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[PREDICTORS Regarding POOR Digestive tract Prep Pertaining to COLONOSCOPY: A new SINGLE-CENTRE Possible STUDY].
Context.Electroencephalography (EEG) signals are contaminated with diverse types of noises and artifacts, which greatly distort EEG recording and increase the difficulty in obtaining accurate diagnosis.Objective.This paper investigates, for the first time, multi-kernel normalized least mean square with coherence-based sparsification (MKNLMS-CS) algorithm for suppressing different artifact components, and the 1D patch-based non-local means (NLM) algorithm for eliminating white and colored noises.Approach.A novel multi-stage system based on combining the NLM algorithm with the MKNLMS-CS algorithm is proposed for eliminating different noise and artifact sources by targeting each noise or artifact component in a single stage.Main Results.The proposed approach is applied to clinical real EEG data, and the results reveal the superior performance of the proposed system in removing white and colored noises, suppressing different artifact components, preserving the important and tiny features of the original EEG signal, and keeping the morphology of EEG frequency components.Significance.The proposed multi-stage design succeeds not only to suppress different artifact components and noise sources under low and high noise conditions, but also to achieve accurate sleep spindle detection from the filtered high-quality EEG signals. This demonstrates the usefulness of the proposed approach for obtaining high-resolution EEG signal from noisy and contaminated EEG recordings.
The power spectrum of the human electroencephalogram (EEG) as a function of frequency is a mix of brain oscillations (e.g. alpha activity around 10 Hz) and non-oscillations or noise of uncertain origin. "White noise" is uniformly distributed over frequency, while "pink noise" has an inverse power-frequency relation (power ∝ 1/f). Interest in EEG pink noise has been growing, but previous human estimates appear methodologically flawed. We propose a new approach to extract separate valid estimates of pink and white noise from an EEG power spectrum.

We use simulated data to demonstrate its effectiveness compared with established procedures, and provide an illustrative example from a new resting eyes-open (EO) and eyes-closed (EC) dataset. The topographic characteristics of the obtained pink and white noise estimates are examined, as is the alpha power in this sample.

Valid pink and white noise estimates were successfully obtained for each of our 5400 individual spectra (60 participants × 30 electrodes × 3 c and technology.In recent years, artificial intelligence techniques have proved to be very successful when applied to problems in physical sciences. Here we apply an unsupervised machine learning (ML) algorithm called principal component analysis (PCA) as a tool to analyse the data from muon spectroscopy experiments. Specifically, we apply the ML technique to detect phase transitions in various materials. The measured quantity in muon spectroscopy is an asymmetry function, which may hold information about the distribution of the intrinsic magnetic field in combination with the dynamics of the sample. Sharp changes of shape of asymmetry functions-measured at different temperatures-might indicate a phase transition. Existing methods of processing the muon spectroscopy data are based on regression analysis, but choosing the right fitting function requires knowledge about the underlying physics of the probed material. Conversely, PCA focuses on small differences in the asymmetry curves and works without any prior assumptions about the studied samples. We discovered that the PCA method works well in detecting phase transitions in muon spectroscopy experiments and can serve as an alternative to current analysis, especially if the physics of the studied material are not entirely known. selleck kinase inhibitor Additionally, we found out that our ML technique seems to work best with large numbers of measurements, regardless of whether the algorithm takes data only for a single material or whether the analysis is performed simultaneously for many materials with different physical properties.For the first time, we propose using amorphous selenium (a-Se) as the photoconductive material for time-of-flight (TOF) detectors. Advantages of avalanche-modea-Se are having high fill factor, low excess noise due to unipolar photoconductive gain, band transport in extended states with the highest possible mobility, and negligible trapping. The major drawback ofa-Se is its poor single-photon time resolution and low carrier mobility due to shallow-traps, problems that must be circumvented for TOF applications. We propose a nanopattern multi-wella-Se detector to enable both impact ionization avalanche gain and unipolar time-differential (UTD) charge sensing in one device. Our experimental results show that UTD charge sensing in avalanche-modea-Se improves time-resolution by nearly 4 orders-of-magnitude. In addition, we used Cramér -Rao Lower Bound analysis and Monte Carlo simulations to demonstrate the viability of our detector for low statistics photon imaging modalities such as PET despite it being a linear-mode device. Based on our results, our device proves very promising to achieve 100 ps coincidence time resolution with a material that is low cost and uniformly scalable to large area.Scalable fabrication of Si nanowires with a critical dimension of about 100 nm is essential to a variety of applications. Current techniques used to reach these dimensions often involve e-beam lithography or deep-UV (DUV) lithography combined with resolution enhancement techniques. In this study, we report the fabrication of less then 150 nm Si nanowires from SOI substrates using DUV lithography (λ = 248 nm) by adjusting the exposure dose. Irregular resist profiles generated by in-plane interference under masking patterns of width 800 nm were optimized to split the resulting features into twin Si nanowires. However, masking patterns of micrometre size or more on the same photomask does not generate split features. The resulting resist profiles are verified by optical lithography computer simulation based on Huygens-Fresnel diffraction theory. Photolithography simulation results validate that the key factors in the fabrication of subwavelength nanostructures are the air gap value and the photoresist thickness.
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