Improved Blind Decodings of STBC with Unknown and Known Channel Correlation to Transmitter
The subspace algorithm can be utilized for the blind detection of space-time block codes (STBC) without knowledge of channel state information (CSI) both at the transmitter and receiver. However, its performance degrades when the channels are correlated. In this letter, we analyze the impact of channel correlation from the orthogonality loss between the transmit signal subspace (TSS) and the statistical noise subspace (SNS). Based on the decoding property of the subspace algorithm, we propose a revised detection in favor of the channel correlation matrix (CCM) only known to the receiver. Then, a joint transmit-receive preprocessing scheme is derived to obtain a further performance improvement when the CCM is available both at the transmitter and receiver. Analysis and simulation results indicate that the proposed methods can significantly improve the blind detection performance of STBC over the correlated channels.

title: Hyperspectral images lossless compression by a novel three-dimensional wavelet coding
abstract: In this paper, we present a 3D hyperspectral images lossless compression algorithm, which is based on the asymmetric 3D quincunx structured wavelet transform (A3D-QWT) and the adaptive classification coding. A3D-QWT is a simpler 3D transform compared to the classical asymmetric 3D pyramidal wavelet structure (A3D-DWT) with nearly equal entropy; furthermore the spectral correlation after this quincunx structured transform is higher, which is useful to the adaptive classification coding. The adaptive classification coding can make full use of not only the spatial correlation but also the spectral correlation characteristics of hyperspectral images. Experiments show that our method is capable of providing a higher compression performance for hyperspectral images.

title: Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance
abstract: In this paper, we identify a new task for studying the outlying degree (OD) of high-dimensional data, i.e. finding the subspaces (subsets of features) in which the given points are outliers, which are called their outlying subspaces. Since the state-of-the-art outlier detection techniques fail to handle this new problem, we propose a novel detection algorithm, called High-Dimension Outlying subspace Detection (HighDOD), to detect the outlying subspaces of high-dimensional data efficiently. The intuitive idea of HighDOD is that we measure the OD of the point using the sum of distances between this point and itsknearest neighbors. Two heuristic pruning strategies are proposed to realize fast pruning in the subspace search and an efficient dynamic subspace search method with a sample-based learning process has been implemented. Experimental results show that HighDOD is efficient and outperforms other searching alternatives such as the naive top–down, bottom–up and random search methods, and the existing outlier detection methods cannot fulfill this new task effectively.

title: A Novel Approach Using PCA and SVM for Face Detection
abstract: Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on principal components analysis (PCA) and support vector machine (SVM) is proposed. It firsly filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. PCA is used to reduce dimension of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.

title: Research on the Optimal Transmission Design of Planar Bar Mechanism
abstract: A creative method for the optimal transmission design of the planar bar mechanism is presented in this paper. By the method, crank-rocker mechanism, crank-slider mechanism and the corresponding six-bar mechanism with desired dimensions can be devised, in which basic parameters of the mechanisms such as time ratio, the oscillating angle of the rocker and the stroke remain unchanged and in the mean time, the minimum transmission angle obtains its maximum value. By the method, the combination form of the optimal transmission parameters can be obtained according to given design requirements so as to improve transmission property and realize the optimal transmission results. The design method is characterized by simple operation and convenient usage in practice.

title: Spectral Decomposition Approach for Transient Analysis of Multi-Server Discrete-Time Queues
abstract: Discrete time queueing systems are extensively used in modeling the ATM environment at the cell level. This paper examines the transient behavior of a discrete time FCFS multiple server queue. For the arrival process we assume a general discrete time Markovian batch arrival process, where the batch size distribution of the arrivals in successive slots is governed by a finite state discrete time Markov chain. We show that once the spectral decomposition of the “probability generating matrix” of the arrival process is obtained, the complete solution in the transform domain may be given. Using the complex analysis technique and Cauchy's integral formula, we present an efficient numerical method for the numerical calculation of a few performance measures of interest, namely transient probability of queue being empty, and the mean of the queue length distribution. We also generalize our numerical method in the situations where the superposition of a number of independent arrival sources are fed to the queue. It is shown that the numerical complexity of obtaining the transient solution in this case can be substantially reduced by using the approach based on the Kronecker product. The finite buffer case is also analyzed.

title: Personalized QoS Prediction forWeb Services via Collaborative Filtering
abstract: Many researchers propose that, not only functional but also non-functional properties, also known as quality of service (QoS), should be taken into consideration when consumers select services. Consumers need to make prediction on quality of unused web services before selecting. Usually, this prediction is based on other consumers' experiences. Being aware of different QoS experiences of consumers, this paper proposes a collaborative filtering based approach to making similarity mining and prediction from consumers' experiences. Experimental results demonstrate that this approach can make significant improvement on the effectiveness of QoS prediction for web services.