Automatic classification of digital communication signal modulations

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityAutomatic modulation classification detects the modulation type of received communication signals. It has important applications in military scenarios to facilitate jamming, intelligence, surveillance, and threat analysis. The renewed interest from civilian scenes has been fuelled by the development of intelligent communications systems such as cognitive radio and software defined radio. More specifically, it is complementary to adaptive modulation and coding where a modulation can be deployed from a set of candidates according to the channel condition and system specification for improved spectrum efficiency and link reliability. In this research, we started by improving some existing methods for higher classification accuracy but lower complexity. Machine learning techniques such as k-nearest neighbour and support vector machine have been adopted for simplified decision making using known features. Logistic regression, genetic algorithm and genetic programming have been incorporated for improved classification performance through feature selection and combination. We have also developed a new distribution test based classifier which is tailored for modulation classification with the inspiration from Kolmogorov-Smirnov test. The proposed classifier is shown to have improved accuracy and robustness over the standard distribution test. For blind classification in imperfect channels, we developed the combination of minimum distance centroid estimator and non-parametric likelihood function for blind modulation classification without the prior knowledge on channel noise. The centroid estimator provides joint estimation of channel gain and carrier phase o set where both can be compensated in the following nonparametric likelihood function. The non-parametric likelihood function, in the meantime, provide likelihood evaluation without a specifically assumed noise model. The combination has shown to have higher robustness when different noise types are considered. To push modulation classification techniques into a more timely setting, we also developed the principle for blind classification in MIMO systems. The classification is achieved through expectation maximization channel estimation and likelihood based classification. Early results have shown bright prospect for the method while more work is needed to further optimize the method and to provide a more thorough validation.School of Engineering and Design Brunel University London, the Faculty of Engineering University of Liverpool, and the University of Liverpool Graduate Association (Hong Kong)

Using the heart rate variability for classifying patients with and without chronic heart failure and periodic breathing

Assessment of the dynamic interactions between cardiovascular signals can provide valuable information that improves the understanding of cardiovascular control. Heart rate variability (HRV) analysis is known to provide information about the autonomic heart rate modulation mechanism. Using the HRV signal, we aimed to obtain parameters for classifying patients with and without chronic heart failure (CHF), and with periodic breathing (PB), non-periodic breathing (nPB), and Cheyne-Stokes respiration (CSR) patterns. An electrocardiogram (ECG) and a respiratory flow signal were recorded in 36 elderly patients: 18 patients with CHF and 18 patients without CHF. According to the clinical criteria, the patients were classified into the follow groups: 19 patients with nPB pattern, 7 with PB pattern, 4 with Cheyne-Stokes respiration (CSR), and 6 non-classified patients (problems with respiratory signal). From the HRV signal, parameters in the time and frequency domain were calculated. Frequency domain parameters were the most discriminant in comparisons of patients with and without CHF: PTOT, PLF and fpHF. For the comparison of the nPB vs CSR patients groups, the best parameters were RMSSD and SDSD. Therefore, the parameters appear to be suitable for enhanced diagnosis of decompensated CHF patients and the possibility of developed periodic breathing and a CSR pattern

Spatial encoding in primate hippocampus during free navigation.

The hippocampus comprises two neural signals-place cells and θ oscillations-that contribute to facets of spatial navigation. Although their complementary relationship has been well established in rodents, their respective contributions in the primate brain during free navigation remains unclear. Here, we recorded neural activity in the hippocampus of freely moving marmosets as they naturally explored a spatial environment to more explicitly investigate this issue. We report place cells in marmoset hippocampus during free navigation that exhibit remarkable parallels to analogous neurons in other mammalian species. Although θ oscillations were prevalent in the marmoset hippocampus, the patterns of activity were notably different than in other taxa. This local field potential oscillation occurred in short bouts (approximately .4 s)-rather than continuously-and was neither significantly modulated by locomotion nor consistently coupled to place-cell activity. These findings suggest that the relationship between place-cell activity and θ oscillations in primate hippocampus during free navigation differs substantially from rodents and paint an intriguing comparative picture regarding the neural basis of spatial navigation across mammals

A Novel Graph Neural Network-based Framework for Automatic Modulation Classification in Mobile Environments

