Smallest Euclidean Distance Research Articles

Speech tempo and fundamental frequency patterns: A case study of male monozygotic twins and an age- and sex-matched sibling

This case study describes an investigation into the speaking characteristics of a set of male monozygotic (MZ) twins (T1 and T2) and an age- and sex-matched sibling (S). Measures of speech tempo and fundamental frequency (F0) were analysed in the speech samples of a reading passage. Results showed significant between-sibling differences for sentence durations and F0 parameters; however, Euclidean distance (ED) measures revealed the smallest distances between the F0 parameters of the MZ twins. The smallest ED values were also observed between T1 and T2 for word durations, pause durations, all-voiced sample durations, and all the all-voiced sample F0 parameters. Greater similarities were observed across all three siblings for the speech tempo and dynamic F0 parameters.

WSNHA-GAHR: a greedy and A* heuristic routing algorithm for wireless sensor networks in home automation

The use of wireless sensor networks in home automation (WSNHA) is a growing trend, on account of their self-organisation, low cost and potential for rapid deployment. Routing in WSNHA is a challenging research topic, because of the requirements of low energy consumption, low storage, low computational complexity, fast response, self-adaptation to unpredictable topological changes and robustness with respect to discontinuous interference. With the development of localisation techniques, localised routing offers a number of attractive benefits, but not all are suitable for WSNHA. In this study, the authors propose a new routing algorithm for WSNHA, called WSNHA-GAHR (greedy and A* heuristic routing for WSNHA). The algorithm uses greedy forwarding, based on the smallest Euclidean distance, to minimise the number of hops for data packet transmission, and thus conserve energy. It also automatically adjusts the route, using the A* heuristic algorithm, when local minimum problems and unpredictable topological changes occur in WSNHA. In addition, link priority classification gives WSNHA-GAHR a greater tolerance than ZigBee routing algorithm for discontinuous interference. Simulation results show that this routing protocol dramatically improves reliability and reduces average packet delay, without introducing additional overhead.

A hybrid approach for tomographic inversion of crosshole seismic first-arrival times

A sequential hybrid approach was presented here to invert crosshole seismic first-arrival times. The proposed tomographic scheme combined a simple simulated annealing algorithm with a linearized smoothness-constrained least-squares inversion. The simulated annealing was implemented to obtain a background velocity distribution used by the linearized inversion for the initial guess. The linearized component was based on the functional description of traveltimes. This indicates a nonlinear function, the eikonal equation, providing traveltimes for a given slowness model. Thus an explicit ray tracing was not required by the linearized scheme. The velocity updates were obtained by a matrix inversion based on an iterative conjugate gradient-like LSQR algorithm. Second-difference regularization was used to stabilize the solutions. The Jacobian matrix giving the partial derivatives with respect to the model parameters was constructed by a finite-difference approximation based on the perturbation of the cell slowness. The traveltimes for both the hybrid and linearized schemes were calculated by a fast finite-difference eikonal solver. The hybrid scheme was tested by using both synthetic and field data sets based on the crosshole geometry. According to the tests studies, the tomograms resulted from the hybrid approach better imaged the subsurface velocity distribution. Also the hybrid optimization was characterized by quicker convergence rate than the conventional optimization based on only the linearized inversion. The tests with the synthetic data set also showed that the hybrid approach yielded a solution having lower rms residual, smaller Euclidean distance and lower relative errors in the cell velocities.

Recognition of Isolated Handwritten Characters in Gurmukhi Script

Isolated handwritten character recognition has been the subject of intensive research during last decades because it is useful in wide range of real world problems. It also provides a solution for processing large volumes of data automatically. Work has been done in recognizing handwritten characters in many languages like Chinese, Arabic, Devnagari, Urdu and English. The work presented in this thesis, focuses on the problem of recognition of isolated handwritten characters in Gurmukhi script. The whole process consists of two stages. The first, feature extraction stage analyzes the set of isolated characters and selects a set of features that can be used to uniquely identify characters. The performance of recognition system depends heavily on what features are being used. The selection of stable and representative set of features is the heart of recognition system. The feature extraction method Zoning, is used for extracting features of the character under consideration in this problem. In Zoning method, the frame containing the character is divided into several overlapping or non-overlapping zones and the densities of object pixels in each zone are calculated. Densities are used to form a representation. The final, classification stage is the main decision making stage of the recognition system. It uses features extracted in the feature extraction stage to identify the character. K-Nearest Neighbor and Support Vector Machine are the two classifiers used for identifying the character in the problem. In k-nearest classification method, the Euclidean distance between the test point and all the reference points is calculated in order to find K nearest neighbors, and then the obtained distances are ranked in ascending order and the reference points corresponding to the k smallest Euclidean distances are taken. The Support Vector Machine (SVM) is learning machine with very good generalization ability. SVM implements the Structural Risk Minimization Principal which seeks to minimize an upper bound of the generalization error. An SVM classifier discriminates two classes of feature vectors by generating hyper-surfaces in the feature space, which are optimal in a specific sense that is the hyper-surface obtained by the SVM optimization is guaranteed to have the maximum distance to the nearest” support vectors. SVM operate on kernel evaluations of the feature vectors. An annotated sample image database of isolated handwritten characters in Gurmukhi script has been prepared which has been used for training and testing of the system.

Contourlet-based super resolution restoration for remote sensing images

This paper presented a Contourlet-based super resolution for remote sensing images,which adopted Contourlet coefficients as the features.It described a better degree of directionality and anisotropy,and used the smallest Euclidean distance as the computed feature by global searching.According to the distributions of the found coefficients in finer scale,the Hidden Markov Tree(HMT)model was introduced to the remote sensing images in Contourlet domain.And the Expectation Maximization(EM)algorithm was applied to estimate the parameters of the HMT model.With the parameters,the Contourlet coefficients were renewed by using Bayesian estimation theory.Finally,the super resolution restorationfor remote sensing images has achieved better effect.

