Contour-Based Multi-Source Information Fusion for Motion Segmentation

A motion segmentation framework that effectively exploited the multiple sources of image information and fused these sources of the information synergisti-cally was proposed to serve the purpose of motion segmen- tation. A Markov process was formulated for motion seg- mentation in which two feature spaces were established to estimate the state transition Probability density function （PDF） and the initial state, respectively. An information fusion space was developed such that each motion struc-ture was described as a single distribution in this space. The proposed framework can naturally embed the evolution equations of the active contour methods into the seg-mentation to achieve contour-based segmentation results. Extensive empirical evaluations demonstrate the robust-ness and the promise of this framework.     

A Part-of-speech Tagging Model Employing Word Clustering and Syntactic Parsing

Part-Of-Speech tagging is a basic task in the field of natural language processing. This paper builds a POS tagger based on improved Hidden Markov model, by employing word clustering and syntactic parsing model. Firstly, In order to overcome the defects of the classi- cal HMM, Markov family model （MFM）, a new statisti- cal model was introduced. Secondly, to solve the prob- lem of data sparseness, we propose a bottom-to-up hierar- chical word clustering algorithm. Then we combine syn- tactic parsing with part-of-speech tagging. The Part-of- Speech tagging experiments show that the improved Part- Of-Speech tagging model has higher performance than Hidden Markov models （HMMs） under the same test- ing conditions, the precision is enhanced from 94.642% to 97.235%.     

Performance Analysis of Capture Effect in High Speed Wireless Networks

The influence of capture effect on the high speed wireless networks is investigated in this paper. A new garkov chain model considering capture effect for the binary exponential back-off scheme in the MAC layer has been proposed. A new throughput model used in the IEEE 802.11 networks is proposed Based on this new Markov chain model, and then we analyzed throughput impacted by the capture effect under different transmission speeds. The performance analysis shows the capture effect has more impact on the throughput of high speed wireless networks than that on the low speed wireless networks.     

A Learning Method for Pedestrian Activity Classification

Analysis of human activity and online anomaly detection from video sequences is one of the hottest and difficult research areas in computer visions. This paper describes a method for pedestrian gait classifi- cation in video sequence and deals with the classification of human gait types based on the notion that gait types can be analyzed into a series of consecutive postures types. First, silhouettes are extracted using the Background subtraction method which is combined with the time-stepping method. Then a method using recursion method for establishment of the standard gait state sequence is proposed. Mean- while, wavelet moment method is used to extract features of the human body image, and the result matrix leads to Discrete hidden Markov models. Finally, Discrete hidden Markov models is used for human posture training, model- ing and activity matching to recognize the human activity. The experiment tests show some encouraging results also indicates the algorithm has very small leak-examining and mistake-examining-rate, also shows the capability of real- time performance, which indicate that the method could be a choice for solving the problem but more tests are re- quired.     

Optimal and Suboptimal Access and Transmission Polices for Dynamic Spectrum Access over Fading Channels in Cognitive Radio Networks

We propose a cross-layer optimal access and transmission framework for dynamic spectrum access to maximize expected long-term average throughput under power and collision constraints by a dynamic programming method namely Constrained Markov decision process （CMDP）. The optimal policy for CMDP is capable of guiding transmitter to choose an available channel and transmission rate at the beginning of each frame for its long-term goals according to current channel sensing results and prior channel fading information. The complexity of finding the optimal policy by Linear programming （LP） approach increases exponentially with the number of channels and fading levels, which incurs so-called curse of dimensionality. Therefore we propose two complexity- reduced suboptimal policies, namely, policy separation and heuristic algorithms. Finally, we compare the performances of policies by numerical results.     

Performance Analysis of the IEEE 802.15.3 CSMA/CA in Unsaturated Traffic Conditions

In IEEE 802.15.3 the traffic communicated by Carrier sense multi access/Collision avoidance （CSiVIA/CA） mechanism are unsaturated in most cases that are different in IEEE 802.11. This paper presented an extension analytical model based on the Bianchi model in IEEE 802.11 considering unsaturated traffic conditions in error-prone channel. By using this model we analyzed the throughput, frame discard probability and average frame delay performance of IEEE 802.15.3 CSMA/CA. This model is validated through extensive simulation results. Effects of unsaturated situation and channel error condition on the performance were analyzed.     

Multi-Feature Based Bayesian Segmentation of Cerebrovascular from Time-of-flight Magnetic Resonance Angiography

Vessel analysis in medical images is important both for diagnostic and intervention planning purposes, especially, a three-dimensional representation of vasculature can be extremely important in image-guided neurosurgery and pre-surgical planning. In this paper, a Bayesian approach is proposed to aggregating geometric shape and intensity features for whole cerebrovascular tree extraction from Time-of-flight Magnetic resonance angiography （TOF-MRA）, while most of the current segmentation methods solely deal with the latter. In this method, we first utilige scale space analysis to get shape feature of blood vessels, then both shape and speed features are incorporated into a Bayesian segmentation framework. Maximum a posterior （MAP） method is used to estimate the posterior probabilities of vessel and background for classi- fication. The experimental results show that the proposed method can obtain a better quality of segmentation than those sole feature utilized methods.     

