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PubMed2002

On the properties of stochastic intermittency in rainfall processes.

In this work we propose a mixed approach to deal with the modelling of rainfall events, based on the analysis of geometrical and statistical properties of rain intermittency in time, combined with the predictability power derived from the analysis of no-rain periods distribution and from the binary decomposition of the rain signal. Some recent hypotheses on the nature of rain intermittency are reviewed too. In particular, the internal intermittent structure of a high resolution pluviometric time series covering one decade and recorded at the tipping bucket station of the University of Genova is analysed, by separating the internal intermittency of rainfall events from the inter-arrival process through a simple geometrical filtering procedure. In this way it is possible to associate no-rain intervals with a probability distribution both in virtue of their position within the event and their percentage. From this analysis, an invariant probability distribution for the no-rain periods within the events is obtained at different aggregation levels and its satisfactory agreement with a typical extreme value distribution is shown.

Environmental Monitoring↗
PubMed2003

Performance evaluation of a modular gamma camera using a detectability index.

UNLABELLED: The performance of a modular gamma camera for the task of detecting signals in random noisy backgrounds was evaluated experimentally. The results were compared with a theoretical computer simulation. METHODS: The camera uses a 10 x 10 cm thallium-doped sodium iodide crystal, a 2 x 2 array of 53 x 53 mm photomultiplier tubes, and a parallel-hole collimator (1.5-mm bore width, 23.6-mm bore length). The camera was positioned to look down into a 10-cm-deep water bath that filled its field of view (FOV). The top surface of the water was 5 cm from the front face of the camera. The camera has 3-mm intrinsic spatial resolution (SR) in the center of its FOV and 9-mm system SR for objects 5 cm below the top surface of the water. Uniform and nonuniform random background data were collected by imaging the bath containing 740 MBq (20 mCi) (99m)Tc. Nonuniformities were created by placing water-filled objects in the bath. Each signal dataset was collected by imaging a water-filled plastic sphere, injected with (99m)Tc and set at a specific depth (Z) in the bath. Data were collected for many signal diameters (D) (4, 7, 10, 13, 16, 28 mm) at 1 depth (5 cm) and for 1 signal diameter (10 mm) at several depths (1, 3, 5, 7, 9 cm). Sets of signal-present/signal-absent image pairs (380 pairs, 10(5) events per image) for known contrasts (C) were generated for use in ideal-observer studies in which the detectability (d') was calculated. Contrast-detail (log C vs. log D) plots were created. The theoretical simulation, developed for uniform backgrounds, provided data for comparison. RESULTS: The detectability increased linearly with C and decreased nonlinearly with decreasing D or increasing Z. The C required to achieve a specific d' increased sharply for D < SR. For C = 5, D = 10 mm, and d' = 1.2, the camera consistently detected signals for Z < 6 cm. Similar results were found for nonuniform backgrounds. The theoretical simulation verified the results for uniform backgrounds. CONCLUSION: The methodology presented here provides a way of evaluating gamma cameras on the basis of signal-detection performance for specified lesions, with particular application to scintimammography.

Algorithms↗
PubMed2003

Wigner-Ville distribution and Gabor transform in Doppler ultrasound signal processing.

Time-frequency distributions have been used extensively for nonstationary signal analysis, they describe how the frequency content of a signal is changing in time. The Wigner-Ville distribution (WVD) is the best known. The draw back of WVD is cross-term artifacts. An alternative to the WVD is Gabor transform (GT), a signal decomposition method, which displays the time-frequency energy of a signal on a joint t-f plane without generating considerable cross-terms. In this paper the WVD and GT of ultrasound echo signals are computed analytically.

Algorithms↗
PubMed2003

Using the discrete Gabor expansion for the Doppler ultrasound signal processing.

In this paper we have synthesized the Doppler signal with a known time varying mean frequency, then used the orthogonal-like Discrete Gabor Transform (DGT) and the spectrogram for analyzing the signal. Mean square error has been computed for each method respectively and at last we have analyzed the real clinical signal too.

Algorithms↗
PubMed2003

A wavelet coefficient smoothened RLS adaptive denoising model for ECG.

