Hardward and algorithm architectures for real-time additive synthesis

Additive synthesis is a fundamental computer music synthesis paradigm tracing its origins to the work of Fourier and Helmholtz. Rudimentary implementation linearly combines harmonic sinusoids (or partials) to generate tones whose perceived timbral characteristics are a strong function of the partial amplitude spectrum. Having evolved over time, additive synthesis describes a collection of algorithms each characterised by the time-varying linear combination of basis components to generate temporal evolution of timbre. Basis components include exactly harmonic partials, inharmonic partials with time-varying frequency or non-sinusoidal waveforms each with distinct spectral characteristics. Additive synthesis of polyphonic musical instrument tones requires a large number of independently controlled partials incurring a large computational overhead whose investigation and reduction is a key motivator for this work. The thesis begins with a review of prevalent synthesis techniques setting additive synthesis in context and introducing the spectrum modelling paradigm which provides baseline spectral data to the additive synthesis process obtained from the analysis of natural sounds. We proceed to investigate recursive and phase accumulating digital sinusoidal oscillator algorithms, defining specific metrics to quantify relative performance. The concepts of phase accumulation, table lookup phase-amplitude mapping and interpolated fractional addressing are introduced and developed and shown to underpin an additive synthesis subclass - wavetable lookup synthesis (WLS). WLS performance is simulated against specific metrics and parameter conditions peculiar to computer music requirements. We conclude by presenting processing architectures which accelerate computational throughput of specific WLS operations and the sinusoidal additive synthesis model. In particular, we introduce and investigate the concept of phase domain processing and present several “pipeline friendly” arithmetic architectures using this technique which implement the additive synthesis of sinusoidal partials

Numerical Evaluation of Classification Techniques for Flaw Detection

Nondestructive testing is used extensively throughout the industry for quality assessment and detection of defects in engineering materials. The range and variety of anomalies is enormous and critical assessment of their location and size is often complicated. Depending upon final operational considerations, some of these anomalies may be critical and their detection and classification is therefore of importance. Despite the several advantages of using Nondestructive testing for flaw detection, the conventional NDT techniques based on the heuristic experience-based pattern identification methods have many drawbacks in terms of cost, length and result in erratic analysis and thus lead to discrepancies in results. The use of several statistical and soft computing techniques in the evaluation and classification operations result in the development of an automatic decision support system for defect characterization that offers the possibility of an impartial standardized performance. The present work evaluates the application of both supervised and unsupervised classification techniques for flaw detection and classification in a semi-infinite half space. Finite element models to simulate the MASW test in the presence and absence of voids were developed using the commercial package LS-DYNA. To simulate anomalies, voids of different sizes were inserted on elastic medium. Features for the discrimination of received responses were extracted in time and frequency domains by applying suitable transformations. The compact feature vector is then classified by different techniques: supervised classification (backpropagation neural network, adaptive neuro-fuzzy inference system, k-nearest neighbor classifier, linear discriminate classifier) and unsupervised classification (fuzzy c-means clustering). The classification results show that the performance of k-nearest Neighbor Classifier proved superior when compared with the other techniques with an overall accuracy of 94% in detection of presence of voids and an accuracy of 81% in determining the size of the void in the medium. The assessment of the various classifiers’ performance proved to be valuable in comparing the different techniques and establishing the applicability of simplified classification methods such as k-NN in defect characterization. The obtained classification accuracies for the detection and classification of voids are very encouraging, showing the suitability of the proposed approach to the development of a decision support system for non-destructive testing of materials for defect characterization

Maximizing the optical network capacity

Most of the digital data transmitted are carried by optical fibres, forming the great part of the national and international communication infrastructure. The information-carrying capacity of these networks has increased vastly over the past decades through the introduction of wavelength division multiplexing, advanced modulation formats, digital signal processing and improved optical fibre and amplifier technology. These developments sparked the communication revolution and the growth of the Internet, and have created an illusion of infinite capacity being available. But as the volume of data continues to increase, is there a limit to the capacity of an optical fibre communication channel? The optical fibre channel is nonlinear, and the intensity-dependent Kerr nonlinearity limit has been suggested as a fundamental limit to optical fibre capacity. Current research is focused on whether this is the case, and on linear and nonlinear techniques, both optical and electronic, to understand, unlock and maximize the capacity of optical communications in the nonlinear regime. This paper describes some of them and discusses future prospects for success in the quest for capacity

