Hardware Implementation of the Spot Payload for Orbiting Objects Detection Using Star Sensors

Space debris issue has become an attractive challenge for many applications in the framework of Space Situational Awareness (SSA) and Space Surveillance and Tracking (SST). The Star sensor image on-board Processing for orbiting Objects deTection (SPOT) fits in this field as an innovative space based autonomous and versatile system for Resident Space Objects’ optical detection via star sensors and for different Earth orbits scenarios. This system is planned to be a payload for an In-Orbit Validation (IOV) activity in the next future. The purpose of this paper is to show the architecture of the SPOT system together with its implementation on a System on Chip (SoC)/Field Programmable Gate Array (FPGA) space representative board. The SPOT algorithms involve several layers of filters which are relatively expensive in terms of computational latency, limiting their applicability to real-time image processing applications. This work presents the design and implementation of SPOT algorithm on the Zynq-7000 SoC using Xilinx FPGA and ARM CPU. Algorithms have been modelled with Simulink and implemented on FPGA using Xilinx system generator with aiming to optimize both processing time and area usage. A Hardware-In-the-Loop (HIL) setup was developed as well, to verify the performances and robustness of the SPOT algorithms and simulating critical scenario by using real night sky images from acquisition campaig

YOLO v4 Based Algorithm for Resident Space Object Detection and Tracking

Resident Space Objects (RSOs) detection and tracking are relevant challenges in the framework of Space Situational Awareness (SSA). The growing number of active and inactive platforms and the incoming era of mega constellations is increasing the traffic in the near Earth segment. Recently, more and more research efforts have been focused on this problem. This, combined with the popularity of Artificial Intelligence (AI) applications, has led to interesting solutions. The potential of an AI based approach for image processing, objects detection and tracking oriented to space optical sensors applications has already been proved. In this work, the architecture of a Convolutional Neural Network (CNN) based algorithm has been developed and tested. The image processing and object detection tasks are demanded to Neural Network (NN) modules (U-Net and YOLO v4, respectively) while the tracking of objects inside the sensor’s Field Of View (FOV) is formulated as an optimization problem. A performance comparison in terms of detection capabilities has been carried out with respect to a previous version of the algorithm based on YOLO v3. Reported results, based on real and simulated night sky images, show a notable performance improvement from v3 to v4

International Conference on Scalable Quantum Computing with Trapped Atoms

The conference will be the occasion to have an update on the progress towards the realisation of a quantum information processors, by using individually controlled atoms, ions and photons in order to encode, store, process and transmit qubits

Mobile Apps Development: A Framework for Technology Decision Making.

Developers of a new Mobile App have to undertake a number of decisions, including the target platform and the development technology to utilize. Even though there is no one-size-fits-all solution, which could meet all needs for all contexts, this paper is concerned with an exploratory study aimed to provide developers with a framework to support their technology selection process, including practical guidelines on how to select the technology that best fits the given context and requirements. The exploited research methods are survey, interview, and case study. Results consist in a model of, and a collection of data and experts’ experiences about, some advanced platforms. Results are packed in a tool-prototype: once entered the needs and required device features, the tool returns measures that allow a decision maker to identify the development technology, among the recommended alternatives, which best fulfills the actual requirements

On the impact of residual strains in the stress analysis of patient-specific atherosclerotic carotid vessels: predictions based on the homogenous stress hypothesis

The identification of carotid atherosclerotic lesion at risk for plaque rupture, eventually resulting in cerebral embolism and stroke, is of paramount clinical importance. High stress in the fibrous plaque cap has been proposed as risk factor. However, among others, residual strains influence said stress predictions, but quantitative and qualitative implications of residual strains in this context are not well explored. We therefore propose a multiplicative kinematics-based Growth and Remodeling (G&R) framework to predict residual strains from homogenizing tissue stress and then investigate its implication on plaque stress. Carotid vessel morphology of four patients was reconstructed from clinical Computed Tomography-Angiography (CT-A) images and equipped with heterogeneous tissue constitutive properties assigned through a histology-based artificial intelligence image segmentation tool. As compared to a purely elastic analysis and depending on patient-specific morphology and tissue distributions, the incorporation of residual strains reduced the maximum wall stress by up to 30% and resulted in a fundamentally different distribution of stress across the atherosclerotic wall. Regardless residual strains homogenized tissue stresses, the fibrous plaque cap may persistently be exposed to spots of high stress. In conclusion, the incorporation of residual strains in biomechanical studies of atherosclerotic carotids may be important for a reliable assessment of fibrous plaque cap stress

Enhancing the System Development Process Performance: a Value-Based Approach

When planning or controlling the system development process, a project leader needs to make decisions which take into account a number of aspects, including: availability of assets and competences, previously enacted processes in the organization, certifications the system is required to obtain, standards to comply with, interactions among process activities, contextual factors and constraints, and allocated budget and schedule. In this paper we propose a value-based approach for supporting decision making. The aim is to provide supportive information for decisions related to the system verification process. This would in turn enhance the performance of system development process by supporting the decision making process for complex systems. We report both academic and industrial empirical evaluations, which demonstrate the feasibility and effectiveness of our proposal, and thus prompt us to refine and extend our approach to sub-processes other than verification

Mobile Apps Development: A Framework for Technology Decision Making.

Developers of a new Mobile App have to undertake a number of decisions, including the target platform and the development technology to utilize. Even though there is no one-size-fits-all solution, which could meet all needs for all contexts, this paper is concerned with an exploratory study aimed to provide developers with a framework to support their technology selection process, including practical guidelines on how to select the technology that best fits the given context and requirements. The exploited research methods are survey, interview, and case study. Results consist in a model of, and a collection of data and experts’ experiences about, some advanced platforms. Results are packed in a tool-prototype: once entered the needs and required device features, the tool returns measures that allow a decision maker to identify the development technology, among the recommended alternatives, which best fulfills the actual requirements