Flight Avionics Hardware Roadmap

As part of NASA's Avionics Steering Committee's stated goal to advance the avionics discipline ahead of program and project needs, the committee initiated a multi-Center technology roadmapping activity to create a comprehensive avionics roadmap. The roadmap is intended to strategically guide avionics technology development to effectively meet future NASA missions needs. The scope of the roadmap aligns with the twelve avionics elements defined in the ASC charter, but is subdivided into the following five areas: Foundational Technology (including devices and components), Command and Data Handling, Spaceflight Instrumentation, Communication and Tracking, and Human Interfaces

Integrated Circuit Design in US High-Energy Physics

This whitepaper summarizes the status, plans, and challenges in the area of integrated circuit design in the United States for future High Energy Physics (HEP) experiments. It has been submitted to CPAD (Coordinating Panel for Advanced Detectors) and the HEP Community Summer Study 2013(Snowmass on the Mississippi) held in Minnesota July 29 to August 6, 2013. A workshop titled: US Workshop on IC Design for High Energy Physics, HEPIC2013 was held May 30 to June 1, 2013 at Lawrence Berkeley National Laboratory (LBNL). A draft of the whitepaper was distributed to the attendees before the workshop, the content was discussed at the meeting, and this document is the resulting final product. The scope of the whitepaper includes the following topics: Needs for IC technologies to enable future experiments in the three HEP frontiers Energy, Cosmic and Intensity Frontiers; Challenges in the different technology and circuit design areas and the related R&D needs; Motivation for using different fabrication technologies; Outlook of future technologies including 2.5D and 3D; Survey of ICs used in current experiments and ICs targeted for approved or proposed experiments; IC design at US institutes and recommendations for collaboration in the future

Neural Network Methods for Radiation Detectors and Imaging

Recent advances in image data processing through machine learning and especially deep neural networks (DNNs) allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration

Compressive Imaging Using RIP-Compliant CMOS Imager Architecture and Landweber Reconstruction

In this paper, we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in CS CMOS image sensors are recursive pseudo-random binary matrices. We have proved that the restricted isometry property of these matrices is limited by a low sparsity constant. The quality of these matrices is also affected by the non-idealities of pseudo-random number generators (PRNG). To overcome these limitations, we propose a hardware-friendly pseudo-random ternary measurement matrix generated on-chip by means of class III elementary cellular automata (ECA). These ECA present a chaotic behavior that emulates random CS measurement matrices better than other PRNG. We have combined this new architecture with a block-based CS smoothed-projected Landweber reconstruction algorithm. By means of single value decomposition, we have adapted this algorithm to perform fast and precise reconstruction while operating with binary and ternary matrices. Simulations are provided to qualify the approach.Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2338-2013Office of Naval Research (USA) N000141410355European Union H2020 76586

Implementation of sub-nanosecond time-to-digital convertor in field-programmable gate array: applications to time-of-flight analysis in muon radiography

International audienceTime-of-flight (tof) techniques are standard techniques in high energy physics to determine particles propagation directions. Since particles velocities are generally close to c, the speed of light, and detectors typical dimensions at the meter level, the state-of-the-art tof techniques should reach sub-nanosecond timing resolution. Among the various techniques already available, the recently developed ring oscillator TDC ones, implemented in low cost FPGA, feature a very interesting figure of merit since a very good timing performance may be achieved with limited processing ressources. This issue is relevant for applications where unmanned sensors should have the lowest possible power consumption. Actually this article describes in details the application of this kind of tof technique to muon tomography of geological bodies. Muon tomography aims at measuring density variations and absolute densities through the detection of atmospheric muons flux's attenuation, due to the presence of matter. When the measured fluxes become very low, an identified source of noise comes from backwards propagating particles hitting the detector in a direction pointing to the geological body. The separation between through-going and backward-going particles, on the basis of the tof information is therefore a key parameter for the tomography analysis and subsequent previsions