The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on finite set statistics. It propagates the PHD function, a first-order moment of the full m...
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The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on finite set statistics. It propagates the PHD function, a first-order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of target states. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. We propose two different schemes according to which PHD filter can provide track-valued estimates of individual targets. Both schemes use the probabilistic data-association functionality albeit in different ways. In the first scheme, the outputs of the PHD filter are partitioned into tracks by performing track-to-estimate association. The second scheme uses the PHD filter as a clutter filter to eliminate some of the clutter from the measurement set before it is subjected to existing data association techniques. In both schemes, the PHD filter effectively reduces the size of the data that would be subject to data association. We consider the use of multiple hypothesis tracking (MHT) for the purpose of data association. The performance of the proposed schemes are discussed and compared with that of MHT.
An algorithm using the unsupervised Bayesian online learning process is proposed for the segmentation of object-based video images. The video image segmentation is solved using a classification method. First, differen...
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An algorithm using the unsupervised Bayesian online learning process is proposed for the segmentation of object-based video images. The video image segmentation is solved using a classification method. First, different visual features (the spatial location, colour and optical-flow vectors) are fused in a probability framework for image pixel clustering. The appropriate modelling of the probability distribution function (PDF) for each feature-cluster is obtained through a Gaussian distribution. The image pixel is then assigned a cluster number in a maximum a posteriori probability framework. Different from the previous segmentation methods, the unsupervised Bayesian online learning algorithm has been developed to understand a cluster's PDF parameters through the image sequence. This online learning process uses the pixels of the previous clustered image and information from the feature-cluster to update the PDF parameters for segmentation of the current image. The unsupervised Bayesian online learning algorithm has shown satisfactory experimental results on different video sequences.
We show in this paper that the BGS model of abstract state machines can be simulated by random access machines with at most a polynomial time overhead. This result is already stated in [5] with a very brief proof sket...
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We show in this paper that the BGS model of abstract state machines can be simulated by random access machines with at most a polynomial time overhead. This result is already stated in [5] with a very brief proof sketch. The present paper gives a detailed proof of the result. We represent hereditarily finite sets, which are the typical BGS ASM objects, by membership graphs of the transitive closure of the sets. Testing for equality between BGS objects can be done in linear time in our representation.
Default unification operations combine strict information with information from one or more defeasible feature structures. Many such operations require finding the maximal subsets of a set of atomic constraints that a...
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Default unification operations combine strict information with information from one or more defeasible feature structures. Many such operations require finding the maximal subsets of a set of atomic constraints that are consistent with each other and with the strict feature structure, where a subset is maximally consistent with respect to the subsumption ordering if no constraint can be added to it without creating an inconsistency. Although this problem is NP-complete, there are a number of heuristic optimizations that can be used to substantially reduce the size of the search space. In this article, we propose a novel optimization, leaf pruning, which in some cases yields an improvement in running time of several orders of magnitude over previously described algorithms. This makes default unification efficient enough to be practical for a wide range of problems and applications.
The advantage of a new scheme for balanced detection has been investigated to reduce the influence of optical interference fringes when performing diode laser gas absorption spectroscopy employing lock-in amplifiers a...
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The advantage of a new scheme for balanced detection has been investigated to reduce the influence of optical interference fringes when performing diode laser gas absorption spectroscopy employing lock-in amplifiers and pigtailed lasers. The influence of the fringes has been reduced by comparing the lock-in 2 f signal due to the gas sample with that of a reference beam. The frequency regions outside the absorption feature have been used to obtain information on the interference fringe impact on the signal of interest. We have demonstrated an efficient way to reduce the influence of such fringes by employing this technique combined with non-linear signal processing methods. The different steps of the algorithm are presented. In the experimental arrangement presented, a reduction of the optical interference fringes by about 10 times is achieved, as demonstrated in measurements on molecular oxygen around 761 nm. The new technique is compared with an analog technique for balanced detection and certain advantages of the computer algorithm are pointed out. In particular, the emerging field of gas spectroscopy in scattering solid media strongly benefits from the technique presented.
