Synchronization and Detection Techniques for Navigation and Communication Systems

Synchronization is a key issue in any communication system, but it becomes fundamental in the navigation systems, which are entirely based on the estimation of the time delay of the signals coming from the satellites. Thus, even if synchronization has been a well known topic for many years, the introduction of new modulations and new physical layer techniques in the modern standards makes the traditional synchronization strategies completely ineffective. For this reason, the design of advanced and innovative techniques for synchronization in modern communication systems, like DVB-SH, DVB-T2, DVB-RCS, WiMAX, LTE, and in the modern navigation system, like Galileo, has been the topic of the activity. Recent years have seen the consolidation of two different trends: the introduction of Orthogonal Frequency Division Multiplexing (OFDM) in the communication systems, and of the Binary Offset Carrier (BOC) modulation in the modern Global Navigation Satellite Systems (GNSS). Thus, a particular attention has been given to the investigation of the synchronization algorithms in these areas.

The role of medial parieto occipital cortex in visuospatial attention and reach planning: electrophysiological studies in human and non-human primates

We usually perform actions in a dynamic environment and changes in the location of a target for an upcoming action require both covert shifts of attention and motor planning update. In this study we tested whether, similarly to oculomotor areas that provide signals for overt and covert attention shifts, covert attention shifts modulate activity in cortical area V6A, which provides a bridge between visual signals and arm-motor control. We performed single cell recordings in monkeys trained to fixate straight-ahead while shifting attention outward to a peripheral cue and inward again to the fixation point. We found that neurons in V6A are influenced by spatial attention demonstrating that visual, motor, and attentional responses can occur in combination in single neurons of V6A. This modulation in an area primarily involved in visuo-motor transformation for reaching suggests that also reach-related regions could directly contribute in the shifts of spatial attention necessary to plan and control goal-directed arm movements. Moreover, to test whether V6A is causally involved in these processes, we have performed a human study using on-line repetitive transcranial magnetic stimulation over the putative human V6A (pV6A) during an attention and a reaching task requiring covert shifts of attention and reaching movements towards cued targets in space. We demonstrate that the pV6A is causally involved in attention reorienting to target detection and that this process interferes with the execution of reaching movements towards unattended targets. The current findings suggest the direct involvement of the action-related dorso-medial visual stream in attentional processes, and a more specific role of V6A in attention reorienting. Therefore, we propose that attention signals are used by the V6A to rapidly update the current motor plan or the ongoing action when a behaviorally relevant object unexpectedly appears at an unattended location.

Distraction by task-irrelevant stimuli: the effects of endogenous spatial attention and visual working memory load

Salient stimuli, like sudden changes in the environment or emotional stimuli, generate a priority signal that captures attention even if they are task-irrelevant. However, to achieve goal-driven behavior, we need to ignore them and to avoid being distracted. It is generally agreed that top-down factors can help us to filter out distractors. A fundamental question is how and at which stage of processing the rejection of distractors is achieved. Two circumstances under which the allocation of attention to distractors is supposed to be prevented are represented by the case in which distractors occur at an unattended location (as determined by the deployment of endogenous spatial attention) and when the amount of visual working memory resources is reduced by an ongoing task. The present thesis is focused on the impact of these factors on three sources of distraction, namely auditory and visual onsets (Experiments 1 and 2, respectively) and pleasant scenes (Experiment 3). In the first two studies we recorded neural correlates of distractor processing (i.e., Event-Related Potentials), whereas in the last study we used interference effects on behavior (i.e., a slowing down of response times on a simultaneous task) to index distraction. Endogenous spatial attention reduced distraction by auditory stimuli and eliminated distraction by visual onsets. Differently, visual working memory load only affected the processing of visual onsets. Emotional interference persisted even when scenes occurred always at unattended locations and when visual working memory was loaded. Altogether, these findings indicate that the ability to detect the location of salient task-irrelevant sounds and identify the affective significance of natural scenes is preserved even when the amount of visual working memory resources is reduced by an ongoing task and when endogenous attention is elsewhere directed. However, these results also indicate that the processing of auditory and visual distractors is not entirely automatic.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.