3,259 research outputs found

    An Automated, yet Interactive and Portable DB designer

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    Tuning tools attempt to configure a database to achieve optimal performance for a given workload. Selecting an optimal set of physical structures is computationally hard since it involves searching a vast space of possible configurations. Commercial DBMSs offer tools that can address this problem. The usefulness of such tools, however, is limited by their dependence on greedy heuristics, the need for a-priori (offline) knowledge of the workload, and lack of an optimal materialization schedule to get the best out of suggested design features. Moreover, the open source DBMSs do not provide any automated tuning tools. This demonstration introduces a comprehensive physical designer for the PostgreSQL open source DBMS. The tool suggests design features for both offline and online workloads. It provides close to optimal suggestions for indexes for a given workload by modeling the problem as a combinatorial optimization problem and solving it by sophisticated and mature solvers. It also determines the interaction between indexes to suggest an effective materialization strategy for the selected indexes. The tool is interactive as it allows the database administrator (DBA) to suggest a set of candidate features and shows their benefits and interactions visually. For the demonstration we use large realworld scientific datasets and query workloads

    Assessing the compliance of a product with an eco-label: from standards to constraints

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    The new awareness of the consumers regarding environmental issues should allow companies to gain a competitive advantage by obtaining eco-labels which certify the low impact of a product on the environment. Getting such label requires to analyse a product according to rules expressed in natural language which may be difficult to interpret but also to apply when the product is complex. In order to address this problem, we suggest a method aiming at providing support to the user when checking the compliance of a product with an eco-label. The method is applied on an illustrative example of the literature

    Study to determine potential flight applications and human factors design guidelines for voice recognition and synthesis systems

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    A study was conducted to determine potential commercial aircraft flight deck applications and implementation guidelines for voice recognition and synthesis. At first, a survey of voice recognition and synthesis technology was undertaken to develop a working knowledge base. Then, numerous potential aircraft and simulator flight deck voice applications were identified and each proposed application was rated on a number of criteria in order to achieve an overall payoff rating. The potential voice recognition applications fell into five general categories: programming, interrogation, data entry, switch and mode selection, and continuous/time-critical action control. The ratings of the first three categories showed the most promise of being beneficial to flight deck operations. Possible applications of voice synthesis systems were categorized as automatic or pilot selectable and many were rated as being potentially beneficial. In addition, voice system implementation guidelines and pertinent performance criteria are proposed. Finally, the findings of this study are compared with those made in a recent NASA study of a 1995 transport concept

    Procedural Constraint-based Generation for Game Development

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    On-site customer analytics and reporting (OSCAR):a portable clinical data warehouse for the in-house linking of hospital and telehealth data

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    This document conveys the results of the On-Site Customer Analytics and Reporting (OSCAR) project. This nine-month project started on January 2014 and was conducted at Philips Research in the Chronic Disease Management group as part of the H2H Analytics Project. Philips has access to telehealth data from their Philips Motiva tele-monitoring and other services. Previous projects within Philips Re-search provided a data warehouse for Motiva data and a proof-of-concept (DACTyL) solution that demonstrated the linking of hospital and Motiva data and subsequent reporting. Severe limitations with the DACTyL solution resulted in the initiation of OSCAR. A very important one was the unwillingness of hospitals to share personal patient data outside their premises due to stringent privacy policies, while at the same time patient personal data is required in order to link the hospital data with the Motiva data. Equally important is the fact that DACTyL considered the use of only Motiva as a telehealth source and only a single input interface for the hospitals. OSCAR was initiated to propose a suitable architecture and develop a prototype solution, in contrast to the proof-of-concept DACTyL, with the twofold aim to overcome the limitations of DACTyL in order to be deployed in a real-life hospital environment and to expand the scope to an extensible solution that can be used in the future for multiple telehealth services and multiple hospital environments. In the course of the project, a software solution was designed and consequently deployed in the form of a virtual machine. The solution implements a data warehouse that links and hosts the collected hospital and telehealth data. Hospital data are collected with the use of a modular service oriented data collection component by exposing web services described in WSDL that accept configurable XML data messages. ETL processes propagate the data, link, and load it on the OS-CAR data warehouse. Automated reporting is achieved using dash-boards that provide insight into the data stored in the data warehouse. Furthermore, the linked data is available for export to Philips Re-search in de-identified format

