Frame aggregation is one of the several enhancements proposed by IEEE 802.11 Task Group n to improve channel utilization. In frame aggregation, more than one data frame is encapsulated to form an aggregate, and once an aggregate is formed, a station contends to access the medium to transmit the entire aggregate. We refer to the number of data frames encapsulated within an aggregated frame as aggregate size. We claim that a static assignment of aggregate size leads to the following performance trade-off: a small value might be insufficient to mitigate the transmission overheads, thereby nullifying the whole purpose of frame aggregation; whereas, a large value might affect the quality of service experienced by higher layers due to the extra wait time to build an aggregate. In this paper, we characterize this trade-off by studying the impact of aggregate size on metrics like frame latency and channel utilization. To estimate these metrics, we model the transmission queue of an 802.11n station as a bulk service queuing system. We study the impact of aggregate size over a wide range of operating conditions covering several traffic arrival rates from higher layers, service distribution and data frame sizes. Apart from validating the existence of above-mentioned performance trade-off, our results indicate that the choice of aggregate size not only depends on the traffic arrival rate, but also (more interestingly) on data frame sizes. This calls for a dynamic assignment of aggregate size.

A prior probability estimator, a candidate for asymptotic efficiency, from within the class of recursive estimators proposed by the authors (1990) is synthesized. The authors prove asymptotic efficiency and convergence with probability one by involving a stochastic approximation theorem. The estimator can be implemented in practice for continuous, discrete, and mixed class conditional density functions, although continuous and mixed densities generally require repeated evaluation of expectations of certain functions through numerical techniques. Results of a simulation. experiment with discrete densities are included. Variations of the estimator, for computational simplicity, are discussed.>

The problem of estimating prior probabilities in a mixture of M classes with known class conditional distributions is studied. The observation is a sequence of n independent, identically distributed mixture random variables. The first moments of appropriately formulated functions of observations are used to facilitate estimation. The complexity of these functions may vary from linear functions of the observations (in some cases) to complex functions of class conditional density functions of observations, depending on the desired balance between computational simplicity and theoretical properties. A closed-form, recursive, unbiased, convergent estimator using the density function is presented: the result is valid for any problem in which prior probabilities are identifiable. Discrete and mixed densities require a minor modification. Three application examples are described. The class conditional expectations of density functions, required for the initialization of the estimator algorithm, are analytically evaluated for Gaussian and exponential densities.>

An edge model is developed along with the associated edge detection and contour determination algorithms. An edge point along any scan direction is defined as a sufficient jump in the mean of a time series that is assumed to be white Gaussian around the edge point with the same variance on either side. An edge contour is defined as a sequence of such edge points, each point denoted by the two coordinates of the image plane. Each coordinate sequence is modeled as first-order autoregressive Gaussian over and above a straight line sequence of arbitrary, finite slope and intercept. The first stage of the overall algorithm examines the time series along each row and each column, forms a window around each potential edge point, detects the location and estimates the variance of the error in the location of the edge point, by pattern recognition techniques. The second stage forms noisy edge contours by graph searching techniques. The third stage is smoothing of an edge contour, formulated as a well-defined Kalman smoothing problem. It is shown that the parameters of the system and noise can all be estimated from the data itself. Experimental results on a simple image are discussed.>

Develops an edge model in images and the associated edge detection and contour determination algorithms based on statistical signal processing approaches. An edge point along any scan direction is defined as a sufficient jump in the mean of a time series which is assumed to be white Gaussian around the edge point with the same variance on either side. An edge contour is defined as a sequence of such edge points, each point denoted by the two coordinates of the image plane. Each coordinate sequence is modeled as first order auto regressive Gaussian over and above a straight line sequence of arbitrary, finite slope and intercept. The first stage of our overall algorithm examines the time series along each row and each column, forms a window around each potential edge point, detects the location and estimates the variance of the error in the location of the edge point, by pattern recognition techniques. The second stage forms noisy edge contours by graph searching techniques. The third stage smoothing of an edge contour is formulated as a well defined Kalman smoothing problem. It is shown that the parameters of the system and noise can all be estimated from the data itself. Experimental results on a simple image are discussed.>

Pattern recognition with unknown costs of classification is formulated as a problem of adaptively learning the optimal scheme starting from an ad hoc decision scheme. It is shown that unsupervised learning is adequate to compute converging estimates of the mean values of the MN random classification costs, one for each combination of M classes and N decisions. The quantities required for estimation are 1) the decision taken, 2) the outcome of the cost random variable corresponding to the unknown class and the implemented decision, and 3) the a posteriori probabilities of all the classes. Some of the variations of the above learning scheme are discussed. An application of the proposed methodology for adaptively improving the performance of pattern-recognition trees is presented along with simulation results.

The minimum cost classifier when general cost functions are associated with the tasks of feature measurement and classification is formulated as a decision graph which does not reject class labels at intermediate stages. Noting its complexities, a heuristic procedure to simplify this scheme to a binary decision tree is presented. The optimization of the binary tree in this context is carried out using dynamic programming. This technique is applied to the voiced-unvoiced-silence classification in speech processing.

Speaker recognition schemes which work satisfactorily for small populations often fail when the number of classes is very large. One way of solving such problems is to go in for multistage classification schemes. The basic technique is to successively reduce the number of classes in several stages using one feature at each stage and when the number of classes is less than a predetermined value then the final decision is made. The whole scheme is designed so that the probability of error is fixed at an acceptable level. The computational cost of such a multistage scheme depends on the features used at each stage and the cost of measurement of each feature. The features to be used at each stage are determined so as to reduce the average computational cost for making a decision. This procedure is formulated as a stochastic optimal control problem and is illustrated by designing speaker recognition system for 60 speakers. The overall accuracy of the system is 97.2 %