Application of the Wigner distribution to monitoring cutting tool condition

This thesis is about the application of the Wigner distribution to cutting tool monitoring and control. After reviewing traditional methods, a new method is proposed. This is to regard the surface texture and geometric error of form of a machined workpiece as the fingerprint of a cutting process, to analyse it, and to extract cutting tool vibration information from it, which can then be used for cutting tool monitoring. In order to analyse the surface texture effectively, three analysing tools, i.e. the Fourier transform, the ambiguity function, the Wigner distribution (WD), are examined and compared with each other, and it is concluded that the WD is best able to analyse both stationary and nonstationary signals. Furthermore, computer simulation of both chirp signals and frequency modulated signals is then carried out, and it is shown that the WD can be used to extract useful parameters successively. In order to demonstrate the suitability of the WD for machine tool condi- tion monitoring, first cutting tool vibration are measured directly by two linear variable differential transformers mounted on the cutting tool, and then these measured data about vibration are used to verify those parameters extracted from the surface of the machined workpiece by the WD. It is found that • the extracted frequencies in both horizontal and vertical direction are within 10% of those measured, • the extracted amplitudes in both horizontal and vertical direction are highly correlated with those measured. This result confirms the feasibility of this technique. In spite of being an off-line process, this technique is simple, reliable, and can reveal the direct effect of cutting processes

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

Doctor of Philosophy

dissertationChirp signals arise in many applications of digital signal processing. In this dissertation, we address the problem of detection of chirp signals that are encountered in a bistatic radar which we are developing for remote sensing of cosmic ray induced air showers. The received echoes from the air showers are characterized by their large Doppler shift (several tens of MHz), and very short sweep period (~ 10 ^s). This makes our astrophysical problem a challenging one, since a very short sweep period is equivalent to a very low energy chirp signal. Furthermore, the related parameters of the received echoes are nondeterministic since they are tied to the physical parameters of the air showers that are stochastic in nature. In addition, our problem is characterized by the rarity of the expected chirp-echoes to be received, few events per week, and thus, background noise reception is the case most of the time. The primary focus of this research is to address these challenges and find an optimized detection approach under the existing receiver environment which contains non-Gaussian noise and is characterized by low signal-to-noise ratio (SNR). Matched filters are commonly used in radar systems when the chirp signal is known. In our first method, we revisit this context and use a matched filter as a basis of building a rake-like receiver that consists of a set of filters matched to quantized chirp rates, logarithmically distributed within the chirp-rate interval of interest. We examine the detection capability of the proposed structure through extensive theoretical and numerical analysis. Theoretical analysis and simulation results prove that the proposed detector has high detection capability for a range of chirp slopes in a low SNR environment. A major source of false-alarms was found to be due to sudden noise spikes that cover wide frequency bands. These transient signals have high amplitudes and occur at random time instants. This leads to erroneous detection decision. We study the influence of amplitude limiting the noisy signal on reducing the received false-alarms and enhancing the detection performance of the proposed rake-like receiver. In our second method, we use Hough transform (HT), which is widely used in the area of image processing for the purpose of finding parameterized patterns, as a basis of building a robust detection technique. We examine the detection capability of the proposed structure through theoretical and numerical analysis. Our results prove that the proposed detector has high detection capability for a range of chirp slopes in a low SNR environment. The introduced detection algorithms are implemented over a Virtex-5 FPGA. National Instruments modules are used as a high-performance custom hardware. Due to rarity of received echoes, we emulate the expected radar echoes to evaluate the system performance. The detection performance of the emulated echoes is examined using the implemented receiver at the field. Also, we compare the performance of both detectors

AN EFFECTIVE BLOCK WEIGHTAGE BASED TECHNIQUE FOR IRIS RECOGNITION USING EMPIRICAL MODE DECOMPOSITION

with the growing demands in security systems, iris recognition continues to be a significant solution for biometrics-based identification systems. There are several techniques for Iris Recognition such as Phase Based Technique, Non Filter-based Technique, Based on Wavelet Transform, Based on Empirical Mode Decomposition and many more. In this paper, we have developed a block weightage based iris recognition technique using Empirical Mode Decomposition (EMD) taking into consideration the drawbacks of the baseline technique. EMD is an adaptive multiresolution decomposition technique that is used for extracting the features from each block of the iris image. For matching the features of iris images with the test image, we make use of block weightage method that is designed in accordance with the irrelevant pixels contained in the blocks. For experimental evaluation, we have used the CASIA iris image database and the results clearly demonstrated that applying EMD in each block of normalized iris images makes it possible to achieve better accuracy in iris recognition than the baseline technique

