<p dir="ltr">The industry's interest in electrified powertrain-equipped vehicles has increased due to environmental and economic reasons. Electrified powertrains, in general, produce lower sound and vibration level than those equipped with internal combustion engines, making noise and vibration (N&V) from other non-engine powertrain components more perceptible. One such N&V type that arouses concern to both vehicle manufacturers and passengers is gear growl, but the signal characteristics of gear growl noise and vibration and the threshold of those characteristics that can be used to determine whether a gear growl requires attention are not yet well understood. This study focuses on developing a method to detect gear-growl based on the N\&V measurements and determining thresholds on various severities of gear-growl using supervised machine learning classification. In general, a machine learning classifier requires sufficient high-quality training data with strong information independence to ensure accurate classification performance. In industrial practices, acquiring high-quality vehicle NVH data is expensive in terms of finance, time, and effort. A physically informed data augmentation method is, thus, proposed to generate realistic powertrain NVH signals based on high-quality measurements which not only provides a larger training data set but also enriches the signal feature variations included in the data set. More specifically, this method extracts physical information such as angular speed, tonal amplitudes distribution, and broadband spectrum shape from the measurement data. Then, it recreates a synthetic signal that mimics the measurement data. The measured and simulated (via data augmentation) are transformed into feature matrix representation so that the N\&V signals can be used in the classification model training process. Features describing signal characteristics are studied, extracted, and selected. While the root-mean-square (RMS) of the vibration signal and spectral entropy were sufficient for detecting gear-growl with a test accuracy of 0.9828, the acoustic signal required more features due to background noise, making data linearly inseparable. The minimum Redundancy Maximum Relevance (mRMR) feature scoring method was used to assess the importance of acoustic signal features in classification. The five most important features based on the importance score were the angular acceleration of the driveshaft, the time derivative of RMS, the tone-to-noise ratio (TNR), the time derivative of the spectral spread of the tonal component of the acoustic signal, and the time derivative of the spectral spread of the original acoustic signal (before tonal and broadband separation). A supervised classification model is developed using a support vector machine from the extracted acoustic signal features. Data used in training and testing consists of steady-state vehicle operations of 25, 35, 45, and 55 mph, with two vehicles with two different powertrain specs: axles with 4.56 and 6.14 gear ratios. The dataset includes powertrains with swapped axles (four different configurations). Techniques such as cost weighting, median filter, and hyperparameter tuning are implemented to improve the classification performance where the model classifies if a segment in the signal represents a gear-growl event or no gear-growl event. The average accuracy of test data was 0.918. A multi-class classification model is further implemented to classify different severities based on preliminary subjective listening studies. Data augmentation using signal simulation showed improvement in binary classification applications. In this study, only gear-growl was used as a fault type. Still, data augmentation, feature extraction and selection, and classification methods can be generalized for NVH signal-based fault diagnosis applications. Further listening studies are suggested for improved classification of multi-class classification applications.</p>

Information is often stored and transmitted through electrical signals. This information may need refinement, which may be done by processing and altering the electrical signals, in which it is transmitted. When refining a signal, a frequency selective filter is often used. It can be implemented through digital signal processing (DSP). DSP is a concept where signals are refined using a digital compute system. Digital systems are designed to replace their analog counterpart, mitigating their flaws in scalability, complexity and cost. A DSP system is typically implemented using software on a small computer, while analog systems are implemented through various electronic components. The objective of this project is to design a DSP system that filters analog input data using automatically synthesised filters from user-defined input specifications. The DSP system is implemented using a microcontroller. The system designed the filters and found the filter coefficients. It then uses analog to digital converter (ADC) to sample an input signal and applies the filter. Lastly, it uses the digital to analog converter (DAC) to reconstruct a filtered, analog result. A user interface is not designed for the system, and only a limited number of filters are implemented. However, the system is successful in designing filters and finding their coefficients.

