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This paper presents pattern classification to a predefined set of classes as a missing data task. This is achieved by first augmenting the feature vector of each training pattern with the corresponding binary codeword representing its class. A Restricted Boltzmann Machine (RBM) or a Dictionary Learning (DL) algorithm is then trained on the augmented feature space. During the classification stage,...
In this paper, we describe an algorithm for sensor network localization (SNL) that proceeds by dividing the whole network into smaller subnetworks, then localizes them in parallel using some fast and accurate algorithm, and finally registers the localized subnetworks in a global coordinate system. We demonstrate that this divide-and-conquer algorithm can be used to leverage existing high-precision...
In this paper we propose the application of spatial decomposition methods for improving the prediction accuracy of linear regression models when used for establishing radio environment maps through large-scale pathloss estimation. We discuss two basic decomposition schemes, namely square and radial decomposition, and apply them in a reference scenario for a high-power TV transmitter. Our results show...
prosodic focus, using a paradigm based on digit strings, in which the same material and discourse contexts can be used in different languages. We found a striking difference between languages like English and Mandarin Chinese, where prosodic focus is clearly marked in production and accurately recognized in perception, and languages like Korean, where prosodic focus is neither clearly marked in production...
We study the problem of recovering sparse vectors given possibly erroneous support estimates. First, we provide necessary and sufficient conditions for weighted ℓ1 minimization to successfully recovery all sparse signals whose support estimate is sufficiently accurate. We relate these conditions to the analogous ones for ℓ1 minimization, showing that they are equivalent when the support estimate is...
Recently, ESPRIT-based parameter estimation algorithms have been developed to exploit the structure of signals from strictly second-order (SO) non-circular (NC) sources. They achieve a higher estimation accuracy and can resolve up to twice as many sources. However, these NC methods assume that all the received signals are strictly non-circular. In this paper, we present the C-NC Standard ESPRIT and...
Spectral subtraction is one of the earliest and longest standing, popular approaches to noise compensation and speech enhancement. A literature search reveals an abundance of recent research papers that report the successful application of spectral subtraction to noise robust automatic speech recognition (ASR). However, as with many alternative approaches, the benefits lessen as noise levels in the...
Belief propagation (BP), also called “sum-product algorithm”, is one of the best-known graphical model for inference in statistical physics, artificial intelligence, computer vision, etc. Furthermore, a recent research in distributed sensor network localization showed us that BP is an efficient way to obtain sensor location as well as appropriate uncertainty. However, BP convergence is not guaranteed...
In comparison with standard HMM (Hidden Markov Model) with forced alignment, this paper discusses two automatic segmentation algorithms from different points of view: the probabilities of insertion and omission, and the accuracy. The first algorithm, hereafter named the refined HMM algorithm, aims at refining the segmentation performed by standard HMM via a GMM (Gaussian Mixture Model) of each boundary...
The sensors that acquire 3D data play an important role in many applications. In addition, they have been used in the robotic field for several purposes, for instance, enhancing the navigation of mobile robots, object detection, scene reconstruction, 3D inspection of parts and others. Moreover, a significant amount of devices with distinct cost, accuracy and features have been released in the recent...
In this paper we provide a method for detection of peaks in spectral representations of music signals for the purpose of partial tracking. The basic idea is to detect local maxima in any digitized signal and use statistical techniques for rejecting spurious peaks. Detected peaks are then connected to each other to form partial tracks. The performance of our algorithm is investigated in two levels...
Super-resolution mapping (SRM) aims to locate subpixel class fractions geographically in the area represented by a mixed pixel. The accuracy of small sub-pixel class patches are represented by the popular SRM method is explored. It is shown that the accuracy of predicted patch location from the Hopfield Neural of SRM is a function of patch size. Specifically, the accuracy with which patch location...
On modern parallel architectures, floating-point computations may become non-deterministic and, therefore, non-reproducible mainly due to non-associativity of floating-point operations. We propose an algorithm to solve dense triangular systems by leveraging the standard parallel triangular solver and our, recently introduced, multi-level exact summation approach. Finally, we present implementations...
Evaluating workers is a critical aspect of any crowdsourcing system. In this paper, we devise techniques for evaluating workers by finding confidence intervals on their error rates. Unlike prior work, we focus on “conciseness”—that is, giving as tight a confidence interval as possible. Conciseness is of utmost importance because it allows us to be sure that we have the best guarantee possible on worker...
The automatic recognition of planes in aerial images is an important application in the image analysis field. However, it remains a problem despite many years of work due to the arbitrary original poses and the variation in the shapes of planes. This paper proposes a novel approach for automatic aircraft detection based on statistical theory and common features of different kinds of planes. Experiments...
In this report a new algorithm is presented for the spectral factorization of a two-sided symmetric polynomial. The method is based on the discrete Fourier transform theory (DFT) and its relationship to the Z-transform. Involving DFT computational techniques, namely the famous fast Fourier transform routine (FFT), brings high computational efficiency and reliability. The power of the proposed procedure...
Pool of knowledge available to the mankind depends on the source of learning resources, which can vary from ancient printed documents to present electronic material. The rapid conversion of material available in traditional libraries to digital form needs a significant amount of work if we are to maintain the format and the look of the electronic documents as same as their printed counterparts. Most...
We designed and evaluated SplitVector, a new vector field display approach to help scientists perform new discrimination tasks on scientific data shown in virtual environments (VEs). Our empirical study compared the SplitVector approach with three other approaches of direct linear representation, log, and text display common in information-rich VEs or IRVEs. Our results suggest the following: (1)...
We develop an analytical modeling framework for efficient prediction of cache miss ratios based on reuse distance distributions. The only input needed for our predictions is the reuse distance distribution of a program execution: previous work has shown that they can be obtained with very small overhead by sampling from native executions. This should be contrasted with previous approaches that base...
Aflatoxin is one of the mycotoxins released by Aspergillus flavus and Aspergillus parasiticus. It is carcinogenic and has stringent regulations in terms of residue limit across the globe. Various food commodities like chilli, groundnut, maize and nutmeg are susceptible to aflatoxin and face challenges in meeting the residue limits set by various importing countries. ITC Limited, an FMCG Conglomerate...
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