This paper mainly concentrate on Noise Cancellation methodology for bio-signals. In this paper we have smoothened the Wavelet based signal coefficient and adapted to the MSE using RLS Algorithm. We have applied the Noisy ECG to After reconstruction model (ARM) and Before reconstruction model (BRM) and is implemented and tested. The proposed model combines the advantage of Wavelet Transform (co-efficient smoothening) and Adaptive Filter. This new proposed model performed better and faster. This find many application in the filed of noise elimination.

Algorithms↗
PubMed2006

[The application of adaptive algorithm and wavelet transform in the filtering of ECG signal].

Electrocardiographic (ECG) signal are a kind of basic physiological signals of human body, and are very important in clinical diagnosis. But the ECG signals from body surface are often interfered by noises such as 50 Hz noise, baseline displacemant, electromyography (EMG) noise and edv. These noises bring obstacle to the diagnosis of cardiovascular diseases. To eliminate the ECG signals noises mentioned above,this paper adopts LMS adaptive algorithm and wavelet transform theory to design three kinds of digital adaptive filters-adaptive noise cancellation filter, wavelet transform filter and adaptive signal dividing filter to filter the corresponding noises. The results show that the three kinds of noises existing in the ECG signal have been efficiently eliminated.

Algorithms↗
PubMed2006

[Quadrature Doppler ultrasound signal denoising based on adapted local cosine transform].

The spectrogram of Doppler ultrasound signal has been widely used in clinical diagnosis. The additional frequency components arising from internal or external noise to the system will produce adverse effects on its subjective and quantitative analysis. A novel approach based on the adapted local cosine transform and the non-negative Garrote thresholding method was proposed to remove noise from quadrature Doppler signal. At first, the directional information was extracted from the quadrature signal. And then the denoising method based on the adapted local cosine transform is performed on the forward and backward flow signals, respectively. At last, the estimated signal was reconstructed from the denoised signals using Hilbert transform. In the simulation study, both the mean frequency and spectral width waveform were studied for the denoised signal. The simulation results had shown that this approach was superior to that based on the wavelet transform, especially under low SNR conditions.

Algorithms↗
PubMed1993

Time series analysis of complex dynamics in physiology and medicine.

A variety of mathematical methods have been developed to characterize complex rhythms that are observed in physiological systems. These methods include classical techniques such as the mean, standard deviation, and power spectrum, as well as newer methods suggested by nonlinear dynamics including the dimension, Lyapunov number, and entropy. This paper reviews the various ways in which these measures have been applied to analyze physiological dynamics with emphasis on the potential advantages and pitfalls of the various approaches. We conclude that these methods may be useful to help characterize complex time series, but only rarely is it possible to use these methods to establish deterministic chaos in a given time series.

Animals↗
PubMed1994

Model-based biosignal interpretation.

Two relatively new approaches to model-based biosignal interpretation, qualitative simulation and modelling by causal probabilistic networks, are compared to modelling by differential equations. A major problem in applying a model to an individual patient is the estimation of the parameters. The available observations are unlikely to allow a proper estimation of the parameters, and even if they do, the task appears to have exponential computational complexity if the model is non-linear. Causal probabilistic networks have both differential equation models and qualitative simulation as special cases, and they can provide both Bayesian and maximum-likelihood parameter estimates, in most cases in much less than exponential time. In addition, they can calculate the probabilities required for a decision-theoretical approach to medical decision support. The practical applicability of causal probabilistic networks to real medical problems is illustrated by a model of glucose metabolism which is used to adjust insulin therapy in type I diabetic patients.

Bayes Theorem↗
PubMedDate not supplied

Segmentation of depth-EEG seizure signals: method based on a physiological parameter and comparative study.

The analysis of stereoelectroencephalographic (intracerebral recording) signals provides information on the electrical activity of brain structures implied in epileptic seizures. A simple nonparametric adaptive segmentation method, based on a physiologically relevant parameter, is presented and compared with three methods reported in the literature. The comparative frame allows us to objectively test methods for their performances on the same basis. Results show that the proposed method is robust with respect to the types of change studied and easier to conduct, even if it is less accurate about the estimation of instants of change than another method presented in this study. Signals are segmented throughout the duration of seizures without parameter readjustment and generate instants of change in accordance with those interactively delimited by the clinician.

