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This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom's valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future work.

Passive time reversal has aroused considerable interest in underwater communications as a computationally inexpensive means of mitigating the intersymbol interference introduced by the channel using a receiver array. In this paper the basic technique is extended by adaptively weighting sensor contributions to partially compensate for degraded focusing due to mismatch between the assumed and actual medium impulse responses. Two algorithms are proposed, one of which restores constructive interference between sensors, and the other one minimizes the output residual as in widely used equalization schemes. These are compared with plain time reversal and variants that employ postequalization and channel tracking. They are shown to improve the residual error and temporal stability of basic time reversal with very little added complexity. Results are presented for data collected in a passive time-reversal experiment that was conducted during the MREA'04 sea trial. In that experiment a single acoustic projector generated a 2 / 4-PSK ͑phase-shift keyed͒ stream at 200/ 400 baud, modulated at 3.6 kHz, and received at a range of about 2 km on a sparse vertical array with eight hydrophones. The data were found to exhibit significant Doppler scaling, and a resampling-based preprocessing method is also proposed here to compensate for that scaling.

This paper proposes a vector sensor measurement model and the related Bartlett estimator based on particle velocity measurements for generic parameter estimation, illustrating the advantages of the Vector Sensor Array ͑VSA͒. A reliable estimate of the seabed properties such as sediment compressional speed, density and compressional attenuation based on matched-field inversion ͑MFI͒ techniques can be achieved using a small aperture VSA. It is shown that VSAs improve the resolution of seabed parameter estimation when compared with pressure sensor arrays with the same number of sensors. The data considered herein was acquired by a four-element VSA in the 8-14 kHz band, during the Makai Experiment in 2005. The results obtained with the MFI technique are compared with those obtained with a method proposed by C. Harrison, which determines the bottom reflection loss as the ratio between the upward and downward beam responses. The results show a good agreement and are in line with the historical information for the area. The particle velocity information provided by the VSA increases significantly the resolution of seabed parameter estimation and in some cases reliable results are obtained using only the vertical component of the particle velocity.

Abstract. An essential and indispensable component of automated microscopy framework is the automatic focusing system, which determines the in-focus position of a given field of view by searching the maximum value of a focusing function over a range of z-axis positions. The focus function and its computation time are crucial to the accuracy and efficiency of the system. Sixteen focusing algorithms were analyzed for histological and histopathological images. In terms of accuracy, results have shown an overall high performance by most of the methods. However, we included in the evaluation study other criteria such as computational cost and focusing curve shape which are crucial for real-time applications and were used to highlight the best practices.

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Matched-field based methods always involve the comparison of the output of a physical model and the actual data. The method of comparison and the nature of the data varies according to the problem at hand, but the result becomes always largely conditioned by the accurateness of the physical model and the amount of data available. The usage of broadband methods has become a widely used approach to increase the amount of data and to stabilize the estimation process. Due to the difficulties to accurately predict the phase of the acoustic field the problem whether the information should be coherently or incoherently combined across frequency has been an open debate in the last years. This paper provides a data consistent model for the observed signal, formed by a deterministic channel structure multiplied by a perturbation random factor plus noise. The cross-frequency channel structure and the decorrelation of the perturbation random factor are shown to be the main causes of processor performance degradation. Different Bartlett processors, such as the incoherent processor ͓Baggeroer et al., J. Acoust. Soc. Am. 80, 571-587 ͑1988͔͒, the coherent normalized processor ͓Z.-H. Michalopoulou, IEEE J. Ocean Eng. 21, 384 -392 ͑1996͔͒ and the matched-phase processor ͓Orris et al., J. Acoust. Soc. Am. 107, 2563-2375 ͑2000͔͒, are reviewed and compared to the proposed cross-frequency incoherent processor. It is analytically shown that the proposed processor has the same performance as the matched-phase processor at the maximum of the ambiguity surface, without the need for estimating the phase terms and thus having an extremely low computational cost.

Abstract-Underwater acoustic networks (UANs) are an emerging technology for a number of oceanic applications, ranging from oceanographic data collection to surveillance applications. However, their reliable usage in the field is still an open research problem, due to the challenges posed by the oceanic environment. The UAN project, a European-Union-funded initiative, moved along these lines, and it was one of the first cases of successful deployment of a mobile underwater sensor network integrated within a wide-area network, which included above water and underwater sensors. This contribution, together with a description of the underwater network, aims at evaluating the communication performance, and correlating the variation of the acoustic channel to the behavior of the entire network stack. Results are given based on the data collected during the UAN11 (May 2011, Trondheim Fjord area, Norway) sea trial. During the experimental activities, the network was in operation for five continuous days and was composed of up to four Fixed NOdes (FNOs), two autonomous underwater vehicles (AUVs), and one mobile node mounted on the supporting research vessel. Results from the experimentation at sea are reported in terms of channel impulse response (CIR) and signal-to-interference-plus-noise ratio (SINR) as measured by the acoustic modems during the sea tests. The performance of the upper network levels is measured in terms of round trip time (RTT) and probability of packet loss (PL). The analysis shows how the communication performance was dominated by variations in signal-to-noise ratio, and how this impacted the behavior of the whole network. Qualitative explanation of communication performance variations can be accounted, at least in the UAN11 experiment, by standard computation of the CIR and transmission loss estimate.