A Feedback Correction De-noising Algorithm Based on the Wavelet Coefficients

The localization of satellite interference sources under the condition of low SNR has been a hot research topic at current, where the signal de-noising is important in the following positioning work. With the help of the wavelet analysis theory, this paper proposes a denoising algorithm based on the wavelet coefficients in low SNR. After correcting and reconstructing the signal by the correction coefficients, we could obtain the SNR and Root mean square error （RMSE）, and then we could correct the signal for the second time. The simulation results show that this de-noising algorithm can effectively remove the noise of the signal in low SNR, which would have a good effect on the estimation of time delay parameter in the localization of satellite interference sources.     

The Optimization of Combined Beamforming with Space-Time Block Coding

The combination of beamforming and Alamouti space-time block coding may not be optimal in rich scattering since it only uses two equal energy loading beams. A new scheme combining beamforming with spacetime block coding is put forward in this paper. The new scheme requires only the knowledge of channel correlation at the transmitter, the optimization design turns out to be an eigen-beamformer with multiple beams pointing to orthogonal directions along the eigenvectors of the channel correlation matrix and combine with appropriate spacetime block coding. In order to minimize a tight upper bound on the symbol error rate the optimal energy loading algorithm is proposed in this paper. We assume that the channel receive vectors observed on different receive antennas are mutually uncorrelated, but have the same correlation matrix, thus the new scheme can be extended to multiple receivers. The combination of beamforming and Alamouti space-time block coding is a special case of the new scheme. Based on Laplace transform, this paper provides an algorithm to calculate the probability density function of the equivalent signal to noise ratio, thus the closedorm expressions for the symbol error rate of the new scheme are derived. Simulation results demonstrate the superiority of the new scheme.     

Model and Simulation of Multipath Error in DLL for GPS Receiver

Although different multipath error models of Delay lock loop （DLL） used in GPS receiver are established, they have never been put together for comparison. Furthermore, no universal simulation method is developed to get a fair comparison among these models. A new model with implicate expression is hence proposed for the coherent DLL and the noncoherent Dot-product （DOT） power mode DLL. Meanwhile, a new simulation method based on the anonymous function in Matlab, which is especially suitable for models with implicit expressions is also proposed to compare the new model with the existing ones. The theoretical analysis and simulation results show that the existing models are the special case of the proposed one. The new simulation method can be used for the comparison of different multipath error models and the multipath error analysis of other DLLs for which only the implicit model is available.     

State Estimation for Linear Constrained Systems with Unknown Input

This paper deals with the filtering problem for linear discrete constrained dynamic systems with unknown input. The constraint matrix and constraint vector in this system are allowed to vary in the value and in the dimension. The original full state is separated into two parts, and the estimate of the state is reduced to find the optimal estimate of a singular system. The estimable condition is researched and a recursive estimator for the original full state is presented. A rigorous mathematical induction is given to compare the performance of our approach to that of the existing method without constraint. A numerical example is presented to demonstrate the effectiveness of the new method.     

An Adaptive Designing Method of Broadcast Almanac Parameters

A model of the signal acquisition time influenced by the almanac was introduced to evaluate the almanac model. It shows that the six parameters almanac model outperforms the three parameters almanac model. The almanac truncation error was introduced to describe the error between the original data and the interface specification formatted terms. The parameter sen- sitivity and the truncation error were analyzed to validate the necessity to choose the proper definition of the almanac parameters, including the effective range and the scale factor. It demonstrates the parameter definition is the key designing element while the almanac representation is determined. Based on the research above, An Adaptive almanac designing method （AADM） of broadcast almanac parameters was presented. Simulation results reveal that the AADM can generate an optimum set of almanac parameters with the shortest signal acquisition time to a limited bit allocation.     

Time Delay Estimation Based on Entropy Estimation

Abstract--This paper presents a novel time delay estimation （TDE） method using the concept of entropy. The relative delay is estimated by minimizing the estimated joint entropy of multiple sensor output signals. When estimating the entropy, the information about the prior distribution of the source signal is not required. Instead, the Parzen window estimator is employed to estimate the density function of the source signal from multiple sensor output signals. Meanwhile, based on the Parzen window estimator, the Renyi＇s quadratic entropy （RQE） is incorporated to effectively and efficiently estimate the high-dimensional joint entropy of the multichannel outputs. Furthermore, a modified form of the joint entropy for embedding information about reverberation （multipath reflections） for speech signals is introduced to enhance the estimator＇s robustness against reverberation.     

