A near real-time water surface detection method based on HSV transformation of MODIS multi-Spectral time series data

In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10 years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objective of this study is to develop and demonstrate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near-real-time. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. The validation of the algorithm showed very few omission errors and no commission errors. It demonstrates the ability of the proposed algorithm to perform as effectively as human interpretation of the images. The validation of the permanent water surface product with an independent dataset derived from high resolution imagery, showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed 27 is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficulties

The application of counter immunoelectrophoresis (CIE) in ocular protein studies. Part I:time dependent deposition patterns of immunoregulatory proteins on anionic hydrogel contact lenses

This paper focuses on the effects of wear regime on the deposition pattern of important immunoregulatory proteins on FDA Group IV etafilcon-A lenses. Specifically, the aim was to assess the extent to which the daily disposable wear modality produces a different deposition of proteins from the conventional daily wear regime which is coupled with cleaning and disinfection. Counter immunoelectrophoresis (CIE) was employed to detect individual proteins in lens extracts from individual patients and focused on the analysis of five proteins, IgA, IgG, lactoferrin, albumin and kininogen. Deposition was monitored as a function of time; significantly lower deposition was detected on the daily disposable lenses. cr 2002 British Contact Lens Association. Published by Elsevier Science Ltd. All rights reserved.

Time-dependent variation of fiber Bragg grating reflectivity in PMMA-based polymer optical fibers

In this paper we investigate the effects of viscoelasticity on both the strength and resonance wavelength of two fibre Bragg gratings (FBGs) inscribed in microstructured polymer optical fibre (mPOF) made of undoped PMMA. Both FBGs were inscribed under a strain of 1% in order to increase the material photosensitivity. After the inscription the strain was released and the FBGs spectra were monitored. We initially observed a decrease of the reflection down to zero after which it began to increase. After that, strain tests were carried out to confirm the results and finally the gratings were monitored for a further 120 days, with a stable reflection response being observed beyond 50 days.

Time-Dependent Differential Inclusions and Viability

AMS subject classification: Primary 34A60, Secondary 49J52.

Identification of a time-dependent bio-heat blood perfusion coefficient

In the paper the identification of the time-dependent blood perfusion coefficient is formulated as an inverse problem. The bio-heat conduction problem is transformed into the classical heat conduction problem. Then the transformed inverse problem is solved using the method of fundamental solutions together with the Tikhonov regularization. Some numerical results are presented in order to demonstrate the accuracy and the stability of the proposed meshless numerical algorithm.

True real-time change data capture with web service database encapsulation

This research is investigating the claim that Change Data Capture (CDC) technologies capture data changes in real-time. Based on theory, our hypothesis states that real-time CDC is not achievable with traditional approaches (log scanning, triggers and timestamps). Traditional approaches to CDC require a resource to be polled, which prevents true real-time CDC. We propose an approach to CDC that encapsulates the data source with a set of web services. These web services will propagate the changes to the targets and eliminate the need for polling. Additionally we propose a framework for CDC technologies that allow changes to flow from source to target. This paper discusses current CDC technologies and presents the theory about why they are unable to deliver changes in real-time. Following, we discuss our web service approach to CDC and accompanying framework, explaining how they can produce real-time CDC. The paper concludes with a discussion on the research required to investigate the real-time capabilities of CDC technologies. © 2010 IEEE.

Time-dependent Suppression of Oscillatory Power in Evolving Solar Magnetic Fields

Oscillation amplitudes are generally smaller within magnetically active regions like sunspots and plage when compared to their surroundings. Such magnetic features, when viewed in spatially resolved power maps, appear as regions of suppressed power due to reductions in the oscillation amplitudes. Employing high spatial- and temporal-resolution observations from the Dunn Solar Telescope (DST) in New Mexico, we study the power suppression in a region of evolving magnetic fields adjacent to a pore. By utilizing wavelet analysis, we study for the first time how the oscillatory properties in this region change as the magnetic field evolves with time. Image sequences taken in the blue continuum, G-band, Ca ii K, and Hα filters were used in this study. It is observed that the suppression found in the chromosphere occupies a relatively larger area, confirming previous findings. Also, the suppression is extended to structures directly connected to the magnetic region, and is found to get enhanced as the magnetic field strength increased with time. The dependence of the suppression on the magnetic field strength is greater at longer periods and higher formation heights. Furthermore, the dominant periodicity in the chromosphere was found to be anti-correlated with increases in the magnetic field strength.

