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Digital Elevation Models (DEMs) and ortho-rectified images in Borneo (Kalimantan) island, where the probability of cloud coverage is too high to make DEMs by optical stereovision, are produced using ASTER data. By referring to Space Shuttle Radar Topography Mission (SRTM) data as the initial guess, we produced DEMs and orthoimages that make the most of ASTER data acquisition based on statistical estimation...
The problem of guidance control of an interceptor against a ballistic missile flying with some decoys around is studied in this paper. Before the determination of the real target, it is necessary for the interceptor to keep targets reachable as many as possible. First, the terminal guidance problem is analyzed under the complex detection circumstance, and the missile-target relative motion equations...
We develop a stochastic finite element based scheme for constructing a likelihood function for the Bayesian estimation of parameters in a distributed parameter model for thin film layered organic photovoltaic cells. The scheme is based on a distributed parameter model for the propagation of the optical wave through the various layers of the cell and its conversion to an electrical current in the active...
In this paper, we consider an impulsive mixture noise process, which commonly comes across in applications such as multiuser radar communications, astrophysical imaging in the microwave range and kick detection in oil drilling. The mixture process is in the time domain, whose probability density function (PDF) corresponds to the convolution of the components' PDFs. In this work, we concentrate on...
In this paper, the experimental and comparative studies on the decomposition algorithm and the increment estimation algorithm for unmodeled dynamics are studied, it has been confirmed that the increment estimation algorithm exhibits simple structure and the convenience of realization when maintaining the same estimation precision. With the above development, the unmodeled dynamics increment estimation...
A cooperative guidance law is developed for multiple interceptors to intercept multiple high-speed maneuvering targets cooperatively. Since the existence of background noise, uncertainties of the targets and low detection accuracy of the interceptors, only a dynamic region describing the location of targets by a probability density function can be obtained. A framework of cooperative detection is...
Coded pulse compression waveforms with matched filtering suffer from range sidelobe interferences. In this paper, we consider the estimating technique with side lobe suppression capability known as the linear minimum mean square error (LMMSE) based reiterated filtering, i.e. the adaptive pulse compression (APC). We derive the theoretical amplitude probability density function (PDF) of the APC estimation...
In this study, within the hour parametric probability density functions are calculated by using TEC (Total Electron Content) estimates. TEC estimates are obtained from IONOLAB-TEC method by using TNPGN-Active GPS network. GPS stations are placed at intervals with an average range of 80–100 km distance, between the years 2009 and 2012, provided input for calculation of probability density functions...
A distributed location detection problem in wireless sensor networks (WSNs) with M anchors and one node is considered in this paper. In the presence of the transmitting node at a known location, each anchor receives a noisy signal in the presence of node. In the absence of the node, the anchors receive only noise. Each anchor makes a decision as to whether the node is present or not by using a Neyman-Pearson...
In this correspondence, we address the problem of predicting the usable rates of a multiple access communications channel under real world conditions. In particular, we are interested in channels and communications signals that cannot be easily modeled by the usual Gaussian assumptions, and call for alternative methods of analysis. The examples we use in our development typically involve two communicators,...
This paper proposes an energy estimation technique utilizing only timing information from a Multi-channel Digital a SiPM (MD-SiPM) and presents a statistical analysis of the proposed approach for TOF PET applications with a LYSO scintillator. By utilizing only timing information for estimating energy of gamma photons, circuitry in the MD-SiPM is minimized so as to increase fill-factor and the dead...
This paper deals with the bit error rate (BER) semi-analytical prediction in a turbo coded digital communication system. We propose a new prediction method which is based on the kernel estimator of the probability density function (pdf). We assume that no knowledge on the distribution of the received soft samples is available. In the proposed method, we derived a new expression of the smoothing parameter...
In this work, we propose a novel hop-count based localization algorithm able to reduce errors due to mapping the hops into distance units. Using the proposed algorithm, the mean hop size ¯hs is locally derived at each regular or position-unaware node, thereby avoiding its broadcast by anchors (i.e., a few nodes aware of their exact position) as usually required in current state-of-the-art solutions...
Ranging errors are inevitable in all local positioning systems, including those based on Time-of-Flight (ToF) technique. Results of experiments show that the major cause for these errors is a signal degradation from multipath propagation. This effect is especially critical in case of Non-Light-of-Sight (NLOS) conditions. This paper describes causes that affects ranging errors for nanoLOC™-TOF-technology...
A new sparse kernel density estimator is introduced. Our main contribution is to develop a recursive algorithm for the selection of significant kernels one at time using the minimum integrated square error (MISE) criterion for both kernel selection. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate that the...
In order to explore the critical factors that influence the reliability of CNC grinding machine, and find the measures to improve the reliability and performance of CNC grinding machine, the reliability mathematical model of CNC grinding machine is analyzed and formulated. Firstly, the dependence of the failure models is tested based on the field failure data. Secondly, the probability density function...
We consider the problem of merging measurements when the sensors are likely to be affected by multiple malfunctions. An hybrid model is introduced which describes the dynamic behavior of the various sensors not only under each operating mode, but also during mode transitions. The data fusion problem is then written within the Bayesian probabilistic framework, as an estimation problem. Its optimal...
This article proposes to monitor industrial process faults using Kullback Leibler (KL) divergence. The main idea is to measure the difference between the distributions of normal and faulty data. Sensitivity analysis on the KL divergence under Gaussian distribution assumption is performed, which shows that the sensitivity of KL divergence increases with the number of samples. For non-Gaussian data,...
The paper considers the problem of reconstructing a probability density function from a finite set of samples independently drawn from it.We cast the problem in a Bayesian setting where the unknown density is modeled via a nonlinear transformation of a Bayesian prior placed on a Reproducing Kernel Hilbert Space. The learning of the unknown density function is then formulated as a minimum variance...
In this paper, we present a new neural and statistical classification approach. This procedure uses the neural network with competitive training to detect the local maxima of the probabilities density function's (pdf) which are considered as the prototype of the classes in the data distribution. In order to take account of different forms of distributions; Gaussian and non-Gaussian, we used as criteria...
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