Steady-State Response of Periodically Switched Linear Circuits via Augmented Time-Invariant Nodal Analysis

This paper focuses on the simulation of periodically switched linear circuits. The basic notation and theoretical framework is presented, with emphasis on the differences between the linear time-invariant and the time-varying cases. For this important class of circuits and sources defined by periodic signals, the computation of their steady-state response is carried out via the solution of an augmented time-invariant MNA equation in the frequency-domain. The proposed method is based on the expansion of the unknown voltages and currents in terms of Fourier series and on the automatic generation of augmented equivalents of the circuit components. The above equivalents along with the information on circuit topology allow to create, via circuit inspection, a timeinvariant MNA equation, the solution of which provides the coefficients of both the time- and the frequency-domain responses of the circuit. Analytical and numerical examples are used to stress the generality and benefits of the proposed approach

Nonlinear acoustical detection of buried landmines using pulsed standoff excitation

textTo help resolve certain practical issues with acoustical methods for landmine detection, experiments were performed using a pulsed, standoff source consisting of sixteen speakers mounted on a circular arc. This source, as well as a pair of 18-inch subwoofers, were used separately for acoustical excitation of the buried mine, and the response of the target site was examined as a function of source frequency, sound pressure level, and excitation signal type, with a particular focus on multitone signals. In addition, modeling was undertaken to investigate the effects of nonlinearity, including bimodular nonlinearity, on frequency generation. A numerical, time-domain solution based on a lumped-element model proposed by Donskoy et al. [J. Acoust. Soc. Am. 117, 690 (2005)] was developed and used to simulate pulsed excitation and the effects of bimodular nonlinearity, which allowed experimentally observed spectra to be compared with modeled results.Mechanical Engineerin

Blind Estimation of Multi-Path and Multi-User Spread Spectrum Channels and Jammer Excision via the Evolutionary Spectral Theory

Despite the significant advantages of direct sequence spreadspectrum communications, whenever the number of users increases orthe received signal is corrupted by an intentional jammer signal,it is necessary to model and estimate the channel effects in orderto equalize the received signal, as well as to excise the jammingsignals from it. Due to multi-path and Doppler effects in thetransmission channels, they are modeled as random, time-varyingsystems. Considering a wide sense stationary channel during thetransmission of a number of bits, a linear time-varying modelcharacterized by a random number of paths, each beingcharacterized by a delay, an attenuation factor and a Dopplerfrequency shift, is shown to be an appropriate channel model. Itis shown that the estimation of the parameters of such models ispossible by means of the spreading function, related to thetime-varying frequency response of the system and the associatedevolutionary kernels. Applying the time-frequency orfrequency-frequency discrete evolutionary transforms, we show thata blind estimation procedure is possible by computing thespreading function from the discrete evolutionary transform ofthe received signal. The estimation also requires the synchronizedpseudo-noise sequence for either of the users we are interestedin. The estimation procedure requires to adaptively implementingthe discrete evolutionary transform to estimate the spreadingfunction and determine the channel parameters. Once the number ofpaths, delays, Doppler frequencies and attenuations characterizingthe channel are found, a decision parameter can be obtained todetermine the transmitted bit. We will show also that ourestimation approach supports multiuser communication applicationssuch as uplink and downlink in wireless communicationtransmissions. In the case of an intentional jamming, common inmilitary applications, we consider a receiver based onnon-stationary Wiener masking that excises such jammer as well asinterference from other users. Both the mask and the optimalestimator are obtained from the discrete evolutionarytransformation. The estimated parameters from the computedspreading function, corresponding to the closest to the line ofsight signal path, provide an efficient detection scheme. Ourprocedures are illustrated with simulations, that display thebit-error rate for different levels of channel noise and jammersignals

Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR

The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range

Sampling based on local bandwidth

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 105-106).The sampling of continuous-time signals based on local bandwidth is considered in this thesis. In an intuitive sense, local bandwidth refers to the rate at which a signal varies locally. One would expect that signals should be sampled at a higher rate in regions of higher local bandwidth, and at a lower rate in regions of lower local bandwidth. In many cases, sampling signals based on local bandwidth can yield more efficient representations as compared with conventional uniform sampling, which does not exploit local signal characteristics. In the first part of the thesis, a particular definition for a linear time-varying lowpass filter is adopted as a potential model for local bandwidth. A sampling and reconstruction method permitting consistent resampling is developed for signals generated by such filters. The method does not generally result in perfect reconstruction except for a special class of self-similar signals. However, the reconstruction error is shown to decrease with the variation in the cut-off frequency of the filter. In the second part of the thesis, a definition for local bandwidth based on the time-warping of globally bandlimited signals is reviewed. Using this definition, a method is developed for sampling and reconstructing signals according to local bandwidth. The method employs a time-warping to minimize the energy of a signal above a given maximum frequency. A number of techniques for determining the optimal time-warping are examined.by Dennis Wei.M.Eng

Condition Monitoring of Helical Gear Transmissions Based on Vibration Modelling and Signal Processing

Condition monitoring (CM) of gear transmission has attracted extensive research in recent years. In particular, the detection and diagnosis of its faults in their early stages to minimise cost by maximising time available for planned maintenance and giving greater opportunity for avoiding a system breakdown. However, the diagnostic results obtained from monitored signals are often unsatisfactory because mainstream technologies using vibration response do not sufficiently account for the effect of friction and lubrication. To develop a more advanced and accurate diagnosis, this research has focused on investigating the nonlinearities of vibration generation and transmission with the viscoelastic properties of lubrication, to provide an in-depth understanding of vibration generating mechanisms and hence develop more effective signal processing methods for early detection and accurate diagnosis of gear incipient faults. A comprehensive dynamic model has been developed to study the dynamic responses of a multistage helical gear transmission system. It includes not only time-varying stiffness but also tooth friction forces based on an elastohydrodynamic lubrication (EHL) model. In addition, the progression of a light wear process is modelled by reducing stiffness function profile, in which the 2nd and 3rd harmonics of the meshing frequency (and their sidebands) show significant alteration that support fault diagnostic at early stages. Numerical and experimental results show that the friction and progressive wear induced vibration excitations will change slightly the amplitudes of the spectral peaks at both the mesh frequency and its sideband components at different orders, which provides theoretical supports for extracting reliable diagnostic signatures. As such changes in vibrations are extremely small and submerged in noise, it is clear that effective techniques for enhancing the signal-to-noise ratio, such as time synchronous averaging (TSA) and modulation signal bispectrum (MSB) are required to reveal such changes. MSB is preferred as it allows small amplitude sidebands to be accurately characterised in a nonlinear way without information loss and does not impose any addition demands regarding angular displacement measurement as does TSA. With the successful diagnosis of slight wear in helical gears, the research progressed to validate the capability of MSB based methods to diagnose four common gear faults relating to gear tribological conditions; lubrication shortfall, changes in lubrication viscosity, water in oil, and increased bearing clearances. The results show that MSB signatures allows accurate differentiation between these small changes, confirming the model and signal processing proposed in this thesi