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Estimating joint probabilities plays an important role in many data mining and machine learning tasks. In this paper we introduce two methods, minAB and prodAB, to estimate joint probabilities. Both methods are based on a light-weight structure, partition support. The core idea is to maintain the partition support of itemsets over logically disjoint partitions and then use it to estimate joint probabilities of itemsets of higher cardinalitiess. We present extensive mathematical analyses on both methods and compare their performances on synthetic datasets. We also demonstrate a case study of using the estimation methods in Apriori algorithm for fast association mining. Moreover, we explore the usefulness of the estimation methods in other mining/learning tasks [9]. Experimental results show the effectiveness of the estimation methods. [ABSTRACT FROM AUTHOR]

The sustainable management of geographic information through time is fundamental in the field of spatial planning, because handling of long-term statistical indicators is useful for setting up prospective scenarios. Sustainability means the control of complex information which is often incomplete: data come from multiple scales and heterogeneous grids, from various suppliers, at many dates, with a semantic which keeps evolving. Under these conditions, a framework for data quality assessment and estimation of missing values is necessary. This paper draws out a proposition for a system based on an object oriented spatio-temporal data model fit to figure out some relationships of interest when managing missing values, and providing a support for storage and exploitation of metadata through the usage of thematic and geographic ontologies. [ABSTRACT FROM AUTHOR]

In the paper spatial component estimation for Track–Before–Detect (TBD) based motion capture systems is presented. Using TBD algorithms is possible to track markers at low Signal–to–Noise Ratio level that is a typical case in motion capture system. In the paper is considered special and the most popular rectangular capture area (cuboid volumen) for three configurations of image processing schemes: full frame processing, single camera optimized and multiple cameras optimized. In the article separate TBD processing for every camera is assumed. [ABSTRACT FROM AUTHOR]

From the late `70s on, Albrecht΄s Function Point Analysis provided an insightful way to size a software system moving from the elicitation of Functional User Requirement (FUR), making an evaluation more objective than done before using Lines of Code (LOC). This technique has currently a plenty of variants, some of them become international de jure standards (e.g. COSMIC, NESMA, Mark-II and FISMA) - called FSM (Functional Size Measurement) methods - and they are widely adopted worldwide. A common problem when using a FSM for estimation purposes is that the software size (that is a product measure, referring only to its functional side) is used as the solely independent variable to estimate the overall project effort, that includes the effort of both the functional and non-functional activities within the project΄s boundary, as currently stressed more and more in the Scope Management field, also in the Software Engineering domain (see NorthernScope and SouthernScope approaches), not knowing neither the approximated distribution between the two parts. This missing information, usually not gathered in projects΄ repositories, can be one of the reasons leading to a lower capability in estimating project effort. In 2003, a new technique called PSU (Project Size Unit) come out with the aim to size the `project΄ entity from a Project Management viewpoint. It can be used alone or jointly with a FSM unit. In the second case, the joint usage of the two values can improve what a FSM cannot measure and therefore estimate, that is the non-functional side of a software project. This paper presents a case study with 33 projects measured both with IFPUG FPA and COSMIC methods as well as with PSU, showing the obtained results using the different sizes for estimating the overall effort, and providing a rationale for the better results with PSU. [ABSTRACT FROM AUTHOR]

In this paper, we present an interesting filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, in case of multi-target tracking. With this paper, we aim to contribute in solving the problem of model based body motion estimation by using data coming from visual sensors. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets whose state and/or measurement (assumed to be linear) models changes during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate track. In order to deal with this problem, the IMM algorithm was combined with the Unscented Kalman Filter (UKF) [6]. Even if the later algorithm proved its efficacy in nonlinear model case; it presents a serious drawback in case of non Gaussian noise. To deal with this problem we propose to substitute the UKF with the Particle Filter (PF). To overcome the problem of data association, we propose the use of the JPDA approach, [12]. To reduce the computational burden of the latter technique, we choose firstly the most likely feasible events by applying a Genetic Algorithm; finally the derived algorithm from the combination of the IMM-PF algorithm and the GA-JPDA approach is noted GA-JPDA-IMM-PF. [ABSTRACT FROM AUTHOR]

The distributed nature of observations collected by inexpensive wireless sensors necessitates transmission of the individual sensor data under stringent bandwidth and power constraints. These constraints motivate: i) a means of reducing the dimensionality of local sensor observations; ii) quantization of sensor observations prior to digital transmission; and iii) estimators based on the quantized digital messages. These three problems are addressed in the present paper. We start deriving linear estimators of stationary random signals based on reduced-dimensionality observations. For uncorrelated sensor data, we develop mean-square error (MSE) optimal estimators in closed-form; while for correlated sensor data, we derive sub-optimal iterative estimators which guarantee convergence at least to a stationary point. We then determine lower and upper bounds for the Distortion-Rate (D-R) function and a novel alternating scheme that numerically determines an achievable upper bound of the D-R function for general distributed estimation using multiple sensors. We finally derive distributed estimators based on binary observations along with their fundamental error-variance limits for pragmatic signal models including: i) known univariate but generally non-Gaussian noise probability density functions (pdfs); ii) known noise pdfs with a finite number of unknown parameters; and iii) practical generalizations to multivariate and possibly correlated pdfs. Estimators utilizing either independent or colored binary observations are developed, analyzed and tested with numerical examples. [ABSTRACT FROM AUTHOR]

