High frequency millimeter wave (mmWave) band is the key carrier to enable multi-gigabit wireless communications. Due to high directivity and penetration loss of mmWave, a beam pair link (BPL) established between a transmitting beam and a receiving beam may be vulnerable to the blockage effect caused by user body and surrounding obstacles. While the existing work mostly considers the mmWave blockage problem in the outdoor scenario, this work studies the mmWave blockage effect in the indoor hotspot scenario where the shorter link distance and a higher blocker density are expected to magnify the blockage effect, compared to the outdoor counterpart. System-level simulations are carried out to assess the blockage effect based on the 3GPP New Radio (NR) channel and blockage models. To mitigate the blockage effect, a beam cooperation mechanism is used by configuring multiple BPLs for a user equipment. Extensive simulation results are presented that offer insights to the mmWave blockage problem in the indoor environment.

Extracting biologically relevant information from DNA microarrays is a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been proposed for the analysis of gene expression data, but when analyzing the large and heterogeneous collections of gene expression data, conventional clustering algorithms often cannot produce a satisfactory solution. Biclustering algorithm has been presented as an alternative approach to standard clustering techniques to identify local structures from gene expression data set. These patterns may provide clues about the main biological processes associated with different physiological states. In this paper, different from existing bicluster patterns, we first introduce a more general pattern: correlated bicluster, which has intuitive biological interpretation. Then, we propose a novel transform technique based on singular value decomposition so that identifying correlated-bicluster problem from gene expression matrix is transformed into two global clustering problems. The Mixed-Clustering algorithm and the Lift algorithm are devised to efficiently produce δ-corBiclusters. The biclusters obtained using our method from gene expression data sets of multiple human organs and the yeast Saccharomyces cerevisiae demonstrate clear biological meanings.

On face recognition, most previous works on dimensionality reduction and classification would first transform the input image into 1-D vector, which ignores the underlying data structure and often leads to the small sample size problem. More recently, 2-D discriminant analysis has become an interesting technique which can overcome the aforementioned drawbacks. However, 2-D methods extract features based on the rows or the columns of all images, so it is possible that the features using 2-D methods still contain some redundant information. In addition, most existing 2-D methods cannot provide an automatic strategy to choose discriminant vectors. In this paper, we study the combination of 2-D discriminant analysis and 1-D discriminant analysis and propose a two-stage framework: " (2D)(2)MMC + LDA." Because the extracted features based on maximal margin criterion (MMC) is robust, stable, and efficient, in the first stage, a 2-D two-directional feature extraction technique, (2D)(2)MMC , is presented. In the second stage, the linear discriminant analysis (LDA) step is performed in the (2D)(2)MMC subspace. Experiments with Feret, Olivetti and Oracle Research Laboratory, and Carnegie Mellon University Pose, Illumination, and Expression databases are conducted to evaluate our method in terms of classification accuracy and robustness.

High-dimensional data and the small sample size problem occur in many modern pattern classification applications such as face recognition and gene expression data analysis. To deal with such data, one important step is dimensionality reduction. Principal component analysis (PCA) and between-group analysis (BGA) are two commonly used methods, and various extensions of these two methods exist. The principle of these two approaches comes from their best approximation property. From a pattern recognition perspective, we show that PCA, which is based on the total scatter matrix, preserves linear separability, and BGA, which is based on between-class scatter matrix, retains the distance between class centroids. Moreover, we propose an automatic nonparameter uncorrelated discriminant analysis (UDA) algorithm based on the maximum margin criterion (MMC). The extracted features via UDA are statistically uncorrelated. UDA combines rank-preserving dimensionality reduction and constraint discriminant analysis and also serves as an effective solution for the small-sample-size problem. Experiments with face images and gene expression data sets are conducted to evaluate UDA in terms of classification accuracy and robustness.