An Approach to Emulate and Validate the Effects of Single Event Upsets using the PREDICT FUTRE Hardware Integrated Framework

Due to the advances in electronics design automation industry, worldwide, the integrated approach to model and emulate the single event effects due to cosmic radiation, in particular single event upsets or single event transients is gaining momentum. As of now, no integrated methodology to inject the fault in parallel to functional test vectors or to estimate the effects of radiation for a selected function in system on chip at design phase exists. In this paper, a framework, PRogrammable single Event effects Demonstrator for dIgital Chip Technologies (PREDICT) failure assessment for radiation effects is developed using a hardware platform and aided by genetic algorithms addressing all the above challenges. A case study is carried out to evaluate the frameworks capability to emulate the effects of radiation using the co-processor as design under test (DUT) function. Using the ML605 and Virtex-6 evaluation board for single and three particle simulations with the layered atmospheric conditions, the proposed framework consumes approximately 100 min and 300 min, respectively; it consumes 600 min for 3 particle random atmospheric conditions, using the 64 GB RAM, 64-bit operating system with 3.1 GHz processor based workstation. The framework output transforms the 4 MeVcm2/mg linear energy transfer to a single event transient pulse width of 2 μs with 105 amplification factor for visualisation, which matches well with the existing experimental results data. Using the framework, the effects of radiation for the co-processing module are estimated during the design phase and the success rate of the DUT is found to be 48 per cent

Development of a Bonner Sphere neutron spectrometer from a commercial neutron dosimeter

Bonner Spheres have been used widely for the measurement of neutron spectra with neutron energies ranged from thermal up to at least 20 MeV . A Bonner Sphere neutron spectrometer (BSS) was developed by extending a Berthold LB 6411 neutron-dose-rate meter. The BSS consists of a 3He thermal-neutron detector with integrated electronics, a set of eight polyethylene spherical shells and two optional lead shells of various sizes. The response matrix of the BSS was calculated with GEANT4 Monte Carlo simulation. The BSS had a calibration uncertainty of ± 8.6% and a detector background rate of (1.57 ± 0.04) × 10−3 s−1. A spectral unfolding code NSUGA was developed. The NSUGA code utilizes genetic algorithms and has been shown to perform well in the absence of a priori information.postprin

Automatic Design of Artificial Neural Networks for Gamma-Ray Detection

The goal of this work is to investigate the possibility of improving current gamma/hadron discrimination based on their shower patterns recorded on the ground. To this end we propose the use of Convolutional Neural Networks (CNNs) for their ability to distinguish patterns based on automatically designed features. In order to promote the creation of CNNs that properly uncover the hidden patterns in the data, and at same time avoid the burden of hand-crafting the topology and learning hyper-parameters we resort to NeuroEvolution; in particular we use Fast-DENSER++, a variant of Deep Evolutionary Network Structured Representation. The results show that the best CNN generated by Fast-DENSER++ improves by a factor of 2 when compared with the results reported by classic statistical approaches. Additionally, we experiment ensembling the 10 best generated CNNs, one from each of the evolutionary runs; the ensemble leads to an improvement by a factor of 2.3. These results show that it is possible to improve the gamma/hadron discrimination based on CNNs that are automatically generated and are trained with instances of the ground impact patterns.info:eu-repo/semantics/publishedVersio

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 331)

This bibliography lists 129 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during December, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Constraints on the distance duality relation with standard sirens

We use gravitational wave (GW) standard sirens, in addition to Type Ia supernovae (SNIa) and baryon acoustic oscillation (BAO) mock data, to forecast constraints on the electromagnetic and gravitational distance duality relations (DDR). We make use of a parameterised approach based on a specific DDR violation model, along with a machine learning reconstruction method based on the Genetic Algorithms. We find that GW provide an alternative to the use of BAO data to constrain violations of the DDR, reaching $3\%$ constraints on the violation parameter we consider when combined with SNIa, which is only improved by a factor of $\approx1.4$ if one instead considers the combination of BAO and SNIa. We also investigate the possibility that a neglected modification of gravity might lead to a false detection of DDR violations, even when screening mechanisms are active. We find that such a false detection can be extremely significant, up to $\approx10\sigma$ for very extreme modified gravity scenarios, while this reduces to $\approx4\sigma$ in a more realistic case. False detections can also provide a smoking gun for the modified gravity mechanism at play, as a result of the tension introduced between the SNIa+GW and SNIa+BAO combinations.Comment: 31 pages, 13 figures. Version 2 aligns with the published version in JCAP (references and details added to the introduction, more discussion of modified gravity parameterisation and the genetic algorithm results included

SoC-based FPGA architecture for image analysis and other highly demanding applications

