Error-resilient coding of 3-D graphic models via adaptive mesh segmentation
Current coding techniques for 3-D graphic models mainly focus on coding efficiency, which makes them extremely sensitive to channel errors due to the irregular mesh structure. We introduce a new approach for error-resilient coding of arbitrary 3-D graphic models by extending the error-free constructive traversal compression scheme proposed by Li and Kuo (see MPEG-4 Tokyo Meeting, 1998, , and Proc.  vol.86, p.1052-63, 1998). A 3-D mesh of an arbitrary structure is partitioned into pieces of a smaller uniform size with joint boundaries. The size of a piece is determined adaptively based on the channel error rate. The topology and geometry information of each joint boundary and each piece of a connected component is coded independently. The coded topology and first several important bit-planes of the joint-boundary data are protected against channel errors by using the Bose-Chaudhuri-Hocquenghem error-correcting code. At the decoder, each piece is decoded and checked for channel errors. The decoded joint-boundary information is used to perform data recovery and error concealment on the corrupted piece data. All decoded pieces are combined together according to their configuration to reconstruct all connected components of the complete 3-D model. Our experiments demonstrate that the proposed approach has excellent error resiliency at a reasonable bit-rate overhead. The techniques is also capable of incrementally rendering one connected component of the 3-D model at a time.

title: A P1500 compliant programmable BistShell for embedded memories
abstract: We describe the design and implementation of an IEEE P1500 compliant programmable BIST for embedded memories. The proposed design can be embedded in other cores or systems with minimum test generation or test application overhead. The programmability of our BIST is useful when the algorithm is being refined while the memory architecture is under production. A variety of test algorithms can be implemented with the programmability provided in our design with no change to the BIST hardware. As an example we demonstrate the implementation of an algorithm to detect open decoder faults. This example is shown for its didactic content as it brings out the programmable axis of our design. Our design also offers means to perform dedicated delay tests as well as scan tests for diagnosis. We show by synthesis experiments that the extra area cost for the BIST hardware is relatively small for medium to large memories.

title: Wavelet directional histograms for classification of human gestures represented by spatio-temporal templates
abstract: This paper evaluates the efficacy of directional information of wavelet multi-resolution decomposition for histogram-based classification of human gestures represented by spatio-temporal templates. This template collapses temporal component into gesture representation in a way that no explicit sequence matching or temporal analysis is needed, and characterizes the motion from a very high dimensional space to a low dimensional space. These templates are modified to be invariant to translation, rotation and scale. Two dimensional, 3 level dyadic wavelet transform applied on these templates results in one low pass subimage and nine highpass directional subimages. Histograms of wavelet coefficients at different scales are compared to establish significance of available information for classification. The preliminary experiments show that while the statistical properties of the template provide high level of classification accuracy, the available information in high pass or low pass decompositions by itself is not sufficient to provide significant efficiency of accuracy.

title: Semantics of Agent Communication Languages for Group Interaction
abstract: Group communication is the core of societal interactions. Therefore, artificial agents should be able to communicate with groups as well as individuals. However, most contemporary agent communication languages, notably FIPA and KQML, have either no provision or no well-defined semantics for group communication. We give semantics for group communication that we believe can profitably enrich the agent communication languages. In our semantics, individual communication is a special case of group communication wherein each communicating group consists of a single agent.

title: Hybrid transactional memory
abstract: High performance parallel programs are currently difficult to write and debug. One major source of difficulty is protecting concurrent accesses to shared data with an appropriate synchronization mechanism. Locks are the most common mechanism but they have a number of disadvantages, including possibly unnecessary serialization, and possible deadlock. Transactional memory is an alternative mechanism that makes parallel programming easier. With transactional memory, a transaction provides atomic and serializable operations on an arbitrary set of memory locations. When a transaction commits, all operations within the transaction become visible to other threads. When it aborts, all operations in the transaction are rolled back.Transactional memory can be implemented in either hardware or software. A straightforward hardware approach can have high performance, but imposes strict limits on the amount of data updated in each transaction. A software approach removes these limits, but incurs high overhead. We propose a novel hybrid hardware-software transactional memory scheme that approaches the performance of a hardware scheme when resources are not exhausted and gracefully falls back to a software scheme otherwise.

title: An observation-constrained generative approach for probabilistic classification of image regions
abstract: In this paper, we propose a probabilistic region classification scheme for natural scene images. In conventional generative methods, a generative model is learnt for each class using all the available training data belonging to that class. However, if an input image has been generated from only a subset of the model support, use of the full model to assign generative probabilities can produce serious artifacts in the probability assignments. This problem arises mainly when the different classes have multimodal distributions with considerable overlap in the feature space. We propose an approach to constrain the class generative probability of a set of newly observed data by exploiting the distribution of the new data itself and using linear weighted mixing. A Kullback–Leibler Divergence-based fast model selection procedure is also proposed for learning mixture models in a low dimensional feature space. The preliminary results on the natural scene images support the effectiveness of the proposed approach.

title: Man-made structure detection in natural images using a causal multiscale random field
abstract: This paper presents a generative model based approach to man-made structure detection in 2D (two-dimensional) natural images. The proposed approach uses a causal multiscale random field suggested by Bouman and Shapiro (1994) as a prior model on the class labels on the image sites. However, instead of assuming the conditional independence of the observed data, we propose to capture the local dependencies in the data using a multiscale feature vector. The distribution of the multiscale feature vectors is modeled as mixture of Gaussians. A set of robust multi-scale features is presented that captures the general statistical properties of man-made structures at multiple scales without relying on explicit edge detection. The proposed approach was validated on real-world images from the Corel data set, and a performance comparison with other techniques is presented.