Spark (mathematics)

About: Spark (mathematics) is a research topic. Over the lifetime, 7304 publications have been published within this topic receiving 63322 citations.

Technique for the rapid, accurate and strain-free machining of metallic single crystals

Abstract: Spark planing, a new spark erosion method for the production of flat smooth metal surfaces, is described and its application to single metal crystals and to metallographic preparation is illustrated. The use of conventional spark erosion methods for crystal cutting and forming is also described. The spark planing technique is far more rapid and accurate than chemical or electrochemical machining and it causes very much less damage to the surface than the most careful grinding operation. Spark planed surfaces have been examined by x-ray diffraction, reflection electron microscopy and a Talysurf tracer: these results are presented and the nature and extent of surface damage to various metals is discussed.

Ultrawideband Signals in Medicine [Life Sciences]

Abstract: In this column, we aimed to spark interest in the medical uses of UWB signals. As illustrated throughout the article, the role of the signal processing community is crucial for the improvement of existing applications and the search for novel procedures for diagnosing and treating effectively life-threatening diseases. Therefore, further research in this field is encouraged.

Scalable aggregation predictive analytics : a query-driven machine learning approach

Abstract: We introduce a predictive modeling solution that provides high quality predictive analytics over aggregation queries in Big Data environments. Our predictive methodology is generally applicable in environments in which large-scale data owners may or may not restrict access to their data and allow only aggregation operators like COUNT to be executed over their data. In this context, our methodology is based on historical queries and their answers to accurately predict ad-hoc queries’ answers. We focus on the widely used set-cardinality, i.e., COUNT, aggregation query, as COUNT is a fundamental operator for both internal data system optimizations and for aggregation-oriented data exploration and predictive analytics. We contribute a novel, query-driven Machine Learning (ML) model whose goals are to: (i) learn the query-answer space from past issued queries, (ii) associate the query space with local linear regression & associative function estimators, (iii) define query similarity, and (iv) predict the cardinality of the answer set of unseen incoming queries, referred to the Set Cardinality Prediction (SCP) problem. Our ML model incorporates incremental ML algorithms for ensuring high quality prediction results. The significance of contribution lies in that it (i) is the only query-driven solution applicable over general Big Data environments, which include restricted-access data, (ii) offers incremental learning adjusted for arriving ad-hoc queries, which is well suited for query-driven data exploration, and (iii) offers a performance (in terms of scalability, SCP accuracy, processing time, and memory requirements) that is superior to data-centric approaches. We provide a comprehensive performance evaluation of our model evaluating its sensitivity, scalability and efficiency for quality predictive analytics. In addition, we report on the development and incorporation of our ML model in Spark showing its superior performance compared to the Spark’s COUNT method.

Reinvestigation of Flammability Limits Measurement of Methane by the Conventional Vessel Method with AC Discharge Ignition

Abstract: The flammability limits of methane have been reinvestigated by using the AC discharge ignition method. This work has partly been motivated by the necessity of evaluating the flammability characteristics of CFC alternatives. In the study, the effects of spark duration time and spark gap on the flammability limits of methane have extensively been explored. The resulting flammable range tends to become wide if the spark duration time is too long and/or the spark gap is too large. The effects of inadequate spark duration time and spark gap are exaggerated if the height of the experimental vessel is too small. As a result, the spark duration time of 0·1–0·2 sec combined with the spark gap of 6–8 mm has been found to make an optimum condition for the correct measurement of the flammability limits by the AC discharge ignition method.