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)

Demonstration of Universal Parametric Entangling Gates on a Multi-Qubit Lattice

We show that parametric coupling techniques can be used to generate selective entangling interactions for multi-qubit processors. By inducing coherent population exchange between adjacent qubits under frequency modulation, we implement a universal gateset for a linear array of four superconducting qubits. An average process fidelity of $\mathcal{F}=93\%$ is estimated for three two-qubit gates via quantum process tomography. We establish the suitability of these techniques for computation by preparing a four-qubit maximally entangled state and comparing the estimated state fidelity against the expected performance of the individual entangling gates. In addition, we prepare an eight-qubit register in all possible bitstring permutations and monitor the fidelity of a two-qubit gate across one pair of these qubits. Across all such permutations, an average fidelity of $\mathcal{F}=91.6\pm2.6\%$ is observed. These results thus offer a path to a scalable architecture with high selectivity and low crosstalk

Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images

We study filter–based feature selection methods for classification of biomedical images. For feature selection, we use two filters — a relevance filter which measures usefulness of individual features for target prediction, and a redundancy filter, which measures similarity between features. As selection method that combines relevance and redundancy we try out a Hopfield network. We experimentally compare selection methods, running unitary redundancy and relevance filters, against a greedy algorithm with redundancy thresholds [9], the min-redundancy max-relevance integration [8,23,36], and our Hopfield network selection. We conclude that on the whole, Hopfield selection was one of the most successful methods, outperforming min-redundancy max-relevance when\ud more features are selected

Increased markers of cardiac vagal activity in leucine-rich repeat kinase 2-associated Parkinson's disease.

PurposeCardiac autonomic dysfunction manifests as reduced heart rate variability (HRV) in idiopathic Parkinson's disease (PD), but no significant reduction has been found in PD patients who carry the LRRK2 mutation. Novel HRV features have not been investigated in these individuals. We aimed to assess cardiac autonomic modulation through standard and novel approaches to HRV analysis in individuals who carry the LRRK2 G2019S mutation.MethodsShort-term electrocardiograms were recorded in 14 LRRK2-associated PD patients, 25 LRRK2-non-manifesting carriers, 32 related non-carriers, 20 idiopathic PD patients, and 27 healthy controls. HRV measures were compared using regression modeling, controlling for age, sex, mean heart rate, and disease duration. Discriminant analysis highlighted the feature combination that best distinguished LRRK2-associated PD from controls.ResultsBeat-to-beat and global HRV measures were significantly increased in LRRK2-associated PD patients compared with controls (e.g., deceleration capacity of heart rate: p = 0.006) and idiopathic PD patients (e.g., 8th standardized moment of the interbeat interval distribution: p = 0.0003), respectively. LRRK2-associated PD patients also showed significantly increased irregularity of heart rate dynamics, as quantified by Rényi entropy, when compared with controls (p = 0.002) and idiopathic PD patients (p = 0.0004). Ordinal pattern statistics permitted the identification of LRRK2-associated PD individuals with 93% sensitivity and 93% specificity. Consistent results were found in a subgroup of LRRK2-non-manifesting carriers when compared with controls.ConclusionsIncreased beat-to-beat HRV in LRRK2 G2019S mutation carriers compared with controls and idiopathic PD patients may indicate augmented cardiac autonomic cholinergic activity, suggesting early impairment of central vagal feedback loops in LRRK2-associated PD