Adaptive Spectral Analysis Research Articles

A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022

Abstract. Multifaceted regime shifts of Earth's surface are ongoing dramatically and – in turn – considerably alter the global carbon budget, energy balance and biogeochemical cycles. Sustainably managing terrestrial ecosystems necessitates a deeper comprehension of the diverse and dynamic nature of multicomponent information within these environments. However, comprehensive records of global-scale fractional vegetation and soil information that encompass these structural and functional complexities remain limited. Here, we provide a globally comprehensive record of monthly vegetation and soil fractions during the period 2001–2022 using a spatiotemporally adaptive spectral mixture analysis framework. This product is designed to continuously represent Earth's terrestrial surface as a percentage of five physically meaningful vegetation and soil endmembers, including photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), bare soil (BS), ice or snow (IS) and dark surface (DA), with high accuracy and low uncertainty compared to the previous vegetation index and vegetation continuous-field product as well as traditional fully constrained linear spectral mixture models. We also adopt nonparametric seasonal Mann–Kendall tested fractional dynamics to identify shifts based on interactive changes in these fractions. Our results – superior to previous portrayals of the greening planet – not only report a +9.35 × 105 km2 change in photosynthetic vegetation, but also explore decreases in nonphotosynthetic vegetation (−2.19 × 105 km2), bare soil (−5.14 × 105 km2) and dark surfaces (−2.27 × 105 km2). In addition, interactive changes in these fractions yield multifaceted regime shifts with important implications, such as a simultaneous increase in PV and NPV in central and southwestern China during afforestation activities, an increase in PV in cropland of China and India due to intensive agricultural development, a decrease in PV and an increase in BS in tropical zones resulting from deforestation. These advantages emphasize that our dataset provides locally relevant information on multifaceted regime shifts at the required scale, enabling scalable modeling and effective governance of future terrestrial ecosystems. The data about five fractional surface vegetation and soil components are available in the Science Data Bank (https://doi.org/10.57760/sciencedb.13287, Sun and Sun, 2023).

WavelNet: A novel convolutional neural network architecture for arrhythmia classification from electrocardiograms

Background and objectiveAutomated detection of arrhythmias from electrocardiograms (ECGs) can be of considerable assistance to medical professionals in providing efficient treatment for patients with cardiovascular diseases. In recent times, convolutional neural network (CNN)-based arrhythmia classification models have been introduced, but their decision-making processes remain unclear and their performances are not reproducible. This paper proposes an accurate, interpretable, and reproducible end-to-end arrhythmia classification model based on a novel CNN architecture named WavelNet, which is interpretable and optimal for dealing with ECGs. MethodsInspired by SincNet, which is capable of band-pass filtering-based spectral analysis, WavelNet was devised to achieve wavelet transform-based spectral analysis. WavelNet was trained using a subject-oriented five-class ECG arrhythmia dataset generated from the MIT-BIH Arrhythmia Database while following a benchmark scheme. By adopting various mother wavelets, multiple WavelNet-based arrhythmia classification models were implemented. To investigate whether our wavelet transform-based approach outperforms original end-to-end and band-pass filtering-based approaches, our proposed models were compared with vanilla CNN- and SincNet-based models. Model implementation and evaluation processes were repeated ten times in a Google Colab Pro+ environment. Furthermore, our most successful model was compared with state-of-the-art arrhythmia classification models for performance evaluation. ResultsThe proposed WavelNet-based models showed excellent performance on classifying non-ectopic, supraventricular ectopic, and ventricular ectopic beats because of their ability to perform adaptive spectral analysis while preserving temporal ECG information compared with vanilla CNN- and SincNet-based models. In particular, a Symlet 4 wavelet-adopting WavelNet-based model achieved the best performance with nearly 90% overall accuracy as well as the highest levels of sensitivity in classifying each arrhythmia class: 91.4%, 49.3%, and 91.4% for non-ectopic, supraventricular ectopic, and ventricular ectopic beat classifications, respectively. These results were comparable to those of state-of-the-art models. In addition, the results are reproducible, which differentiates our study from previous studies. ConclusionsOur proposed WavelNet-based arrhythmia classification model achieved remarkable performance based on a reasonable decision-making process, in comparison with other models. As its noteworthy performance is clinically reasonable and reproducible, our proposed model can contribute toward implementing a real-world precision healthcare system for patients with cardiovascular diseases.