    ์ž๋™์ฐจ ์‚ฌ์–‘ ๋ณ€๊ฒฝ์„ ์‹ค์‹œ๊ฐ„ ๋ฐ˜์˜ํ•˜๋Š” ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋””์ž์ธ ์ ‘๊ทผ ๋ฐฉ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(์ง€๋Šฅํ˜•์œตํ•ฉ์‹œ์Šคํ…œ์ „๊ณต), 2020. 8. ๊ณฝ๋…ธ์ค€.The automotive industry is entering a new phase in response to changes in the external environment through the expansion of eco-friendly electric/hydrogen vehicles and the simplification of modules during the manufacturing process. However, in the existing automotive industry, conflicts between structured production guidelines and various stake-holders, who are aligned with periodic production plans, can be problematic. For example, if there is a sudden need to change either production parts or situation-specific designs, it is often difficult for designers to reflect those requirements within the preexisting guidelines. Automotive design includes comprehensive processes that represent the philosophy and ideology of a vehicle, and seeks to derive maximum value from the vehicle specifications. In this study, a system that displays information on parts/module components necessary for real-time design was proposed. Designers will be able to use this system in automotive design processes, based on data from various sources. By applying the system, three channels of information provision were established. These channels will aid in the replacement of specific component parts if an unexpected external problem occurs during the design process, and will help in understanding and using the components in advance. The first approach is to visualize real-time data aggregation in automobile factories using Google Analytics, and to reflect these in self-growing characters to be provided to designers. Through this, it is possible to check production and quality status data in real time without the use of complicated labor resources such as command centers. The second approach is to configure the data flow to be able to recognize and analyze the surrounding situation. This is done by applying the vehicles camera to the CCTV in the inventory and distribution center, as well as the direction inside the vehicle. Therefore, it is possible to identify and record the parts resources and real-time delivery status from the internal camera function without hesitation from existing stakeholders. The final approach is to supply real-time databases of vehicle parts at the site of an accident for on-site repair, using a public API and sensor-based IoT. This allows the designer to obtain information on the behavior of parts to be replaced after accidents involving light contact, so that it can be reflected in the design of the vehicle. The advantage of using these three information channels is that designers can accurately understand and reflect the modules and components that are brought in during the automotive design process. In order to easily compose the interface for the purpose of providing information, the information coming from the three channels is displayed in their respective, case-specific color in the CAD software that designers use in the automobile development process. Its eye tracking usability evaluation makes it easy for business designers to use as well. The improved evaluation process including usability test is also included in this study. The impact of the research is both dashboard application and CAD system as well as data systems from case studies are currently reflected to the design ecosystem of the motors group.