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Previous studies have reported the role of some species of acidophilic bacteria in accelerating the dissolution of goethite under aerobic and anaerobic conditions. This has relevance for environments impacted by acid mine drainage and for the potential bioleaching of limonitic laterite ores. In this study, natural well-characterized goethite mineral samples and synthetic goethite were used in aerobic and anaerobic laboratory batch culture incubation experiments with ferric iron-reducing, acidophilic bacteria, including the lithoautotrophic species Acidithiobacillus (At.) thiooxidans, At. ferrooxidans, and At. caldus, as well as two strains of the organoheterotrophic species Acidiphilium cryptum. All bacteria remained alive throughout the experiments and efficiently reduced soluble ferric iron in solution in positive control assays. However, goethite dissolution was low to negligible in all experimental assays with natural goethite, while some dissolution occurred with synthetic goethite in agreement with previous publications. The results indicate that ferric iron-reducing microbial activity at low pH is less relevant for goethite dissolution than the oxidation of elemental sulfur to sulfuric acid. Microbial ferric iron reduction enhances but does not initiate goethite dissolution in very acidic liquors.

Abstract Standard compressive sensing (CS) scenario assumes a single sparsifying basis used to reconstruct the signals from a small set of incoherent measurements. However, in many cases, the signal cannot be sparsely represented using a single transformation. Particularly, in ECG signal analysis, each signal segment is specific in nature and reflects different physical phenomena. Hence, using the same transformation for all segments may be inappropriate for efficient analysis and reconstruction. Moreover, in the CS scenario, it would be necessary to combine different transforms to achieve compact signal support and to provide successful reconstruction from randomly under-sampled data. This work proposes a hybrid CS reconstruction algorithm that combines different transform basis, based on the concept of orthogonal matching pursuit. The performance of the proposed approach is verified experimentally using the combination of the Fourier and the Hermite transform on the real ECG signals.

Abstract This work proposes an analog implementation of gradient-based algorithm for compressive sensing signal reconstruction. Compressive sensing has appeared as a promising technique for efficient acquisition and reconstruction of sparse signals in many real-world applications. It starts from the assumption that sparse signals can be exactly reconstructed using far less samples than in standard signal processing. In this paper, we consider the gradient-based algorithm as the optimal choice that provides lower complexity and competitive accuracy compared with existing methods. Since the efficient hardware implementations of reconstruction algorithms are still an emerging topic, this work is focused on the design of hardware that will provide fast parallel algorithm execution for real-time applications, overcoming the limitations imposed by the large number of nested iterations during the signal reconstruction. The proposed implementation is simple and fast, executing 400 iterations in 1 ms which is sufficient to obtain highly accurate reconstruction results.

The virtual (software) instrument with a statistical analyzer for testing algorithms for biomedical signals’ recovery in compressive sensing (CS) scenario is presented. Various CS reconstruction algorithms are implemented with the aim to be applicable for different types of biomedical signals and different applications with under-sampled data. Incomplete sampling/sensing can be considered as a sort of signal damage, where missing data can occur as a result of noise or the incomplete signal acquisition procedure. Many approaches for recovering the missing signal parts have been developed, depending on the signal nature. Here, several approaches and their applications are presented for medical signals and images. The possibility to analyze results using different statistical parameters is provided, with the aim to choose the most suitable approach for a specific application. The instrument provides manifold possibilities such as fitting different parameters for the considered signal and testing the efficiency under different percentages of missing data. The reconstruction accuracy is measured by the mean square error (MSE) between original and reconstructed signal. Computational time is important from the aspect of power requirements, thus enabling the selection of a suitable algorithm. The instrument contains its own signal database, but there is also the possibility to load any external data for analysis.

Compressive sensing is a computational framework for acquisition and processing of sparse signals at sampling rates below the rates mandated by the Nyquist sampling theorem. In this paper, we present seven MATLAB functions for compressive sensing based time–frequency processing of sparse nonstationary signals. These functions are developed to reproduce figures in our companion review paper. Keywords: Compressive sensing, Time–frequency analysis, Nonstationary signals

The robust time-frequency distributions with complex-lag argument are proposed. They can provide an accurate estimation of fast varying instantaneous frequency in the presence of noise with heavy-tailed probability density function. The L-estimate form of this distribution is defined and it includes the L-estimate form of Wigner distribution as a special case. A modification for multicomponent signal representation is proposed, as well. Theoretical considerations are illustrated by the examples.

Fast Hermite projections have been often used in image-processing procedures such as image database retrieval, projection filtering, and texture analysis. In this paper, we propose an innovative approach for the analysis of one-dimensional biomedical signals that combines the Hermite projection method with time-frequency analysis. In particular, we propose a two-step approach to characterize vibrations of various origins in swallowing accelerometry signals. First, by using time-frequency analysis we obtain the energy distribution of signal frequency content in time. Second, by using fast Hermite projections we characterize whether the analyzed time-frequency regions are associated with swallowing or other phenomena (vocalization, noise, bursts, etc.). The numerical analysis of the proposed scheme clearly shows that by using a few Hermite functions, vibrations of various origins are distinguishable. These results will be the basis for further analysis of swallowing accelerometry to detect swallowing difficulties.

A review of time-frequency analysis and some aspects of its applications in digital watermarking are presented. The main advantages and drawbacks of various time-frequency distributions are first discussed. The aim of this theoretical overview is to facilitate an appropriate distribution selection in a specific application. Different aspects of the time-frequency analysis when applied to digital watermarking are then presented. In particular, the method that maps time-frequency characteristics of a host signal to the pseudo noise watermark sequence is thoroughly discussed. This approach is presented in the multidimensional form and then applied to digital audio, digital image, and digital video watermarking. Finally, the theoretical considerations are illustrated by various numerical and real-life examples.

A hardware implementation of the Nth order complex-lag time-frequency distribution is proposed. The considered distribution provides an arbitrary high concentration for multicomponent signals with fast varying instantaneous frequency. Although the distribution form is quite complex, the proposed realization is very efficient and provides high-speed real-time processing. Further, it allows avoiding miscalculation errors that may appear in the numerical calculation of signal with complex-lag argument. The results of FPGAs (Field Programmable Gate Arrays) implementation are presented as well.

A class of time-frequency distributions with complex-lag argument is proposed. It is based on the ambiguity domain representations of real and complex-lag moment, combined to provide a cross-terms free representation for multicomponent signals. Furthermore, the distributions from the proposed class provide a more effective instantaneous frequency estimation for signals with fast varying phase function than the existing approach. The theory is illustrated by the examples.

An approach to speech watermarking based on the time-frequency signal analysis is proposed. As a time-frequency representation suitable for speech analysis, the S-method is used. The time-frequency characteristics of watermark are modeled by using speech components in the selected region. The modeling procedure is based on the concept of time-varying filtering. A detector form that includes cross-terms in the Wigner distribution is proposed. Theoretical considerations are illustrated by the examples. Efficiency of the proposed procedure has been tested for several signals and under various attacks.

An approach to optimal watermark detection in the DCT domain, based on the probability density function (pdf) modeling, is presented. A new detector is proposed in order to produce reliable detection of weak watermarks for various distributions of DCT image coefficients. Efficacy of the proposed detector is proved on some common attacks, as well. [ABSTRACT FROM AUTHOR]