Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Morphodynamics, sedimentation and sediment dynamics of a gravel beach

The morphodyiiamics of a gravel barrier beadi in Devon, \JK (Slaptou Sands: tau/S 0.15 - 0.25, D50 2 - 8min), was studied with reference to its sedimentology. Three time scales were sampled for nearshore hydrodynamics, intertidal morphologies and sediirientologies. A series of surveys were carried out over individual tidal cycles (samphng every 5 - lOmins for between 6 and 91irs); on •consecutive low tides over half-lunar tidal cycles (1 -2 cross-shore profiles-sampled every 0.5 - Ini, on 2 spring - spring tidal cycles comprising 26 and 24 tides, respectively); and finally eveiy 2 weeks at spring low tide, over 1 calendar year (13-17 profile lines survej'ed and sampled for sediment over 3.25 - 4.251an). In order to further our understanding of gravel beaches, sediment data needs to, be collected at a resolution similar to that of the hydrodynamics. Innovative automatic sediment sizing techniques based on digital images of sediments were therefore developed, and software written, to allow the collection and analysis of high-resolution sediment data. The gi-avel beach step and berm are accretionaiy features, tidally modulated, and evolve under different time scales. A new technique to determine bed mobility from the nearshore, using underwater ^adeo cameras, was devised. Nearsliore sediment transport was suggested as being related to sub-incident wave frequencies. No aspect of morphological change could be found to havea statistically significant association with sedimentological change, but dimensional-reduction techniques did satisfactorily detect association. The lack of co-variance and obvious patterns is stochastic noise, not • parameterisation. Over one year, the barrier underwent asymmetrical rotation over one year, highlighting the importance of alongshore sediment transport processes on this supposedly 'swash aligned' beach. A statistical model based on the log-hj'perbolic distribution of sinface particle sizes was found to be a reasonable predictor of mean net sedimentation over individual tides. Its complicated parameter space could possibly map'onto a simpler plane based on traditional moments. Sediment trend vector models based on sorting alone out-performed a traditional approach. Moments of a surface grain-size'distribution appear to be inappropriate to characterise sedimentological change at time-scales gi-eater than a semi-diurnal tidal cycle. Sub-surface sampling on the intertidal zone on diurnal and semi-lunar time-scales is useful in assessing the dynamics of the step, itself an important mechanism for onshore and offshore net volumetric transport.School of Geograph

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields

Beyong lexical meaning : probabilistic models for sign language recognition

Ph.DDOCTOR OF PHILOSOPH

Cognitive radar network design and applications

PhD ThesisIn recent years, several emerging technologies in modern radar system design are attracting the attention of radar researchers and practitioners alike, noteworthy among which are multiple-input multiple-output (MIMO), ultra wideband (UWB) and joint communication-radar technologies. This thesis, in particular focuses upon a cognitive approach to design these modern radars. In the existing literature, these technologies have been implemented on a traditional platform in which the transmitter and receiver subsystems are discrete and do not exchange vital radar scene information. Although such radar architectures benefit from these mentioned technological advances, their performance remains sub-optimal due to the lack of exchange of dynamic radar scene information between the subsystems. Consequently, such systems are not capable to adapt their operational parameters “on the fly”, which is in accordance with the dynamic radar environment. This thesis explores the research gap of evaluating cognitive mechanisms, which could enable modern radars to adapt their operational parameters like waveform, power and spectrum by continually learning about the radar scene through constant interactions with the environment and exchanging this information between the radar transmitter and receiver. The cognitive feedback between the receiver and transmitter subsystems is the facilitator of intelligence for this type of architecture. In this thesis, the cognitive architecture is fused together with modern radar systems like MIMO, UWB and joint communication-radar designs to achieve significant performance improvement in terms of target parameter extraction. Specifically, in the context of MIMO radar, a novel cognitive waveform optimization approach has been developed which facilitates enhanced target signature extraction. In terms of UWB radar system design, a novel cognitive illumination and target tracking algorithm for target parameter extraction in indoor scenarios has been developed. A cognitive system architecture and waveform design algorithm has been proposed for joint communication-radar systems. This thesis also explores the development of cognitive dynamic systems that allows the fusion of cognitive radar and cognitive radio paradigms for optimal resources allocation in wireless networks. In summary, the thesis provides a theoretical framework for implementing cognitive mechanisms in modern radar system design. Through such a novel approach, intelligent illumination strategies could be devised, which enable the adaptation of radar operational modes in accordance with the target scene variations in real time. This leads to the development of radar systems which are better aware of their surroundings and are able to quickly adapt to the target scene variations in real time.Newcastle University, Newcastle upon Tyne: University of Greenwich

Deep probabilistic methods for improved radar sensor modelling and pose estimation

Radar’s ability to sense under adverse conditions and at far-range makes it a valuable alternative to vision and lidar for mobile robotic applications. However, its complex, scene-dependent sensing process and significant noise artefacts makes working with radar challenging. Moving past classical rule-based approaches, which have dominated the literature to date, this thesis investigates deep and data-driven solutions across a range of tasks in robotics. Firstly, a deep approach is developed for mapping raw sensor measurements to a grid-map of occupancy probabilities, outperforming classical filtering approaches by a significant margin. A distribution over the occupancy state is captured, additionally allowing uncertainty in predictions to be identified and managed. The approach is trained entirely using partial labels generated automatically from lidar, without requiring manual labelling. Next, a deep model is proposed for generating stochastic radar measurements from simulated elevation maps. The model is trained by learning the forward and backward processes side-by-side, using a combination of adversarial and cyclical consistency constraints in combination with a partial alignment loss, using labels generated in lidar. By faithfully replicating the radar sensing process, new models can be trained for down-stream tasks, using labels that are readily available in simulation. In this case, segmentation models trained on simulated radar measurements, when deployed in the real world, are shown to approach the performance of a model trained entirely on real-world measurements. Finally, the potential of deep approaches applied to the radar odometry task are explored. A learnt feature space is combined with a classical correlative scan matching procedure and optimised for pose prediction, allowing the proposed method to outperform the previous state-of-the-art by a significant margin. Through a probabilistic consideration the uncertainty in the pose is also successfully characterised. Building upon this success, properties of the Fourier Transform are then utilised to separate the search for translation and angle. It is shown that this decoupled search results in a significant boost to run-time performance, allowing the approach to run in real-time on CPUs and embedded devices, whilst remaining competitive with other radar odometry methods proposed in the literature