Separation of Agile Waveform Time-Frequency Signatures from Coexisting Multimodal Systems

abstract: As the demand for wireless systems increases exponentially, it has become necessary for different wireless modalities, like radar and communication systems, to share the available bandwidth. One approach to realize coexistence successfully is for each system to adopt a transmit waveform with a unique nonlinear time-varying phase function. At the receiver of the system of interest, the waveform received for process- ing may still suffer from low signal-to-interference-plus-noise ratio (SINR) due to the presence of the waveforms that are matched to the other coexisting systems. This thesis uses a time-frequency based approach to increase the SINR of a system by estimating the unique nonlinear instantaneous frequency (IF) of the waveform matched to the system. Specifically, the IF is estimated using the synchrosqueezing transform, a highly localized time-frequency representation that also enables reconstruction of individual waveform components. As the IF estimate is biased, modified versions of the transform are investigated to obtain estimators that are both unbiased and also matched to the unique nonlinear phase function of a given waveform. Simulations using transmit waveforms of coexisting wireless systems are provided to demonstrate the performance of the proposed approach using both biased and unbiased IF estimators.Dissertation/ThesisMasters Thesis Electrical Engineering 201

A new time-frequency analysis technique for neuroelectric signals

Cataloged from PDF version of article.In the presence of external stimuli, the functioning brain emits neuroelectrical signals which can be recorded as the Event Related Potential (ERP) signals. To understand the brain cognitive functions, ERP signals have been the subject matter of many applications in the field of cognitive psychophysiology. Due to the non–stationary nature of the ERP signals, commonly used time or frequency analysis techniques fail to capture the time–frequency domain localized nature of the ERP signal components. In this study, the newly developed Time–Frequency Component Analyzer (TFCA) approach is adapted to the ERP signal analysis. The results obtained on the actual ERP signals show that the TFCA does not have a precedent in resolution and extraction of uncontaminated individual ERP signal components. Furthermore, unlike the existing ERP analysis techniques, the TFCA based analysis technique can reliably measures the subject dependent variations in the ERP signals, which iiiopens up new possibilities in the clinical studies. Thus, TFCA serves as an ideal tool for studying the intricate machinery of the human brain.Tüfekçi, D. İlhanM.S

Structured Machine Learning for Robotics

Machine Learning has become the essential tool for automating tasks that consist in predicting the output associated to a certain input. However many modern algorithms are mainly developed for the simple cases of classification and regression. Structured prediction is the field concerned with predicting outputs consisting of complex objects such as graphs, orientations or sequences. While these objects are often of practical interest, they do not have many of the mathematical properties that allow to design principled and computationally feasible algorithms with traditional techniques. In this thesis we investigate and develop algorithms for learning manifold-valued functions in the context of structured prediction. Differentiable manifolds are a mathematical abstraction used in many domains to describe sets with continuous constraints and non-Euclidean geometric properties. By taking a structured prediction approach we show how to define statistically consistent estimators for predicting elements of a manifold, in constrast to traditional structured predition algorithms that are restricted to output sets with finite cardinality. We introduce a wide range of applications that leverage manifolds structures. Above all, we study the case of the hyperbolic manifold, a space suited for representing hierarchical data. By representing supervised datasets within hyperbolic space we show how it is possible to invent new concepts in a previously known hierarchy and show promising results in hierarchical classification. We also study how modern structured approaches can help with practical robotics tasks, either improving performances in behavioural pipelines or showing more robust predictions for constrained tasks. Specifically, we show how structured prediction can be used to tackle inverse kinematics problems of redundant robots, accounting for the constraints of the robotic joints. We also consider the task of biological motion detection and show that by leveraging the sequence structure of video streams we significantly reduce the latency of the application. Our studies are complemented by empirical evaluations on both synthetic and real data

Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching

Sequences of feature vectors are a natural way of representing temporal data. Given a database of sequences, a fundamental task is to find the database entry which is the most similar to a query. In this thesis, we present learning-based methods for efficiently and accurately comparing sequences in order to facilitate large-scale sequence search. Throughout, we will focus on the problem of matching MIDI files (a digital score format) to a large collection of audio recordings of music. The combination of our proposed approaches enables us to create the largest corpus of paired MIDI files and audio recordings ever assembled. Dynamic time warping (DTW) has proven to be an extremely effective method for both aligning and matching sequences. However, its performance is heavily affected by factors such as the feature representation used and its adjustable parameters. We therefore investigate automatically optimizing DTW-based alignment and matching of MIDI and audio data. Our approach uses Bayesian optimization to tune system design and parameters over a synthetically-created dataset of audio and MIDI pairs. We then perform an exhaustive search over DTW score normalization techniques to find the optimal method for reporting a reliable alignment confidence score, as required in matching tasks. This results in a DTW-based system which is conceptually simple and highly accurate at both alignment and matching. We also verify that this system achieves high performance in a large-scale qualitative evaluation of real-world alignments. Unfortunately, DTW can be far too inefficient for large-scale search when sequences are very long and consist of high-dimensional feature vectors. We therefore propose a method for mapping sequences of continuously-valued feature vectors to downsampled sequences of binary vectors. Our approach involves training a pair of convolutional networks to map paired groups of subsequent feature vectors to a Hamming space where similarity is preserved. Evaluated on the task of matching MIDI files to a large database of audio recordings, we show that this technique enables 99.99\% of the database to be discarded with a modest false reject rate while only requiring 0.2\% of the time to compute. Even when sped-up with a more efficient representation, the quadratic complexity of DTW greatly hinders its feasibility for very large-scale search. This cost can be avoided by mapping entire sequences to fixed-length vectors in an embedded space where sequence similarity is approximated by Euclidean distance. To achieve this embedding, we propose a feed-forward attention-based neural network model which can integrate arbitrarily long sequences. We show that this approach can extremely efficiently prune 90\% of our audio recording database with high confidence. After developing these approaches, we applied them together to the practical task of matching 178,561 unique MIDI files to the Million Song Dataset. The resulting ``Lakh MIDI Dataset'' provides a potential bounty of ground truth information for audio content-based music information retrieval. This can include transcription, meter, lyrics, and high-level musicological features. The reliability of the resulting annotations depends both on the quality of the transcription and the accuracy of the score-to-audio alignment. We therefore establish a baseline of reliability for score-derived information for different content-based MIR tasks. Finally, we discuss potential future uses of our dataset and the learning-based sequence comparison methods we developed

An efficient implementation of lattice-ladder multilayer perceptrons in field programmable gate arrays

The implementation efficiency of electronic systems is a combination of conflicting requirements, as increasing volumes of computations, accelerating the exchange of data, at the same time increasing energy consumption forcing the researchers not only to optimize the algorithm, but also to quickly implement in a specialized hardware. Therefore in this work, the problem of efficient and straightforward implementation of operating in a real-time electronic intelligent systems on field-programmable gate array (FPGA) is tackled. The object of research is specialized FPGA intellectual property (IP) cores that operate in a real-time. In the thesis the following main aspects of the research object are investigated: implementation criteria and techniques. The aim of the thesis is to optimize the FPGA implementation process of selected class dynamic artificial neural networks. In order to solve stated problem and reach the goal following main tasks of the thesis are formulated: rationalize the selection of a class of Lattice-Ladder Multi-Layer Perceptron (LLMLP) and its electronic intelligent system test-bed – a speaker dependent Lithuanian speech recognizer, to be created and investigated; develop dedicated technique for implementation of LLMLP class on FPGA that is based on specialized efficiency criteria for a circuitry synthesis; develop and experimentally affirm the efficiency of optimized FPGA IP cores used in Lithuanian speech recognizer. The dissertation contains: introduction, four chapters and general conclusions. The first chapter reveals the fundamental knowledge on computer-aideddesign, artificial neural networks and speech recognition implementation on FPGA. In the second chapter the efficiency criteria and technique of LLMLP IP cores implementation are proposed in order to make multi-objective optimization of throughput, LLMLP complexity and resource utilization. The data flow graphs are applied for optimization of LLMLP computations. The optimized neuron processing element is proposed. The IP cores for features extraction and comparison are developed for Lithuanian speech recognizer and analyzed in third chapter. The fourth chapter is devoted for experimental verification of developed numerous LLMLP IP cores. The experiments of isolated word recognition accuracy and speed for different speakers, signal to noise ratios, features extraction and accelerated comparison methods were performed. The main results of the thesis were published in 12 scientific publications: eight of them were printed in peer-reviewed scientific journals, four of them in a Thomson Reuters Web of Science database, four articles – in conference proceedings. The results were presented in 17 scientific conferences

