Sparse Representation of FHSS Signals in the Hermite Transform Domain

Signal sparsity is exploited in various signal processing approaches. Signal compression, classification, coding, as well as the recently introduced compressed sensing are some examples where the possibility to represent a signal sparsely determines the efficiency of the applied processing technique. However, the possibility of a sparse signal representation in a transform basis is highly dependent on the signal nature. Therefore, finding a suitable basis where the signal exhibits a compact support is a challenging task. In this paper, the Hermite Transform (HT) is considered as a sparsity domain for the FHSS wireless communication signals. The transform coefficients sparsification is done by optimizing the scaling factor and time-shift of basis functions. The optimization is done by minimizing the concentration measure of HT coefficients. The theory is verified by numerical examples with synthetic FHSS signals

LNG carrier main steam turbine reliability in the exploatation period of time

In this paper the LNG carrier with steam turbine propulsion plant maintenance records has been analysed. Actual observed data from the ship, built in 2001, are from ship maintenance history data from September 2002 until August 2010. During the analysed period, main propulsion turbine had one major failure and several minor failures. The ship had three dry docks and one was prolonged due to increased requirements for cargo transport. Total running hours of the main propulsion turbine in the observed period of time were 63204 hours. The list of failures and influence of each mentioned failure of main turbine propulsion machinery is discussed and analysed in respect to the propulsion autonomy of the vessel

Single-Iteration Algorithm for Compressive Sensing Reconstruction

A single-iteration algorithm is proposed for the reconstruction of sparse signal from its incomplete set of observations. Recently, the reconstruction algorithms have been intensively developed within the Compressive Sensing framework. Most of the existing solutions are based either on l1-norm optimization methods or greedy iterative procedures with a priori known number of components or predefined number of iterations. We propose a simple non-iterative algorithm based on the analysis of noise-effect that appears in the frequency domain as a consequence of missing samples. The noise variance can be related and controlled by the number of missing samples. Accordingly, it is possible to keep the level of spectral noise below the signal components, such as to be able to accurately detect signal support and to reconstruct the entire signal. The theory is proven on various examples with multicomponent signals

Signal content estimation based on the short-term time-frequency Rényi entropy of the S-method time-frequency distribution

A key characteristic of a nonstationary signal, when analyzed in the time-frequency domain, is the signal complexity, quantified as the number of components in the signal. This paper describes a method for the estimation of this number of components of a signal using the short-term Rényi entropy of its time-frequency distribution (TFD). We focus on the characteristics of TFDs that make them suitable for such a task. The performance of the proposed algorithm is studied with respect to the parameters of the S-method TFD, which combines the virtues of both the spectrogram and the Wigner-Ville distribution. Once the optimal parameters of the TFD have been determined, the applicability of the method in the analysis of signals in low SNRs and real life signals is assessedScopu

Signal content estimation based on the short-term time-frequency Rényi entropy of the S-method time-frequency distribution

A key characteristic of a nonstationary signal, when analyzed in the time-frequency domain, is the signal complexity, quantified as the number of components in the signal. This paper describes a method for the estimation of this number of components of a signal using the short-term Rényi entropy of its time-frequency distribution (TFD). We focus on the characteristics of TFDs that make them suitable for such a task. The performance of the proposed algorithm is studied with respect to the parameters of the S-method TFD, which combines the virtues of both the spectrogram and the Wigner-Ville distribution. Once the optimal parameters of the TFD have been determined, the applicability of the method in the analysis of signals in low SNRs and real life signals is assessedScopu