Development of the method for decomposition of superpositions of unknown pulsed signals using the second­order adaptive spectral analysis

We considered the issue of intuitive analysis, processing, and synthesis of unknown pulse sequences in a detailed form. We studied both classical methods of analysis with all pluses and minuses and the developed prospective method created on their basis. The developed method is adaptive, it is based on the consistent use of various methods of spectral analysis, which increases reliability, sensitivity, and resolution capability of visual analysis of echo-pulse images. Thus, we solved the problem on processing pulse signals. The proposed method makes it possible to increase resolution capability in the processing of signals and images without using a priori information on the form of elementary pulses. It is resistant to the influence of measuring noise. We presented the results of numerical simulation and actual verification on the example of a seismic image. The considered method has a significant development potential, both in theoretical and application aspects (first of all, in areas of ultrasonic medical diagnosis, seismic imaging, and non-destructive testing).

Decorrelating MVDR Filterbanks Using the Non-Uniform Discrete Fourier Transform

Minimum variance distortionless response (MVDR) is a classic design criteria in signal adaptive spectral analysis as well as filter design. We extend this approach to filterbanks with the constraint that transform domain signal components must be uncorrelated. Our analysis shows that filterbanks based on Vandermonde decomposition of the autocorrelation matrix correspond to the non-uniform discrete Fourier transform and satisfies the MVDR criteria. Namely, the columns of the inverse Vandermonde matrix corresponds to filters with unit response at the pass-band while leakage is minimized with the constraint that components remain uncorrelated. In the special case that the autocorrelation matrix is rank deficient, the proposed filterbank coincides with Pisarenko's harmonic decomposition, which thus also satisfies the MVDR criteria.

Tree species mapping in tropical forests using multi-temporal imaging spectroscopy: Wavelength adaptive spectral mixture analysis

The use of imaging spectroscopy for florisic mapping of forests is complicated by the spectral similarity among co-existing species. Here we evaluated an alternative spectral unmixing strategy combining a time series of EO-1 Hyperion images and an automated feature selection in Multiple Endmember Spectral Mixture Analysis (MESMA). The temporal analysis provided a way to incorporate species phenology while feature selection indicated the best phenological time and best spectral feature set to optimize the separability between tree species. Instead of using the same set of spectral bands throughout the image which is the standard approach in MESMA, our modified Wavelength Adaptive Spectral Mixture Analysis (WASMA) approach allowed the spectral subsets to vary on a per pixel basis. As such we were able to optimize the spectral separability between the tree species present in each pixel. The potential of the new approach for floristic mapping of tree species in Hawaiian rainforests was quantitatively assessed using both simulated and actual hyperspectral image time-series. With a Cohen's Kappa coefficient of 0.65, WASMA provided a more accurate tree species map compared to conventional MESMA (Kappa=0.54; p-value<0.05. The flexible or adaptive use of band sets in WASMA provides an interesting avenue to address spectral similarities in complex vegetation canopies.

A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution

Spectral mixture analysis (SMA) has been widely employed in analyzing urban environments, especially for estimating urban impervious surface distribution at the subpixel level. When implementing SMA, endmember selection is considered an important step, and an inappropriate endmember set could severely affect the accuracy of fractional land covers. Although different degrees of success have been achieved in existing SMA approaches, the paradox of endmember selection is still unsolved: theoretically “purest” endmembers that can be selected with relative ease do not always yield optimal results, while the selection of the most “representative” endmembers is very difficult with simple SMA. To address this problem, we propose a spatially adaptive SMA (SASMA) technique to automatically extract and synthesize the “most representative” endmembers for SMA through considering both between-class and within-class variations. In particular, we developed a classification tree method to automatically extract endmember candidates through incorporating spectral and spatial information. In addition, to mitigate the effect of within-class variation, we employed synthetic spectra of neighboring endmember candidates in a local search window as the most “representative” endmember signature. Results indicate that SASMA performs well in estimating subpixel impervious surface distribution with relatively high precision (mean absolute error of 8.50%, root mean square error of 15.25%, and R2 of 0.701) and small bias (systematic error of −0.93%).

