Time-varying signal processing using multi-wavelet basis functions and a modified block least mean square algorithm

This paper introduces a novel parametric modeling and identification method for linear time-varying systems using a modified block least mean square (LMS) approach where the time-varying parameters are approximated using multi-wavelet basis functions. This approach can be used to track rapidly or even sharply varying processes and is more suitable for recursive estimation of process parameters by combining wavelet approximation theory with a modified block LMS algorithm. Numerical examples are provided to show the effectiveness of the proposed method for dealing with severely nonstatinoary processes

Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. A new efficient Common Model Structure Selection (CMSS) algorithm is proposed to select a common model structure. The main idea and key procedure is: First, generate K 1 data sets (the first K data sets are used for training, and theK 1 th one is used for testing) using an online sliding window method; then detect significant model terms to form a common model structure which fits over all the K training data sets using the new proposed CMSS approach. Finally, estimate and refine the time-varying parameters for the identified common-structured model using a Recursive Least Squares (RLS) parameter estimation method. The new method can effectively detect and adaptively track the transient variation of nonstationary signals. Two examples are presented to illustrate the effectiveness of the new approach including an application to an EEG data set

Time-varying model identification for time-frequency feature extraction from EEG data

A novel modelling scheme that can be used to estimate and track time-varying properties of nonstationary signals is investigated. This scheme is based on a class of time-varying AutoRegressive with an eXogenous input (ARX) models where the associated time-varying parameters are represented by multi-wavelet basis functions. The orthogonal least square (OLS) algorithm is then applied to refine the model parameter estimates of the time-varying ARX model. The main features of the multi-wavelet approach is that it enables smooth trends to be tracked but also to capture sharp changes in the time-varying process parameters. Simulation studies and applications to real EEG data show that the proposed algorithm can provide important transient information on the inherent dynamics of nonstationary processes

TWITTER IN THE MARKETING

We report on a compact and highly efficient diode-end-pumped TEM00 Nd:YVO4 slab laser with an output power of 103 W and beam quality M2 1.5. The optical-to-optical efficiency was 41.5%. In electro-optically Q-switched operation. 83 W of average power at a pulse-repetition rate of 50 kHz with a pulse length of 11.3 ns was obtained. At a pulse-repetition rate of 10 kHz, 5.6 mJ of pulse energy, and 870 kW of peak power were measured

Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV

Performance comparison of doubly salient reluctance machine topologies supplied by sinewave currents

This paper comprehensively investigates the electromagnetic performance of 3-phase, 12-slot, and 8-pole switched reluctance machines (SRMs) with different winding configurations, i.e. double/single layer, short pitched (concentrated) and fully pitched (distributed). These SRMs are supplied by sinewave currents so that a conventional 3-phase converter can be employed, leading to behavior which is akin to that of synchronous reluctance type machines. Comparisons in terms of static and dynamic performances such as d- and q-axis inductances, on-load torque, torque-speed curve, efficiency map, etc. have been carried out using two-dimensional finite element method (2-D FEM). It is demonstrated for the given size of machine considered, that for same copper loss and without heavy magnetic saturation, both single and double layer mutually coupled SRMs can produce higher on-load torque compared to conventional SRMs. Additionally, double layer mutually coupled SRM achieved the highest efficiency compared to other counterparts. When it comes to single layer SRMs, they are more suitable for middle speed applications and capable of producing higher average torque while lower torque ripple than their double layer counterparts at low phase current. Two prototype SRMs, both single layer and double layer, are built to validate the predictions

Modular Permanent Magnet Machines with Alternate Teeth Having Tooth Tips

This paper presents single layer modular permanent magnet machines with either wound or unwound teeth with tooth tips. The structures with wound teeth having tooth tips are suitable for modular machines with slot number higher than pole number to compensate for the drop in winding factor due to the flux gaps in alternate stator teeth, accordingly to maintain or even to increase their average torques. However, the structures with unwound teeth having tooth tips are suitable for modular machines with slot number lower than pole number to increase the winding factor and hence to further improve the machine performance. The phase back-EMF, on-load torque, iron and copper losses as well as efficiency have been calculated using finite element analysis for different slot/pole number combinations, and for different flux gap and tooth tip widths. It is found that by properly choosing the flux gap and tooth tip widths, both the on-load torque performance and the efficiency can be optimized for the investigated machines with different slot/pole number combinations. Experiments have been carried out to validate the finite element results