Location-based services (LBSs) have become pervasive, and the demand for these systems and services is rising. Indoor Positioning Systems (IPSs) are key to extend location-based services indoors where the Global Positioning System (GPS) is not reliable due to low signal strength and complicated signal propagation environment. Most existing IPSs either require the installation of special hardware devices or build a fingerprint map, which is expensive, time-consuming, and labor-intensive. Developments in microelectromechanical systems (MEMS) have resulted in significant advancements in the low-cost compact MARG inertial sensors, making it possible to achieve low-cost and high-accuracy IPSs. This research considers the indoor positioning problem and aims to design and develop an infrastructure-free self-contained indoor positioning and tracking system based on Pedestrian Dead Reckoning (PDR) using MEMS MARG inertial sensors. PDR-based systems rely on MARG inertial sensor measurements to estimate the current position of the object by using a previously determined position without external references. Many issues still exist in developing such systems, such as cumulative errors, high-frequency sensor noises, the gyro drift issue, magnetic distortions, etc. As the MARG sensors are inherently error-prone, the most significant challenge is how to design sensor fusion models and algorithms to accurately extract useful location-based information from individual motion and magnetic sensors. The objective of this thesis is to solve these issues and mitigate the challenges. The proposed positioning system is designed with four main modules at the system level and a dual-mode feature. Specifically, the four main modules are mode detection, step detection and moving distance estimation, heading and orientation estimation, and position estimation. To address the cumulative error issue of using low-cost inertial sensors, signal processing and sensor fusion techniques are utilized for algorithm design. Experimental evaluations show that the proposed position estimation algorithm is able to achieve high positioning accuracy at low costs for the indoor environment. / Thesis / Master of Applied Science (MASc) / With the maturity of microelectromechanical systems (MEMS) technology in recent years, Magnetic, Angular Rate, and Gravity (MARG) sensors are embedded in most smart devices. This research considers the indoor positioning problem and aims to design and develop an infrastructure-free self-contained MEMS MARG inertial sensor-based indoor positioning and tracking system with high precision. The proposed positioning system uses the Pedestrian Dead Reckoning (PDR) approach and includes four main modules at the system level with a dual-mode feature. Specifically, the four main modules are mode detection, step detection and moving distance estimation, heading and orientation estimation, and position estimation. The two modes are static mode and dynamic mode. To address the cumulative error issue of using low-cost inertial sensors, signal processing and sensor fusion techniques are utilized for algorithm design. The detection and estimation algorithms of each module are presented in the system design chapter. Experimental evaluations including trajectory results under five scenarios show that the proposed position estimation algorithm achieves a higher position accuracy than that of conventional estimation methods.

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As an indirect consequence of increased heat cooling requirements, personal computers (PC) have become noisier due to the increased use of fans. Hard disk drives also contribute to the annoying noise radiated by personal computers, creating a need for the control of computer noise. Due to size constraints, the implementation of passive noise control techniques in PC is difficult. Alternatively, active noise control (ANC) may provide a compact solution to the noise problems discussed above, which is the subject of this work. First, the computer noise sources were characterized. The structure-borne path was altered passively through the decoupling of the vibrating sources from the chassis. Global noise control strategy was then investigated with a hybrid passive/active noise control technique based on folded lined ducts, integrating microphones and speakers, that were added to the PC air inlet and outlet. While the ducts were effective above 1000Hz, the use of a MIMO adaptive feedforward digital controller lead to significant noise reduction at the ducts outlets below 1000Hz. However, global performance was limited due to important airborne flanking paths. Finally, the same type of controller was used to create a zone of quiet around the PC user head location. It was implemented using multimedia speakers and microphones, while the computer was placed in a semi-reverberant environment. A large zone of quiet surrounding the head was created at low frequencies (250Hz), and its size would reduce with increasing frequency (up to 1000Hz). / Master of Science

