A sub-Nyquist co-prime sampling music spectral approach for natural frequency identification of white-noise excited structures

Motivated by practical needs to reduce data transmission payloads in wireless sensors for vibration-based monitoring of civil engineering structures, this paper proposes a novel approach for identifying resonant frequencies of white-noise excited structures using acceleration measurements acquired at rates significantly below the Nyquist rate. The approach adopts the deterministic co-prime sub-Nyquist sampling scheme, originally developed to facilitate telecommunication applications, to estimate the autocorrelation function of response acceleration time-histories of low-amplitude white-noise excited structures treated as realizations of a stationary stochastic process. This is achieved without posing any sparsity conditions to the signals. Next, the standard MUSIC algorithm is applied to the estimated autocorrelation function to derive a denoised super-resolution pseudo-spectrum in which natural frequencies are marked by prominent spikes. The accuracy and applicability of the proposed approach is numerically assessed using computer-generated noise-corrupted acceleration time-history data obtained by a simulation-based framework pertaining to a white-noise excited structural system with two closely-spaced modes of vibration carrying the same amount of energy, and a third isolated weakly excited vibrating mode. All three natural frequencies are accurately identified by sampling at as low as 78% below Nyquist rate for signal to noise ratio as low as 0dB (i.e., energy of additive white noise equal to the signal energy), suggesting that the proposed approach is robust and noise-immune while it can reduce data transmission requirements in acceleration wireless sensors for natural frequency identification of engineering structures

Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles

Minimising latency of pitch detection algorithms for live vocals on low-cost hardware

A pitch estimation device was proposed for live vocals to output appropriate pitch data through the musical instrument digital interface (MIDI). The intention was to ideally achieve unnoticeable latency while maintaining estimation accuracy. The projected target platform was low-cost, standalone hardware based around a microcontroller such as the Microchip PIC series. This study investigated, optimised and compared the performance of suitable algorithms for this application. Performance was determined by two key factors: accuracy and latency. Many papers have been published over the past six decades assessing and comparing the accuracy of pitch detection algorithms on various signals, including vocals. However, very little information is available concerning the latency of pitch detection algorithms and methods with which this can be minimised. Real-time audio introduces a further latency challenge that is sparsely studied, minimising the length of sampled audio required by the algorithms in order to reduce overall total latency. Thorough testing was undertaken in order to determine the best-performing algorithm and optimal parameter combination. Software modifications were implemented to facilitate accurate, repeatable, automated testing in order to build a comprehensive set of results encompassing a wide range of test conditions. The results revealed that the infinite-peak-clipping autocorrelation function (IACF) performed better than the other autocorrelation functions tested and also identified ideal parameter values or value ranges to provide the optimal latency/accuracy balance. Although the results were encouraging, testing highlighted some fundamental issues with vocal pitch detection. Potential solutions are proposed for further development

On line power spectra identification and whitening for the noise in interferometric gravitational wave detectors

In this paper we address both to the problem of identifying the noise Power Spectral Density of interferometric detectors by parametric techniques and to the problem of the whitening procedure of the sequence of data. We will concentrate the study on a Power Spectral Density like the one of the Italian-French detector VIRGO and we show that with a reasonable finite number of parameters we succeed in modeling a spectrum like the theoretical one of VIRGO, reproducing all its features. We propose also the use of adaptive techniques to identify and to whiten on line the data of interferometric detectors. We analyze the behavior of the adaptive techniques in the field of stochastic gradient and in the Least Squares ones.Comment: 28 pages, 21 figures, uses iopart.cls accepted for pubblication on Classical and Quantum Gravit

Wideband Channel Estimation and Prediction in Single-Carrier Wireless Systems

Abstract—In this contribution wideband channel estimation and prediction designed for single-carrier wideband wireless communications systems are investigated. Specifically, the single-carrier wideband pilot signal received by the receiver is first converted to the frequency-domain. Then, the envelope of the channel transfer function (CTF) is estimated in the frequency-domain, in order to reduce the effects of background noise on the channel prediction step to be invoked. Finally, channel prediction is carried out based on the estimated CTF in the frequency-domain, where a Kalman filter assisted long-range channel prediction algorithm is employed. Our simulation results show that for a reasonable signal-to-noise ratio (SNR) value the proposed frequency-domain based wideband channel estimator is capable of efficiently mitigating the effects of the background noise, hence enhancing the performance of wideband channel prediction

Low Complexity Blind Equalization for OFDM Systems with General Constellations

This paper proposes a low-complexity algorithm for blind equalization of data in OFDM-based wireless systems with general constellations. The proposed algorithm is able to recover data even when the channel changes on a symbol-by-symbol basis, making it suitable for fast fading channels. The proposed algorithm does not require any statistical information of the channel and thus does not suffer from latency normally associated with blind methods. We also demonstrate how to reduce the complexity of the algorithm, which becomes especially low at high SNR. Specifically, we show that in the high SNR regime, the number of operations is of the order O(LN), where L is the cyclic prefix length and N is the total number of subcarriers. Simulation results confirm the favorable performance of our algorithm

VLSI implementation of an energy-aware wake-up detector for an acoustic surveillance sensor network

We present a low-power VLSI wake-up detector for a sensor network that uses acoustic signals to localize ground-base vehicles. The detection criterion is the degree of low-frequency periodicity in the acoustic signal, and the periodicity is computed from the "bumpiness" of the autocorrelation of a one-bit version of the signal. We then describe a CMOS ASIC that implements the periodicity estimation algorithm. The ASIC is functional and its core consumes 835 nanowatts. It was integrated into an acoustic enclosure and deployed in field tests with synthesized sounds and ground-based vehicles.Fil: Goldberg, David H.. Johns Hopkins University; Estados UnidosFil: Andreou, Andreas. Johns Hopkins University; Estados UnidosFil: Julian, Pedro Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Pouliquen, Philippe O.. Johns Hopkins University; Estados UnidosFil: Riddle, Laurence. Signal Systems Corporation; Estados UnidosFil: Rosasco, Rich. Signal Systems Corporation; Estados Unido