Robust Cyclic MUSIC Algorithm for Finding Directions in Impulsive Noise Environment

This paper addresses the issue of direction finding of a cyclostationary signal under impulsive noise environments modeled by α-stable distribution. Since α-stable distribution does not have finite second-order statistics, the conventional cyclic correlation-based signal-selective direction finding algorithms do not work effectively. To resolve this problem, we define two robust cyclic correlation functions which are derived from robust statistics property of the correntropy and the nonlinear transformation, respectively. The MUSIC algorithm with the robust cyclic correlation matrix of the received signals of arrays is then used to estimate the direction of cyclostationary signal in the presence of impulsive noise. The computer simulation results demonstrate that the two proposed robust cyclic correlation-based algorithms outperform the conventional cyclic correlation and the fractional lower order cyclic correlation based methods

Breathing pattern characterization in patients with respiratory and cardiac failure

El objetivo principal de la tesis es estudiar los patrones respiratorios de pacientes en proceso de extubación y pacientes con insuficiencia cardiaca crónica (CHF), a partirde la señal de flujo respiratorio. La información obtenida de este estudio puede contribuir a la comprensión de los procesos fisiológicos subyacentes,y ayudar en el diagnóstico de estos pacientes. Uno de los problemas más desafiantes en unidades de cuidados intensivos es elproceso de desconexión de pacientes asistidos mediante ventilación mecánica. Más del 10% de pacientes que se extuban tienen que ser reintubados antes de 48 horas. Una prueba fallida puede ocasionar distrés cardiopulmonar y una mayor tasa de mortalidad. Se caracterizó el patrón respiratorio y la interacción dinámica entre la frecuenciacardiaca y frecuencia respiratoria, para obtener índices no invasivos que proporcionen una mayor información en el proceso de destete y mejorar el éxito de la desconexión.Las señales de flujo respiratorio y electrocardiográfica utilizadas en este estudio fueron obtenidas durante 30 minutos aplicando la prueba de tubo en T. Se compararon94 pacientes que tuvieron éxito en el proceso de extubación (GE), 39 pacientes que fracasaron en la prueba al mantener la respiración espontánea (GF), y 21 pacientes quesuperaron la prueba con éxito y fueron extubados, pero antes de 48 horas tuvieron que ser reintubados (GR). El patrón respiratorio se caracterizó a partir de las series temporales. Se aplicó la dinámica simbólica conjunta a las series correspondientes a las frecuencias cardiaca y respiratoria, para describir las interacciones cardiorrespiratoria de estos pacientes. Técnicas de "clustering", ecualización del histograma, clasificación mediante máquinasde soporte vectorial (SVM) y técnicas de validación permitieron seleccionar el conjunto de características más relevantes. Se propuso una nueva métrica B (índice de equilibrio) para la optimización de la clasificación con muestras desbalanceadas. Basado en este nuevo índice, aplicando SVM, se seleccionaron las mejores características que mantenían el mejor equilibrio entre sensibilidad y especificidad en todas las clasificaciones. El mejor resultado se obtuvo considerando conjuntamente la precisión y el valor de B, con una clasificación del 80% entre los grupos GE y GF, con 6 características. Clasificando GE vs. el resto de los pacientes, el mejor resultado se obtuvo con 9 características, con 81%. Clasificando GR vs. GE y GR vs. el resto de pacientes la precisión fue del 83% y 81% con 9 y 10 características, respectivamente. La tasa de mortalidad en pacientes con CHF es alta y la estratificación de estospacientes en función del riesgo es uno de los principales retos de la cardiología contemporánea. Estos pacientes a menudo desarrollan patrones de respiraciónperiódica (PB) incluyendo la respiración de Cheyne-Stokes (CSR) y respiración periódica sin apnea. La respiración periódica en estos pacientes se ha asociadocon una mayor mortalidad, especialmente en pacientes con CSR. Por lo tanto, el estudio de estos patrones respiratorios podría servir como un marcador de riesgo y proporcionar una mayor información sobre el estado fisiopatológico de pacientes con CHF. Se pretende identificar la condición de los pacientes con CHFde forma no invasiva mediante la caracterización y clasificación de patrones respiratorios con PBy respiración no periódica (nPB), y patrón de sujetos sanos, a partir registros de 15minutos de la señal de flujo respiratorio. Se caracterizó el patrón respiratorio mediante un estudio tiempo-frecuencia estacionario y no estacionario, de la envolvente de la señal de flujo respiratorio. Parámetros relacionados con la potencia espectral de la envolvente de la señal presentaron losmejores resultados en la clasificación de sujetos sanos y pacientes con CHF con CSR, PB y nPB. Las curvas ROC validan los resultados obtenidos. Se aplicó la "correntropy" para una caracterización tiempo-frecuencia mas completa del patrón respiratorio de pacientes con CHF. La "corretronpy" considera los momentos estadísticos de orden superior, siendo más robusta