Automatic modulation classification (AMC) refers to a signal processing procedure through which the modulation type and order of an observed signal are identified without any prior information about the communications setup. AMC has been recognized as one of the essential measures in various communications research fields such as intelligent modem design, spectrum sensing and management, and threat detection. The research literature in AMC is limited to accounting only for the noise that affects the received signal, which makes their models applicable for stationary environments. However, a more practical and real-world application of AMC can be found in mobile environments where a higher number of distorting effects is present. Hence, in this dissertation, we have developed a solution in which the distorting effects of mobile environments, e.g., multipath, Doppler shift, frequency, phase and timing offset, do not influence the process of identifying the modulation type and order classification. This solution has two major parts: recording an emulated dataset in mobile environments with real-world parameters (MIMOSigRef-SD), and developing an efficient feature-based AMC classifier. The latter itself includes two modules: feature extraction and classification. The feature extraction module runs upon a dynamic spatio-temporal graph convolutional neural network architecture, which tackles the challenges of statistical pattern recognition of received samples and assignment of constellation points. After organizing the feature space in the classification module, a support vector machine is adopted to be trained and perform classification operation. The designed robust feature extraction modules enable the developed solution to outperform other state-of-the-art AMC platforms in terms of classification accuracy and efficiency, which is an important factor for real-world implementations. We validated the performance of our developed solution in a prototyping and field-testing process in environments similar to MIMOSigRef-SD. Therefore, taking all aspects into consideration, our developed solution is deemed to be more practical and feasible for implementation in the next generations of communication systems. Advisor: Hamid R. Sharif-Kashan

Temporal information processing across primary visual cortical layers in normal and red light reared tree shrews.

Visual neuroscience research has benefitted from decades of efforts of comparative studies of different species, since exploring and understanding the diversity of functional properties of visual system in different species has helped us identify both general organization rules and unique traits of certain species. In this study, spatio-temporal receptive fields (STRFs), together with some other functional properties (etc. stimulus preference to different visual stimuli, orientation tuning, temporal frequency tuning and the F1/F0 ratio of responses to sine-wave grating stimuli), of primary visual cortex (V1) cells were measured in normally reared and red-light reared tree shrews (Tupaia), a species considered the closest non-primate relative to human being. All data were sampled in anesthetized animals using extracellular recording techniques. In the current study, a diversity of STRFs structures were found in tree shrew V1, and the STRFs found were classified into two categories, Type I receptive fields (RFs) that had spatially discontinuous on- and off-regions, or had spatio-temporal inseparable RFs, and Type II RFs that had spatially overlapped circular or elliptical on- and off- regions, and spatio-temporal separable RFs. Spatial and temporal profile analysis indicated this Type I and Type II classification did not correspond to simple and complex RF types previously described in primates and carnivores. It was also found in the current study that the linear prediction based on STRFs did not predict temporal frequency tuning, orientation tuning or the F1/F0 ratio very well in tree shrew V1. In tree shrew V1, both low-pass and band-pass cells for temporal frequency were found, and the proportion of cells with different types of tuning curves also differed across layers, resulting in a low-pass filter between layer II/II and layer IV. Last but not least, it was found in this study that red light rearing after birth changes the population stimulus preference in layer IV in tree shrew V1

Lung cancer assessment in CT scans: results from a Chinese dedicated cancer hospital

Lung cancer is the leading cause of cancer death in the world. It accounts for nearly 20% of all cancer deaths. In China, lung cancer incidence increases rapidly both in men and women. This causes a series of public problems. It is estimated that from 2015 to 2030, lung cancer mortality in China is likely to increase by nearly 40%. Smoking and air pollution are two significant risk factors for lung cancer. Since lung cancer usually presented at a relatively late stage, the long-term survival rate is still low. The combination of early detection of lung nodules and early treatment of lung cancer can improve the prognosis of lung cancer. The aims of this thesis are first to explore the status of lung cancer in China and to evaluate the characteristics and work-up of pulmonary nodules in a Chinese dedicated cancer hospital. The second aim is to develop lung nodule classification models using CT characteristics suited for the Chinese clinical population. The third aim is to evaluate the feasibility of an automatic nodule detection system in a Chinese lung cancer screening program

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed