Towards cost-efficient prospection and 3D visualization of underwater structures using compact ROVs

The deployment of Remotely Operated Vehicles (ROV) for underwater prospection and 3D visualization has grown significantly in civil applications for a few decades. The demand for a wide range of optical and physical parameters of underwater environments is explained by an increasing complexity of the monitoring requirements of these environments. The prospection of engineering constructions (e.g. quay walls or enclosure doors) and underwater heritage (e.g. wrecks or sunken structures) heavily relies on ROV systems. Furthermore, ROVs offer a very flexible platform to measure the chemical content of the water. The biggest bottleneck of currently available ROVs is the cost of the systems. This constrains the availability of ROVs to a limited number of companies and institutes. Fortunately, as with the recent introduction of cost-efficient Unmanned Aerial Vehicles on the consumer market, a parallel development is expected for ROVs. The ability to participate in this new field of expertise by building Do It Yourself (DIY) kits and by adapting and adding on-demand features to the platform will increase the range of this new technology. In this paper, the construction of a DIY OpenROV kit and its implementation in bathymetric research projects are elaborated. The original platform contains a modified webcam for visual underwater prospection and a Micro ElectroMechanical System (MEMS) based depth sensor, allowing relative positioning. However, the performance of the standard camera is limited and an absolute positioning system is absent. It is expected that 3D visualizations with conventional photogrammetric qualities are limited with the current system. Therefore, modifications to improve the standard platform are foreseen, allowing the development of a cost-efficient underwater platform. Preliminary results and expectations on these challenges are reported in this paper

Measuring the software process and product: Lessons learned in the SEL

The software development process and product can and should be measured. The software measurement process at the Software Engineering Laboratory (SEL) has taught a major lesson: develop a goal-driven paradigm (also characterized as a goal/question/metric paradigm) for data collection. Project analysis under this paradigm leads to a design for evaluating and improving the methodology of software development and maintenance

Methods of Technical Prognostics Applicable to Embedded Systems

Hlavní cílem dizertace je poskytnutí uceleného pohledu na problematiku technické prognostiky, která nachází uplatnění v tzv. prediktivní údržbě založené na trvalém monitorování zařízení a odhadu úrovně degradace systému či jeho zbývající životnosti a to zejména v oblasti komplexních zařízení a strojů. V současnosti je technická diagnostika poměrně dobře zmapovaná a reálně nasazená na rozdíl od technické prognostiky, která je stále rozvíjejícím se oborem, který ovšem postrádá větší množství reálných aplikaci a navíc ne všechny metody jsou dostatečně přesné a aplikovatelné pro embedded systémy. Dizertační práce přináší přehled základních metod použitelných pro účely predikce zbývající užitné životnosti, jsou zde popsány metriky pomocí, kterých je možné jednotlivé přístupy porovnávat ať už z pohledu přesnosti, ale také i z pohledu výpočetní náročnosti. Jedno z dizertačních jader tvoří doporučení a postup pro výběr vhodné prognostické metody s ohledem na prognostická kritéria. Dalším dizertačním jádrem je představení tzv. částicového filtrovaní (particle filtering) vhodné pro model-based prognostiku s ověřením jejich implementace a porovnáním. Hlavní dizertační jádro reprezentuje případovou studii pro velmi aktuální téma prognostiky Li-Ion baterii s ohledem na trvalé monitorování. Případová studie demonstruje proces prognostiky založené na modelu a srovnává možné přístupy jednak pro odhad doby před vybitím baterie, ale také sleduje možné vlivy na degradaci baterie. Součástí práce je základní ověření modelu Li-Ion baterie a návrh prognostického procesu.The main aim of the thesis is to provide a comprehensive overview of technical prognosis, which is applied in the condition based maintenance, based on continuous device monitoring and remaining useful life estimation, especially in the field of complex equipment and machinery. Nowadays technical prognosis is still evolving discipline with limited number of real applications and is not so well developed as technical diagnostics, which is fairly well mapped and deployed in real systems. Thesis provides an overview of basic methods applicable for prediction of remaining useful life, metrics, which can help to compare the different approaches both in terms of accuracy and in terms of computational/deployment cost. One of the research cores consists of recommendations and guide for selecting the appropriate forecasting method with regard to the prognostic criteria. Second thesis research core provides description and applicability of particle filtering framework suitable for model-based forecasting. Verification of their implementation and comparison is provided. The main research topic of the thesis provides a case study for a very actual Li-Ion battery health monitoring and prognostics with respect to continuous monitoring. The case study demonstrates the prognostic process based on the model and compares the possible approaches for estimating both the runtime and capacity fade. Proposed methodology is verified on real measured data.

Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper