The acquisition of age and growth data is of key importance for fisheries research (assessment, marine ecology issues, etc.). Consequently, automating this task is of great interest. In this paper, we investigate the use of statistical learning techniques for fish age estimation. The core of this study lies in the definition of relevant image-related features. We rely on the computation of a 1D representation summing up the content of otolith images within a predefined area of interest. Features are then extracted from this non-stationary representation depicting the alternation of seasonal growth rings. Thus, fish age estimation can be viewed as a multi-class classification issue using statistical learning strategies. In particular, a procedure based on demodulation and remodulation of fish growth patterns is used to improve the generalization properties of the trained classifiers. The experimental evaluation is carried out over a dataset of 320 plaice otolith images from age groups 1-6. We analyze both, the performances of several statistical classifiers, namely SVMs (support vector machines) and neural networks, and the relevance of the proposed image-based feature sets. In addition, the combination of additional biological and shape features to the image-related ones is considered. We reach a rate of correct age estimation of 88% w.r.t. the expert ground truth. This demonstrates the relevance of the proposed approach for the automation of routine aging and for computer-assisted aging.
In this paper, we describe an efficient approach to overcome the need for matrix inversion required in most wired applications encoutered in practice. In particular, MMSE equalization based on series expansion to approximate the matrix inversion is addressed. By adjusting a scaling factor, the series expansion is directly optimized according to a fixed order with respect to a system performance criterion. In comparison with previous approaches, the resulting equalizer enables improved BER performance according to a fixed order, in addition to low complexity without the need for a complicated eigenvalue calculation procedure.
This paper presents a simple approach to assess performance that can be achieved by the MMSE turbo equalizer in ST-BICM systems over multipaths Rayleigh block fading channels with i.i.d fading statistics. By considering perfect information exchange between the SISO decoder and the linear MMSE equalizer, the performance reaches the matched filter bound, and a closed form expression of the corresponding probability of bit error can be derived at the output of the equalizer. In particular, we emphasize that the suggested approach provides an attractive and reliable tool for performance validation consistently with the proposed expression of the probability of bit error. Simulations for 4-PSK and 8-PSK modulated signals show the relevance of the proposed approach and the full benefit provided by the MMSE turbo equalizer. In addition some clarification of the signal-tonoise ratio definition is pointed out.Index Terms-Space-Time BICM, MMSE Turbo equalization, performance validation, Rayleigh block fading multipath channel, probability of bit error expression.
In this paper, we investigate the use of statistical learning techniques for fish age estimation from otolith images. The core of this study lies in the definition of relevant imagerelated features. We rely on the characterization of a 1D signal summing up the image content within a predefined area of interest. Fish age estimation is then viewed as a multiclass classification issue using neural networks and SVMs. A procedure based on demodulation and remodulation of fish growth patterns is used to improve the generalization properties of the trained classifiers. We also investigate the combination of additional biological and shape features to the image-related ones. The performances are evaluated for a database of several hundred of plaice otoliths.
In this paper, a closed-form solution minimizing the Godard or Constant Modulus (CM) cost function under the practical conditions of finite SNR and finite equalizer length is derived. While previous work has been reported by Zeng et al., IEEE Trans. Information Theory. 1998, to establish the link between the constant modulus and Wiener receivers, we show that under the Gaussian approximation of intersymbol interference at the output of the equalizer, the CM finite-length receiver is equivalent to the nonblind MMSE equalizer up to a complex gain factor. Some simulation results are provided to support the Gaussian approximation assumption.
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