Controlled Coherence Plasmonic Light Sources

Through a computational model, we study the coherence converting capabilities of an array of holes in a surface plasmon-supporting metal plate, with an eye towards the creation of controlled coherence plasmonic light sources. We evaluate how the average coherence and transmission of the hole array depends on the parameters of the array, such as the array geometry, lattice constant, and hole size. We show that the location of coherence bandgaps and resonances can be estimated through a simple formula and that increases in coherence are strongly correlated with increases in transmission.

A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry

Personalized health monitoring of neural signals usually results in a very large dataset, the processing and transmission of which require considerable energy, storage, and processing time. We present bioinspired electroceptive compressive sensing (BeCoS) as an approach for minimizing these penalties. It is a lightweight and reliable approach for the compression and transmission of neural signals inspired by active electroceptive sensing used by weakly electric fish. It uses a signature signal and a sensed pseudo-sparse differential signal to transmit and reconstruct the signals remotely. We have used EEG datasets to compare BeCoS with the block sparse Bayesian learning-bound optimization (BSBL-BO) technique—A popular compressive sensing technique used for low-energy wireless telemonitoring of EEG signals. We achieved average coherence, latency, compression ratio, and estimated per-epoch power values that were 35.38%, 62.85%, 53.26%, and 13 mW better than BSBL-BO, respectively, while structural similarity was only 6.295% worse. However, the original and reconstructed signals remain visually similar. BeCoS senses the signals as a derivative of a predefined signature signal resulting in a pseudo-sparse signal that significantly improves the efficiency of the monitoring process. The results show that BeCoS is a promising approach for the health monitoring of neural signals.

The explicability factor of the algorithm in the problems of searching for the similarity of text documents

С развитием все более сложных методов автоматического анализа текста повышается важность задачи объяснения пользователю, почему прикладная интеллектуальная информационная система выделяет некоторые тексты как схожие по смыслу. В работе рассмотрены ограничения, которые такая постановка накладывает на используемые интеллектуальные алгоритмы. Проведенный авторами эксперимент показал, что абсолютное значение схожести документов не универсально по отношению к интеллектуальному алгоритму, поэтому оптимальную пороговую величину схожести необходимо устанавливать отдельно для каждой решаемой задачи. Полученные результаты могут быть использованы при оценке применимости различных методов установления смысловой схожести между документами в прикладных информационных системах, а также при выборе оптимальных параметров модели с учетом требований объяснимости решения. The problem of providing a comprehensive explanation to any user why the applied intelligent information system suggests meaning similarity in certain texts imposes significant requirements on the intelligent algorithms. The article covers the entire set of technologies involved in the solution of the text clustering problem and several conclusions are stated thereof. Matrix decomposition aimed at reducing the dimension of the vector representation of a corpus does not provide clear explanatiom of the algorithmic principles to a user. Ranking using the TF-IDF function and its modifications finds a few documents that are similar in meaning, however, this method is the easiest for users to comprehend, since algorithms of this type detect specific matching words in the compared texts. Topic modeling methods (LSI, LDA, ARTM) assign large similarity values to texts despite a few matching words, while a person can easily tell that the general subject of the texts is the same. Yet the explanation of how topic modeling works requires additional effort for interpretation of the detected ones. This interpretation gets easier as the model quality grows, while the quality can be optimized by its average coherence. The experiment demonstrated that the absolute value of documents similarity is not invariant for different intelligent algorithms, so the optimal threshold value of similarity must be set separately for each problem to be solved. The results of the work can be further used to assess which of the various methods developed to detect meaning similarity in texts can be effectively implemented in applied information systems and to determine the optimal model parameters based on the solution explicability requirements.

Graphene Oxide Concentration Effect on the Optoelectronic Properties of ZnO/GO Nanocomposites

In this work, the effects of graphene oxide (GO) concentrations (1.5 wt.%, 2.5 wt.%, and 5 wt.%) on the structural, morphological, optical, and luminescence properties of zinc oxide nanorods (ZnO NRs)/GO nanocomposites, synthesized by a facile hydrothermal process, were investigated. X-ray diffraction (XRD) patterns of NRs revealed the hexagonal wurtzite structure for all composites with an average coherence length of about 40–60 nm. A scanning electron microscopy (SEM) study confirmed the presence of transparent and wrinkled, dense GO nanosheets among flower-like ZnO nanorods, depending on the GO amounts used in preparation. Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), ultraviolet–visible (UV–Vis) absorption spectroscopy, and photoluminescence (PL) measurements revealed the impact of GO concentration on the optical and luminescence properties of ZnO NRs/GO nanocomposites. The energy band gap of the ZnO nanorods was independent of GO concentration. Photoluminescence spectra of nanocomposites showed a significant decrease in the intensities in the visible light range and red shifted suggesting a charge transfer process. The nanocomposites’ chromaticity coordinates for CIE 1931 color space were estimated to be (0.33, 0.34), close to pure white ones. The obtained results highlight the possibility of using these nanocomposites to achieve good performance and suitability for optoelectronic applications.