Identity-Based Encryption on RSA Without Pairings and Key Escrow

We propose a new construction of identitybased encryption without key escrow over the tradition RSA cryptosystems. The security of our scheme follows from the decisional Diffie=Hellman assumption and the difficulty of Modular inversion hidden number problem with error （MIHNPwE）, which can be seen as a generalization of the modular inversion hidden number problem. We give an analysis on the hardness of MIHNPwE by lattice techniques. In our construction） we generate each user＇s partial private key in the form of an MIHNPwE instance. The hardness of MII-INPwE provides our scheme with resistance against key-collusion attacks from any number of traitors. Our prototype implementation of the proposed scheme shows that it can be more computation efficient and easy-to-implementation than the influential pairingfriendly elliptic-curve based IBE scheme.     

Interference Mitigation and Joint Decoding for Uplink LDPC-Coded Relay Cooperation

This paper proposes an efficient inter- ference mitigation and joint decoding scheme for uplink LDPC-coded relay cooperation over a Rayleigh fading channel, where a concatenation of Minimum-mean-squared error linear detectors （MMSE） and BP-based joint itera- tire decoding based on the introduced treble-layer Tanner graph are effectively designed to filter and decode the cor- rupted received sequence at a base station. It is demon- strated by theoretical analysis and numerical simulations that the proposed design can well combine the gains from coding and diversity, which consequently leads to a sig- nificant performance improvement over the conventional cooperation system under the same conditions.     

Multiuser Frequency Synchronization for Interleaved-OFDMA Uplink Systems

This paper investigates the frequency synchronization in the uplink of the Orthogonal frequency- division multiple access （OFDMA） system with interleaved subcarrier assignment. In such a system, one of the key problems is the multiuser frequency synchronization, which focuses on the multiple-parameter estimation of the Carrier frequency offsets （CFOs）. In this paper, we propose a two-stage frequency offset estimation algorithm. The main advantage of the proposed method is that it can obtain the CFOs of all users simultaneously using only one OFDMA block. In addition, a novel CFO-compensation method is presented. Based on the inner signal structure of interleaved-OFDMA uplink, the new scheme uses adaptive beamformer to compensate the CFO of each user and isolate the signals of all users at the same time. Compared to the previously known methods, the proposed algorithm can provide accurate frequency synchronization and data detection without feeding CFO estimates back to active users for frequency adjustment.     

Single Beacon Cruise Positioning Algorithm in Wireless Sensor Networks

Node localization is important in WSNs （Wireless sensor networks） applications. We present a new cruise positioning algorithm, where a single beacon node cruises an entire network, broadcasting its position periodically with fixed signal strength. Meawhile, an unknown node obtains the location and the corresponding distance of the beacon node. Two possible position coordinate sets for the unknown node can then be calculated, and the coordinate mean of the point set with a small variance is an unbiased estimator of the unknown node＇s coordinate. The positioning algorithm＇s computation is simple, positioning accuracy is not limited by a physical distance measuring tool, and there are no restrictions on the path of motion, which makes it more realistic. An algorithm simulation is presented to compare the original and cruise location algorithm. Numerical results show that the cruise localization algorithm can achieve good positioning accuracy.     

Nonlinear Sum Statistic to Improve Signal Detection in Gaussian Mixture Noise

This paper discusses the binary decision problem in Gaussian mixture noise by the nonlinear sum statistic （nonlinear detector） based on a Maximum a posteriori probability （MAP） criterion. When the signal is suprathreshold, the nonlinear detector can obtain a smaller the probability of error Per compared to the standard lin- ear detector which can minimize Per in Gaussian noise. When the signal is subthreshold, the nonlinear detector can also obtain a smaller Per compared to the standard linear detector for Gaussian mixture noise with stronger noise intensity. Noise can improve signal detection, i.e., Stochastic resonance （SR） exists. According to the variation of the Probability density function （PDF）, we discuss how the noise parameters affect the detection performance, and why SR occurs for the subthreshold signal. These results confirm further that there are some simple nonlinear statistics that can improve signal detection in non-Gaussian noise, and also show the robustness of SR to noise.     

多基地雷达马氏定位算法优化

通过研究多基地雷达定位的马尔可夫算法,提出了一种适用于大数据量情形的简化目标定位方法,解决了因算法简化而衍生的测量误差协方差矩阵非正定问题.通过实例仿真,对定位结果的"定位精度的几何稀释"曲线进行分析,证明了本算法能够保证定位结果有效,并提高了运算效率.     

Robust Quantization of Discrete-Time Signals with Independent Samples

TheN-level uniform quantizer on[-c,c]plus the assignment ofy_{0}deg = -(a _{s}+ c)/2andy_{N+1}deg = (a_{s}+ c)/2to signal values falling in the saturation regions[-a_{s},- c) and (c,a_{s}], respectively, is shown to be the minimax(N + 2)-level quantizer with a nonsaturating input range[-c,c]. The performance criterion considered is the mean weighted quantization error and the input signals are only required to be amplitude bounded bypm a_{s}wherea_{s} > c > 0. The worst case input signal marginal probability distributions are shown to be discrete. From the derivation of this result, the minimax error can be computed. An example is given which illustrates the performance of the minimax quantizer for several input ranges against different input signal probability distributions.