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.

Odin: Context-Aware Middleware for Mobile Services

Abstract—Mobile devices such as smart phones are increas-ing permeating society. With strides in computational power, coupled with the ability to connect to other small devices, smart phones are able to host novel services. To address the repetitive problems associated with mobile service development, namely service reachability, scalability and availability, we have developed Odin, which is a middleware platform for mobile service provisioning. Beyond providing a provisioning solution, Odin conserves scarce resources such as network bandwidth and device power supply. However, Odin has previously lacked an ability to take into account operational context. In this paper, we present context-aware extensions to Odin that further optimise resource usage. Augmented with support for context types that include location, performance, power and network, Odin is able to propagate context information to applications and dynamic adapt the middleware’s behaviour. Novelty of the work lies in a solution whose device overhead is very low, and one that offers a coherent approach to context dis-semination and adaptation. Based on quantitative evaluation, context-aware Odin’s low overhead is demonstrated along with significant gains in resource conservation. I

Opinion mining of Phone Sitting Feature

This report aims to evaluate opinion mining of customers about phone sitting features in cell phones in different brands across the world by using data mining techniques. Therefore, Data for the report has been collected from data scrapping in Qoura which collects opinion of customers. The collected information from the data base has evaluated by using sentiment analysis. The collected information from Qoura transforms the content and extracts data from API’s. In relation to this, information obtains from the research contributed for providing useful information for these features. The collected information from application scrapping has been analysed through sentiment analysis. The application used in this project is python for analysing the data. This working contributed for developing understanding of customer’s feedback on this features through which they get benefited from it. The outcomes of the project provide information that most of the customer provides positive sentiments about Mobile phone sitting features by using data scraping method as this method provide the process of having data in addition On the other hand, negative sentiments of customers specify that they prefer not limit screen time as it support for managing their work. This contributed for developing an understanding of mobile phone sitting feature and to know users consumptions

DSP.Ear: Leveraging co-processor support for continuous audio sensing on smartphones

The rapidly growing adoption of sensor-enabled smartphones has greatly fueled the proliferation of applications that use phone sensors to monitor user behavior. A central sensor among these is the microphone which enables, for instance, the detection of valence in speech, or the identification of speakers. Deploying multiple of these applications on a mobile device to continuously monitor the audio environment allows for the acquisition of a diverse range of sound-related contextual inferences. However, the cumulative processing burden critically impacts the phone battery. To address this problem, we propose DSP.Ear - an integrated sensing system that takes advantage of the latest low-power DSP co-processor technology in commodity mobile devices to enable the continuous and simultaneous operation of multiple established algorithms that perform complex audio inferences. The system extracts emotions from voice, estimates the number of people in a room, identifies the speakers, and detects commonly found ambient sounds, while critically incurring little overhead to the device battery. This is achieved through a series of pipeline optimizations that allow the computation to remain largely on the DSP. Through detailed evaluation of our prototype implementation we show that, by exploiting a smartphone's co-processor, DSP.Ear achieves a 3 to 7 times increase in the battery lifetime compared to a solution that uses only the phone's main processor. In addition, DSP.Ear is 2 to 3 times more power efficient than a naive DSP solution without optimizations. We further analyze a large-scale dataset from 1320 Android users to show that in about 80-90% of the daily usage instances DSP.Ear is able to sustain a full day of operation (even in the presence of other smartphone workloads) with a single battery charge.This work was supported by Microsoft Research through its PhD Scholarship Program.This is the author's accepted manuscript. The final version is available from ACM in the proceedings of the ACM Conference on Embedded Networked Sensor Systems: http://dl.acm.org/citation.cfm?id=2668349

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Offloading for Mobile Device Performance Improvement

Mobile devices are increasingly becoming part of everyday life. These include smart phones, tablets, wearable devices etc. Due to their mobility aspect, they are always constrained in their size and weight, which limits their resource capacity, e.g. processing power, and battery life. One possible solution for augmentation of such resource-constrained devices is through efficient usage of their surrounding resources, i.e. using some offloading technique. This paper studies how offloading of tasks to the surrounding resources affects on both the performance of task execution as well as the battery life of the mobile device. Two mobile phones and two tablets (from two different manufacturers) are studied in the experiments to find out the impact of the device characteristics. Two computationally demanding tasks, namely image processing and encryption/decryption, are used in these experiments. These results are compared to our earlier results on mobile devices of previous generations. We assumed that the increased computing power of new devices would make offloading obsolete. Our results show gains both in energy saving and in computational performance with these mobile devices. The comparison to our earlier results show that the performance increase of newer mobile device generations has not diminished the benefits of offloading. These results are in line with results presented in literature and they show that the offloading could offer a viable approach for resource augmentation of mobile devices towards edge/fog resources emphasized by the new 5G technology

A framework for implementing formally verified resource-bounded smart space systems

© 2017, Springer Science+Business Media New York. Context-aware computing is a mobile computing paradigm that helps designing and implementing next generation smart applications, where personalized devices interact with users in smart environments. Development of such applications is inherently complex due to these applications adapt to changing contextual information and they often run on resource-bounded devices. Most of the existing context-aware development frameworks are centralized, adopt client–server architecture, and do not consider resource limitations of context-aware devices. This paper presents a systematic framework to modelling and implementation of resource-bounded multi-agent context-aware systems on Android devices. The proposed framework makes use of semantic technologies for context modelling and reasoning about resource-bounded context-aware agents, Android powered smartphones as development platform, a suitable communication model and declarative rule-based programming as a preferred development language

Promoting product longevity. How can the EU product safety and compliance framework help promote product durability and tackle planned obsolescence, foster the production of more sustainable products, and achieve more transparent supply chains for consumers?

Product longevity can play a useful role in achieving the Paris Agreement goals – material efficiency is an important contributor to energy efficiency and is also important in its own right. The product safety and compliance instruments available at European level can contribute to these efforts, if wisely applied. This document was prepared for Policy Department A at the request of the IMCO Committee