Notes in Pure Mathematics & Mathematical Structures in Physics

These Notes deal with various areas of mathematics, and seek reciprocal combinations, explore mutual relations, ranging from abstract objects to problems in physics.Comment: Small improvements and addition

Caractérisation des phénomènes physiques par analyse parcimonieuse des signaux transitoires

For their uniqueness, transient are really difficult to characterize. They are met everywhere and are generally the result of very complex physical phenomena that contain a lot of information such as the transient at its origin, the effect of the propagation through the medium and the effects induced by the transducers. They can correspond to communication between mammals as well as being the reflection of a fault in electrical or hydraulic networks for instance. Hence their study is of great importance even though it is quite complicated. Numerous signal processing methods have been developed in the last decades: they often rely on statistical approaches, linear projections of the signal onto dictionaries and data-driven techniques. All those methods have pros and cons since they often provide good detections, nevertheless their characterization for classification and discrimination purposes remains complicated. In this spirit, this thesis proposes new approaches to study transients. After a brief overview of the existing methods, this work first focuses on the representation of signals having tight-varying time-frequency components. Generally, general complex-time distributions present a proper framework to study them but remain limited to narrow band signals. In a first part, we propose to overcome this limitation in the case of signals with a spread time-frequency variation. This method is based on the compression of the signal's spectrum to a bandwidth that ensures the efficiency of the technique. A second part then focuses on the extraction of nonlinear modulation phase signals in the context of nonstationary noise and other coherent signals. This is performed with warping operators and compressive sensing reconstruction techniques. The third chapter then focuses on data-driven methods based on the representation of the signal in phase space. The main contribution takes advantage of the lag diversity that enables to highlight time scale transformations as well as amplitude modifications between transients. Hence, we develop different techniques enabling to highlight those properties. Finally, works presented in the first chapters are developed in applicative contexts such as: ECG segmentation, electrical transient characterization, a passive acoustic configuration and the study of acoustic signals in an immerse environment. We then end up by some conclusions and perspectives for future works.Les signaux transitoires, de par leur unicité, sont très difficiles à caractériser. Ils se rencontrent partout et sont généralement le reflet d'un phénomène physique très complexe traduisant de nombreuses informations telles que le signal à l'origine, les effets de la propagation dans le milieu considéré et aussi les effets induits par les capteurs. Ils peuvent aussi bien correspondre à un phénomène de communication entre animaux, qu'être le reflet d'un défaut dans un système électrique ou hydraulique par exemple. Tout ceci rend leur étude très difficile, mais aussi primordiale. De nombreuses techniques en traitement du signal ont été développées ces dernières années pour les étudier: elles reposent souvent sur des approches statistiques, des approches projectives sur différents dictionnaires et des techniques auto-adaptatives. Toutes ces méthodes présentent des avantages et des inconvénients, puisqu'elles permettent souvent de les détecter correctement, néanmoins leur caractérisation à des fins de classification et de discrimination reste compliquée. Cette thèse s'inscrit dans cette optique et propose de nouvelles approches d'étude des transitoires. Après un rapide descriptif des techniques d'étude des signaux transitoires, ce travail s'intéressera dans un premier temps à la représentation des signaux ayant des composantes fréquentielles variant très rapidement. De manière générale l'utilisation des distributions généralisées à temps complexe présente un cadre d'analyse adéquat, mais il est limité aux signaux possédant une bande passante étroite, nous proposons dans une première partie d'étendre cette utilisation à des signaux possédant une bande passante plus large en appliquant un changement d'échelle des signaux. Une deuxième partie s'intéressera davantage à l'extraction de signaux à modulation de phase dans le contexte d'un mélange de bruit non-stationnaire et d'autres signaux cohérents. Ceci sera effectué par des opérateurs de warping couplé à des techniques de débruitage basée sur la compression de données. Le troisième chapitre s'intéressera aux techniques guidées par les données basées sur la représentation des signaux en diagrammes de phase. La contribution principale porte sur la diversité des lags qui permet en effet de mettre en évidence les effets des opérateurs de temps-échelles, mais aussi de modification d'amplitude entre des signaux. Nous développerons donc des méthodes permettant de mettre en évidence ces propriétés. Finalement, les travaux présentés dans les premiers chapitres seront développés dans le cadre de quatre domaines applicatifs qui sont : la segmentation d'ECG, la caractérisation de transitoires électriques, un cas d'acoustique passive et l'étude de signaux acoustiques en milieu immergé. Nous terminerons enfin par une conclusion et quelques perspectives de travail