Active noise control (ANC) is an active noise mitigation method that has in recent years become increasingly prevalent. The method relies on the principle of superposition, canceling unwanted noise through the addition of a second sound wave with the same amplitude but an inverted phase to the first. One of the most common applications of ANC is in hearables, particularly in wireless earbuds. Because of individual differences in ear anatomy, the requirements for an effective ANC system will vary slightly among different users. However, the static nature of most ANC systems in hearables means that they are unable to account for these anatomical differences, resulting in inconsistent noise reduction across individuals. The aim of this project is to develop an adaptive ANC system capable of accounting for individual variations in ear anatomy through the use of optimization algorithms and adaptive filters. The proposed adaptive ANC system is designed to operate as a separate layer alongside the static ANC system and is implemented in a simulated environment with the help of Python. The effectiveness of the adaptive system is evaluated relative to the static system in terms of overall sound pressure level (OASPL) as well as power spectral density (PSD) across several test participants. The results indicate that the adaptive system indeed provides a noticeable improvement over the standalone static system.

Switching-mode power converters are one of the most ubiquitous electronic devices thatare used in all fields of life - from simple home appliances to cutting-edge space technologies.Often the power converter is one of the weakest links in the equipment since it isusually directly connected to the power grid and will be affected first from voltage spikesand other irregularities. As a lot of the applications of switching-mode power convertersare mission-critical, it is important to minimize the risks of unexpected breakdowns of thedevices. The aim of this project is to find a simple and reliable way to diagnose a failingpower converter in order to make it possible to react before the failure could affect themain equipment. For that, a basis for a Discrete Time Fourier Transform based methodwas developed, which could allow to diagnose some of the most common potential failuresof equipment using simple and robust devices for measurement and analysis. This reportdetails patterns in the general time-domain shapes of the voltage load and current as wellas the high-frequency components of the voltage. In addition, it also looks at these patternsin the frequency domain and suggests a few possibilities for how a method could bedeveloped. The results of the work show the potential of the approach for further developmentof an embedded diagnostic unit that would make it possible to forecast emergingfailure of a power unit.

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

Large-scale networks are becoming more prevalent, with applications in healthcare systems, financial networks, social networks, and traffic systems. The detection of normal and abnormal behaviors (signals) in these systems presents a challenging problem. State-of-the-art approaches such as principal component analysis and graph signal processing address this problem using signal projections onto a space determined by an eigendecomposition or singular value decomposition. When a graph is directed, however, applying methods based on the graph Laplacian or singular value decomposition causes information from unidirectional edges to be lost. Here we present a novel formulation and graph signal processing framework that addresses this issue and that is well suited for application to extremely large, directed, sparse networks. In this thesis, we develop and demonstrate a graph Fourier transform for which the spectral components are the Jordan subspaces of the adjacency matrix. In addition to admitting a generalized Parseval’s identity, this transform yields graph equivalence classes that can simplify the computation of the graph Fourier transform over certain networks. Exploration of these equivalence classes provides the intuition for an inexact graph Fourier transform method that dramatically reduces computation time over real-world networks with nontrivial Jordan subspaces. We apply our inexact method to four years of New York City taxi trajectories (61 GB after preprocessing) over the NYC road network (6,400 nodes, 14,000 directed edges). We discuss optimization strategies that reduce the computation time of taxi trajectories from raw data by orders of magnitude: from 3,000 days to less than one day. Our method yields a fine-grained analysis that pinpoints the same locations as the original method while reducing computation time and decreasing energy dispersal among spectral components. This capability to rapidly reduce raw traffic data to meaningful features has important ramifications for city planning and emergency vehicle routing.

