8483401
Optimum spatial-temporal receiver for wireless systems with ISI and CCI
Spatial-temporal processing is an effective technique for wireless systems with antenna arrays to suppress co-channel interference. In this paper, spatial-temporal receive filters for multiple receiver systems with intersymbol interference (ISI), co-channel interference (CCI), and additive white Gaussian noise (AWGN) are investigated. An analogical spatial-temporal matched filter that is optimum for any following T-spaced processor with any reasonable criterion of goodness is found. Therefore, it allows co-channel interference suppression and equalization to be treated separately.
5907828
Error correcting least-squares Subspace algorithm for blind identification and equalization
Abstract A subspace based blind channel identification algorithm using only the fact that the received signal can be oversampled is proposed. No direct use is made in this algorithm of either the statistics of the input sequence or even of the fact that the symbols are from a finite set and therefore this algorithm can be used to identify even channels in which arbitrary symbols are sent. Using this algorithm as a base and using the extra information which becomes available when the transmitted symbols are from a known finite set, the EC-LS-Subspace algorithm is derived. The EC-LS-Subspace algorithm operates directly on the data domain and therefore avoids the problems associated with other algorithms which use the statistical information contained in the received signal directly. In the noiseless case, if some conditions are met, it is possible for the proposed Basic Subspace algorithm to identify the channel exactly using an observation interval of just ( J +2) T , if the length of the impulse response of a channel is JT , T being the symbol interval. In the noisy case, simulations have shown that the channel can be identified accurately by using a very small observation interval (comparable to ( J +2) T ).
39154147
Translating a Continuous-Time Temporal Logic into Timed Automata
LTLC is a continuous-time linear temporal logic for the specification of real-time systems. It can express both real-time systems and their properties. With LTLC, real-time systems can be described at different levels of abstraction, from high-level requirement specifications to low-level implementation models, and the conformance between different descriptions can be expressed by logical implication. The full logic of LTLC is undecidable. This paper will show that the existentially quantified fragment of LTLC is decidable. We achieve this goal by showing that the fragment can be translated into timed automata. Because the emptiness problem for timed automata is decidable, we then get a decision procedure for satisfiability for this fragment. This decidable part of LTLC is quite expressive. Many important real-time properties, such as bounded-response and bounded-invariance properties, can be expressed in it. The translation also enables us to develop a decision procedure for model checking real-time systems with quantifier-free LTLC specifications.
1247256
Differential Geometric Consistency Extends Stereo to Curved Surfaces
Traditional stereo algorithms implicitly use the frontal parallel plane assumption when exploiting contextual information, since the smoothness prior biases towards constant disparity (depth) over a neighborhood. For curved surfaces these algorithms introduce systematic errors to the matching process. These errors are non-negligible for detailed geometric modeling of natural objects (e.g. a human face). We propose to use contextual information geometrically. In particular, we perform a differential geometric study of smooth surfaces and argue that geometric contextual information should be encoded in Cartan's moving frame model over local quadratic approximations of the smooth surfaces. The result enforces geometric consistency for both depth and surface normal. We develop a simple stereo algorithm to illustrate the importance of using such geometric contextual information and demonstrate its power on images of the human face.
10816332
Statistically quantitative volume visualization
Visualization users are increasingly in need of techniques for assessing quantitative uncertainty and error in the images produced. Statistical segmentation algorithms compute these quantitative results, yet volume rendering tools typically produce only qualitative imagery via transfer function-based classification. This paper presents a visualization technique that allows users to interactively explore the uncertainty, risk, and probabilistic decision of surface boundaries. Our approach makes it possible to directly visualize the combined "fuzzy" classification results from multiple segmentations by combining these data into a unified probabilistic data space. We represent this unified space, the combination of scalar volumes from numerous segmentations, using a novel graph-based dimensionality reduction scheme. The scheme both dramatically reduces the dataset size and is suitable for efficient, high quality, quantitative visualization. Lastly, we show that the statistical risk arising from overlapping segmentations is a robust measure for visualizing features and assigning optical properties.
37978882
Hamiltonian cycles in 3-connected Claw-free graphs
It is shown that every 3-connected claw-free graph having at most 6δ - 7 vertices is hamiltonian, where δ is the minimum degree.
6029798
Efficient Holistic Twig Joins in Leaf-to-Root Combining with Root-to-Leaf Way
Finding all the occurrences of a twig pattern on multiple elements in an XML document is a core operation for efficient evaluation of XML queries. Holistic twig join algorithms, TwigStack and TSGeneric, have been recognized as optimal solutions when the twig pattern only involves A-D(ancestordescendant) relationships, while iTwigJoin can be optimal for partial twig patterns that contain A-D only or P-C (parent-child) only relationships. However, existing algorithms involve unnecessary computations and CPU cost of them can be further improved, and we in this paper mainly address this problem. We first propose three effective optimization rules to avoid those unnecessary computations, and then present two algorithms incorporated with these optimization rules to effectively answer twig patterns in leaf-to-root combining with root-to-leaf way. Experimental results on various datasets indicate that our algorithms perform significantly better than existing proposals.