Detection in strongly nonhomogeneous data and application to airborne RADAR

The constant false alarm rate detection of punctual targets embedded in strongly nonhomogeneous data is a difficult task. Indeed, the standard detection techniques do not allow one to get an appropriate regulation of the false alarm rate. In this manuscript, we present three new solutions for this issue. The developed methods are applied on simulated range/doppler maps that arise on the airborne RADAR context. These three approaches are compared by numerical simulations to the classical detection methods. Compared to the standard techniques and within the scope of the studied model, the proposed solutions allow one to get a better false alarm rate regulation and better detection capacities.

Magraner, Eric — Fresnel Institute

Nonlinear Noise Cancellation

Noise or interference is often assumed to be a random process. Conventional linear filtering, control or prediction techniques are used to cancel or reduce the noise. However, some noise processes have been shown to be nonlinear and deterministic. These nonlinear deterministic noise processes appear to be random when analysed with second order statistics. As nonlinear processes are widespread in nature it may be beneficial to exploit the coherence of the nonlinear deterministic noise with nonlinear filtering techniques. The nonlinear deterministic noise processes used in this thesis are generated from nonlinear difference or differential equations which are derived from real world scenarios. Analysis tools from the theory of nonlinear dynamics are used to determine an appropriate sampling rate of the nonlinear deterministic noise processes and their embedding dimensions. Nonlinear models, such as the Volterra series filter and the radial basis function ...

Strauch, Paul E. — University Of Edinburgh

Advanced GPR data processing algorithms for detection of anti-personnel landmines

Ground Penetrating Radar (GPR) is seen as one of several promising technologies aimed to help mine detection. GPR is sensitive to any inhomogeneity in the ground. Therefore any APM regardless of the metal content can be detected. On the other hand, all the inhomogeneities, which do not represent mines, show up as a clutter in GPR images. Moreover, it is known that reflectivity of APM is often weaker than that of stones, pieces of shrapnel and barbed wire, etc. Altogether these factors cause GPR to produce unacceptably high false alarm rate whilst it reaches the 99.6% detection rate which is prescribed by an UN resolution as a standard for humanitarian demining. The main goal of the work presented in the thesis is reduction of the false alarm rate while keeping the 99.6% detection rate intact. To reach this goal a ...

Kovalenko, Vsevolod — Delft University of Technology

Automatic Signature and Graphical Password Verification: Discriminant Features and New Application Scenarios

The proliferation of handheld devices such as smartphones and tablets brings a new scenario for biometric authentication, and in particular to automatic signature verification. Research on signature verification has been traditionally carried out using signatures acquired on digitizing tablets or Tablet-PCs. This PhD Thesis addresses the problem of user authentication on handled devices using handwritten signatures and graphical passwords based on free-form doodles, as well as the effects of biometric aging on signatures. The Thesis pretends to analyze: (i) which are the effects of mobile conditions on signature and doodle verification, (ii) which are the most distinctive features in mobile conditions, extracted from the pen or fingertip trajectory, (iii) how do different similarity computation (i.e. matching) algorithms behave with signatures and graphical passwords captured on mobile conditions, and (iv) what is the impact of aging on signature features and verification ...

Martinez-Diaz, Marcos — Universidad Autonoma de Madrid

Analysis of energy based signal detection

The focus of this thesis is on the binary signal detection problem, i.e., if a signal or signals are present or not. Depending on the application, the signal to be detected can be either unknown or known. The detection is based on some function of the received samples which is compared to a threshold. If the threshold is exceeded, it is decided that signal(s) is (are) present. Energy detectors (radiometers) are often used due to their simplicity and good performance. The main goal here is to develop and analyze energy based detectors as well as power-law based detectors. Different possibilities for setting the detection threshold for a quantized total power radiometer are analyzed. The main emphasis is on methods that use reference samples. In particular, the cell-averaging (CA) constant false alarm rate (CFAR) threshold setting method is analyzed. Numerical examples ...

