This thesis presents a comprehensive study of the evaluation of the Quality of Experience (QoE) perceived by the users of 3D video systems, analyzing the impact of effects introduced by all the elements of the 3D video processing chain. Therefore, various subjective assessment tests are presented, particularly designed to evaluate the systems under consideration, and…
Due to significant advances made over the last few decades in the areas of (wireless) networking, communications and microprocessor fabrication, the use of sensor networks to observe physical phenomena is rapidly becoming commonplace. Over this period, many aspects of sensor networks have been explored, yet a thorough understanding of how to analyse and process the…
Signal processing techniques for multiple-antenna transmission can exploit the spatial dimension of the wireless channel to serve multiple users simultaneously, achieving high spectral efficiencies. Realizing such gains; however, is strongly dependent on the availability of highly accurate and up-to-date Channel State Information at the Transmitter (CSIT). This stems from the necessity to deal with multiuser…
This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts….
The task of robust regression is of particular importance in signal processing, statistics and machine learning. Ordinary estimators, such as the Least Squares (LS) one, fail to achieve sufficiently good performance in the presence of outliers. Although the problem has been addressed many decades ago and several methods have been established, it has recently attracted…
Sparse signal recovery consists of algorithms that are able to recover undersampled high dimensional signals accurately. These algorithms require fewer measurements than traditional Shannon/Nyquist sampling theorem demands. Sparse signal recovery has found many applications including magnetic resonance imaging, electromagnetic inverse scattering, radar/sonar imaging, seismic data collection, sensor array processing and channel estimation. The focus of…
Gliomas represent about 80% of all malignant primary brain tumors. Despite recent advancements in glioma research, patient outcome remains poor. The 5 year survival rate of the most common and most malignant subtype, i.e. glioblastoma, is about 5%. Magnetic resonance imaging (MRI) has become the imaging modality of choice in the management of brain tumor…
OFDM is a multicarrier modulation system widely used in wireline and wireless applications such as DVB-T/T2, Wifi, and LTE, due to its resilience against frequency selective channels compared with the single carrier modulation systems. However, the OFDM signal suffers from large amplitude variations. The fluctuations of the OFDM envelope generate non-linear distortions when we introduce…
Matrices, as natural representation of linear mappings in finite dimension, play a crucial role in signal processing and machine learning. Multiplying a vector by a full rank matrix a priori costs of the order of the number of non-zero entries in the matrix, in terms of arithmetic operations. However, matrices exist that can be applied…
Distant talking scenarios, such as hands-free calling or teleconference meetings, are essential for natural and comfortable human-machine interaction and they are being increasingly used in multiple contexts. The acquired speech signal in such scenarios is reverberant and affected by additive noise. This signal distortion degrades the performance of speech recognition and diarization systems creating troublesome…