Nonparametric Missing Sample Spectral Analysis and Its Applications to Interrupted SAR

We consider nonparametric adaptive spectral analysis of complex-valued data sequences with missing samples occurring in arbitrary patterns. We first present two high-resolution missing-data spectral estimation algorithms: the Iterative Adaptive Approach (IAA) and the Sparse Learning via Iterative Minimization (SLIM) method. Both algorithms can significantly improve the spectral estimation performance, including enhanced resolution and reduced sidelobe levels. Moreover, we consider fast implementations of these algorithms using the Conjugate Gradient (CG) technique and the Gohberg-Semencul-type (GS) formula. Our proposed implementations fully exploit the structure of the steering matrices and maximize the usage of the fast Fourier transform (FFT), resulting in much lower computational complexities as well as much reduced memory requirements. The effectiveness of the adaptive spectral estimation algorithms is demonstrated via several numerical examples including both 1-D spectral estimation and 2-D interrupted synthetic aperture radar (SAR) imaging examples.

Data Adaptive Spectral Analysis of Unsteady Leakage Flow in an Axial Turbine

A data adaptive spectral analysis method is applied to characterize the unsteady loss generation in the leakage flow of an axial turbine. Unlike conventional spectral analysis, this method adapts a model dataset to the actual data. The method is illustrated from the analysis of the unsteady wall pressures in the labyrinth seal of an axial turbine. Spectra from the method are shown to be in good agreement with conventional spectral estimates. Furthermore, the spectra using the method are obtained with data records that are 16 times shorter than for conventional spectral analysis, indicating that the unsteady processes in turbomachines can be studied with substantially shorter measurement schedules than is presently the norm.

Online Multi-step Ahead Prediction of Time-Varying Solar and Geomagnetic Activity Indices via Adaptive Neurofuzzy Modeling and Recursive Spectral Analysis

The time-varying Sun as the main source of space weather affects the Earth’s magnetosphere by emitting hot magnetized plasma in the form of solar wind into interplanetary space. Solar and geomagnetic activity indices and their chaotic characteristics vary abruptly during solar and geomagnetic storms. This variation depicts the difficulties in modeling and long-term prediction of solar and geomagnetic storms. On the other hand, the combination of neurofuzzy models and spectral analysis has been a subject of interest due to their many practical applications in modeling and predicting complex phenomena. However, these approaches should be trained by algorithms that need to be carried out by an offline data set, which influences their performance in online modeling and prediction of time-varying phenomena. This paper proposes an adaptive approach for multi-step ahead prediction of space weather indices by extending the regular singular spectrum analysis and locally linear neurofuzzy models to adaptive approaches. The combination of these recursive approaches fulfills requirements of long-term prediction of solar and geomagnetic activity indices. The results demonstrate the power of the proposed method in online prediction of space weather indices.

Resolving Nonstationary Spectral Information in Wind Speed Time Series Using the Hilbert–Huang Transform

Abstract This work is motivated by the observation that large-amplitude wind fluctuations on temporal scales of 1–10 h present challenges for the power management of large offshore wind farms. Wind fluctuations on these scales are analyzed at a meteorological measurement mast in the Danish North Sea using a 4-yr time series of 10-min wind speed observations. An adaptive spectral analysis method called the Hilbert–Huang transform is chosen for the analysis, because the nonstationarity of time series of wind speed observations means that they are not well described by a global spectral analysis method such as the Fourier transform. The Hilbert–Huang transform is a local method based on a nonparametric and empirical decomposition of the data followed by calculation of instantaneous amplitudes and frequencies using the Hilbert transform. The Hilbert–Huang transformed 4-yr time series is averaged and summarized to show climatological patterns in the relationship between wind variability and time of day. First, by integrating the Hilbert spectrum along the frequency axis, a scalar time series representing the total variability within a given frequency range is calculated. Second, by calculating average spectra conditional to time of day, the time axis of the Hilbert spectrum is “remapped” to show climatological patterns. Third, the daily patterns in wind variability and wind speed are compared for the four seasons of the year. It is found that the most intense wind variability occurs in autumn even though the strongest observed wind speeds occur in winter.