frente a los "outliers". Con la densidad espectral de correntropy (CSD) tanto la frecuencia de modulación como la dela respiración se representan en su posición real en el eje frecuencial. Los pacientes con PB y nPB, presentan diferentesgrados de periodicidad en función de su condición, mientras que los sujetos sanos no tienen periodicidad marcada. Con único parámetro se obtuvieron resultados del 88.9% clasificando pacientes PB vs. nPB, 95.2% para CHF vs. sanos, 94.4% para nPB vs. sanos.The main objective of this thesis is to study andcharacterize breathing patterns through the respiratory flow signal applied to patients on weaning trials from mechanicalventilation and patients with chronic heart failure (CHF). The aim is to contribute to theunderstanding of the underlying physiological processes and to help in the diagnosis of these patients. One of the most challenging problems in intensive care units is still the process ofdiscontinuing mechanical ventilation, as over 10% of patients who undergo successfulT-tube trials have to be reintubated in less than 48 hours. A failed weaning trial mayinduce cardiopulmonary distress and carries a higher mortality rate. We characterize therespiratory pattern and the dynamic interaction between heart rate and breathing rate toobtain noninvasive indices that provide enhanced information about the weaningprocess and improve the weaning outcome. This is achieved through a comparison of 94 patients with successful trials (GS), 39patients who fail to maintain spontaneous breathing (GF), and 21 patients who successfully maintain spontaneous breathing and are extubated, but require thereinstitution of mechanical ventilation in less than 48 hours because they are unable tobreathe (GR). The ECG and the respiratory flow signals used in this study were acquired during T-tube tests and last 30 minute. The respiratory pattern was characterized by means of a number of respiratory timeseries. Joint symbolic dynamics applied to time series of heart rate and respiratoryfrequency was used to describe the cardiorespiratory interactions of patients during theweaning trial process. Clustering, histogram equalization, support vector machines-based classification (SVM) and validation techniques enabled the selection of the bestsubset of input features. We defined a new optimization metric for unbalanced classification problems, andestablished a new SVM feature selection method, based on this balance index B. The proposed B-based SVM feature selection provided a better balance between sensitivityand specificity in all classifications. The best classification result was obtained with SVM feature selection based on bothaccuracy and the balance index, which classified GS and GFwith an accuracy of 80%, considering 6 features. Classifying GS versus the rest of patients, the best result wasobtained with 9 features, 81%, and the accuracy classifying GR versus GS, and GR versus the rest of the patients was 83% and 81% with 9 and 10 features, respectively.The mortality rate in CHF patients remains high and risk stratification in these patients isstill one of the major challenges of contemporary cardiology. Patients with CHF oftendevelop periodic breathing patterns including Cheyne-Stokes respiration (CSR) and periodic breathing without apnea. Periodic breathing in CHF patients is associated withincreased mortality, especially in CSR patients. Therefore it could serve as a risk markerand can provide enhanced information about thepathophysiological condition of CHF patients. The main goal of this research was to identify CHF patients' condition noninvasively bycharacterizing and classifying respiratory flow patterns from patients with PB and nPBand healthy subjects by using 15-minute long respiratory flow signals. The respiratory pattern was characterized by a stationary and a nonstationary time-frequency study through the envelope of the respiratory flow signal. Power-related parameters achieved the best results in all of the classifications involving healthy subjects and CHF patients with CSR, PB and nPB and the ROC curves validated theresults obtained for the identification of different respiratory patterns. We investigated the use of correntropy for the spectral characterization of respiratory patterns in CHF patients. The correntropy function accounts for higher-order moments and is robust to outliers. Due to the former property, the respiratory and modulationfrequencies appear at their actual locations along the frequency axis in the correntropy spectral density (CSD). The best results were achieved with correntropy and CSD-related parameters that characterized the power in the modulation and respiration discriminant bands, definedas a frequency interval centred on the modulation and respiration frequency peaks,respectively. All patients, i.e. both PB and nPB, exhibit various degrees of periodicitydepending on their condition, whereas healthy subjects have no pronounced periodicity.This fact led to excellent results classifying PB and nPB patients 88.9%, CHF versushealthy 95.2%, and nPB versus healthy 94.4% with only one parameter.Postprint (published version

Blind parameter estimation of M-FSK signals in the presence of alpha-stable noise