由於在盲語音分離的應用，線性準平穩源訊號混合的盲識別獲得了巨大的研究興趣。在這個問題上，我們利用準穩態源訊號的時變特性來識別未知的混合系統系數。傳統的方法有二：i)基於張量分解的平行因子分析(PARAFAC);ii)基於對多個矩陣的聯合對角化的聯合對角化算法(JD)。一般來說，PARAFAC和JD 都採用了源聯合的提取方法；即是說，對應所有訊號源的系統係數在升法上是用時進行識別的。 / 在這篇論文中，我利用Khati-Rao(KR)子空間來設計一種新的盲識別算法。在我設計的算法中提出一種與傳統的方法不同的提法。在我設計的算法中，盲識別問題被分解成數個結構上相對簡單的子問題，分別對應不同的源。在超定混合模型，我們提出了一個專門的交替投影算法(AP)。由此產生的算法，不但能從經驗發現是非常有競爭力的，而且更有理論上的利落收斂保證。另外，作為一個有趣的延伸，該算法可循一個簡單的方式應用於欠混合模型。對於欠定混合模型，我們提出啟發式的秩最小化算法從而提高算法的速度。 / Blind identification of linear instantaneous mixtures of quasi-stationary sources (BI-QSS) has received great research interest over the past few decades, motivated by its application in blind speech separation. In this problem, we identify the unknown mixing system coefcients by exploiting the time-varying characteristics of quasi-stationary sources. Traditional BI-QSS methods fall into two main categories: i) Parallel Factor Analysis (PARAFAC), which is based on tensor decomposition; ii) Joint Diagonalization (JD), which is based on approximate joint diagonalization of multiple matrices. In both PARAFAC and JD, the joint-source formulation is used in general; i.e., the algorithms are designed to identify the whole mixing system simultaneously. / In this thesis, I devise a novel blind identification framework using a Khatri-Rao (KR) subspace formulation. The proposed formulation is different from the traditional formulations in that it decomposes the blind identication problem into a number of per-source, structurally less complex subproblems. For the over determined mixing models, a specialized alternating projections algorithm is proposed for the KR subspace for¬mulation. The resulting algorithm is not only empirically found to be very competitive, but also has a theoretically neat convergence guarantee. Even better, the proposed algorithm can be applied to the underdetermined mixing models in a straightforward manner. Rank minimization heuristics are proposed to speed up the algorithm for the underdetermined mixing model. The advantages on employing the rank minimization heuristics are demonstrated by simulations. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Lee, Ka Kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 72-76). / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Settings of Quasi-Stationary Signals based Blind Identification --- p.4 / Chapter 2.1 --- Signal Model --- p.4 / Chapter 2.2 --- Assumptions --- p.5 / Chapter 2.3 --- Local Covariance Model --- p.7 / Chapter 2.4 --- Noise Covariance Removal --- p.8 / Chapter 2.5 --- Prewhitening --- p.9 / Chapter 2.6 --- Summary --- p.10 / Chapter 3 --- Review on Some Existing BI-QSS Algorithms --- p.11 / Chapter 3.1 --- Joint Diagonalization --- p.11 / Chapter 3.1.1 --- Fast Frobenius Diagonalization [4] --- p.12 / Chapter 3.1.2 --- Pham’s JD [5, 6] --- p.14 / Chapter 3.2 --- Parallel Factor Analysis --- p.16 / Chapter 3.2.1 --- Tensor Decomposition [37] --- p.17 / Chapter 3.2.2 --- Alternating-Columns Diagonal-Centers [12] --- p.21 / Chapter 3.2.3 --- Trilinear Alternating Least-Squares [10, 11] --- p.23 / Chapter 3.3 --- Summary --- p.25 / Chapter 4 --- Proposed Algorithms --- p.26 / Chapter 4.1 --- KR Subspace Criterion --- p.27 / Chapter 4.2 --- Blind Identification using Alternating Projections --- p.29 / Chapter 4.2.1 --- All-Columns Identification --- p.31 / Chapter 4.3 --- Overdetermined Mixing Models (N > K): Prewhitened Alternating Projection Algorithm (PAPA) --- p.32 / Chapter 4.4 --- Underdetermined Mixing Models (N <K) --- p.34 / Chapter 4.4.1 --- Rank Minimization Heuristic --- p.34 / Chapter 4.4.2 --- Alternating Projections Algorithm with Huber Function Regularization --- p.37 / Chapter 4.5 --- Robust KR Subspace Extraction --- p.40 / Chapter 4.6 --- Summary --- p.44 / Chapter 5 --- Simulation Results --- p.47 / Chapter 5.1 --- General Settings --- p.47 / Chapter 5.2 --- Overdetermined Mixing Models --- p.49 / Chapter 5.2.1 --- Simulation 1 - Performance w.r.t. SNR --- p.49 / Chapter 5.2.2 --- Simulation 2 - Performance w.r.t. the Number of Available Frames M --- p.49 / Chapter 5.2.3 --- Simulation 3 - Performance w.r.t. the Number of Sources K --- p.50 / Chapter 5.3 --- Underdetermined Mixing Models --- p.52 / Chapter 5.3.1 --- Simulation 1 - Success Rate of KR Huber --- p.53 / Chapter 5.3.2 --- Simulation 2 - Performance w.r.t. SNR --- p.54 / Chapter 5.3.3 --- Simulation 3 - Performance w.r.t. M --- p.54 / Chapter 5.3.4 --- Simulation 4 - Performance w.r.t. N --- p.56 / Chapter 5.4 --- Summary --- p.56 / Chapter 6 --- Conclusion and Future Works --- p.58 / Chapter A --- Convolutive Mixing Model --- p.60 / Chapter B --- Proofs --- p.63 / Chapter B.1 --- Proof of Theorem 4.1 --- p.63 / Chapter B.2 --- Proof of Theorem 4.2 --- p.65 / Chapter B.3 --- Proof of Observation 4.1 --- p.65 / Chapter B.4 --- Proof of Proposition 4.1 --- p.66 / Chapter C --- Singular Value Thresholding --- p.67 / Chapter D --- Categories of Speech Sounds and Their Impact on SOSs-based BI-QSS Algorithms --- p.69 / Chapter D.1 --- Vowels --- p.69 / Chapter D.2 --- Consonants --- p.69 / Chapter D.1 --- Silent Pauses --- p.70 / Bibliography --- p.72