Performances Study of a New Speech Coding Strategy with Reduced Channels for Cochlear Implants

A new cochlear implant speech coding strategy for representing acoustic information with reduced number of channels will be developed. After a brief description of the actual cochlear implant stimulation methods, the authors pre- sent a processing algorithm describing the new adaptive spectral analysis strategy (ASAS-GC). This technique is based on a Gammachirp perception model and an optimal stimulation rate selection. The excited electrodes signals and recognition performances are presented and compared with those of others strategies.

High accuracy peak location and amplitude spectral estimation via tuning APES method

In this paper, we introduce a new approach to the method of non-parametric adaptive spectral analysis by using the Amplitude and Phase Estimation (APES) method, and taking into account the small sample errors of the sample covariance matrix. This approach is referred to as Adaptive Tuning Amplitude and Phase Estimation method (ATAPES). The main advantage of the ATAPES algorithm is its elimination of biased estimation exists with APES method, which is a biased peak location and corresponding problem of the biased amplitude estimation. The ATAPES method provides more accurate peak location and amplitude estimation with higher resolution than APES method.

Pan-European forest/non-forest mapping with Landsat ETM+ and CORINE Land Cover 2000 data

This paper describes a simple and adaptive methodology for large area forest/non-forest mapping using Landsat ETM+ imagery and CORINE Land Cover 2000. The methodology is based on scene-by-scene analysis and supervised classification. The fully automated processing chain consists of several phases, including image segmentation, clustering, adaptive spectral representativity analysis, training data extraction and nearest-neighbour classification. This method was used to produce a European forest/non-forest map through the processing of 415 Landsat ETM+ scenes. The resulting forest/non-forest map was validated with three independent data sets. The results show that the map’s overall point-level agreement with our validation data generally exceeds 80%, and approaches 90% in central European conditions. Comparison with country-level forest area statistics shows that in most cases the difference between the forest proportion of the derived map and that computed from the published forest area statistics is below 5%.

Identification of small-scale structure of plankton communities of the Black and Ionian Seas by their bioluminescence characteristics

Small-scale heterogeneity of bioluminescence intensity was studied in offshore regions of the Black and Ionian Seas in September/October, 1989. Bathyphotometric casts were deployed every 3 min over a period of several hours from a drifting vessel. Casts were accompanied by temperature profiles and zooplankton sampling. Taking into account the vessel drift, the sampling interval was equal to a spatial resolution from 15 to 25 m. Measurements were carried out during the day and night periods. From adaptive spectral analysis it was shown that the horizontal dimensions of the bioluminescent heterogeneities were between 75 and 1500 m in the Black Sea and 89–800 m in the Ionian Sea. The horizontal dimensions of the heterogeneities were 85–818 m and 85–500 m for zooplankton biomass and 80–1800 m and 85–600 m for the temperature field in the Black and Ionian Seas, respectively. Therefore, it is assumed that physical and dynamical factors dominate in the formation of small-scale spatial heterogeneity of the studied fields. Biological factors, such as diel vertical migrations, trophic interactions etc. will also contribute to the formation of patches at night.

Real-Time Analysis of Passage Through Resonance by Adaptive Spectral Analysis. Experimental Analysis of Time-Varying Amplitudes and Phase Spectra.

Passage through resonance of a Duffing-type oscillator can be analyzed using the amplitude equations which are obtained by the method of multiple scales and averaging method. Using the analyses, the growth, which includes jumps of the amplitude and phase angle, has been theoretically clarified. On the other hand, to our knowledge, no experiment has been conducted to examine the above character of passage through resonance of a Duffing-type oscillator which has multiple frequencies. The reason seems to be that because the passage through resonance is a nonstationary resonance, FFT, which is Widely used in spectral analysis for stationary vibrations is not an appropriate method. Recently, the method of experimental spectral analysis for the transient state of a parametric resonance of a Duffing-type oscillator using an adaptive filter is proposed. In this paper, we apply this method for passage through resonance of Duffing type oscillator which has multiple frequencies. The time-varying amplitudes and phase angle which include jumps are experimentally clarified in real-time. By comparison with the theoretical results obtained from the amplitude equations, we show that the adaptive spectral analysis is effective for experimental analysis of the passage through resonance.

