplings without reconstruct ing signal. Simulations show t hat the proposed algorithm still perform well in unknown signal detection with l ow signal to noise ratio.

Abstract:To make the best use of all phase method in signal compression, spectrum and mul ti resolution analysis fields 〖WTBZ〗etc〖WTBZ〗, all phase signal processing algorithm in three orthogonal transforms is designed which improves weak Gibbs effectively, and the mathematical expressions and inverse transformation formula s of all phase kernel in DFT, DCT and DCT orthogonal bases are derived. Then imp lementation structure of windowed all phase processing is presented for improvi ng the performance of the algorithm. In addition, window function is analyzed fo r the linearity. Finally, two experiments, designing transmission property based on DCT and discrete wavelet transform (DWT) and sub band dividing, testify the correctness and feasibility of the presented algorithm.