When a speaker is addressing an audience, a PA system consisting of a microphone and a loudspeaker is often used. If the microphone picks up too much of the loudspeaker energy, acoustic feedback in the form of an unwanted characteristic howling can occur. Limes Audio is a software company that specializes in improving sound quality in digital communications, mainly conference telephony, and has developed a reference product, the Magneto mixer, to demonstrate the capability of their software TrueVoice. The company now wishes to expand the field of usage for the Magneto mixer to enable it to work as a microphone mixer in PA scenarios, and for this, a feedback suppression feature is needed. This master’s thesis aims at surveying the market and the literature in the field and specifying the requirements for a feedback suppression feature. Three methods for suppressing howling feedback are evaluated through simulations and compared in terms of maximum stable gain (MSG) and subjective listening experience. The method that performed the best based on these criteria was acoustic feedback cancellation with a 5 Hz frequency shift on the loudspeaker signal. This method makes use of an adaptive filter to model the acoustic feedback path and to remove the feedback component from the microphone signal. In the simulations, the method was able to increase the stable gain by approximately 10 dB while maintaining a good sound quality. / När en talare talar för en publik används ofta ett PA system bestående av en mikrofon och en högtalare. Om mikrofonen tar upp för mycket av ljudet från högtalaren finns en överhängande risk för akustisk rundgång i form av ett karaktäristiskt oönskat tjut. Limes Audio är ett företag som utvecklar mjukvara för att förbättra ljudkvaliten i digital kommunikation, främst inom konferenstelefoni. De har utvecklat en demonstrationsprodukt, Magnetomixern, som kan användas som en konferenstelefon för att demonstrera deras programvara TrueVoice. Företaget önskar nu utveckla Magnetomixern till att även fungera som en ljudmixer för PA-scenarion, eller konferenstelefoni där intern ljudförstärkning i rummet behövs, och för detta behövs en funktion för att ta bort eventuell rundgång. Detta examensarbete har som mål att lägga grunden för en sådan funktion i Magnetomixern genom att undersöka marknaden och litteraturen på området. Tre metoder för att eliminera rundgång utvärderas i simuleringar och jämförs beträffande maximal stabil förstärkning (MSG) och subjektiv ljudkvalitet. Metoden ”Acoustic feedback cancellation” tillsammans med ett 5 Hz frekvensskifte på högtalarsignalen gav högst MSG och bäst ljudkvalitet. Metoden använder ett adaptivt filter för att approximera den akustiska återkopplingsvägen mellan högtalare och mikrofon samt tar bort rundgångskomponenter från mikrofonsignalen. I simuleringarna kunde metoden öka den maximala stabila förstärkningen med upp till 10 dB medan en god ljudkvalitet på talet bibehölls.