Analysis of Parametric Resonance by Adaptive Spectral Analysis. Experimental Analysis of the Time-varying Amplitude and Phase Spectra.

Analysis of Parametric Resonance by Adaptive Spectral Analysis. Experimental Analysis of the Time-varying Amplitude and Phase Spectra.

Spectral analysis of episodic rhythmic variations in the cutaneous electrogastrogram.

Electrical activity of the stomach can be measured using surface electrodes. The cutaneous recording of gastric electrical activity is called the electrogastrogram (EGG). Gastric electrical dysrhythmic events associated with abnormal conditions of the stomach may be detected from the EGG. An adaptive spectral analysis method which is based on autoregressive moving average modeling has previously been developed. The aim of this paper is to demonstrate the ability of the previous method in detecting gastric dysrhythmic events from the EGG. A series of bench tests stimulating typical problems with the analysis of nonstationary electric potentials was conducted. The application of the adaptive spectral analysis method to dysrhythmic events and rhythmic variations of the gastric slow wave is presented in this paper. The adaptive spectral analysis approach provides several advantages: narrow frequency peaks permitting more precise frequency identification, determination of changes in frequency components at any time point, and enhanced interpretation of cutaneous EGG recordings.

Adaptive spectral analysis of cutaneous electrogastric signals using autoregressive moving average modelling

The recording of the human gastric myoelectrical activity by means of cutaneous electrodes is called electrogastrography (EGG). It provides a noninvasive method of studying electrogastric behaviour. The normal frequency of the gastric signal is about 0.05 Hz. However, sudden changes of its frequency have been observed and are generally considered to be related to gastric motility disorders. Thus, spectral analysis, especially online spectral analysis, can serve as a valuable tool for practical purposes. The paper presents a new method of the adaptive spectral analysis of cutaneous electrogastric signals using autoregressive moving average (ARMA) modelling. It is based on an adaptive ARMA filter and provides both time and frequency information of the signal. Its performance is investigated in comparison with the conventional FFT-based periodogram method. Its properties in tracking time-varying instantaneous frequencies are shown. Its applications to the running spectral analysis of cutaneous electrogastric signals are presented. The proposed adaptive ARMA spectral analysis method is easy to implement and is efficient in computations. The results presented in the paper show that this new method provides a better performance and is very useful for the online monitoring of cutaneous electrogastric signals.

Adaptive spectral estimation by the conjugate gradient method

This paper proposes an alternative technique for adaptive spectral estimation. The new technique applies the method of conjugate gradient, which is used for iteratively finding the generalized eigenvector corresponding to the minimum generalized eigenvalue of a semidefinite Hermitian matrix, to the adaptive spectral analysis problem. Computer simulations have been performed to compare the new method to existing ones. From the limited examples presented, it is seen that the new method is computationally more efficient at the expense of more core storage. Also, this method is effective for small data records and can implement noise correction to yield unbiased spectral estimates if an estimate of the noise covariance matrix is available. The technique performs well for both narrow-band and wide-band signals.

ENHANCEMENT OF DIRECTIONAL WAVE SPECTRUM ESTIMATES

Determination of the directional distribution of ocean surface waves is of practical importance and analytical schemes for it are developed and discussed here. Based on a generalized representation of wave properties such as surface elevation, subsurface pressure or horizontal components of water particle velocity, acceleration or wave force, two general schemes of analysis are developed. In one scheme the predictive equations for the directional distribution of both the amplitude and phase of waves are derived. Distribution of energy as a function of direction for random waves is obtained in the other scheme. Fourier series parameterization is used to represent directional spectrum. The truncation of the series dictated by data limitations introduce directional spread and negative side lobes for the estimated directional spectrum. A procedure to remove these undesirable side lobes by a non-negative smoothing function is described. The smoothing causes further directional spread. Methods for obtaining better directional resolution are discussed. Data adaptive spectral analysis techniques such as Maximum Likelihood Method and Maximum Entropy Method are suggested.