Algorithms↗
PubMed2026

Depth-dependent microbial succession and interspecies hydrogen transfer drive pit mud maturation in Chinese strong-flavor baijiu fermentation.

Microbial communities in fermentation pit mud play a key role in determining the quality of Chinese strong-flavor baijiu (CSFB). However, the ecological processes underlying pit mud maturation across spatial and temporal scales remain unclear. In this study, amplicon sequencing and metagenomic analyses were employed to investigate the taxonomic succession, community assembly, and metabolic functions of bacterial and archaeal communities during the transition from fresh pit mud (FPM) to new pit mud (NPM) and old pit mud (OPM). A pronounced depth-dependent succession pattern was observed, with 4&#xa0;cm representing a critical ecological boundary separating distinct community structures and maturation trajectories. During surface-layer maturation, community assembly shifted from stochastic to deterministic processes, accompanied by homogeneous selection and increasing network complexity. In contrast, stochastic processes remained dominant throughout deep-layer maturation. Metagenomic analyses revealed a functional transition from lactate and acetate production, primarily associated with Lactobacillus in FPM and NPM, to butyrate and caproate production associated with Clostridium and Caproiciproducens in OPM. This functional transition was accompanied by enhanced amino acid metabolism, which was associated with the enrichment of Proteiniphilum and Aminobacterium. Notably, methanogen-mediated interspecies hydrogen transfer (IHT) emerged as a key ecological feature during pit mud maturation. In OPM, IHT networks primarily involving Methanobacterium and Methanosarcina linked methanogenesis with reverse &#x3b2;-oxidation through diverse hydrogen-transfer pathways, reinforcing metabolic interactions underlying caproate production. These findings provide new insights into the ecological mechanisms underlying pit mud maturation and offer a theoretical basis for the directed cultivation of high-quality pit mud in CSFB production.

Hydrogen↗
PubMed1996

Direct speech feature estimation using an iterative EM algorithm for vocal fold pathology detection.

The focus of this study is to formulate a speech parameter estimation algorithm for analysis/detection of vocal fold pathology. The speech processing algorithm proposed estimates features necessary to formulate a stochastic model to characterize healthy and pathology conditions from speech recordings. The general idea is to separate speech components under healthy and assumed pathology conditions. This problem is addressed using an iterative maximum-likelihood (ML) estimation procedure, based on the estimation-maximization (EM) algorithm. A new feature for characterizing pathology, termed enhanced-spectral-pathology component (ESPC), is estimated and shown to vary consistently between healthy and pathology conditions. It is also shown that the mean-area-peak-value (MAPV) and the weighted-slope (WSLOPE) indexes, which are obtained from the ESPC estimate, are meaningful measures of speech pathology conditions. For classification purposes, a five-state hidden-Markov-model (HMM) recognizer was formulated, based on the MAPV, WSLOPE, and ESPC spectral features. A set of log Mel-frequency filter bank coefficients were used to parameterize the ESPC feature. An evaluation of the HMM-based classifier was performed using speech recordings from healthy and vocal fold cancer patients of sustained vowel sounds. It is shown that while both MAPV and WSLOPE are useful features for vocal fold pathology detection, superior performance was achieved using a finer spectral representation of ESPC (e.g., a detection rate of 88.7% for pathology and 92.8% for healthy condition). One main advantage of the proposed method is that it does not require direct estimation of the glottal flow waveform. Therefore, the limitation of the inability to characterize vocal fold pathology, due to incomplete glottal closure, is no longer an issue. The results suggest that general analysis of the ESPC feature can provide a quantitative, noninvasive approach for analysis, detection, and characterization of speech production under vocal fold pathology.

Algorithms↗
PubMed2002

A Monte Carlo EM approach for partially observable diffusion processes: theory and applications to neural networks.