The analog to digital converter (ADC) samples a continuous analog signal and produces a stream of digital words which approximate the analog signal. The conversion process introduces noise into the digital signal. In the case of an ideal ADC, where all noise sources are ignored, the noise due to the quantization process remains. The resolution of the ADC is defined by how many bits are in the digital output word. The amount of quantization noise is clearly related to the resolution of the ADC. Reducing the quantization noise results in higher effective resolution.

Random Telegraph Signal (RTS) noise is a random noise source defined by discrete and metastable changes in the magnitude of a signal. Though observed in a variety of physical processes, RTS is of particular interest to image sensor fabrication where progress in the suppression of other noise sources has elevated its noise contribution to the point of approaching the limiting noise source in scientific applications. There have been two basic physical sources of RTS noise reported in image sensors. The first involves a charge trap in the oxide layer of the source follower in a CMOS image sensor. The capture and emission of a charge changes the conductivity across the source follower, altering the signal level. The second RTS source in image sensors has been reported in CCD and CMOS architectures and involves some metastability in the structure of the device within the light collection area. A methodology is presented for the analysis of RTS noise. Utilizing wavelets, a time-based signal has white noise removed, while RTS transitions are preserved. This allows for the simple extraction of RTS parameters, which provide valuable insight into defects in semiconductor devices. The scheme is used to extract RTS transition amplitudes and time constants from radiation damaged CMOS image sensor pixels. Finally, the generation of ionizing radiation induced RTS centers is investigated and discussed. Surprisingly, the number of RTS centers does not scale linearly with absorbed dose, but instead follows a quadratic dependence. The implications and possible mechanisms behind the generation of these RTS centers are discussed.

The Global Positioning System (GPS) has become popular research and application interests in surveying and many other areas. Nowadays, the accuracy of the Differential GPS can easily reach the order of a few meters. Yet, there are still many ways to exploit the GPS system signal carrier to improve the accuracy to less than meter level. In this thesis, a new approach to improve the accuracy to less than meter level is presented while the observer is in the dynamic situation. In order to reach the sub-meter accuracy, we measure on the carrier phase difference (The L1 carrier frequency is 1575.42 Mhz, 1=19 em) between a reference point A and a primary point B. This actually means we work on the accuracy of centimeter. In this proposed method of the precision survey, first the Differential GPS is used to fix the position in the accuracy of meter level, and then by measuring the signal carrier relative difference we can work on the accuracy in the accuracy level of the wavelength (19 cm). The measuring on the relative carrier phase will introduce the problem of initial modulo 2π phase (integer wavelength) ambiguity. To solve the initial integer ambiguity, A Multiple Model Estimation Algorithm (MMEA) which was developed by D.T. Magill in 1965 is applied. The MMEA is composed of a bank of parallel Kalman filters, all operating on the input measurement sequence simultaneously. Each filter in the bank of filters is modeled around a different hypothesis. The number of the required parallel filters is the number of hypothesis of integer ambiguity which is determined by the error range of the differential phase measurement. And the error range of the differential phase measurement is related to the accuracy of the Differential GPS. The precision positioning by MMEA method has some advantage compares with other methods now being used . . It does not require continuous observation of the satellites initially. . Kalman filter is recursive technique. So it has the potential of on-line . . Kalman filter is widely used in navigation and approved to be very efficient and versatile. Computer simulation results are given for a hypothetical GPS system. They demonstrate that the MMEA can effectively solve the integer wavelength ambiguity problem in dynamic situation. The simulation results presented are especially encouraging with regard to the flexibility and efficiency in precision survey. A further improvement of precision surveying by GPS is also discussed in the last Chapter. By using Markov Model and Verterbi Algorithm, a more flexible and reliable precision surveying method could be available.

<p>The goal of this bachelor thesis has been to investigate if the laboratory exercises in the courses digital filters and linear systems can be moved from matlab to a digital signal processor. The processor is a TMS320C6713 floating point processor mounted on a development board.</p><p> </p><p>The original laboratories have been implemented and analyzed and some suggested changes have been presented for the digital filter laboration. For the laboration in linear systems, the exercise can be implemented as it is today. Furthermore, a transmultiplexer has been implemented and tested for real time execution.</p><p> </p><p>Finally, an application programming interface has also been implemented, with common functions, used in the laboratories.</p><p> </p>