Automatic lung nodule matching on sequential CT images

We propose an automatic segmentation and registration method that provides more efficient and robust matching of lung nodules in sequential chest computed tomography (CT) images. Our method consists of four steps. First, the lungs are extracted from chest CT images by the automatic segmentation method. Second, gross translational mismatch is corrected by optimal cube registration. This initial alignment does not require extracting any anatomical landmarks. Third, the initial alignment is step-by-step refined by hierarchical surface registration. To evaluate the distance measures between lung boundary points, a three-dimensional distance map is generated by narrow-band distance propagation, which drives fast and robust convergence to the optimal value. Finally, correspondences of manually detected nodules are established from the pairs with the smallest Euclidean distances. Experimental results show that our segmentation method accurately extracts lung boundaries and the registration method effectively finds the nodule correspondences.

Using Geometrical Properties for Fast Indexation of Gaussian Vector Quantizers

Vector quantization is a classical method used in mobile communications. Each sequence of samples of the discretized vocal signal is associated to the closest -dimensional codevector of a given set called codebook. Only the binary indices of these codevectors (the codewords) are transmitted over the channel. Since channels are generally noisy, the codewords received are often slightly different from the codewords sent. In order to minimize the distortion of the original signal due to this noisy transmission, codevectors indexed by one-bit different codewords should have a small mutual Euclidean distance. This paper is devoted to this problem of index assignment of binary codewords to the codevectors. When the vector quantizer has a Gaussian structure, we show that a fast index assignment algorithm based on simple geometrical and combinatorial considerations can improve the SNR at the receiver by 5dB with respect to a purely random assignment. We also show that in the Gaussian case this algorithm outperforms the classical combinatorial approach in the field.

Bit-interleaved coded modulation with iterative decoding and 8 PSK signaling

We have suggested bit-interleaved coded modulation with soft decision iterative decoding (BICM-ID) for bandwidth-efficient transmission over Gaussian and fading channels. Unlike trellis coded modulation, BICM-ID has a small free Euclidean distance but large diversity order due to bit interleaving. With iterative decoding, soft bit decisions can be employed to significantly improve the conditional intersignal Euclidean distance. This leads to a large coding gain, comparable to that of turbo TCM, over both Gaussian and Rayleigh fading channels with much less system complexity. We address critical design issues to enhance the decoding performance and provide the analytical bounds on the performance with an ideal feedback assumption. We investigate the performance characteristics of BICM-ID through extensive simulations and show that at high signal to noise ratios, the performance of BICM-ID converges to the performance assuming error-free feedback.

A single multidimensional model for discrimination, identification and preferential choice

Two forms of a mathematical model for tasks involving three alternatives are given. The model can be applied to triad discrimination, preferential choice and two-stimulus identification. In all cases multivariate normal probabilistic assumptions are made with a decision rule based on the smallest Euclidean distance between one of the alternatives and the other two. The model is presented as an indefinite quadratic form and as a weighted sum of central F distribution functions. The model should have numerous applications in marketing, sensory evaluation, experimental psychology and economics.

Joint trellis coded quantization/modulation

A joint source/channel coding system constructed using trellis coded quantization (TCQ) and trellis coded modulation (TCM) is described. Identical trellises are used in the TCQ and TCM systems, and a straightforward mapping of TCQ codewords to TCM symbols is presented which guarantees that Euclidean squared distance in the channel is commensurate with quantization mean-square error (MSE). Hence, likely TCM error events of small Euclidean distance cause only a small increase in overall source coding MSE. >

Automatic Speaker Recognition Based on Pitch Contours

An automatic speaker-recognition method, using temporal variations of pitch in speech as a speaker-identifying characteristic, is described. The pitch data was obtained from 60 utterances, consisting of six repetitions of the same sentence, spoken by 10 speakers. The pitch data for each utterance was represented by a 20-dimensional vector in the Karhunen-Loève coordinate system. The 20-dimensional vectors representing the pitch contours were linearly transformed so that the ratio of interspeaker to intraspeaker variance in the transformed space was maximized. A reference vector was formed for each speaker by averaging the transformed vectors of that speaker. The recognition procedure was based on measuring the Euclidean distance between the test vector and the reference vectors in the transformed space; the speaker corresponding to the reference vector with the smallest distance was selected as the speaker of the test utterance. The percentage of correct identifications was found to be 97%. The results suggest that temporal variations of pitch could be used effectively for automatic speaker recognition.

Automatic Speaker Recognition Based on Pitch Contours

The results of a study aimed at finding the importance of pitch for automatic speaker recognition are presented. Pitch contours were obtained for 60 utterances, each approximately 2-sec in duration, of 10 female speakers. A data-reduction procedure based on the Karhunen-Loeve representation was found effective in representing the pitch information in each contour in a 20-dimensional space. The data were divided into two portions; one part was used to design the speaker recognition system, while the other part was used to test the effectiveness of the design. The 20-dimensional vectors representing the pitch contours of the design set were linearly transformed so that the ratio of interspeaker to intraspeaker variance in the transformed space was maximum. A reference utterance was formed for each speaker by averaging the transformed vectors of that speaker. The test utterance was assigned to the speaker corresponding to the reference utterance with the smallest Euclidean distance in the transformed space. The percentage of correct identifications using the above procedure was found to be 97%. The recognition rate increased to 98% with the addition of duration as an independent measure.