Smart applications like speaker verification have become essential in verifying the user’s identity for availing of personal assistants or online banking services based on the user’s voice characteristics. However, far-field or distant speaker verification is constantly affected by surrounding noises which can severely distort the speech signal. Moreover, speech signals propagating in long-range get reflected…
Cancer, with its varying and hard to detect types, became one of the most dangerous diseases for humans. Melanoma is a type of skin cancer that has the most mortality rate among its type. The usual melanoma detection process is based on awareness of the patient and the experience of the visual investigator. Even though…
This dissertation is the combination of three manuscripts ?either published in or submitted to journals? on compressive sensing of propeller noise for detection, identification and localization of water crafts. Propeller noise, as a result of rotating blades, is broadband and radiates through water dominating underwater acoustic noise spectrum especially when cavitation develops. Propeller cavitation yields…
Wireless connectivity, with its relative ease of over-the-air information sharing, is a key technological enabler that facilitates many of the essential applications, such as satellite navigation, cellular communication, and media broadcasting, that are nowadays taken for granted. However, that relative ease of over-the-air communications has significant drawbacks too. On one hand, the broadcast nature of…
Wireless sensor networks (WSNs) paved the way for accessing data previously unavailable by deploying sensors in various locations in space, each collecting local measurements of a target source signal. By exploiting the information resulting from the multitude of signals measured at the different sensors of the network, various tasks can be achieved, such as denoising…
Single-channel source separation for radio-frequency (RF) systems is a challenging problem relevant to key applications, including wireless communications, radar, and spectrum monitoring. This thesis addresses the challenge by focusing on data-driven approaches for source separation, leveraging datasets of sample realizations when source models are not explicitly provided. To this end, deep learning techniques are employed…
Inference and data analysis over networks have become significant areas of research due to the increasing prevalence of interconnected systems and the growing volume of data they produce. Many of these systems generate data in the form of multivariate time series, which are collections of time series data that are observed simultaneously across multiple variables….
In recent years Machine Learning and in particular Deep Learning have excelled in object recognition and classification tasks in computer vision. As these methods extract features from the data itself by learning features that are relevant for a particular task, a key aspect of this remarkable success is the amount of data on which these…
The localization and tracking of sound sources using microphone arrays is a problem that, even if it has attracted attention from the signal processing research community for decades, remains open. In recent years, deep learning models have surpassed the state-of-the-art that had been established by classic signal processing techniques, but these models still struggle with…
The characterization of Clock and Ephemeris error of the Global Navigation Satellite Systems is a key element to validate the assumptions for the integrity analysis of GNSS Safety of Life (SoL) applications. Specifically, the performance metrics of SoL applications require the characterization of the nominal User Range Errors (UREs) as well as the knowledge of…
