43356203
Robust transforms for channel estimator in clustered OFDM for high rate wireless data
Clustered OFDM can provide in-band diversity gain for wideband wireless channels. It is a promising technique for high-rate wireless packet data access, such as mobile multimedia communications. Due to smaller size of each cluster for clustered OFDM than for classical(non-clustered) OFDM, edge effects can be very large. We present new transforms for channel estimators in clustered OFDM systems. The new transforms are independent of the channel delay profiles and can effectively mitigate the edge effects. Computer simulation shows that the performance of clustered OFDM with the estimator using the new transforms is very close to the performance with the optimum estimator that depends on the channel delay profile. For the typical-urban or hilly-terrain delay profiles, clustered OFDM using the new transform based estimator is almost as good as classical OFDM with transmitter diversity.
8467998
Truncation for Low-Complexity MIMO Signal Detection
Joint maximum-likelihood (JML) detector may be used in memoryless multiple-input multiple-output (MIMO) systems to obtain optimal detection performance. However, JML detector performs an exhaustive search and has prohibitively large decoding complexity. To reduce the complexity of MIMO signal detection, minimum mean-square-error (mmse) linear detector (LD), decision-feedback detector (DFD), group detector, and sphere detector (SD) may be used. In this correspondence, we propose a truncation based detector for low-complexity MIMO signal detection, and give theoretical insight into the design and performance of such a detector. We study bitruncation in detail and present two bitruncation approaches. These approaches have low-complexity, and computer simulation results show that they outperform mmse-LD and mmse-DFD
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 ).
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.
16653782
Genetic Algorithm Based on Independent Component Analysis for Global Optimization
We design a new Genetic Algorithm based on Independent Component Analysis for unconstrained global optimization of continuous function. We use Independent Component Analysis to linearly transform the original dimensions of the problem into new components which are independent from each other with respect to the fitness. We project the population on the independent components and obtain corresponding sub-populations. We apply genetic operators on the sub-populations to generate new sub-populations, and combine them as a new population. In other words, we use Genetic Algorithm to find the optima on the independent components, and combine the optima as the global optimum for the problem. As we actually reduce the original high-dimensional problem into sub-problems of much fewer dimensions, the solution space decreases exponentially and thus the problem becomes easier for Genetic Algorithm to solve. The experiment results verified that our algorithm produced optimal or close-to-optimal solutions better than or comparable to those produced by some of other Genetic Algorithms and it required much less fitness evaluations of individuals.
7034186
Semiautomatic simplification
We present semisimp, a tool for semiautomatic simplification of three dimensional polygonal models. Existing automatic simplification technology is quite mature, but is not sensitive to the heightened importance of distinct semantic model regions such as faces and limbs, nor to simplification constraints imposed by model usage such as animation. semisimp allows users to preserve such regions by intervening in the simplification process. Users can manipulate the order in which basic simplifications are applied to redistribute model detail, improve the simplified models themselves by repositioning vertices with propagation to neighboring levels of detail, and adjust the hierarchical partitioning of the model surface to segment simplification and improve control of reordering and position propagation. ACM Category and Subject Descriptor: I.3.5 [Computer Graphics] Computational Geometry and Object Modeling hierarchy and geometric transformations Additional
7761968
A PTAS for Semiconductor Burn-in Scheduling
In this paper a polynomial time approximation scheme, PTAS for short, is presented for the problem of scheduling jobs in a batch processing system. Each job has a pre-defined release date, which indicates when the job is available, and a pre-defined burn-in time, which is the least time needed for processing the job. At one time, at most B jobs can be processed together, where B is a pre-given number. No preemption is permitted.
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.