Bioelectric signal analysis and measurement

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

On semi-Classical Questions Related to Signal Analysis

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

Mixed-Signal Testability Analysis for Data-Converter IPs

In this paper, a new procedure to derive testability measures is presented. Digital testability can be calculated by means of probability, while in analog it is possible to calculate testability using impedance values. Although attempts have been made to reach compatibility, matching was somewhat arbitrary and therefore not necessarily compatible. The concept of the new approach is that digital and analog can be integrated in a more consistent way. More realistic testability figures are obtained, which makes testability of true mixed-signal systems and circuits feasible. To verify the results, our method is compared with a sensitivity analysis, for a simple 3-bit ADC

Bolt Detection Signal Analysis Method Based on ICEEMD

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