We present a Monte Carlo approach for training partially observable diffusion processes. We apply the approach to diffusion networks, a stochastic version of continuous recurrent neural networks. The approach is aimed at learning probability distributions of continuous paths, not just expected values. Interestingly, the relevant activation statistics used by the learning rule presented here are inner products in the Hilbert space of square integrable functions. These inner products can be computed using Hebbian operations and do not require backpropagation of error signals. Moreover, standard kernel methods could potentially be applied to compute such inner products. We propose that the main reason that recurrent neural networks have not worked well in engineering applications (e.g., speech recognition) is that they implicitly rely on a very simplistic likelihood model. The diffusion network approach proposed here is much richer and may open new avenues for applications of recurrent neural networks. We present some analysis and simulations to support this view. Very encouraging results were obtained on a visual speech recognition task in which neural networks outperformed hidden Markov models.

Algorithms↗
PubMed1991

Ornstein-Uhlenbeck process and its application.

The paper deals with the Ornstein-Uhlenbeck process (O-U), its approximation by discrete random processes designed for modelling of the O-U process and some methods of statistical evaluation. The known elementary properties of the O-U process and its discretization are summarized. As to the statistical methods, the maximum-likelihood estimates of parameters of the O-U process and some methods for hypothesis testing are demonstrated on results obtained by simulation of the O-U process on the computer.

Stochastic Processes↗
PubMed1978

[Labeled mitosis curve in the presence of different states of cell proliferation kinetics. IV. Additional remarks on the method of a posteriori modeling].

Using the conditional probability density function of phase duration--h (a, t), defined for a cell just completing a given phase of the mitotic cycle at the moment t it is possible to construct a mathematical description of the fraction labeled mitoses curve. The function h (a, t) defined for any separate phase can be described by means of a certain mathematical expression which is applicable even in the presence of transient processes in cell kinetics and serves as an appropriate generalization of the same result from the theory of exponentially growing cell populations (the model of age-dependent branching process). When the sum of r successive phases with stochastically dependent durations is under consideration and the age of a cell is measured from the start of phase r, it is necessary to find out the expression for the function hr (ar, t). The derivation of such an expression is given.

Cytological Techniques↗
PubMed2006

Sustainable ecosystem management using optimal control theory: part 2 (stochastic systems).

Sustainable development of ecosystems through external ecosystem management is assuming importance for the environmentalists. To that effect, previous work by the authors looked at the option of manipulating population dynamics of the species in an ecosystem to achieve sustainability. Fisher information is used as the quantifying measure of sustainability and optimal control theory is used to derive the control profiles. However, that work considered only deterministic systems. Uncertainty being prevalent in all systems, particularly in natural systems, this paper extends that work to analyse uncertain systems. Predator-prey models are used to model the species populations and different control philosophies are compared. Ito mean reverting process is used to model the stochastic process, and stochastic maximum principle is used to derive the control profiles. The results for the objective of FI variance minimization qualitatively agree with those for the deterministic system, while the results for the FI maximization objective differ. It is observed that the instability associated with the FI maximization objective for deterministic systems is absorbed by the noise introduced by the uncertainty. Quantitatively, it is observed that the degree of uncertainty, along with its presence, is also important to identify the most appropriate management strategy.

Animals↗
PubMed1994

Comparative assessment of some algorithms for differentiating noisy biomechanical data.

In this paper, a comparison is carried out between various algorithms for smoothing and differentiating noisy (non-exact) discrete time series, a problem frequently encountered in experimental movement studies. The algorithms compared are: the 'implicit procedure' of Anderssen and Bloomfield (Numer Math, 22 (1974) 157-182) with a refinement by Kosarev and Pantos (J Phys E Sci Instrum, 16 (1983) 537-543); the 'explicit procedure', a digital filter method in which the filter coefficients are computed starting from the measurements; the regularized Fourier series method (these two algorithms were also presented by Anderssen and Bloomfield); and, finally, natural B-splines regularized by the generalized cross-validation criterion. The comparison was performed mainly by analytical, noise-corrupted test sequences, whose derivatives were known a priori. The testing procedure proved capable of showing the different characteristics of the various algorithms, and providing criteria to choose that best suited to a given practical situation. In most cases, the regularized Fourier series method and the implicit procedure proved to have the best tradeoff between accuracy and speed.

Algorithms↗