With recent advances in electronics and communication technologies, our daily lives are immersed in an environment of Internet-connected smart things. Despite the great convenience brought by the development of these technologies, privacy concerns and security issues are two topics that deserve more attention. On one hand, as smart things continue to grow in their abilities to sense the physical world and capabilities to send information out through the Internet, they have the potential to be used for surveillance of any individuals secretly. Nevertheless, people tend to adopt wearable devices without fully understanding what private information can be inferred and leaked through sensor data. On the other hand, security issues become even more serious and lethal with the world embracing the Internet of Things (IoT). Failures in computing systems are common, however, a failure now in IoT may harm people's lives. As demonstrated in both academic research and industrial practice, a software vulnerability hidden in a smart vehicle may lead to a remote attack that subverts a driver's control of the vehicle. Our approach to the aforementioned challenges starts by understanding privacy leakage in the IoT era and follows with adding defense layers to the IoT system with attackers gaining increasing capabilities. The first question we ask ourselves is "what new privacy concerns do IoT bring". We focus on discovering information leakage beyond people's common sense from even seemingly benign signals. We explore how much private information we can extract by designing information extraction systems. Through our research, we argue for stricter access control on newly coming sensors. After noticing the importance of data collected by IoT, we trace where sensitive data goes. In the IoT era, edge nodes are used to process sensitive data. However, a capable attacker may compromise edge nodes. Our second research focuses on applying trusted hardware to build trust in large-scale networks under this circumstance. The application of trusted hardware protects sensitive data from compromised edge nodes. Nonetheless, if an attacker becomes more powerful and embeds malicious logic into code for trusted hardware during the development phase, he still can secretly steal private data. In our third research, we design a static analyzer for detecting malicious logic hidden inside code for trusted hardware. Other than the privacy concern of data collected, another important aspect of IoT is that it affects the physical world. Our last piece of research work enables a user to verify the continuous execution state of an unmanned vehicle. This way, people can trust the integrity of the past and present state of the unmanned vehicle. / Doctor of Philosophy / The past few years have witnessed a rising in computing and networking technologies. Such advances enable the new paradigm, IoT, which brings great convenience to people's life. Large technology companies like Google, Apple, Amazon are creating smart devices such as smartwatch, smart home, drones, etc. Compared to the traditional internet, IoT can provide services beyond digital information by interacting with the physical world by its sensors and actuators. While the deployment of IoT brings value in various aspects of our society, the lucrative reward from cyber-crimes also increases in the upcoming IoT era. Two unique privacy and security concerns are emerging for IoT. On one hand, IoT brings a large volume of new sensors that are deployed ubiquitously and collect data 24/7. User's privacy is a big concern in this circumstance because collected sensor data may be used to infer a user's private activities. On the other hand, cyber-attacks now harm not only cyberspace but also the physical world. A failure in IoT devices could result in loss of human life. For example, a remotely hacked vehicle could shut down its engine on the highway regardless of the driver's operation. Our approach to emerging privacy and security concerns consists of two directions. The first direction targets at privacy protection. We first look at the privacy impact of upcoming ubiquitous sensing and argue for stricter access control on smart devices. Then, we follow the data flow of private data and propose solutions to protect private data from the networking and cloud computing infrastructure. The other direction aims at protecting the physical world. We propose an innovative method to verify the cyber state of IoT devices.

No / An investigation into the wavelet packet transform (WPT) modulation scheme for Multiple Input Multiple Output (MIMO) band-limited systems is presented. The implementation involves using the WPT as the base multiplexing technology at baseband, instead of the traditional Fast Fourier Transform (FFT) common in Orthogonal Frequency Division Multiplexing (OFDM) systems. An investigation for a WPT-MIMO multicarrier system, using the Alamouti diversity technique, is presented. Results are consistent with those in the original Alamouti work. The scheme is then implemented for WPT-MIMO and FFTMIMO cases with extended receiver diversity, namely 2 ×Nr MIMO systems, where Nr is the number of receiver elements. It is found that the diversity gain decreases with increasing receiver diversity and that WPT-MIMO systems can be more advantageous than FFT-based MIMO-OFDM systems.

Résumé : Afin de répondre aux besoins de communication et de divertissement, il ne fait aucun doute que la parole et l’audio doivent être encodés sous forme numérique. En qualité CD, cela nécessite un débit numérique de 1411.2 kb/s pour un signal stéréo-phonique. Une telle quantité de données devient rapidement prohibitive pour le stockage de longues durées d’audio ou pour la transmission sur certains réseaux, particulièrement en temps réel (d’où l’adhésion universelle au format MP3). De plus, ces dernières années, la quantité de productions musicales et cinématographiques disponibles en cinq canaux et plus ne cesse d’augmenter. Afin de maintenir le débit numérique à un niveau acceptable pour une application donnée, il est donc naturel pour un codeur audio à bas débit d’exploiter la redondance entre les canaux et la psychoacoustique binaurale. Le codage perceptuel et plus particulièrement le codage paramétrique permet d’atteindre des débits manifestement inférieurs en exploitant les limites de l’audition humaine (étudiées en psychoacoustique). Cette recherche se concentre donc sur le codage paramétrique à bas débit de plus d’un canal audio. // Abstract : In order to fulfill our communications and entertainment needs, there is no doubt that speech and audio must be encoded in digital format. In"CD" quality, this requires a bit-rate of 1411.2 kb/s for a stereo signal. Such a large amount of data quickly becomes prohibitive for long-term storage of audio or for transmitting on some networks, especially in real-time (leading to a universal adhesion to the MP3 format). Moreover, throughout the course of these last years, the number of musical and cinematographic productions available in five channels or more continually increased.In order to maintain an acceptable bit-rate for any given application, it is obvious that a low bit-rate audio coder must exploit the redundancies between audio channels and binaural psychoacoustics. Perceptual audio coding, and more specifically parametric audio coding, offers the possibility of achieving much lower bit-rates by taking into account the limits of human hearing (psychoacoustics). Therefore, this research concentrates on parametric audio coding of more than one audio channel.

Résumé : L’audio numérique s’est déployé de façon phénoménale au cours des dernières décennies, notamment grâce à l’établissement de standards internationaux. En revanche, l’imposition de normes introduit forcément une certaine rigidité qui peut constituer un frein à l’amélioration des technologies déjà déployées et pousser vers une multiplication de nouveaux standards. Cette thèse établit que les codecs existants peuvent être davantage valorisés en améliorant leur qualité ou leur débit, même à l’intérieur du cadre rigide posé par les standards établis. Trois volets sont étudiés, soit le rehaussement à l’encodeur, au décodeur et au niveau du train binaire. Dans tous les cas, la compatibilité est préservée avec les éléments existants. Ainsi, il est démontré que le signal audio peut être amélioré au décodeur sans transmettre de nouvelles informations, qu’un encodeur peut produire un signal amélioré sans ajout au décodeur et qu’un train binaire peut être mieux optimisé pour une nouvelle application. En particulier, cette thèse démontre que même un standard déployé depuis plusieurs décennies comme le G.711 a le potentiel d’être significativement amélioré à postériori, servant même de cœur à un nouveau standard de codage par couches qui devait préserver cette compatibilité. Ensuite, les travaux menés mettent en lumière que la qualité subjective et même objective d’un décodeur AAC (Advanced Audio Coding) peut être améliorée sans l’ajout d’information supplémentaire de la part de l’encodeur. Ces résultats ouvrent la voie à davantage de recherches sur les traitements qui exploitent une connaissance des limites des modèles de codage employés. Enfin, cette thèse établit que le train binaire à débit fixe de l’AMR WB+ (Extended Adaptive Multi-Rate Wideband) peut être compressé davantage pour le cas des applications à débit variable. Cela démontre qu’il est profitable d’adapter un codec au contexte dans lequel il est employé. / Abstract : Digital audio applications have grown exponentially during the last decades, in good part because of the establishment of international standards. However, imposing such norms necessarily introduces hurdles that can impede the improvement of technologies that have already been deployed, potentially leading to a proliferation of new standards. This thesis shows that existent coders can be better exploited by improving their quality or their bitrate, even within the rigid constraints posed by established standards. Three aspects are studied, being the enhancement of the encoder, the decoder and the bit stream. In every case, the compatibility with the other elements of the existent coder is maintained. Thus, it is shown that the audio signal can be improved at the decoder without transmitting new information, that an encoder can produce an improved signal without modifying its decoder, and that a bit stream can be optimized for a new application. In particular, this thesis shows that even a standard like G.711, which has been deployed for decades, has the potential to be significantly improved after the fact. This contribution has even served as the core for a new standard embedded coder that had to maintain that compatibility. It is also shown that the subjective and objective audio quality of the AAC (Advanced Audio Coding) decoder can be improved, without adding any extra information from the encoder, by better exploiting the knowledge of the coder model’s limitations. Finally, it is shown that the fixed rate bit stream of the AMR-WB+ (Extended Adaptive Multi-Rate Wideband) can be compressed more efficiently when considering a variable bit rate scenario, showing the need to adapt a coder to its use case.

Advances in fiber optic communications and the convergence of the optical-wireless network will dramatically increase the network heterogeneity and complexity. The goal of our research is to create smart receivers that can autonomously identify and demodulate, without prior knowledge, nearly any signal emerging from the next-generation of high-speed optical communication networks.