Fast iterative solvers for time-dependent PDE-constrained optimization problems

Otto-von-Guericke-Universität Magdeburg, Fakultät für Mathematik, Kumulative Habilitation, 2016

Time-Dependent Deformation of Non-Composite and Composite Sand-Lightweight Prestressed Concrete Structures, Phase 1, HR-137, 1969

Presented in this report is an investigation of the use of "sand-lightweight" concrete in prestressed concrete structures. The sand-lightweight concrete consists of 100% sand substitution for fines, along with Idealite coarse and medium lightweight aggregate and Type I Portland Cement.

Time-Dependent Effects of Acute Exercise-Induced Arousal on Long-Term Memory for Emotional and Neutral Stimuli

Psychological research has strongly documented the memory-enhancing effects of emotional arousal, while the effects of acute aerobic exercise on memory are not well understood. Manipulation of arousal has been shown to enhance long-term memory for emotional stimuli in a time-dependent fashion. This presents an opportunity to investigate the role of acute exercise in memory modulation. The purpose of this study was to determine the time-dependent relationship between acute exercise-induced arousal and long-term emotional memory. Participants viewed pleasant, neutral, and unpleasant images before or after completing a high-intensity session of cycling exercise. Salivary alpha-amylase, a biomarker of central norepinephrine, was measured as an indicator of arousal. No effects of exercise on recognition memory were revealed, however; a single session of high-intensity cycling increased salivary alpha-amylase. Our results also indicate that the influence of exercise on emotional responsiveness should be considered in further exploration of the memory-enhancing potential of acute exercise.

Analysis and control of transient spectra using time-dependent density functional theory.

215 p.

Similarity solutions of a Becker-Döring system with time-dependent monomer input

We formulate the Becker-Döring equations for cluster growth in the presence of a time-dependent source of monomer input. In the case of size-independent aggregation and ragmentation rate coefficients we find similarity solutions which are approached in the large time limit. The form of the solutions depends on the rate of monomer input and whether fragmentation is present in the model; four distinct types of solution are found.

Sparse time-frequency data analysis : a multi-scale approach

In this work, we further extend the recently developed adaptive data analysis method, the Sparse Time-Frequency Representation (STFR) method. This method is based on the assumption that many physical signals inherently contain AM-FM representations. We propose a sparse optimization method to extract the AM-FM representations of such signals. We prove the convergence of the method for periodic signals under certain assumptions and provide practical algorithms specifically for the non-periodic STFR, which extends the method to tackle problems that former STFR methods could not handle, including stability to noise and non-periodic data analysis. This is a significant improvement since many adaptive and non-adaptive signal processing methods are not fully capable of handling non-periodic signals. Moreover, we propose a new STFR algorithm to study intrawave signals with strong frequency modulation and analyze the convergence of this new algorithm for periodic signals. Such signals have previously remained a bottleneck for all signal processing methods. Furthermore, we propose a modified version of STFR that facilitates the extraction of intrawaves that have overlaping frequency content. We show that the STFR methods can be applied to the realm of dynamical systems and cardiovascular signals. In particular, we present a simplified and modified version of the STFR algorithm that is potentially useful for the diagnosis of some cardiovascular diseases. We further explain some preliminary work on the nature of Intrinsic Mode Functions (IMFs) and how they can have different representations in different phase coordinates. This analysis shows that the uncertainty principle is fundamental to all oscillating signals.

Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data

Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals. Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and heavy structures with the risk of overfitting small datasets, and architectures are often defined empirically. Furthermore, CNNs are mainly validated by designing within-subject decoders. Crucially, the automatically learned features mainly remain unexplored; conversely, interpreting these features may be of great value to use decoders also as analysis tools, highlighting neural signatures underlying the different decoded brain or behavioral states in a data-driven way. Lastly, SOA DL-based algorithms used to decode single-cell recordings rely on more complex, slower to train and less interpretable networks than CNNs, and the use of CNNs with these signals has not been investigated. This PhD research addresses the previous limitations, with reference to P300 and motor decoding from EEG, and motor decoding from single-neuron activity. CNNs were designed light, compact, and interpretable. Moreover, multiple training strategies were adopted, including transfer learning, which could reduce training times promoting the application of CNNs in practice. Furthermore, CNN-based EEG analyses were proposed to study neural features in the spatial, temporal and frequency domains, and proved to better highlight and enhance relevant neural features related to P300 and motor states than canonical EEG analyses. Remarkably, these analyses could be used, in perspective, to design novel EEG biomarkers for neurological or neurodevelopmental disorders. Lastly, CNNs were developed to decode single-neuron activity, providing a better compromise between performance and model complexity.