Chirp Signaling Offers Modulation Scheme for Underwater Communications

Information contained in the slope of chirp signals can be employed for digital underwater acoustic communications across a broad range of applications

Classification of Cylindrical Targets Buried in Seafloor Sediments

This paper presents the development and evaluation of a time-frequency processing technique for detection and classification of buried cylindrical targets from chirpbased parametric sonar data. The software is designed to discriminate between cylindrical targets —such as cables— of different diameters, which need to be identified as different from other strong reflectors or point targets. The method is evaluated on synthetic data generated with an acoustic scattering model for elastic cylinders for seven different diameters. The model generates characteristic responses of targets acquired by a parametric sonar system. The signal at the sonar receiver hydrophones is first windowed to reduce the data to the region of interest (buried target return). This return is then transformed using joint timefrequency transforms (we use the Wigner and Choi-Williams distributions) to produce a 2D image of the return. Dimensionality reduction and feature extraction are performed by singular value decomposition of this time-frequency image. Linear, quadratic, and Mahalanobis discriminant functions are then applied to the most significant singular values to produce the final classification. The study is carried out for various scenarios of free field response of targets as well as for responses from targets buried in sediment

Determination of Head Kinematics from Impact Acceleration Test Data Using Neural Networks

This paper presents a study of feed-forward neural network (NN) systems developed to determine the head kinematics of subjects who are exposed to impact accelerations. The neural networks process accelerometer data collected during short-duration impact acceleration tests conducted at the National Biodynamcis Laboratory of the University of New Orleans. During an impact acceleration experiment, the subject sits on the sled chair and a piston gives impetus to the sled to travel down a track. Head data is gathered by an array of nine accelerometers. Two more accelerometers are mounted on the sled. The neural processing systems produce the history of the rotational and translational position, velocity, and acceleration of the origin of the accelerometer array mounted on the mouth. Output produced by a least squares algorithm that uses both photographic and accelerometer raw data are used as a baseline and to provide training data for the neural networks. The main disadvantages of the NNs are their speed, and that statistical information and accurate modeling of the testing system are not required. Results show that the neural networks provide accurate information about the kinematics of the subject even when no photographic data are used

Chirp Slope Keying for Underwater Communications

This paper presents a novel broadband modulation method for digital underwater communications: Chirp Slope Keying (CSK). In its simplest form, the binary information modulates the slope of a linear chirp, with up-chirps representing ones and down-chirps representing zeros. Performance evaluation in the form of probability of error vs. SNR show that the system performs as expected for AWGN environments and very well for more realistic models for underwater acoustical communications, such as the Raylegih channel with Doppler, delays, phase offset, and multipath

Determination of Head Kinematics from Impact Acceleration Test Data Using Neural Networks

This paper presents a study of feed-forward neural network (NN) systems developed to determine the head kinematics of subjects who are exposed to impact accelerations. The neural networks process accelerometer data collected during short-duration impact acceleration tests conducted at the National Biodynamcis Laboratory of the University of New Orleans. During an impact acceleration experiment, the subject sits on the sled chair and a piston gives impetus to the sled to travel down a track. Head data is gathered by an array of nine accelerometers. Two more accelerometers are mounted on the sled. The neural processing systems produce the history of the rotational and translational position, velocity, and acceleration of the origin of the accelerometer array mounted on the mouth. Output produced by a least squares algorithm that uses both photographic and accelerometer raw data are used as a baseline and to provide training data for the neural networks. The main disadvantages of the NNs are their speed, and that statistical information and accurate modeling of the testing system are not required. Results show that the neural networks provide accurate information about the kinematics of the subject even when no photographic data are used

Chirp Slope Keying for Underwater Communications

This paper presents a novel broadband modulation method for digital underwater communications: Chirp Slope Keying (CSK). In its simplest form, the binary information modulates the slope of a linear chirp, with up-chirps representing ones and down-chirps representing zeros. Performance evaluation in the form of probability of error vs. SNR show that the system performs as expected for AWGN environments and very well for more realistic models for underwater acoustical communications, such as the Raylegih channel with Doppler, delays, phase offset, and multipath

TCM Decoding Using Neural Networks

This paper presents a neural decoder for trellis coded modulation (TCM) schemes. Decoding is performed with Radial Basis Function Networks and Multi-Layer Perceptrons. The neural decoder effectively implements an adaptive Viterbi algorithm for TCM which learns communication channel imperfections. The implementation and performance of the neural decoder for trellis encoded 16-QAM with amplitude imbalance are analyzed

Asymptotic Performance of the Pth Power-Law Phase Estimator

An expression for the true variance of the Pth powerlaw phase estimator, as the number of samples approaches infinity, is given. This expression is an extension to the linear approximation of Moeneclaey and de Jonghe [1] which is known to be inadequate in some practical systems. Our new expression covers general 2π/P-rotationally symmetric constellations that include those of PAM, QAM, PSK, Star M-QAM, MR-DPSK, and others. This expression also generalizes the known expressions for QAM and PSK. Additionally, our expression reduces to the Cramer-Rao bound given by Steendam and Moeneclaey [9], as SNR goes to zero. Monte Carlo simulations provide experimental verification of the theoretical expression for various constellations

Image Classification using Version Spaces

This paper presents the candidate elimination implementation of the version space strategy for classification of photographic data. It is shown that very accurate classification is easily achieved and that only a small number of training samples are needed to generate the rules

Image Classification using Version Spaces

This paper presents the candidate elimination implementation of the version space strategy for classification of photographic data. It is shown that very accurate classification is easily achieved and that only a small number of training samples are needed to generate the rules