Ultrasound Signal Processing: From Models to Deep Learning

Medical ultrasound imaging relies heavily on high-quality signal processing algorithms to provide reliable and interpretable image reconstructions. Hand-crafted reconstruction methods, often based on approximations of the underlying measurement model, are useful in practice, but notoriously fall behind in terms of image quality. More sophisticated solutions, based on statistical modelling, careful parameter tuning, or through increased model complexity, can be sensitive to different environments. Recently, deep learning based methods have gained popularity, which are optimized in a data-driven fashion. These model-agnostic methods often rely on generic model structures, and require vast training data to converge to a robust solution. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge. These model-based solutions yield high robustness, and require less trainable parameters and training data than conventional neural networks. In this work we provide an overview of these methods from the recent literature, and discuss a wide variety of ultrasound applications. We aim to inspire the reader to further research in this area, and to address the opportunities within the field of ultrasound signal processing. We conclude with a future perspective on these model-based deep learning techniques for medical ultrasound applications

ANALYSIS AND APPLICATION OF CAPACITIVE DISPLACEMENT SENSORS TO CURVED SURFACES

Capacitive displacement sensors have many applications where non-contact, high precision measurement of a surface is required. Because of their non-contact nature they can easily measure conductive surfaces that are flexible or otherwise unable to be measured using a contact probe. Since the output of the capacitance gage is electrical, data points can be collected quickly and averaged to improve statistics. It is often necessary for capacitive displacement sensors to gage the distance from a curved (non-flat) surface. Although displacements can easily be detected, the calibration of this output can vary considerably from the flat case. Since a capacitance gage is typically factorycalibrated against a flat reference, the experimental output contains errors in both gain and linearity. A series of calibration corrections is calculated for rectifying this output. Capacitance gages are also limited in their overall displacement travel. A support stage is described that, along with control electronics, allow the properties of the capacitance gage to be combined with an interferometer to overcome this displacement limitation. Finally, an application is proposed that would make use of the capacitance sensor and support stage assembly

Design and Characterization of a High-resolution Cardiovascular Imager

Fluoroscopic imaging devices for interventional radiology and cardiovascular applications have traditionally used image-intensifiers optically coupled to either charge-coupled devices (CCDs) or video pick-up tubes. While such devices provide image quality sufficient for most clinical applications, there are several limitations, such as loss of resolution in the fringes of the image-intensifier, veiling glare and associated contrast loss, distortion, size, and degradation with time. This work is aimed at overcoming these limitations posed by image-intensifiers, while improving on the image quality. System design parameters related to the development of a high-resolution CCD-based imager are presented. The proposed system uses four 8 x 8-cm three-side buttable CCDs tiled in a seamless fashion to achieve a field of view (FOV) of 16 x 16-cm. Larger FOVs can be achieved by tiling more CCDs in a similar manner. The system employs a thallium-doped cesium iodide (CsI:Tl) scintillator coupled to the CCDs by straight (non-tapering) fiberoptics and can be operated in 78, 156 or 234-microns pixel pitch modes. Design parameters such as quantum efficiency and scintillation yield of CsI:Tl, optical coupling efficiency and estimation of the thickness of fiberoptics to provide reasonable protection to the CCD, linearity, sensitivity, dynamic range, noise characteristics of the CCD, techniques for tiling the CCDs in a seamless fashion, and extending the field of view are addressed. The signal and noise propagation in the imager was modeled as a cascade of linear-systems and used to predict objective image quality parameters such as the spatial frequency-dependent modulation transfer function (MTF), noise power spectrum (NPS) and detective quantum efficiency (DQE). The theoretical predictions were compared with experimental measurements of the MTF, NPS and DQE of a single 8 x 8-cm module coupled to a 450-microns thick CsI:Tl at x-ray beam quality appropriate for cardiovascular fluoroscopy. The measured limiting spatial resolution (10% MTF) was 3.9 cy/mm and 3.6 cy/mm along the two orthogonal axes. The measured DQE(0) was ~0.62 and showed no dependence with incident exposure rate over the range of measurement. The experimental DQE measurements demonstrated good agreement with the theoretical estimate obtained using the parallel-cascaded linear-systems model. The temporal imaging properties were characterized in terms of image lag and showed a first frame image lag of 0.9%. The imager demonstrated the ability to provide images of high and uniform spatial resolution, while preserving and potentially improving on DQE performance at dose levels lower than that currently used in clinical practice. These results provide strong support for potential adaptation of this type of imager for cardiovascular and pediatric angiography

Metamaterials and their applications towards novel imaging technologies

Thesis advisor: Willie J. PadillaThis thesis will describe the implementation of novel imaging applications with electromagnetic metamaterials. Metamaterials have proven to be host to a multitude of interesting physical phenomena and give rich insight electromagnetic theory. This thesis will explore not only the physical theory that give them their interesting electromagnetic properties, but also the many applications of metamaterials. There is a strong need for efficient, low cost imaging solutions, specifically in the longer wavelength regime. While this technology has often been at a standstill due to the lack of natural materials that can effectively operate at these wavelengths, metamaterials have revolutionized the creation of devices to fit these needs. Their scalability has allowed them to access regimes of the electromagnetic spectrum previously unobtainable with natural materials. Along with metamaterials, mathematical techniques can be utilized to make these imaging systems streamlined and effective. Chapter 1 gives a background not only to metamaterials, but also details several parts of general electromagnetic theory that are important for the understanding of metamaterial theory. Chapter 2 discusses one of the most ubiquitous types of metamaterials, the metamaterial absorber, examining not only its physical mechanism, but also its role in metamaterial devices. Chapter 3 gives a theoretical background of imaging at longer wavelengths, specifically single pixel imaging. Chapter 3 also discusses the theory of Compressive Sensing, a mathematical construct that has allowed sampling rates that can exceed the Nyquist Limit. Chapter 4 discusses work that utilizes photoexcitation of a semiconductor to modulate THz radiation. These physical methods were used to create a dynamic THz spatial light modulator and implemented in a single pixel imaging system in the THz regime. Chapter 5 examines active metamaterial modulation through depletion of carriers in a doped semiconductor via application of a bias voltage and its implementation into a similar single pixel imaging system. Additionally, novel techniques are used to access masks generally unobtainable by traditional single pixel imagers. Chapter 6 discusses a completely novel way to encode spatial masks in frequency, rather than time, to create a completely passive millimeter wave imager. Chapter 7 details the use of telecommunication techniques in a novel way to reduce image acquisition time and further streamline the THz single pixel imager. Finally, Chapter 8 will discuss some future outlooks and draw some conclusions from the work that has been done.Thesis (PhD) — Boston College, 2015.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Physics

Portal Imaging Using a CSI (TL) Scintillator Coupled to a Cooled CCD Camera

The purpose of this research was to design a high performance digital portal imaging system, using a transparent x-ray scintillator coupled to a cooled CCD camera. Theoretical analysis using Monte Carlo simulation was performed to calculate the QDE, SNR and DQE of the system. A prototype electronic portal imaging device (EPID) was built, using a 12.7 mm thick, 20.32 cm diameter, CsI (Tl) scintillator, coupled to an Astromed ® liquid nitrogen cooled CCD TV camera. The system geometry of the prototype EPID was optimized to achieve high spatial resolution. Experimental evaluation of the prototype EPID was performed, by determining its spatial resolution, contrast resolution, depth of focus and light scatter. Images of phantoms, animals and human subjects were acquired using the prototype EPID and were compared with those obtained using conventional and high contrast portal film and a commercial EPID. An image processing protocol was developed. The protocol was comprised of preprocessing, noise removal and image enhancement algorithms. An adaptive median filter algorithm for the removal of impulse noise was developed, analyzed and incorporated into the image processing protocol. Results from the theoretical analysis and experimental evaluation have indicated that the performance of the CsI (Tl) - CCD system is comparable or superior to that of current commercial and experimental portal imaging technologies, such as high contrast portal film, commercial TV camera based EPIDs, and amorphous silicon based flat panel EPIDs