Janne Lehtomäki — University of Oulou

Heart rate variability : linear and nonlinear analysis with applications in human physiology

Cardiovascular diseases are a growing problem in today’s society. The World Health Organization (WHO) reported that these diseases make up about 30% of total global deaths and that heart diseases have no geographic, gender or socioeconomic boundaries. Therefore, detecting cardiac irregularities early-stage and a correct treatment are very important. However, this requires a good physiological understanding of the cardiovascular system. The heart is stimulated electrically by the brain via the autonomic nervous system, where sympathetic and vagal pathways are always interacting and modulating heart rate. Continuous monitoring of the heart activity is obtained by means of an ElectroCardioGram (ECG). Studying the fluctuations of heart beat intervals over time reveals a lot of information and is called heart rate variability (HRV) analysis. A reduction of HRV has been reported in several cardiological and noncardiological diseases. Moreover, HRV also has a prognostic ...

Vandeput, Steven — KU Leuven

Discrete-time speech processing with application to emotion recognition

The subject of this PhD thesis is the efficient and robust processing and analysis of the audio recordings that are derived from a call center. The thesis is comprised of two parts. The first part is dedicated to dialogue/non-dialogue detection and to speaker segmentation. The systems that are developed are prerequisite for detecting (i) the audio segments that actually contain a dialogue between the system and the call center customer and (ii) the change points between the system and the customer. This way the volume of the audio recordings that need to be processed is significantly reduced, while the system is automated. To detect the presence of a dialogue several systems are developed. This is the first effort found in the international literature that the audio channel is exclusively exploited. Also, it is the first time that the speaker utterance ...

Kotti, Margarita — Aristotle University of Thessaloniki

Automated detection of epileptic seizures in pediatric patients based on accelerometry and surface electromyography

Epilepsy is one of the most common neurological diseases that manifests in repetitive epileptic seizures as a result of an abnormal, synchronous activity of a large group of neurons. Depending on the affected brain regions, seizures produce various severe clinical symptoms. There is no cure for epilepsy and sometimes even medication and other therapies, like surgery, vagus nerve stimulation or ketogenic diet, do not control the number of seizures. In that case, long-term (home) monitoring and automatic seizure detection would enable the tracking of the evolution of the disease and improve objective insight in any responses to medical interventions or changes in medical treatment. Especially during the night, supervision is reduced; hence a large number of seizures is missed. In addition, an alarm should be integrated into the automated seizure detection algorithm for severe seizures in order to help the ...

Milošević, Milica — KU Leuven

Statistical Signal Processing for Data Fusion

In this dissertation we focus on statistical signal processing for Data Fusion, with a particular focus on wireless sensor networks. Six topics are studied: (i) Data Fusion for classification under model uncertainty; (ii) Decision Fusion over coherent MIMO channels; (iii) Performance analysis of Maximum Ratio Combining in MIMO decision fusion; (iv) Decision Fusion over non-coherent MIMO channels; (v) Decision Fusion for distributed classification of multiple targets; (vi) Data Fusion for inverse localization problems, with application to wideband passive sonar platform estimation. The first topic of this thesis addresses the problem of lack of knowledge of the prior distribution in classification problems that operate on small data sets that may make the application of Bayes' rule questionable. Uniform or arbitrary priors may provide classification answers that, even in simple examples, may end up contradicting our common sense about the problem. Entropic ...

Ciuonzo, Domenico — Second University of Naples

Robust Estimation and Model Order Selection for Signal Processing

In this thesis, advanced robust estimation methodologies for signal processing are developed and analyzed. The developed methodologies solve problems concerning multi-sensor data, robust model selection as well as robustness for dependent data. The work has been applied to solve practical signal processing problems in different areas of biomedical and array signal processing. In particular, for univariate independent data, a robust criterion is presented to select the model order with an application to corneal-height data modeling. The proposed criterion overcomes some limitations of existing robust criteria. For real-world data, it selects the radial model order of the Zernike polynomial of the corneal topography map in accordance with clinical expectations, even if the measurement conditions for the videokeratoscopy, which is the state-of-the-art method to collect corneal-height data, are poor. For multi-sensor data, robust model order selection selection criteria are proposed and applied ...