Received Probability of Vortex Modes Carried by Localized Wave of Bessel–Gaussian Amplitude Envelope in Turbulent Seawater

By using the two-frequency coherence function model of a beam in a turbulent medium and the localized wave theory of the polychromatic beam, we develop the spectrum average mutual coherence function of the localized wave of Bessel–Gaussian amplitude envelope and the spectrum average coherence length of spherical wave. By the spectrum average coherence length and the spectrum average mutual coherence function, we construct a received probability of vortex modes carried by localized wave of Bessel–Gaussian amplitude envelope in anisotropic turbulent seawater. Our results show that the received probability of signal vortex modes increases with the increase of half-modulated pulse width of the input pulse, turbulent inner scale, anisotropic factor of turbulence and rate of dissipation of kinetic energy per unit mass of fluid, but it increases with the decrease of the Bessel cone angle and the dissipation rate of the mean-squared temperature. We also find that there is a maximum effective beam waist for a given receiving aperture, and the vortex mode is more sensitive to salinity fluctuations than to temperature fluctuations in turbulence. Our conclusions show that localized wave of Bessel–Gaussian amplitude envelope is a more suitable beam for the vortex mode communication than conventional vortex waves.

Wavelet Analysis of Daily Energy Demand and Weather Variables

In this paper, we applied the Wavelet Transform Coherence (WTC) and phase analysis to analyze the relationship between the daily electricity demand (DED) and weather variables such as temperature, relative humidity, wind speed, and radiation. The DED data presents both seasonal fluctuations and increasing trend while the weather variables depict only seasonal variation. The results obtained from the WTC and phase analysis permit us to detect the period of time when the DED significantly correlates with the weather variables. We found a strong seasonal interdependence between the air temperature and DED for a periodicity of 256-512 days and 128-256 days. The relationship between the humidity and DED also shows a significant interdependence for a periodicity of 256-512 days with average coherence equal to 0.8. Regarding the radiation and wind speed, the correlation is low with average coherence less than 0.5. These results provide an insight into the properties of the impacts of weather variables on electricity demand on the basis of which power planners can rely to improve their forecasting and planning of electricity demand.

New Approaches to Processing Ground-based SAR (GBSAR) Data for Deformation Monitoring

In this paper, aiming at the limitation of persistence scatterers (PS) points selection, a new method for selecting PS points has been introduced based on the average coherence coefficient, amplitude dispersion index, estimated signal-to-noise ratio and displacement standard deviation of multiple threshold optimization. The stability and quality of this method are better than that of a single model. In addition, an atmospheric correction model has also been proposed to estimate the atmospheric effects on Ground-based synthetic aperture radar (GBSAR) observations. After comparing the monitoring results before and after correction, we clearly found that the results are in good agreement with the actual observations after applying the proposed atmospheric correction approach.

Evoked Potentials Investigations of Deficit Versus Nondeficit Schizophrenia: EEG-MEG Preliminary Data

Heterogeneity of schizophrenia is a major obstacle toward understanding the disorder. One likely subtype is the deficit syndrome (DS) where patients suffer from predominantly negative symptoms. This study investigated the evoked responses and the evoked magnetic fields to identify the neurophysiological deviations associated with the DS. Ten subjects were recruited for each group (Control, DS, and Nondeficit schizophrenia [NDS]). Subjects underwent magnetoencephalography (MEG) and electroencephalography (EEG) testing while listening to an oddball paradigm to generate the P300 as well as a paired click paradigm to generate the mid-latency auditory-evoked responses (MLAER) in a sensory gating paradigm. MEG–coherence source imaging (CSI) during P300 task revealed a significantly higher average coherence value in DS than NDS subjects in the gamma band (30-80 Hz), when listening to standard stimuli but only NDS subjects had a higher average coherence level in the gamma band than controls when listening to the novel sounds. P50, N100, and P3a ERP amplitudes (EEG analysis) were significantly decreased in NDS compared with DS subjects. The data suggest that the deviations in the 2 patient groups are qualitatively different. Deviances in NDS patients suggest difficulty in both early (as in the gating paradigm), as well as later top-down processes (P300 paradigm). The main deviation in the DS group was an exaggerated responsiveness to ongoing irrelevant stimuli detected by EEG whereas NDS subjects had an exaggerated response to novelty.