Blind estimation of parameters for M-ary frequency-shift-keying (M-FSK) signals is great of importance in intelligent receivers. Many existing algorithms have assumed white Gaussian noise. However, their performance severely degrades when grossly corrupted data, i.e., outliers, exist. This paper solves this issue by developing a novel approach for parameter estimation of M-FSK signals in the presence of alpha-stable noise. Specifically, the proposed method exploits the generalized first- and second-order cyclostationarity of M-FSK signals with alpha-stable noise, which results in closed-form solutions for unknown parameters in both time and frequency domains. As a merit, it is computationally efficient and thus can be used for signal preprocessing, symbol timing estimation, signal and noise power estimation. Furthermore, substantial theoretical analysis on the performance of the proposed approach is provided. Simulations demonstrate that the proposed method is robust to alpha-stable noise and that it outperforms the state-of-the-art algorithms in many challenging scenarios

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

Reliable detection of transmit-antenna number for MIMO systems in cognitive radio-enabled Internet of Things

Identification of transmit-antenna number is of importance in cognitive Internet of Things with multiple-input multiple-output (MIMO). Previous studies on transmit-antenna number detection only consider Gaussian noise and ignore impulsive interference. In the practical wireless communication, impulsive interference may exist due to low-frequency atmospheric noise, multiple access and electromagnetic disturbance. Such interference can usually be modeled as symmetric alpha stable (SαS), which cause the performance degradation of conventional algorithms based on Gaussian model. In this paper, we present a novel scheme to detect the transmit-antenna number for MIMO systems in cognitive Internet of Things, assuming that signals are corrupted by both SαS interference and Gaussian noise. We first introduce a new approach to characterize the generalized correlation matrix, and provide its bound with SαS interference. Then, the discriminating feature vector is constructed by utilizing the higher-order moments (HOM) of eigenvalues of the generalized correlation matrix. Finally, an advanced clustering algorithm is employed to detect the transmit-antenna number, using the cluster where the minimum eigenvalue is located. The proposed algorithm avoids the need for a priori information about the transmitted signals, such as coding mode, modulation type and pilot patterns. Simulation experiments demonstrate the feasibility of the proposed transmit-antenna number detection scheme in MIMO systems with Gaussian noise and SαS interference

Transmit antenna number identification for MIMO cognitive radio systems in the presence of Alpha-Stable noise

Identification of communication parameters, a major task of intelligent receivers, has important applications in intelligent systems, especially cognitive radio systems. Multiple antennas make the identification problem more challenging. In this paper, we focus on the problem of detecting the number of transmit antennas in multiple-input multiple-output (MIMO) cognitive radio systems. A novel identification algorithm is proposed to determine the number of transmit antennas for MIMO systems in the presence of alpha-stable noise. We first introduce the correlation matrix based on the fractional lower order statistics (FLOS) and provide a particular structure of FLOS-based correlation matrix. Then, the eigenvalues of the FLOS-based correlation matrix are employed to construct a test statistic and the central limit theorem is exploited to obtain the decision threshold. Finally, the transmit-antenna number is detected using a serial binary hypothesis test. Simulation results are demonstrated to evaluate the effectiveness of the proposed transmit-antenna number detection algorithm for MIMO systems in the presence of alpha-stable noise

Tracking the severity of naturally developed spalls in rolling element bearings

Condition monitoring of rolling element bearing is vital for condition-based maintenance (CBM) in many industries. A key obstacle at present is the ability to accurately quantify the severity of the bearing faults, which is commonly measured in terms of the bearing defect size. Limitations of previous studies in the area include: (i) most accelerometer-based approaches were developed for artificial bearing faults instead of naturally developed spalls, and (ii) a systematic comparison between accelerometers and alternative measurements is not available. Therefore, this thesis aims at obtaining effective methods to estimate and track the growth of bearing spalls. This has been achieved by both advancing the processing of accelerometer signals and exploiting the capabilities of alternative measurements. Firstly, a novel approach based on accelerometers is proposed, which utilises natural frequency perturbations to estimate spall size. By comparing it with the well-established existing methods, it was found that all methods are effective for artificial spalls, but only the newly proposed approach is successful for naturally developed faults. Then, three alternative measurements (acoustic emission, instantaneous angular speed, and radial load) are investigated and benchmarked against acceleration on UNSW’s bearing test rig. It was found that radial load was far superior in fault-size estimation comparing to all other sensors, and achieved more precise results than accelerometers with less complex processing. This was justified considering radial load as a proxy for radial displacement, whose potential was recently suggested by theoretical studies. To confirm this, in the last part of this work, actual displacement sensors (proximity probes) were installed on the bearing test rig and a larger gearbox facility. Both experiments demonstrated that the proposed displacement approach can effectively estimate the size of natural spalls, with very limited signal processing required. This thesis has therefore provided three significant novel contributions to the field of bearing fault severity assessment: (i) the development of a new acceleration-based approach, effective on natural spalls for the first time, (ii) the collection and analysis of a new and comprehensive database of alternative measurements, obtained on naturally developed spalls, (iii) the discovery of the superior effectiveness of direct displacement measurements