This paper presents real-time control characteristics of an electrorheological (ER) suspension system via a fuzzy sliding mode control algorithm which is formulated in a discrete-time manner by considering the samplin...
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This paper presents real-time control characteristics of an electrorheological (ER) suspension system via a fuzzy sliding mode control algorithm which is formulated in a discrete-time manner by considering the sampling rate of an electronic control unit for a vehicle system. A quarter-vehicle system consisting of sprung mass, spring, tire and a cylindrical ER damper (shock absorber) is constructed for the real-time control. After deriving the governing equation of motion of the proposed system, a discrete-time control model with system uncertainties is formulated. A stable sliding surface is then designed and followed by the formulation of the discrete-time sliding mode controller which consists of a discontinuous part and an equivalent part. In the controller formulation, the fuzzy control algorithm is also adopted to enhance system robustness to the mass variation and reaching time to the sliding surface. The controller is then experimentally realized for the manufactured quarter-vehicle ER suspension system. Control performances such as vertical acceleration are evaluated under various road conditions and presented in both time and frequency domains.
It is often desirable that referring expressions be chosen in such a way that their referents are easy to identify. This article focuses on referring expressions in hierarchically structured domains, exploring the hyp...
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It is often desirable that referring expressions be chosen in such a way that their referents are easy to identify. This article focuses on referring expressions in hierarchically structured domains, exploring the hypothesis that referring expressions can be improved by including logically redundant information in them if this leads to a significant reduction in the amount of search that is needed to identify the referent, Generation algorithms are presented that implement this idea by including logically redundant information into the generated expression, in certain well-circumscribed situations. To test our hypotheses, and to assess the performance of our algorithms, two controlled experiments with human subjects were conducted. The first experiment confirms that human judges have a preference for logically redundant expressions in the cases where our model predicts this to be the case. The second experiment suggests that readers benefit from the kind of logical redundancy that our algorithms produce, as measured in terms of the effort needed to identify the referent of the expression.
A new robust adaptive processor based on reiterative application of the median cascaded canceler (MCC) is presented and called the reiterative median cascaded canceler (RMCC). It is shown that the RMCC processor is a ...
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A new robust adaptive processor based on reiterative application of the median cascaded canceler (MCC) is presented and called the reiterative median cascaded canceler (RMCC). It is shown that the RMCC processor is a robust replacement for the sample matrix inversion (SMI) adaptive processor and for its equivalent implementations. The MCC, though a robust adaptive processor, has a convergence rate that is dependent on the rank of the input interference-plus-noise covariance matrix for a given number of adaptive degrees of freedom (DOF), N. In contrast, the RMCC, using identical training data as the MCC, exhibits the highly desirable combination of: 1) convergence-robustness to outliers/targets in adaptive weight training data, like the MCC, and 2) fast convergence performance that is independent of the input interference-plus-noise covariance matrix, unlike the MCC. For a number of representative examples, the RMCC is shown to converge using similar to 2.8N samples for any interference rank value as compared with similar to 2N samples for the SMI algorithm. However, the SMI algorithm requires considerably more samples to converge in the presence of outliers/targets, whereas the RMCC does not. Both simulated data as well as measured airborne radar data from the multichannel airborne radar measurements (MCARM) space-time adaptive processing (STAP) database are used to illustrate performance improvements over SMI methods.
This article describes an analytic study of the process used by John Chowning for the composition of Stria. This article is intended to complete the description of the compositional process given in a previous work (M...
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This article describes an analytic study of the process used by John Chowning for the composition of Stria. This article is intended to complete the description of the compositional process given in a previous work (Meneghini 2003), largely restated by Bossis in a subsequent paper (Bossis 2005). Stria was composed in 1977 and was fully generated by means of computer algorithms (Chowning 1977c) and the corresponding input files (Chowning 1977a, b): all the parameters of the sounds generated in Stria are determined and calculated by these algorithms on the basis of specific mathematical rules and of numerical parameters chosen by Mr. Chowning as input for the programs. For this reason, an accurate analysis of these algorithms is fundamental for a complete comprehension of the compositional process.
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