Automatic seizure detection with different time delays using SDFT and time-domain feature extraction

Automatic seizure detection is important for fast detection of the seizure because the way that the expert denotes and searches for seizure in the long signal takes time. The most common way to detect seizures automatically is to use an electroencephalogram(EEG). Many studies have used feature extraction that needs time for calculation. In this study, sliding discrete Fourier transform(SDFT) was applied for conversion to a frequency domain without using a window, which was compared with using window for feature selection. SDFT was calculated for each time series sample directly without any delay by using a simple infinite impulse response(IIR)structure. The EEG database of Bonn University was used to test the proposed method, and two cases were defined to examine a two-classifier feedforward neural network and an adaptive network-based fuzzy inference system. Results revealed that the maximum accuracies were 93% without delay and 99.8% with a one-second delay. This delay accrued because the average was taken for the results with a one-second window.

Finite-time Stabilization of a General Class of Nonholonomic Dynamic Systems via Terminal Sliding Mode

This paper presents control strategies for finite-time stabilization of a class of nonholonomic dynamic systems with unknown virtual control coefficients and system parameters. The minimal dilation degree technique and the terminal sliding mode control scheme with finite-time convergence are used to design the controllers. The systematic control strategy development involves the introduction of state transformations and the application of recursive terminal sliding mode structure. Depending on whether the system in question can be converted into a time-invariant linear system or not, two control schemes are proposed respectively guaranteeing that system states converge to zero in finite time. The effectiveness and the robust feature of the developed control approaches are testified by two practical examples： the simplified underactuated hovercraft system and the parking problem for a mobile robot of the unicycle type.

Improvement of signal processing in Coriolis mass flowmeters for gas-liquid two-phase flow

As an increasingly popular flow metering technology,Coriolis mass flowmeter exhibits high measurement accuracy under single-phase flow condition and is widely used in the industry.However,under complex flow conditions,such as two-phase flow,the measurement accuracy is greatly decreased due to various factors including improper signal processing methods.In this study,three digital signal processing methods—the quadrature demodulation(QD)method,Hilbert method,and sliding discrete time Fourier transform method—are analyzed for their applications in processing sensor signals and providing measurement results under gas-liquid two-phase flow condition.Based on the analysis,specific improvements are applied to each method to deal with the signals under two-phase flow condition.For simulation,sensor signals under single-and two-phase flow conditions are established using a random walk model.The phase difference tracking performances of these three methods are evaluated in the simulation.Based on the digital signal processor,a converter program is implemented on its evaluation board.The converter program is tested under single-and two-phase flow conditions.The improved signal processing methods are evaluated in terms of the measurement accuracy and complexity.The QD algorithm has the best performance under the single-phase flow condition.Under the two-phase flow condition,the QD algorithm performs a little better in terms of the indication error and repeatability than the improved Hilbert algorithm at 160,250,and 420 kg/h flow points,whereas the Hilbert algorithm outperforms the QD algorithm at the 600 kg/h flow point.