Design of Cloud Robotic Services for Senior Citizens to Improve Independent Living and Personal Health Management

A cloud robotics solution was designed and initially tested with a mobile robotic platform and a smart environment, in order to provide health-care management services to senior citizens and improve their independent living. The solution was evaluated in terms of Quality of Service (QoS) and tested in the realistic scenario of the DomoCasa Living Lab, Peccioli, Italy. In particular, a medication reminding service, a remote home monitoring and a user indoor localization algorithm were outsourced in the cloud and provided to the robots, users and carers. The system acquired data from a smart environment and addressed the robot to the user for service delivery. Experiments showed a service's Reliability of Response at least of the 0.04 % and a Time of Response of the same order of magnitude of the processing time required by the user localization algorithm

Night vision imaging systems design, integration, and verification in military fighter aircraft

This paper describes the developmental and testing activities conducted by the Italian Air Force Official Test Centre (RSV) in collaboration with Alenia Aerospace, Litton Precision Products and Cranfiled University, in order to confer the Night Vision Imaging Systems (NVIS) capability to the Italian TORNADO IDS (Interdiction and Strike) and ECR (Electronic Combat and Reconnaissance) aircraft. The activities consisted of various Design, Development, Test and Evaluation (DDT&E) activities, including Night Vision Goggles (NVG) integration, cockpit instruments and external lighting modifications, as well as various ground test sessions and a total of eighteen flight test sorties. RSV and Litton Precision Products were responsible of coordinating and conducting the installation activities of the internal and external lights. Particularly, an iterative process was established, allowing an in-site rapid correction of the major deficiencies encountered during the ground and flight test sessions. (cont.

GPU-based Real-time Triggering in the NA62 Experiment

Over the last few years the GPGPU (General-Purpose computing on Graphics Processing Units) paradigm represented a remarkable development in the world of computing. Computing for High-Energy Physics is no exception: several works have demonstrated the effectiveness of the integration of GPU-based systems in high level trigger of different experiments. On the other hand the use of GPUs in the low level trigger systems, characterized by stringent real-time constraints, such as tight time budget and high throughput, poses several challenges. In this paper we focus on the low level trigger in the CERN NA62 experiment, investigating the use of real-time computing on GPUs in this synchronous system. Our approach aimed at harvesting the GPU computing power to build in real-time refined physics-related trigger primitives for the RICH detector, as the the knowledge of Cerenkov rings parameters allows to build stringent conditions for data selection at trigger level. Latencies of all components of the trigger chain have been analyzed, pointing out that networking is the most critical one. To keep the latency of data transfer task under control, we devised NaNet, an FPGA-based PCIe Network Interface Card (NIC) with GPUDirect capabilities. For the processing task, we developed specific multiple ring trigger algorithms to leverage the parallel architecture of GPUs and increase the processing throughput to keep up with the high event rate. Results obtained during the first months of 2016 NA62 run are presented and discussed

Logistic regression to predict malignancy of breast tumors using IVIM parameters

The goal of this work is to predict the malignancy of a lesion from the analysis of DW-MRI in a retrospective study. The DW-MRI sequence is used to compute the intravoxel incoherent motion (IVIM) parameters that allow to divide the water movement into diffusion (due to the water present in the tissues) and perfusion (due to the water present in blood flowing in the capillaries). This second movement is not random, but oriented in the direction of the capillaries, but if we recall that capillaries are very short, randomly oriented and with a high density per volume, we can consider the perfusion as a \u2018\u2018pseudo-diffusion\u2019\u2019. Knowing that benign and malign breast tumour have different perfusion characteristics, if we could identify and quantify this feature, we might be able to determine the type of the tumour. In this work, we use state of the art algorithms to compute the IVIM parameters which are then plugged into a learning algorithm, based on retrospective data, that infer the malignancy of the lesion