Low Complexity Spectral Analysis of Heart-Rate-Variability through a Wavelet based FFT

In this paper, a low complexity system for spectral analysis of heart rate variability (HRV) is presented. The main idea of the proposed approach is the implementation of the Fast-Lomb periodogram that is a ubiquitous tool in spectral analysis, using a wavelet based Fast Fourier transform. Interestingly we show that the proposed approach enables the classification of processed data into more and less significant based on their contribution to output quality. Based on such a classification a percentage of less-significant data is being pruned leading to a significant reduction of algorithmic complexity with minimal quality degradation. Indeed, our results indicate that the proposed system can achieve up-to 45% reduction in number of computations with only 4.9% average error in the output quality compared to a conventional FFT based HRV system.

Green Political Economy: beyond orthodox undifferentiated economic growth as a permanent feature of the economy

This chapter outlines the main features of green political economy and the principal ways in which it differs from dominant mainstream or orthodox neoclassical economics. Neoclassical economics is critiqued on the grounds of denying its normative and ideological commitments in its false presentation of itself as ‘objective’ and ‘value neutral’. It is also critiqued for its ecologically irrational commitment to the imperative of orthodox economic growth as a permanent feature of the economy, compromising its ability to offer realistic or normatively compelling guides to how we might make the transition to a sustainable economy. Green political economy is presented as an alternative or heterodox form of economic thinking but one which explicitly expresses its normative/ideological value bases (hence it represents a return to ‘political economy’, the origins of modern economics). Green political economy also challenges the commitment to undifferentiated economic growth as a permanent objective of the human economy. In its place, green political economy promotes ‘economic security’ as a better objective for a sustainable, post-growth economy. The latter includes the transition to a low-carbon energy economy, and is also one which maximises quality of life (as oppose to formal employment, income and wealth), and actively seeks to lower socio-economic inequality. Green political economy views orthodox economic growth as having passed the threshold in most ‘advanced’ capitalist societies beyond which it has undermined quality of life and at best manages rather than reduces socially and ecologically damaging inequalities.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Spitzer Space Telescope spectra of post-AGB stars in the large magellanic Cloud-polycyclic aromatic hydrocarbons at low metallicities

This paper reports variations of polycyclic aromatic hydrocarbons (PAHs) features that were found in Spitzer Space Telescope spectra of carbon-rich post-asymptotic giant branch (post-AGB) stars in the Large Magellanic Cloud (LMC). The paper consists of two parts. The first part describes our Spitzer spectral observing programme of 24 stars including post-AGB candidates. The latter half of this paper presents the analysis of PAH features in 20 carbon-rich post-AGB stars in the LMC, assembled from the Spitzer archive as well as from our own programme.We found that five post-AGB stars showed a broad feature with a peak at 7.7 μm, that had not been classified before. Further, the 10-13 μm PAH spectra were classified into four classes, one of which has three broad peaks at 11.3, 12.3 and 13.3 μm rather than two distinct sharp peaks at 11.3 and 12.7 μm, as commonly found in HII regions. Our studies suggest that PAHs are gradually processed while the central stars evolve from post-AGB phase to planetary nebulae, changing their composition before PAHs are incorporated into the interstellar medium. Although some metallicity dependence of PAH spectra exists, the evolutionary state of an object is more significant than its metallicity in determining the spectral characteristics of PAHs for LMC and Galactic post-AGB stars. © 2014 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.

Towards Multi-Sensor Spectral Alignment Through Post Measurement Calibration Correction

Semiconductor manufactures are increasing reliant on optical emission spectroscopy (OES) to source information on plasma characteristics and process change. However, nonlinearities in the response of OES sensors and errors in their calibration lead to discrepancies in observed wavelength detector response. This paper presents a technique for the retrospective spectral calibration of multiple OES sensors. Underlying methodology is given, and alignment performance is evaluated using OES recordings from a semiconductor plasma process. The paper concludes with a discussion of results and suggests avenues for future work.

Spectral gamma-ray logs and palaeoclimate change? Permian – Triassic, Persian Gulf

Spectral gamma ray (SGR) logs are used as stratigraphic tools in correlation, sequence stratigraphy and most recently, in clastic successions as a proxy for changes in hinterland palaeoweathering. In this study we analyse the spectral gamma ray signal recorded in two boreholes that penetrated the carbonate and evaporate-dominated Permian–Triassic boundary (PTB) in the South Pars Gasfield (offshore Iran, Persian Gulf) in an attempt to analyse palaeoenvironmental changes from the upper Permian (Upper Dalan Formation) and lower Triassic (Lower Kangan Formation). The results are compared to lithological changes, total organic carbon (TOC) contents and published stable isotope (δ18O, δ13C) results. This work is the first to consider palaeoclimatic effects on SGR logs from a carbonate/evaporate succession. While Th/U ratios compare well to isotope data (and thus a change to less arid hinterland climates from the Late Permian to the Early Triassic), Th/K ratios do not, suggesting a control not related to hinterland weathering. Furthermore, elevated Th/U ratios in the Early Triassic could reflect a global drawdown in U, rather than a more humid episode in the sediment hinterlands, with coincident changes in TOC. Previous work that used spectral gamma ray data in siliciclastic successions as a palaeoclimate proxy may not apply in carbonate/evaporate sedimentary rocks.

A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability

Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case. 

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.