We present a new bit and power allocation algorithm for OFDM systems with low comp.tational comp.exity. In order to reduce the comp.tational comp.exity, we divide all subcarriers into groups, and the same number of bi...
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We present a new bit and power allocation algorithm for OFDM systems with low comp.tational comp.exity. In order to reduce the comp.tational comp.exity, we divide all subcarriers into groups, and the same number of bits is allocated to the subcarriers in the same group. To minimize the performance loss due to grouping, subcarriers are grouped according to the channel gains. Based on this scheme, the bit and power allocation problem is redefined and the algorithm for the optimal solution is proposed. Simulation results verify that the proposed algorithm shows comp.rable throughput performance in comp.rison to an ideal allocation algorithm while comp.tational load is significantly reduced.
This paper proposes a new digital beamforming algorithm for vestigial sideband (VSB) based digital TV receivers. We consider the selection beamforming (SBF) algorithm combined with the spatial equalizer. To realize th...
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Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomp.ete ver...
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ISBN:
(纸本)0780383591
Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomp.ete version of an input pattern. In this paper, we introduce implicative fuzzy associative memories (IFAM's), a class of associative neural memories models based on fuzzy set theory. An IFAM consists of a network of comp.etely interconnected Pedrycz logic neurons whose connection weights are determined by the minimum of implications of presynaptic and postsynaptic activations. We present a series of results for autoassociative models including one pass convergence, unlimited storage capacity and tolerance with respect to eroded patterns.
k is the most important parameter in a text categorization system based on the k-nearest neighbor algorithm (kNN). To classify a new document, the k-nearest documents in the training set are determined first. The pred...
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k is the most important parameter in a text categorization system based on the k-nearest neighbor algorithm (kNN). To classify a new document, the k-nearest documents in the training set are determined first. The prediction of categories for this document can then be made according to the category distribution among the k nearest neighbors. Generally speaking, the class distribution in a training set is not even;some classes may have more samples than others. The system's performance is very sensitive to the choice of the parameter k. And it is very likely that a fixed k value will result in a bias for large categories, and will not make full use of the information in the training set. To deal with these problems, an improved kNN strategy, in which different numbers of nearest neighbors for different categories are used inst.ad of a fixed number across all categories, is proposed in this article. More samples (nearest neighbors) will be used to decide whether a test document should be classified in a category that has more samples in the training set. The numbers of nearest neighbors selected for different categories are adaptive to their sample size in the training set. Experiments on two different datasets show that our methods are less sensitive to the parameter k than the traditional ones, and can properly classify documents belonging to smaller classes with a large k. The strategy is especially applicable and promising for cases where estimating the parameter k via cross-validation is not possible and the class distribution of a training set is skewed.
In this paper, we propose a multi-user detection scheme using a hidden training sequence. The hidden training sequence, which uses a fraction of the informative sequence's transmitting power as a training sequence...
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An efficient packet transmission scheme is proposed to avoid unnecessary retransmission of correctly received symbols. In the proposed scheme, we implement small packets (referred to as minipackets) in a payload of tr...
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Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks such as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool fo...
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ISBN:
(纸本)0780388739
Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks such as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed on the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its comp.ex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake will not operate well because the moving object may have large differences in its position or shape, between successive images. Snake's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model (ACSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.
Multimodality often exhibits synergistic effects: each modality comp.iments and comp.nsates for other modalities in transferring coherent, unambiguous, and enriched information for higher interaction efficiency and im...
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Multimodality often exhibits synergistic effects: each modality comp.iments and comp.nsates for other modalities in transferring coherent, unambiguous, and enriched information for higher interaction efficiency and improved sense of presence. In this paper, we explore one such phenomenon: a positive interaction among the geometric field of view, proprioceptive interaction, and tactile feedback. We hypothesize that, with proprioceptive interaction and tactile feedback, the geometric field of view and thus visibility can be increased such that it is larger than the physical field of view, without causing a significant distortion in the user's distance perception. This, in turn, would further help operation of the overall multimodal interaction scheme as the user is more likely to receive the multimodal feedback simultaneously. We tested our hypothesis with an experiment to measure the user's change in distance perception according to different values of egocentric geometric field of view and feedback conditions. Our experimental results have shown that, when coupled with physical interaction, the GFOV could be increased by up to 170 percent of the physical field of view without introducing significant distortion in distance perception. Second, when tactile feedback was introduced, in addition to visual and proprioceptive cues, the GFOV could be increased by up to 200 percent. The results offer a useful guideline for effectively utilizing of modality comp.nsation and building multimodal interfaces for close range spatial tasks in virtual environments. In addition, it demonstrates one way to overcome the shortcomings of the narrow (physical) fields of views of most contemporary HMDs.
We propose a fast convergence direction-of-arrival (DOA) estimation scheme with a very small error margin for employing an electronically passive array radiator (ESPAR) antenna. The proposed scheme maximizes the cross...
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We propose a fast convergence direction-of-arrival (DOA) estimation scheme with a very small error margin for employing an electronically passive array radiator (ESPAR) antenna. The proposed scheme maximizes the cross-correlation between a reference signal and the antenna output by using a conjugate gradient algorithm. Simulation results show that DOA estimation is performed with an average error margin within 2.22° and average convergence within 21 iterations. The performance of DOA estimation is not affected by interference signals when non-correlated pilot symbols are used, and the best efficiency is achieved when orthogonal codes such as Walsh codes are used. comp.risons with current techniques show the superior performance of our proposed scheme. Accordingly, the proposed scheme yields a big improvement over adaptive control of ESPAR antenna in the aspects of convergence speed and DOA estimation precision, and thus it can be a practical approach to small-size and low-cost smart antennas for mobile terminals and location detection applications.
This article discusses some fundamental issues that arise as robots become much larger or much smaller than everyday human scale. The article's title is respectfully derived from C.J. Pennycuick's "Newton...
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This article discusses some fundamental issues that arise as robots become much larger or much smaller than everyday human scale. The article's title is respectfully derived from C.J. Pennycuick's "Newton Rules Biology: A physical approach to biological problems" (1992), from which robot and robotic system designers can learn much from the performance, successes, and failings of biological evolution. Much of the discussion will focus on scaling issues that were first recognized in biological versus technological domains. These issues are discussed mostly from the biological perspective, with the assumption that the reader will have no difficulty extending the underlying principles to technological domains in general, and to the robotics domain specifically. Discussion is also focused on a hypothetical family of geometrically similar vacuum cleaners.
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