An underwater acoustic communications scheme with inherent scale diversity for multiple users

Wideband underwater acoustic communication channels can cause undesirable multipath and Doppler scaling distortions to propagating acoustic signals. In this paper, we propose to exploit a time-scale canonical representation for wideband time-varying channels to achieve joint multipath-scale diversity. We design a signaling scheme with hyperbolic time-frequency signatures that is matched to the underwater acoustic environment to achieve scale diversity. The signaling scheme, combined with code-division multiple-access, is extended to multiple user transmission to improve multiuser detection performance, as demonstrated with simulations. © 2013 MTS

A new signaling scheme for Underwater Acoustic communications

Underwater Acoustic (UWA) communications has attracted a lot of interest in recent years motivated by a wide range of applications. Different signaling solutions have been developed to date including non-coherent communications, phase coherent systems, multi-input multi-output (MIMO) solutions and multi-carrier based approaches. In this paper, we develop a novel UWA communications paradigm using biomimetic signals. In our scheme, digital information is mapped to the parameters of a class of biomimetic signal set and at the receiver an estimator to obtain the parameter values is utilized. To facilitate this, we develop analytical signal models with nonlinear instantaneous frequencies matching mammalian sound signatures in the time-frequency plane. We provide suitable receiver structures, and present decoding results using data recorded during the Kauai Acomms MURI 2011 (KAM11) UWA communications experiment. © 2013 MTS

An underwater acoustic communication scheme exploiting biological sounds

Underwater acoustic (UWA) communications have attracted a lot of interest in recent years motivated by a wide range of applications including offshore oil field exploration and monitoring, oceanographic data collection, environmental monitoring, disaster prevention, and port security. Different signaling solutions have been developed to date including non-coherent communications, phase coherent systems, multi-input and multi-output solutions, time-reversal-based communication systems, and multi-carrier transmission approaches. This paper deviates from the traditional approaches to UWA communications and develops a scheme that exploits biomimetic signals. In the proposed scheme, a transmitter maps the information bits to the parameters of a biomimetic signal, which is transmitted over the channel. The receiver estimates the parameters of the received signal and demaps them back to bits to estimate the message. As exemplary biomimetic signals, analytical signal models with nonlinear instantaneous frequency are developed that match mammal sound signatures in the time-frequency plane are developed. Suitable receiver structures as well as performance analysis are provided for the proposed transmission scheme, and some results using data recorded during the Kauai Acomms MURI 2011 UWA communications experiment are presented. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd

Measuring Multi-Joint Stiffness during Single Movements: Numerical Validation of a Novel Time-Frequency Approach

This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases

Detection, classification, and estimation in the (t, f ) domain

Several studies involving real-life applications have shown that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency ((t,f)) characteristics of such signals. Such (t,f) formulations are described in this chapter and include (t,f) matched filtering for detection and extraction of (t,f) features for classification. The topic is covered in six sections with appropriate internal cross-referencing to this and other chapters. The structure of (t,f) methods is suitable for designing and implementing optimal detectors. Several approaches exist, such as decomposition of TFDs into sets of spectrograms (Section 12.1). For both analysis and classification, a successful (t,f) methodology requires matching of TFDs with the structure of the signal. This can be achieved by a matching pursuit algorithm using (t,f) atoms adapted to the analyzed signals (Section 12.2). We can perform system identification by exciting linear systems with a linear FM signal and relating TFDs of the input and output using (t,f) filtering techniques (Section 12.3). Methods for (t,f) signal estimation and detection can be carried out using time-varying Wiener filters (Section 12.4). Then, advanced formulations and methods for (t,f) matched filtering are described and applied to abnormality detection (Section 12.5). Finally, the formulation of (t,f) features for classification (Section 12.6) is derived and applied to a serious medical problem as an illustration of the performance gained.Scopu

Characterization of cyclostationary signals and their generalizations

In this section, cyclostationary signals are characterized and their spectral analysis is provided. Links between the considered statistical functions and quadratic time-frequency representations are highlighted. Generalizations of the concept of cyclostationarity are addressed. The considered classes of signals are suitable models in several fields of application including communications, radar/sonar, telemetry, climatology, astronomy, acoustics, mechanics, biology, bioengineering, econometrics, and finance (see Refs. [58–61] and references therein)