์ž๋™์ฐจ ์‚ฐ์—…์€ ์นœํ™˜๊ฒฝ ์ „๊ธฐ/์ˆ˜์†Œ ์ž๋™์ฐจ์˜ ํ™•๋Œ€์™€ ์ œ์กฐ ๊ณต์ •์—์„œ์˜ ๋ชจ๋“ˆ ๋‹จ์ˆœํ™”๋ฅผ ํ†ตํ•ด์„œ ์™ธ๋ถ€ ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์ƒˆ๋กœ์šด ๊ตญ๋ฉด์„ ๋งž์ดํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ธฐ์กด์˜ ์ž๋™์ฐจ ์‚ฐ์—…์—์„œ ๊ตฌ์กฐํ™”๋œ ์ƒ์‚ฐ ๊ฐ€์ด๋“œ๋ผ์ธ๊ณผ ๊ธฐ๊ฐ„ ๋‹จ์œ„ ์ƒ์‚ฐ ๊ณ„ํš์— ๋งž์ถฐ์ง„ ์—ฌ๋Ÿฌ ์ดํ•ด๊ด€๊ณ„์ž๋“ค๊ณผ์˜ ๊ฐˆ๋“ฑ์€ ๋ณ€ํ™”์— ๋Œ€์‘ํ•˜๋Š” ๋ฐฉ์•ˆ์ด ๊ด€์„ฑ๊ณผ ๋ถ€๋”ชํžˆ๋Š” ๋ฌธ์ œ๋กœ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ๊ฐ‘์ž‘์Šค๋Ÿฝ๊ฒŒ ์ƒ์‚ฐ์— ํ•„์š”ํ•œ ๋ถ€ํ’ˆ์„ ๋ณ€๊ฒฝํ•ด์•ผ ํ•˜๊ฑฐ๋‚˜ ํŠน์ • ์ƒํ™ฉ์— ์ ์šฉ๋˜๋Š” ๋””์ž์ธ์„ ๋ณ€๊ฒฝํ•  ๊ฒฝ์šฐ, ์ฃผ์–ด์ง„ ๊ฐ€์ด๋“œ๋ผ์ธ์— ๋”ฐ๋ผ ๋””์ž์ด๋„ˆ๊ฐ€ ์ง์ ‘ ์˜๊ฒฌ์„ ๋ฐ˜์˜ํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ์ž๋™์ฐจ ๋””์ž์ธ์€ ์ฐจ์ข…์˜ ์ฒ ํ•™๊ณผ ์ด๋…์„ ๋‚˜ํƒ€๋‚ด๊ณ  ํ•ด๋‹น ์ฐจ๋Ÿ‰์ œ์›์œผ๋กœ ์ตœ๋Œ€์˜ ๊ฐ€์น˜๋ฅผ ๋Œ์–ด๋‚ด๊ณ ์ž ํ•˜๋Š” ์ข…ํ•ฉ์ ์ธ ๊ณผ์ •์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์—ฌ๋Ÿฌ ์›์ฒœ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ž๋™์ฐจ ๋””์ž์ธ ๊ณผ์ •์—์„œ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๋””์ž์ธ์— ํ•„์š”ํ•œ ๋ถ€ํ’ˆ/๋ชจ๋“ˆ ๊ตฌ์„ฑ์š”์†Œ๋“ค์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ํ‘œ์‹œํ•ด์ฃผ๋Š” ์‹œ์Šคํ…œ์„ ๊ณ ์•ˆํ•˜์˜€๋‹ค. ์ด๋ฅผ ์ ์šฉํ•˜์—ฌ ์ž๋™์ฐจ ๋””์ž์ธ ๊ณผ์ •์—์„œ ์˜ˆ์ƒ ๋ชปํ•œ ์™ธ๋ถ€ ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ–ˆ์„ ๋•Œ ์„ ํƒํ•  ๊ตฌ์„ฑ ๋ถ€ํ’ˆ์„ ๋Œ€์ฒดํ•˜๊ฑฐ๋‚˜ ์‚ฌ์ „์— ํ•ด๋‹น ๋ถ€ํ’ˆ์„ ์ดํ•ดํ•˜๊ณ  ๋””์ž์ธ์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ์„ธ ๊ฐ€์ง€ ์ •๋ณด ์ œ๊ณต ์ฑ„๋„์„ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ์ž๋™์ฐจ ๊ณต์žฅ ๋‚ด ์‹ค์‹œ๊ฐ„ ๋ฐ์ดํ„ฐ ์ง‘๊ณ„๋ฅผ Google Analytics๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์‹œ๊ฐํ™”ํ•˜๊ณ , ์ด๋ฅผ ๊ณต์žฅ ์ž์ฒด์˜ ์ž๊ฐ€ ์„ฑ์žฅ ์บ๋ฆญํ„ฐ์— ๋ฐ˜์˜ํ•˜์—ฌ ๋””์ž์ด๋„ˆ์—๊ฒŒ ์ œ๊ณตํ•˜๋Š” ๋ฐฉ์‹์ด๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ข…ํ•ฉ์ƒํ™ฉ์‹ค ๋“ฑ์˜ ๋ณต์žกํ•œ ์ธ๋ ฅ ์ฒด๊ณ„ ์—†์ด๋„ ์ƒ์‚ฐ ๋ฐ ํ’ˆ์งˆ ํ˜„ํ™ฉ ๋ฐ์ดํ„ฐ๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ํ™•์ธ ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋Š” ์ฐจ๋Ÿ‰์šฉ ์ฃผ์ฐจ๋ณด์กฐ ์„ผ์„œ ์นด๋ฉ”๋ผ๋ฅผ ์ฐจ๋Ÿ‰ ๋ถ€์ฐฉ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ธ๋ฒคํ† ๋ฆฌ์™€ ๋ฌผ๋ฅ˜์„ผํ„ฐ์˜ CCTV์—๋„ ์ ์šฉํ•˜์—ฌ ์ฃผ๋ณ€์ƒํ™ฉ์„ ์ธ์‹ํ•˜๊ณ  ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์ฐจ๋Ÿ‰์˜ ์กฐ๋ฆฝ ์ƒ์‚ฐ ๋‹จ๊ณ„์—์„œ ๋ถ€ํ’ˆ ๋‹จ์œ„์˜ ์ด๋™, ์šด์†ก, ์ถœํ•˜๋ฅผ ๊ฑฐ์ณ ์™„์„ฑ์ฐจ์˜ ์ฃผํ–‰ ๋‹จ๊ณ„์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ๋ฐ์ดํ„ฐ ํ๋ฆ„์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด ๋””์ž์ธ ๋ถ€๋ฌธ์— ํ•„์š”ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ํ™œ์šฉ๋˜์—ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ธฐ์กด ์ดํ•ด๊ด€๊ณ„์ž๋“ค์˜ ํฐ ๋ฐ˜๋ฐœ ์—†์ด ๋‚ด๋ถ€์˜ ์นด๋ฉ”๋ผ ๊ธฐ๋Šฅ์œผ๋กœ๋ถ€ํ„ฐ ๋ถ€ํ’ˆ ๋ฆฌ์†Œ์Šค์™€ ์šด์†ก ์ƒํƒœ๋ฅผ ์‹ค์‹œ๊ฐ„ ํŒŒ์•… ๋ฐ ๊ธฐ๋ก ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๊ณต๊ณต API์™€ ์„ผ์„œ ๊ธฐ๋ฐ˜์˜ ์‚ฌ๋ฌผ์ธํ„ฐ๋„ท์„ ํ™œ์šฉํ•ด์„œ ๋„๋กœ ์œ„ ์ฐจ๋Ÿ‰ ์‚ฌ๊ณ ๊ฐ€ ๋ฐœ์ƒํ•œ ์œ„์น˜์—์„œ์˜ ํ˜„์žฅ ์ˆ˜๋ฆฌ๋ฅผ ์œ„ํ•œ ์ฐจ๋Ÿ‰ ๋ถ€ํ’ˆ ์ฆ‰์‹œ ์ˆ˜๊ธ‰ ๋ฐ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šคํ™” ๋ฐฉ๋ฒ•๋„ ๊ฐœ๋ฐœ ๋˜์—ˆ๋‹ค. ์ด๋Š” ๋””์ž์ด๋„ˆ๋กœ ํ•˜์—ฌ๊ธˆ ๊ฐ€๋ฒผ์šด ์ ‘์ด‰ ์‚ฌ๊ณ ์—์„œ์˜ ๋ถ€ํ’ˆ ๊ต์ฒด ํ–‰ํƒœ์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์–ป๊ฒŒ ํ•˜์—ฌ ์ฐจ๋Ÿ‰์˜ ๋””์ž์ธ์— ๋ฐ˜์˜ ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ด ์„ธ ๊ฐ€์ง€ ์ •๋ณด ์ œ๊ณต ์ฑ„๋„์„ ํ™œ์šฉํ•  ๊ฒฝ์šฐ, ์ž๋™์ฐจ ๋””์ž์ธ ๊ณผ์ •์—์„œ ๋ถˆ๋Ÿฌ๋“ค์—ฌ์˜ค๋Š” ๋ถ€ํ’ˆ ๋ฐ ๋ชจ๋“ˆ์˜ ๊ตฌ์„ฑ ์š”์†Œ๋“ค์„ ๋””์ž์ด๋„ˆ๊ฐ€ ์ •ํ™•ํžˆ ์•Œ๊ณ  ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ์ด ๋ถ€๊ฐ๋˜์—ˆ๋‹ค. ์ •๋ณด ์ œ๊ณต์˜ ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ์‰ฝ๊ฒŒ ๊ตฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ, ์‹ค์ œ๋กœ ๋””์ž์ด๋„ˆ๋“ค์ด ์ž๋™์ฐจ ๊ฐœ๋ฐœ ๊ณผ์ •์—์„œ ๋””์ž์ธ ํ”„๋กœ์„ธ์Šค ์ƒ์—์„œ ํ™œ์šฉํ•˜๋Š” CAD software์— ์„ธ ๊ฐ€์ง€ ์ฑ„๋„๋“ค๋กœ๋ถ€ํ„ฐ ๋“ค์–ด์˜ค๋Š” ์ •๋ณด๋ฅผ ์‚ฌ๋ก€๋ณ„ ์ปฌ๋Ÿฌ๋กœ ํ‘œ์‹œํ•˜๊ณ , ์ด๋ฅผ ์‹œ์„ ์ถ”์  ์‚ฌ์šฉ์„ฑ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ํ˜„์—… ๋””์ž์ด๋„ˆ๋“ค์ด ์‚ฌ์šฉํ•˜๊ธฐ ์‰ฝ๊ฒŒ ๊ฐœ์„ ํ•œ ๊ณผ์ •๋„ ๋ณธ ์—ฐ๊ตฌ์— ํฌํ•จ์‹œ์ผœ ์„ค๋ช…ํ•˜์˜€๋‹ค.1 Introduction 1 1.1 Research Background 1 1.2 Objective and Scope 2 1.3 Environmental Changes 3 1.4 Research Method 3 1.4.1 Causal Inference with Graphical Model 3 1.4.2 Design Thinking Methodology with Co-Evolution 4 1.4.3 Required Resources 4 1.5 Research Flow 4 2 Data-driven Design 7 2.1 Big Data and Data Management 6 2.1.1 Artificial Intelligence and Data Economy 6 2.1.2 API (Application Programming Interface) 7 2.1.3 AI driven Data Management for Designer 7 2.2 Datatype from Automotive Industry 8 2.2.1 Data-driven Management in Automotive Industry 8 2.2.2 Automotive Parts Case Studies 8 2.2.3 Parameter for Generative Design 9 2.3 Examples of Data-driven Design 9 2.3.1 Responsive-reactive 9 2.3.2 Dynamic Document Design 9 2.3.3 Insignts from Data-driven Design 10 3 Benchmark of Data-driven Automotive Design 12 3.1 Method of Global Benchmarking 11 3.2 Automotive Design 11 3.2.1 HMI Design and UI/UX 11 3.2.2 Hardware Design 12 3.2.3 Software Design 12 3.2.4 Convergence Design Process Model 13 3.3 Component Design Management 14 4 Vehicle Specification Design in Mobility Industry 16 4.1 Definition of Vehicle Specification 16 4.2 Field Study 17 4.3 Hypothesis 18 5 Three Preliminary Practical Case Studies for Vehicle Specification to Datadriven 21 5.1 Production Level 31 5.1.1 Background and Input 31 5.1.2 Data Process from Inventory to Designer 41 5.1.3 Output to Designer 51 5.2 Delivery Level 61 5.2.1 Background and Input 61 5.2.2 Data Process from Inventory to Designer 71 5.2.3 Output to Designer 81 5.3 Consumer Level 91 5.3.1 Background and Input 91 5.3.2 Data Process from Inventory to Designer 101 5.3.3 Output to Designer 111 6 Two Applications for Vehicle Designer 86 6.1 Real-time Dashboard DB for Decision Making 123 6.1.1 Searchable Infographic as a Designer's Tool 123 6.1.2 Scope and Method 123 6.1.3 Implementation 123 6.1.4 Result 124 6.1.5 Evaluation 124 6.1.6 Summary 124 6.2 Application to CAD for vehicle designer 124 6.2.1 CAD as a Designer's Tool 124 6.2.2 Scope and Method 125 6.2.3 Implementation and the Display of the CAD Software 125 6.2.4 Result 125 6.2.5 Evaluation: Usability Test with Eyetracking 126 6.2.6 Summary 128 7 Conclusion 96 7.1 Summary of Case Studies and Application Release 129 7.2 Impact of the Research 130 7.3 Further Study 131Docto

