The performance of inhibit sense multiple access (ISMA) in a wireless CDMA environment for short burst data service is analyzed.. Data users are allowed to transmit short messages (up to several frames at a time) in a contention manner using the reverse common channels. In this paper, we assume that one such common traffic channel is allocated for this data service. Data users contend for the traffic channel using either the ISMA or slotted Aloha protocol. Markovian analysis is given and the throughput as well as the delay are computed assuming a fixed number of voice users in the system. Numerical results show that for this short data message scenario ISMA has a higher throughput than slotted Aloha.

Algorithms for multiscale basis selection and feature extraction for pattern classification problems are presented. The basis selection algorithm is based on class separability measures rather than energy or entropy. At each level the "accumulated" tree-structured class separabilities obtained from the tree which includes a parent node and the one which includes its children are compared. The decomposition of the node (or subband) is performed (creating the children), if it provides larger combined separability. The suggested feature extraction algorithm focuses on dimensionality reduction of a multiscale feature space subject to maximum preservation of information useful for classification. At each level of decomposition, an optimal linear transform that preserves class separabilities and results in a reduced dimensional feature space is obtained. Classification and feature extraction is then performed at each scale and resulting "soft decisions" obtained for each area are integrated across scales. The suggested algorithms have been tested for classification and segmentation of one-dimensional (1-D) radar signals and two-dimensional (2-D) texture and document images. The same idea can be used for other tree structured local basis, e.g., local trigonometric basis functions, and even for nonorthogonal, redundant and composite basis dictionaries.

We present an algorithm for layout-independent document page segmentation based on document texture using multiscale feature vectors and fuzzy local decision information. Multiscale feature vectors are classified locally using a neural network to allow soft/fuzzy multi-class membership assignments. Segmentation is performed by integrating soft local decision vectors to reduce their "ambiguities".

The discriminatory power of different segments of a human face is studied end a new scheme for face recognition is proposed. We first focus on the linear discriminant analysis (LDA) of human faces in spatial and wavelet domains, which enables us to objectively evaluate the significant of visual information in different parts of the face for identifying the person. The results of this study can be compared with subjective psychovisual findings. The LDA of faces also provides us with a small set of features that carry the most relevant information for face recognition. The features are obtained through the eigenvector analysis of scatter matrices with the objective of maximizing between class variations and minimizing within class variations. The result is an efficient projection based feature extraction and classification scheme for recognition of human faces. For a midsize database of faces excellent classification accuracy is achieved with only four features.

An algorithm for classification task dependent multiscale feature extraction is suggested. The algorithm focuses on dimensionality reduction of the feature space subject to maximum preservation of classification information. It has been shown that, for classification tasks, class separability based features are appropriate alternatives to features selected based on energy and entropy criteria. Application of this idea to feature extraction from multi-scale wavelet packets is presented. At each level of decomposition an optimal linear transform that preserves class separabilities and results in a reduced dimensional feature space is obtained. Classification and feature extraction is performed at each scale and resulting "soft decisions" are integrated across scales. The suggested scheme can also be applied to other orthogonal or non-orthogonal multiscale transforms e.g. local cosine transform or Gabor transform. The suggested algorithm has been tested on classification and segmentation of some radar target signatures as well as textured and document images.

A new algorithm for task dependent selection of wavelet packet trees for signal classification is suggested. The algorithm is based on a class separability measure rather than energy or entropy. At each level the class separabilities obtained from a parent node and its children are computed and compared. The decomposition of the node (or subband) is performed if it provides larger separability. The suggested algorithm is tested for texture classification. The method can also be used with other tree structured local basis e.g. local trigonometric basis functions. Also it can be applied to detection, classification or segmentation of different l-D and 2-D signals.>

A new algorithm for layout-independent document page segmentation is suggested. Text, image and graphics regions in a document image are treated as three different "texture" classes. Soft local decisions on small blocks are made using wavelet packet based feature vectors. Segmentation is performed by propagating and integrating soft local decisions over neighboring blocks, within and across scales. The "uncertainties" associated with local decisions are reduced as more contextual evidence is incorporated in the process of decision integration. The majority, taken over weighted combined votes, determines the final decision. The suggested algorithm is based on parallel independent computations which have low complexity. It can also be applied to other signal and image segmentation tasks.