Al giorno d'oggi, lo sviluppo di algoritmi si concentra su calcoli efficienti in termini di prestazioni ed efficienza energetica. Tecnologie come il field programmable gate array (FPGA) e il system on chip (SoC) basato su FPGA (FPGA/SoC) hanno dimostrato la loro capacità di accelerare applicazioni di calcolo intensive risparmiando al contempo il consumo energetico, grazie alla loro capacità di elevato parallelismo e riconfigurazione dell'architettura. Attualmente, i cicli di progettazione esistenti per FPGA/SoC sono lunghi, a causa della complessità dell'architettura. Pertanto, per colmare il divario tra le applicazioni e le architetture FPGA/SoC e ottenere un design hardware efficiente per l'analisi delle immagini e altri applicazioni altamente demandanti utilizzando lo strumento di sintesi di alto livello, vengono prese in considerazione due strategie complementari: tecniche ad hoc e stima delle prestazioni. Per quanto riguarda le tecniche ad-hoc, tre applicazioni molto impegnative sono state accelerate attraverso gli strumenti HLS: discriminatore di forme di impulso per i raggi cosmici, classificazione automatica degli insetti e re-ranking per il recupero delle informazioni, sottolineando i vantaggi quando questo tipo di applicazioni viene attraversato da tecniche di compressione durante il targeting dispositivi FPGA/SoC. Inoltre, in questa tesi viene proposto uno stimatore delle prestazioni per l'accelerazione hardware per prevedere efficacemente l'utilizzo delle risorse e la latenza per FPGA/SoC, costruendo un ponte tra l'applicazione e i domini architetturali. Lo strumento integra modelli analitici per la previsione delle prestazioni e un motore design space explorer (DSE) per fornire approfondimenti di alto livello agli sviluppatori di hardware, composto da due motori indipendenti: DSE basato sull'ottimizzazione a singolo obiettivo e DSE basato sull'ottimizzazione evolutiva multiobiettivo.Nowadays, the development of algorithms focuses on performance-efficient and energy-efficient computations. Technologies such as field programmable gate array (FPGA) and system on chip (SoC) based on FPGA (FPGA/SoC) have shown their ability to accelerate intensive computing applications while saving power consumption, owing to their capability of high parallelism and reconfiguration of the architecture. Currently, the existing design cycles for FPGA/SoC are time-consuming, owing to the complexity of the architecture. Therefore, to address the gap between applications and FPGA/SoC architectures and to obtain an efficient hardware design for image analysis and highly demanding applications using the high-level synthesis tool, two complementary strategies are considered: ad-hoc techniques and performance estimator. Regarding ad-hoc techniques, three highly demanding applications were accelerated through HLS tools: pulse shape discriminator for cosmic rays, automatic pest classification, and re-ranking for information retrieval, emphasizing the benefits when this type of applications are traversed by compression techniques when targeting FPGA/SoC devices. Furthermore, a comprehensive performance estimator for hardware acceleration is proposed in this thesis to effectively predict the resource utilization and latency for FPGA/SoC, building a bridge between the application and architectural domains. The tool integrates analytical models for performance prediction, and a design space explorer (DSE) engine for providing high-level insights to hardware developers, composed of two independent sub-engines: DSE based on single-objective optimization and DSE based on evolutionary multi-objective optimization

Novel techniques for measuring the effect of neighbouring bases on mutation and their applications

Understanding factors influencing mutations can improve detection of novel mutations, the diagnostic signatures of disease-causing mutagens, and facilitate the development of more accurate models of genetic divergence. Hypermutability of CpG demonstrates the existence of mutation motifs, sequences of flanking bases that influence point mutation processes. These motifs can also be indicative of specific underlying mutation mechanisms. I developed novel log-linear models for identifying mutation motifs that allow further comparisons of these mutation motifs, and of the complete mutation spectra between samples. Mutation motifs are visualised using a sequence logo type method. In this thesis, I applied the methods to examine each of the possible 12 point mutations in about 13.6 million human germline mutations (inferred from single-nucleotide polymorphisms recorded in the Ensembl database) and about 181,000 melanoma mutations from the COSMIC database. My method recovered the well-known CpG effect, which a conventional motif detection method failed to do. I established that all point mutations have significant and distinct mutation motifs. While the major effects of flanking bases lie within 2 bp of the mutated position, I refute previous reports that the effect magnitude decays monotonically with distance. Comparison between autosomes and X-chromosomes supported a reduced contribution from methylation-induced C to T mutation on the X-chromosome, consistent with a previous prediction. In addition, analyses of malignant melanoma confirmed reported characteristic features of this cancer, such as strand asymmetry of mutation processes. Further, I found that neighbouring influences in malignant melanoma differ significantly from those affecting germline mutations. Interestingly, for C to T mutation, the CpG effect was no longer evident, and was largely substituted by different neighbouring mechanisms. Moreover, the observed neighbouring influence is able to reflect the chemical influences of mutagenic processes after exposure to ultraviolet light. Based on this observation, I hypothesised that information regarding the mechanistic origin of point mutations is present in surrounding DNA sequences, and sequence neighbourhood can be used to identify the mechanistic origin of particular mutations. Machine learning classifiers were developed to assess the above hypothesis and discriminate between N-ethyl-N-nitrosourea (ENU)-induced and spontaneous point mutations in the mouse germline. ENU is a synthetic chemical employed in mutagenesis studies, introducing novel point mutations to genomes. My classification results reveal that a combination of k-mer size and representation of second-order interactions among nucleotides was able to improve classification performance compared to the naive classifier approach. In conclusion, this work demonstrates that neighbouring bases have a profound effect on the occurrence of mutations. The statistical methods reported in this research can be used to examine the role of flanking sequence on mutation processes from polymorphism data, which further enable identification of differences in the operation of mechanisms of mutation between genomic regions, cell types or species. In addition, the machine learning classification results have important implications for modelling context-dependent effects on sequence evolution