A signal complexity-based approach for AM–FM signal modes counting

I segnali modulati in frequenza appaiono in molte discipline applicate, tra cui la geologia, la comunicazione, la biologia e l'acustica. Questi sono multicomponenti, cioè consistono in forme d'onda multiple, con frequenza specifica dipendente dal tempo (frequenza istantanea). Nella maggior parte delle applicazioni pratiche, il numero di modalità - che è sconosciuto - è necessario per analizzare correttamente un segnale; per esempio per separare ogni singolo componente e per stimare la sua frequenza istantanea. Il rilevamento del numero di componenti è un problema impegnativo, specialmente nel caso di modalità che interferiscono. L'approccio basato sull'entropia di Rényi si è dimostrato adatto per il conteggio delle modalità di un segnale, ma è limitato a componenti ben separate. Il presente documento affronta questo problema introducendo una nuova nozione di complessità del segnale. In particolare, lo spettrogramma di un segnale multicomponente è visto come un processo non stazionario in cui l'interferenza si alterna alla non interferenza. La complessità relativa alla transizione tra sezioni consecutive dello spettrogramma viene valutata mediante la Run Length Encoding. Sulla base di una legge di evoluzione tempo-frequenza dello spettrogramma, le variazioni di complessità sono studiate per stimare accuratamente il numero di componenti. Il metodo presentato è adatto a segnali multicomponente con modalità non separabili, così come ad ampiezze variabili nel tempo e mostra robustezza al rumore.Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally 1multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise

Physics, Astrophysics and Cosmology with Gravitational Waves

Gravitational wave detectors are already operating at interesting sensitivity levels, and they have an upgrade path that should result in secure detections by 2014. We review the physics of gravitational waves, how they interact with detectors (bars and interferometers), and how these detectors operate. We study the most likely sources of gravitational waves and review the data analysis methods that are used to extract their signals from detector noise. Then we consider the consequences of gravitational wave detections and observations for physics, astrophysics, and cosmology.Comment: 137 pages, 16 figures, Published version <http://www.livingreviews.org/lrr-2009-2

All-sky search for periodic gravitational waves in LIGO S4 data

We report on an all-sky search with the LIGO detectors for periodic gravitational waves in the frequency range 50–1000 Hz and with the frequency’s time derivative in the range −1×10−8  Hz s−1 to zero. Data from the fourth LIGO science run (S4) have been used in this search. Three different semicoherent methods of transforming and summing strain power from short Fourier transforms (SFTs) of the calibrated data have been used. The first, known as StackSlide, averages normalized power from each SFT. A “weighted Hough” scheme is also developed and used, which also allows for a multi-interferometer search. The third method, known as PowerFlux, is a variant of the StackSlide method in which the power is weighted before summing. In both the weighted Hough and PowerFlux methods, the weights are chosen according to the noise and detector antenna-pattern to maximize the signal-to-noise ratio. The respective advantages and disadvantages of these methods are discussed. Observing no evidence of periodic gravitational radiation, we report upper limits; we interpret these as limits on this radiation from isolated rotating neutron stars. The best population-based upper limit with 95% confidence on the gravitational-wave strain amplitude, found for simulated sources distributed isotropically across the sky and with isotropically distributed spin axes, is 4.28×10−24 (near 140 Hz). Strict upper limits are also obtained for small patches on the sky for best-case and worst-case inclinations of the spin axes

The Effects of Instrumental Noise on Searches for Generic Transient Gravitational Waves in Advanced LIGO

One hundred years after Albert Einstein predicted the existence of gravitational waves as a result of his theory of general relativity, the Laser Interferometer Gravitational-Wave Observatory (LIGO), made the first direct detection of a gravitational-wave signal from a binary black hole merger, GW150914. GW150914 was found not only by search methods specifically developed to find the distinctive waveform produced by coalescing binaries, but also by generic searches designed to find any arbitrary short-duration signal in the LIGO data. The impact of noise on the searches must be carefully investigated in order to reduce the search background and enable confident gravitational-wave detections. In this dissertation, I will present my work on characterizing transient noise sources in the detectors and implementing data quality vetoes to reduce their effects on the generic transient gravitational-wave searches. Chapters 3 and 4 describe my work on the data quality of the searches for generic transient gravitational waves. I worked on the development of data quality vetoes during the first observing run and the decisions about which vetoes to implement in the transient searches. I also analyzed the transient noise sources that the vetoes were unable to eliminate, using statistical methods to search for potential instrumental causes. Since the development of data quality vetoes requires a thorough understanding of every component of the detectors, I have also conducted a detailed investigation into the transients in the suspension systems used to isolate the LIGO optics from seismic motion. Chapter 5 presents the details of this work. The first gravitational wave detection was only the beginning an exciting era of gravitational-wave astronomy that will give us a new way of understanding the universe. Even in the first observing run, a second binary black hole merger was observed. The methods used in this dissertation to investigate and reduce background noise will continue to play an important role in making these detections possible. As the detectors improve in the future and continue to take data, more signals will be detected, bringing us a wealth of new information about black holes and other types of sources