Tools and techniques for evaluating control architecture

Author(s):  
J.R. James ◽  
R. McClain
1995 ◽  
Vol 34 (05) ◽  
pp. 475-488
Author(s):  
B. Seroussi ◽  
J. F. Boisvieux ◽  
V. Morice

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.


Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.


Author(s):  
Zhigang Song ◽  
Jochonia Nxumalo ◽  
Manuel Villalobos ◽  
Sweta Pendyala

Abstract Pin leakage continues to be on the list of top yield detractors for microelectronics devices. It is simply manifested as elevated current with one pin or several pins during pin continuity test. Although many techniques are capable to globally localize the fault of pin leakage, root cause analysis and identification for it are still very challenging with today’s advanced failure analysis tools and techniques. It is because pin leakage can be caused by any type of defect, at any layer in the device and at any process step. This paper presents a case study to demonstrate how to combine multiple techniques to accurately identify the root cause of a pin leakage issue for a device manufactured using advanced technology node. The root cause was identified as under-etch issue during P+ implantation hard mask opening for ESD protection diode, causing P+ implantation missing, which was responsible for the nearly ohmic type pin leakage.


Author(s):  
Clifford Howard ◽  
Anusha Weerakoon ◽  
Diana M. Mitro ◽  
Dawn Glaeser

Abstract OBIRCH analysis is a useful technique for defect localization not only for parametric failures, but also for functional analysis. However, OBIRCH results do not always identify the exact defect location. OBIRCH analysis results must be used in conjunction with other analysis tools and techniques to successfully identify defect locations.


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