45,890 research outputs found

    Bioelectric signal analysis and measurement

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    Nonstationary time series techniques are used to analyze EEG signals for the estimation of alertness. A time varying order is extracted in sequential time series measurement of these data and strategies are devised for obtaining optimal representation of the EEG signal

    Bolt Detection Signal Analysis Method Based on ICEEMD

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    The construction quality of the bolt is directly related to the safety of the project, and as such, it must be tested. In this paper, the improved complete ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt detection signal analysis. The ICEEMD is used in order to decompose the anchor detection signal according to the approximate entropy of each intrinsic mode function (IMF). The noise of the IMFs is eliminated by the wavelet soft threshold de-noising technique. Based on the approximate entropy, and the wavelet de-noising principle, the ICEEMD-De anchor signal analysis method is proposed. From the analysis of the vibration analog signal, as well as the bolt detection signal, the result shows that the ICEEMD-De method is capable of correctly separating the different IMFs under noisy conditions, and also that the IMF can effectively identify the reflection signal of the end of the bolt

    Signal Analysis

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    Práce se zabývá problematikou analýzy signálu, získaného měřením rovnoměrnosti chodu harmonických a planetových převodovek. V úvodní části jsou vysvětleny některé vlastnosti FFT a pojem kepstrum. Dále je uveden přehled nejčastěji měřených parametrů uvedených převodovek a potřebné vztahy, které vyjadřují souvislosti mezi jednotlivými mechanickými částmi převodovek. Další část se zabývá popisem SW modulu vytvořeného v prostředí LabView, který je výstupním produktem práce a provádí analýzu na změřených průbězích.This thesis deals with problem of analysis signal acquiring measurement of uniformity motion harmonic drive and planetary gearboxes. In introduction part are explain some features of FFT and concept of cepstrum. Further is present overview of most often measured parameters of these gearboxes and needed formulas, which express connections among particular mechanical parts of gearboxes. The next part deals with description of SW program unit generated in LabView environment, which is output product of work and makes analysis on measured courses.

    On semi-Classical Questions Related to Signal Analysis

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    This study explores the reconstruction of a signal using spectral quantities associated with some self-adjoint realization of an h-dependent Schr\"odinger operator when the parameter h tends to 0. Theoretical results in semi-classical analysis are proved. Some numerical results are also presented. We first consider as a toy model the sech^2 function. Then we study a real signal given by arterial blood pressure measurements. This approach seems to be very promising in signal analysis. Indeed it provides new spectral quantities that can give relevant information on some signals as it is the case for arterial blood pressure signal

    MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis

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    Interpretability has emerged as a crucial aspect of machine learning, aimed at providing insights into the working of complex neural networks. However, existing solutions vary vastly based on the nature of the interpretability task, with each use case requiring substantial time and effort. This paper introduces MARGIN, a simple yet general approach to address a large set of interpretability tasks ranging from identifying prototypes to explaining image predictions. MARGIN exploits ideas rooted in graph signal analysis to determine influential nodes in a graph, which are defined as those nodes that maximally describe a function defined on the graph. By carefully defining task-specific graphs and functions, we demonstrate that MARGIN outperforms existing approaches in a number of disparate interpretability challenges.Comment: Technical Repor

    Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

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    An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly re- lies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.Comment: 10 page
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