Wideband Spectrum Sensing on Real-Time Signals at Sub-Nyquist Sampling Rates in Single and Cooperative Multiple Nodes

The authors would like to acknowledge the Engineering and Physical Sciences Research Council (EPSRC) in the UK for their support of this work with Grant No. EP/L024241/1. Mark D. Plumbley was partly supported by a Leadership Fellowship (EP/G007144/1) from the UK EPSR

Artificial neural network approaches and compressive sensing techniques for stochastic process estimation and simulation subject to incomplete data

This research is themed around development of tools for discrete analysis of stochastic processes subject to limited or missing data; more specifically, estimation of stochastic process power spectra from which new process time-histories may be simulated. In this context, the author proposes three novel approaches to power spectrum estimation subject to missing data which comprise the main body of this work. Of particular importance is the fact that all three approaches are adaptable for use in both stationary and evolutionary power spectrum estimation. Numerous arrangements of missing data are tested to simulate a range of possible scenarios to demonstrate the versatility of the proposed methodologies. The first of the three approaches uses an artificial neural network (ANN) based model for stochastic process power spectrum estimation subject to limited / missing data. In this regard, an appropriately defined ANN is utilized to capture the stochastic pattern in the available data in an “average sense”. Next, the extrapolation capabilities of the ANN are exploited for generating realizations of the underlying stochastic process. Finally, power spectrum estimates are derived based on established frequency (e.g. Fourier analysis), or versatile joint time-frequency analysis techniques (e.g. harmonic wavelets) for the cases of stationary and non-stationary stochastic processes, respectively. One of the significant advantages of the approach relates to the fact that no a priori knowledge about the data is assumed. The second approach uses compressive sensing (CS) to solve the same problem. In this setting, further assumptions are imposed on the nature of the underlying process of interest than in the ANN case, in particular that of sparsity in the frequency domain. The advantages being that when compared to ANN, significant improvements in efficiency and accuracy are achieved with increased reliability for larger amounts of missing data. Specifically, first an appropriate basis is selected for expanding the signal recorded in the time domain. As with the ANN approach, Fourier and harmonic wavelet bases are utilized. Next, an L1 norm minimization procedure is performed for obtaining the sparsest representation of the signal in the selected basis. Further, an adaptive basis procedure is introduced that significantly improves results when working with stochastic process record ensembles. The final approach is somewhat different, in that it aims to quantify uncertainty in power spectrum estimation subject to missing data rather than provide deterministic predictions. By relying on relatively relaxed assumptions for the missing data, utilizing fundamental concepts from probability theory, and resorting to Fourier and harmonic wavelets based representations of stationary and non-stationary stochastic processes, respectively, a closed-form expression is derived for the probability density function (PDF) of the power spectrum value corresponding to a specific frequency. Numerical examples demonstrate the large extent to which any given single estimate using deterministic methods, even for small amounts of missing data, may be unrepresentative of the target spectrum. In this regard, this probabilistic approach can be potentially used to bound deterministic estimates, providing specific validation criteria for missing data reconstruction

Ubicación de fallas en redes de distribución eléctrica basado en sensado comprimido.

In this article, refers on the location of faults in the electrical distribution networks based on compressed sensing, it consists in the reconstruction of decomposed current signals by means of wavelet transform. It described method makes the location of faults optimum distribution systems to reduce reconnection time taking into account as a priority the location of fault in a transient system through the placement of smart meters that will create of voltage signal variation that is used to take pre-fault and failure values as required, forming a tension characteristic signal it will be importance for the reconstruction the same. This procedure allows to carry out the algorithm for the location of faults in distribution networks applying the matrix rule of minimization l1 that allows finding these variations and therefore the identification of failure according to their values. The algorithm proposed by means of the matrix approach rules helps to take measures in terms of the reduction of failures, the reduction of time of restoration of the system and the quality of the service in the node of failure resulting in reliability in the networks in front of disturbance decreasing the affectation to final user.En este artículo hace referencia a la ubicación de fallas en las redes de distribución eléctrica basada en sensado comprimido, esto consiste en la reconstrucción de las señales de corriente descompuestas por medio de la transformada de wavelets. Este método descrito hace que la ubicación de fallas en sistemas de distribución sea el óptimo para reducir el tiempo de reconexión teniendo en cuenta como prioridad la ubicación de la falla en un sistema transitorio por medio de la colocación de medidores inteligentes que servirán como creadores de la señal de la variación de la tensión que se utiliza para tomar valores de pre falla y falla según lo amerite, formando una señal de característica de la tensión la cual será de vital importancia para la reconstrucción de la misma. Este procedimiento permite llevar a cabo el algoritmo para la ubicación de fallas en las redes de distribución aplicando la regla matricial de minimización l1 que permite encontrar dichas variaciones y por consiguiente la identificación de la falla según sus valores. El algoritmo propuesto por medio de las normas matriciales de aproximación ayuda a tomar medidas en cuanto a la reducción de fallas, la reducción de tiempo de restablecimiento del sistema y la calidad del servicio en el nodo de falla teniendo como resultado fiabilidad en las redes frente a una perturbación disminuyendo la afectación al usuario final

Noncontact Vital Signs Detection

Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown

Efficient FFT Algorithms for Mobile Devices

Increased traffic on wireless communication infrastructure has exacerbated the limited availability of radio frequency ({RF}) resources. Spectrum sharing is a possible solution to this problem that requires devices equipped with Cognitive Radio ({CR}) capabilities. A widely employed technique to enable {CR} is real-time {RF} spectrum analysis by applying the Fast Fourier Transform ({FFT}). Today’s mobile devices actually provide enough computing resources to perform not only the {FFT} but also wireless communication functions and protocols by software according to the software-defined radios paradigm. In addition to that, the pervasive availability of mobile devices make them powerful computing platform for new services. This thesis studies the feasibility of using mobile devices as a novel spectrum sensing platform with focus on {FFT}-based spectrum sensing algorithms. We benchmark several open-source {FFT} libraries on an Android smartphone. We relate the efficiency of calculating the {FFT} to both algorithmic and implementation-related aspects. The benchmark results also show the clear potential of special {FFT} algorithms that are tailored for sparse spectrum detection

High-speed surface profilometry based on an adaptive microscope with axial chromatic encoding

An adaptive microscope with axial chromatic encoding is designed and developed, namely the AdaScope. With the ability to confocally address any locations within the measurement volume, the AdaScope provides the hardware foundation for a cascade measurement strategy to be developed, dramatically accelerating the speed of 3D confocal microscopy