A design rework efforts estimation framework is developed in order to identify design rework efforts in the early phase of product design and development. An automotive water pump is used as a case study in this paper. Dependency among components, which are assessed numerically by Analytical Hierarchy Process, is a key driver on design rework efforts estimation. And then, Work transfer matrix method is a mean to estimate design rework efforts. Results based on worst case scenario were validated with experts from an engine manufacturer. [ABSTRACT FROM AUTHOR]

Direction-of-arrival (DOA) estimation is an important algorithm in array processing. Most traditional DOA estimation methods focus on narrow-band sources. A new wideband DOA estimation approach based on arbitrary group delay is proposed in this paper. In the proposed algorithm, echo gain at each direction is calculated based on digital group delay compensation. Digital group delay can realize time delay precisely, and has no restriction on delay step. The proposed method is able to operate arbitrary waveform and is suitable for linear frequency modulation signals, nonlinear frequency modulation signals, and even carrier free signals. Simulations with Gaussian function are carried out to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The aim of our contribution relies on studying the possibility of implementing a genetic algorithm in order to reproduce some characteristics of a simple laboratory experiment with human subjects. The novelty of our paper regards the estimation of the key-parameters of the algorithm, and the analysis of the characteristics of the estimator. [ABSTRACT FROM AUTHOR]

In this paper a method based on wavelet denoising (WD) is presented to estimate event related potentials (ERPs). Firstly, the empirical mode decomposition (EMD) is used to decompose the noisy signals and the intrinsic mode functions (IMFs) are produced; secondly, the IMFs are filtered by WD method with improved semi-soft threshold function and the new IMFs are selected according to the energy distribution; lastly, the ERPs are reconstructed from the selected IMFs. Computer simulation results and the experimental results demonstrate the effectiveness of the WD-based approach in ERPs estimation, which keeps the main waveform characteristics. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of a functional software size measurement experiment. We have conducted this experiment to analyze variances in functional software size measurement results among individuals. We aimed to isolate the factors that cause these variances. At the end of the study, statistical results are displayed. Common measurement problems were presented including their causes. And finally factors leading to discrepancies were identified based on these findings. [ABSTRACT FROM AUTHOR]

In the paper, the estimation algorithm of eye blinking for a computer animation system using biopotentials occurring on an actor΄s face is considered. The measuring system incorporates minimal 3–electrodes configuration, which is relatively cheap solution. The proposed algorithm allows both the measurement of eyes orientation and detection of eyes blinking. [ABSTRACT FROM AUTHOR]

We present an algorithm that makes an appropriate use of a Kalman filter combined with a geometric computation with respect to the localisation of a pollutant-emitting point source. Assuming resource-constrained inexpensive nodes and no specific placement distance to the source, our approach has been shown to perform well in estimating the coordinates and intensity of a source. Using local gossip to directionally propagate estimates, our algorithm initiates a real-time exchange of information that has as an ultimate goal to lead a packet from a node that initially sensed the event to a destination that is as close to the source as possible. The coordinates and intensity measurement of the destination comprise the final estimate. In this paper, we assert that this low-overhead coarse localisation method can rival more sophisticated and computationally-hungry solutions to the source estimation problem. [ABSTRACT FROM AUTHOR]

We consider the problem of allocating limited sampling resources in a ˵real-time″ manner with the purpose of estimating multiple binomial proportions. More specifically, the user is presented with `n΄ sets of data points, S1, S2, ..., Sn, where the set Si has Ni points drawn from two classes {ω1, ω2}. A random sample in set Si belongs to ω1 with probability ui and to ω2 with probability 1 − ui, with {ui}. i = 1, 2, ...n, being the quantities to be learnt. The problem is both interesting and non-trivial because while both n and each Ni are large, the number of samples that can be drawn is bounded by a constant, c. We solve the problem by first modelling it as a Stochastic Non-linear Fractional Knapsack Problem. We then present a completely new on-line Learning Automata (LA) system, namely, the Hierarchy of Twofold Resource Allocation Automata (H-TRAA), whose primitive component is a Twofold Resource Allocation Automaton (TRAA), both of which are asymptotically optimal. Furthermore, we demonstrate empirically that the H-TRAA provides orders of magnitude faster convergence compared to the LAKG which represents the state-of-the-art. Finally, in contrast to the LAKG, the H-TRAA scales sub-linearly. Based on these results, we believe that the H-TRAA has also tremendous potential to handle demanding real-world applications. [ABSTRACT FROM AUTHOR]

The motion parameter estimation for a class of movements in the space by using stereo vision is considered by observing a group of points. The considered motion equation can cover a wide class of practical movements in the space. The observability of this class of movement is clarified. The estimation algorithm for the motion parameters which are all time-varying is developed based on the second method of Lyapunov. The assumptions about the perspective system are reasonable and have apparently physical interpretations. The proposed recursive algorithm requires minor a priori knowledge about the system. Experimental results show the proposed algorithm is effective even in the presence of measurement noises. [ABSTRACT FROM AUTHOR]