Optical Sensors for Planetary Radiant Energy (OSPREy): Calibration and Validation of Current and Next-Generation NASA Missions

A principal objective of the Optical Sensors for Planetary Radiance Energy (OSPREy) activity is to establish an above-water radiometer system as a lower-cost alternative to existing in-water systems for the collection of ground-truth observations. The goal is to be able to make high-quality measurements satisfying the accuracy requirements for the vicarious calibration and algorithm validation of next-generation satellites that make ocean color and atmospheric measurements. This means the measurements will have a documented uncertainty satisfying the established performance metrics for producing climate-quality data records. The OSPREy approach is based on enhancing commercial-off-the-shelf fixed-wavelength and hyperspectral sensors to create hybridspectral instruments with an improved accuracy and spectral resolution, as well as a dynamic range permitting sea, Sun, sky, and Moon observations. Greater spectral diversity in the ultraviolet (UV) will be exploited to separate the living and nonliving components of marine ecosystems; UV bands will also be used to flag and improve atmospheric correction algorithms in the presence of absorbing aerosols. The short-wave infrared (SWIR) is expected to improve atmospheric correction, because the ocean is radiometrically blacker at these wavelengths. This report describes the development of the sensors, including unique capabilities like three-axis polarimetry; the documented uncertainty will be presented in a subsequent report

The development of an on-chip-metering solution

Includes bibliographical references.Energy Measurements Ltd (EML), a joint venture partnership between Siemens and Spescom, manufacture prepaid electricity utility meters for both the local and international markets. Under the brand name CASHPOWER 2000, EML produces single and polyphase prepayment utility meters. Currently, these meters currently utilise a separate module for the measuring of electrical energy. In order to reduce component costs, EML proposed the energy measurement be conducted by the onboard Microcontroller Unit (MCU), a term known as On-Chip-Metering (OCM). It is envisioned that this would quickly translate in an increase in revenue. However, a major concern regarding this has been the increase in the required processor overhead. The CASHPOWER 2000 embedded MCU would be required to conduct all the present metering functionality in addition to the energy measurement. This, together with the cost analysis and compliance with the stipulated IEC1036 regulations, constitute the key criteria in determining the projects viability. This dissertation represents the investigative and development stages of a prototype algorithm and accompanying peripheral hardware as a possible solution for OCM. As part of the preliminary research, several examples of digital power and energy-measurement techniques were investigated. A comparative analysis of these was performed to facilitate the development of a unique solution based on the research conducted. This completed, a prototype was developed and preliminary testing was conducted to determine its compliance with the stipulated regulations for a class 2 meter, as per IEC1O36 specifications

Energy models in data parallel CPU/GPU computations

La tesi affronta il problema della modellazione dei consumi energetici in computazioni data parallel (map) in architetture con GPU. I modelli sviluppati sono utilizzati per valutare il compromesso tra performance e consumi. La tesi include risultati sperimentali che validano sia i modelli sviluppati che la metodologia di ricerca di un compromesso tra prestazioni e consumi

Parallel genetic algorithms in the cloud

2015 - 2016Genetic Algorithms (GAs) are a metaheuristic search technique belonging to the class of Evolutionary Algorithms (EAs). They have been proven to be effective in addressing several problems in many fields but also suffer from scalability issues that may not let them find a valid application for real world problems. Thus, the aim of providing highly scalable GA-based solutions, together with the reduced costs of parallel architectures, motivate the research on Parallel Genetic Algorithms (PGAs). Cloud computing may be a valid option for parallelisation, since there is no need of owning the physical hardware, which can be purchased from cloud providers, for the desired time, quantity and quality. There are different employable cloud technologies and approaches for this purpose, but they all introduce communication overhead. Thus, one might wonder if, and possibly when, specific approaches, environments and models show better performance than sequential versions in terms of execution time and resource usage. This thesis investigates if and when GAs can scale in the cloud using specific approaches. Firstly, Hadoop MapReduce is exploited designing and developinganopensourceframework,i.e.,elephant56, thatreducestheeffortin developing and speed up GAs using three parallel models. The performance of theframeworkisthenevaluatedthroughanempiricalstudy. Secondly, software containers and message queues are employed to develop, deploy and execute PGAs in the cloud and the devised system is evaluated with an empirical study on a commercial cloud provider. Finally, cloud technologies are also exploredfortheparallelisationofotherEAs,designinganddevelopingcCube,a collaborativemicroservicesarchitectureformachinelearningproblems. [edited by author]I Genetic Algorithms (GAs) sono una metaeuristica di ricerca appartenenti alla classe degli Evolutionary Algorithms (EAs). Si sono dimostrati efficaci nel risolvere tanti problemi in svariati campi. Tuttavia, le difficoltà nello scalare spesso evitano che i GAs possano trovare una collocazione efficace per la risoluzione di problemi del mondo reale. Quindi, l’obiettivo di fornire soluzioni basate altamente scalabili, assieme alla riduzione dei costi di architetture parallele, motivano la ricerca sui Parallel Genetic Algorithms (PGAs). Il cloud computing potrebbe essere una valida opzione per la parallelizzazione, dato che non c’è necessità di possedere hardware fisico che può, invece, essere acquistato dai cloud provider, per il tempo desiderato, quantità e qualità. Esistono differenti tecnologie e approcci cloud impiegabili a tal proposito ma, tutti, introducono overhead di computazione. Quindi, ci si può chiedere se, e possibilmente quando, approcci specifici, ambienti e modelli mostrino migliori performance rispetto alle versioni sequenziali, in termini di tempo di esecuzione e uso di risorse. Questa tesi indaga se, e quando, i GAs possono scalare nel cloud utilizzando approcci specifici. Prima di tutto, Hadoop MapReduce è sfruttato per modellare e sviluppare un framework open source, i.e., elephant56, che riduce l’effort nello sviluppo e velocizza i GAs usando tre diversi modelli paralleli. Le performance del framework sono poi valutate attraverso uno studio empirico. Successivamente, i software container e le message queue sono impiegati per sviluppare, distribuire e eseguire PGAs e il sistema ideato valutato, attraverso uno studio empirico, su un cloud provider commerciale. Infine, le tecnologie cloud sono esplorate per la parallelizzazione di altri EAs, ideando e sviluppando cCube, un’architettura a microservizi collaborativa per risolvere problemi di machine learning. [a cura dell'autore]XV n.s