Muma, Michael — Technische Universität Darmstadt

Modulation Spectrum Analysis for Noisy Electrocardiogram Signal Processing and Applications

Advances in wearable electrocardiogram (ECG) monitoring devices have allowed for new cardiovascular applications to emerge beyond diagnostics, such as stress and fatigue detection, athletic performance assessment, sleep disorder characterization, mood recognition, activity surveillance, biometrics, and fitness tracking, to name a few. Such devices, however, are prone to artifacts, particularly due to movement, thus hampering heart rate and heart rate variability measurement and posing a serious threat to cardiac monitoring applications. To address these issues, this thesis proposes the use of a spectro-temporal signal representation called “modulation spectrum”, which is shown to accurately separate cardiac and noise components from the ECG signals, thus opening doors for noise-robust ECG signal processing tools and applications. First, an innovative ECG quality index based on the modulation spectral signal representation is proposed. The representation quantifies the rate-of-change of ECG spectral components, which are shown to ...

Tobon Vallejo, Diana Patricia — INRS-EMT

Sparse approximation and dictionary learning with applications to audio signals

Over-complete transforms have recently become the focus of a wide wealth of research in signal processing, machine learning, statistics and related fields. Their great modelling flexibility allows to find sparse representations and approximations of data that in turn prove to be very efficient in a wide range of applications. Sparse models express signals as linear combinations of a few basis functions called atoms taken from a so-called dictionary. Finding the optimal dictionary from a set of training signals of a given class is the objective of dictionary learning and the main focus of this thesis. The experimental evidence presented here focuses on the processing of audio signals, and the role of sparse algorithms in audio applications is accordingly highlighted. The first main contribution of this thesis is the development of a pitch-synchronous transform where the frame-by-frame analysis of audio data ...

Barchiesi, Daniele — Queen Mary University of London

Bayesian State-Space Modelling of Spatio-Temporal Non-Gaussian Radar Returns

Radar backscatter from an ocean surface is commonly referred to as sea clutter. Any radar backscatter not due to the scattering from an ocean surface constitutes a potential target. This thesis is concerned with the study of target detection techniques in the presence of high resolution sea clutter. In this dissertation, the high resolution sea clutter is treated as a compound process, where a fast oscillating speckle component is modulated in power by a slowly varying modulating component. While the short term temporal correlations of the clutter are associated with the speckle, the spatial correlations are largely associated with the modulating component. Due to the disparate statistical and correlation properties of the two components, a piecemeal approach is adopted throughout this thesis, whereby the spatial and the temporal correlations of high resolution sea clutter are treated independently. As an extension ...

Noga, Jacek Leszek — University of Cambridge

Development of an automated neonatal EEG seizure monitor

Brain function requires a continuous flow of oxygen and glucose. An insufficient supply for a few minutes during the first period of life may have severe consequences or even result in death. This happens in one to six infants per 1000 live term births. Therefore, there is a high need for a method which can enable bedside brain monitoring to identify those neonates at risk and be able to start the treatment in time. The most important currently available technology to continuously monitor brain function is electroEncephaloGraphy (or EEG). Unfortunately, visual EEG analysis requires particular skills which are not always present round the clock in the Neonatal Intensive Care Unit (NICU). Even if those skills are available it is laborsome to manually analyse many hours of EEG. The lack of time and skill are the main reasons why EEG is ...

Deburchgraeve, Wouter — KU Leuven

Robust Methods for Sensing and Reconstructing Sparse Signals

Compressed sensing (CS) is a recently introduced signal acquisition framework that goes against the traditional Nyquist sampling paradigm. CS demonstrates that a sparse, or compressible, signal can be acquired using a low rate acquisition process. Since noise is always present in practical data acquisition systems, sensing and reconstruction methods are developed assuming a Gaussian (light-tailed) model for the corrupting noise. However, when the underlying signal and/or the measurements are corrupted by impulsive noise, commonly employed linear sampling operators, coupled with Gaussian-derived reconstruction algorithms, fail to recover a close approximation of the signal. This dissertation develops robust sampling and reconstruction methods for sparse signals in the presence of impulsive noise. To achieve this objective, we make use of robust statistics theory to develop appropriate methods addressing the problem of impulsive noise in CS systems. We develop a generalized Cauchy distribution (GCD) ...

Carrillo, Rafael — University of Delaware

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