    Soil Moisture Sensor

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    Since water is an important resource and not all communities around the world can afford to be liberal with their water needs; it has become important to use available water as efficiently as possible, especially in agriculture. For the purpose of reducing the overwatering of crops, an unattended ground moisture sensor can be implemented to measure the moisture level in the soil surrounding the plants. This will allow a farmer to know when to water or stop watering crops. For convenience, the moisture data information should be transmitted wirelessly to the user. The design of an unattended ground moisture sensor and wireless communication/user interface system is discussed. The sensor design consists of a Wheatstone bridge for determining the resistance of the soil, followed by a differential amplifier for converting the measured resistance into a voltage. This is done because there exists a correlation between moisture and resistance. This voltage is interpreted by a micro controller as moisture data and sent wirelessly to a Lora receiver. The receiver then relays that information to the user via a mobile or web based app

    Proceedings of the 1994 Monterey Workshop, Increasing the Practical Impact of Formal Methods for Computer-Aided Software Development: Evolution Control for Large Software Systems Techniques for Integrating Software Development Environments

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    Office of Naval Research, Advanced Research Projects Agency, Air Force Office of Scientific Research, Army Research Office, Naval Postgraduate School, National Science Foundatio

    ATLAS Commander: an ATLAS production tool

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    This paper describes the ATLAS production tool AtCom (for ATLAS Commander). The purpose of the tool is to automate as much as possible the task of a production manager: defining and submitting jobs in large quantities, following up upon their execution, scanning log files for known and unknown errors, updating the various ATLAS bookkeeping databases on successful completion of a job whilst cleaning up and resubmitting otherwise. The design of AtCom is modular, separating the generic basic job management functionality from the interactions with the various databases on the one hand, and the computing systems on the other hand. Given the near future reality of different flavors of computing systems (legacy and GRID) deployed concurrently at the various, or even a single ATLAS site, AtCom allows several of them to be used at the same time transparently.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics Conference (CHEP03), La Jolla, Ca, USA, March 2003, 7 pages, PDF, PSN : MONT00
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