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Journal articles on the topic 'Electronic data control'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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1

Hansen, James V., and Ned C. Hill. "Control and Audit of Electronic Data Interchange." MIS Quarterly 13, no. 4 (December 1989): 403. http://dx.doi.org/10.2307/248724.

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2

Neal, Ted A., Judy Whiteman, and John P. Swenson. "Electronic data interchange for pharmacy inventory control." American Journal of Health-System Pharmacy 47, no. 11 (November 1, 1990): 2483–87. http://dx.doi.org/10.1093/ajhp/47.11.2483.

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Aggarwal, Rajesh, Zabihollah Rezaee, and Ramesh Soni. "INTERNAL CONTROL CONSIDERATIONS FOR GLOBAL ELECTRONIC DATA INTERCHANGE." International Journal of Commerce and Management 8, no. 3/4 (March 1998): 71–84. http://dx.doi.org/10.1108/eb047375.

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Yamashita, Mikio, and Minoru Akiyama, Regular Members. "A data flow control electronic exchange dataflex-1." Electronics and Communications in Japan (Part I: Communications) 68, no. 5 (May 1985): 66–74. http://dx.doi.org/10.1002/ecja.4410680508.

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5

Austin, Timothy P., and Michael J. Farrell. "An Examination of Snapshot Data in Caterpillar Electronic Control Modules." SAE International Journal of Passenger Cars - Mechanical Systems 4, no. 1 (April 12, 2011): 611–35. http://dx.doi.org/10.4271/2011-01-0807.

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6

Braae, M. "A Control Project for Electronics Engineering Students." International Journal of Electrical Engineering & Education 29, no. 4 (October 1992): 359–69. http://dx.doi.org/10.1177/002072099202900412.

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A control project for electronics engineering students The relevance of control theory to electrical engineering can be demonstrated vividly to undergraduate students by its application to the design of linear continuous electronic circuits that control the height of a hovering helicopter, animated on a PC screen. The object of the student project is to design altitude control electronics by using control theory. The flexibility of the PC allows for full data logging and graphic display features as well as giving each student a unique set of parameters.
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Barros, Aluísio, and Cauane Blumenberg. "Electronic data collection in epidemiological research." Applied Clinical Informatics 07, no. 03 (July 2016): 672–81. http://dx.doi.org/10.4338/aci-2016-02-ra-0028.

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SummaryThis paper describes the use of Research Electronic Data Capture (REDCap) to conduct one of the follow-up waves of the 2004 Pelotas birth cohort. The aim is to point out the advantages and limitations of using this electronic data capture environment to collect data and control every step of a longitudinal epidemiological research, specially in terms of time savings and data quality.We used REDCap as the main tool to support the conduction of a birth cohort follow-up. By exploiting several REDCap features, we managed to schedule assessments, collect data, and control the study workflow. To enhance data quality, we developed specific reports and field validations to depict inconsistencies in real time.Using REDCap it was possible to investigate more variables without significant increases on the data collection time, when comparing to a previous birth cohort follow-up. In addition, better data quality was achieved since negligible out of range errors and no validation or missing inconsistencies were identified after applying over 7,000 interviews.Adopting electronic data capture solutions, such as REDCap, in epidemiological research can bring several advantages over traditional paper-based data collection methods. In favor of improving their features, more research groups should migrate from paper to electronic-based epidemiological research.Citation: Blumenberg C, Barros AJD. Electronic data collection in epidemiological research: The use of REDCap in the Pelotas birth cohorts
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Leduc, Ryan J., Yu Wang, and Fahim Ahmed. "Sampled-data supervisory control." Discrete Event Dynamic Systems 24, no. 4 (November 8, 2013): 541–79. http://dx.doi.org/10.1007/s10626-013-0172-4.

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Haibing Lu and Yingjiu Li. "Practical Inference Control for Data Cubes." IEEE Transactions on Dependable and Secure Computing 5, no. 2 (April 2008): 87–98. http://dx.doi.org/10.1109/tdsc.2007.70217.

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Dash, Shatarupa, Bharat J. R. Sahu, Navrati Saxena, and Abhishek Roy. "Flooding Control in Named Data Networking." IETE Technical Review 35, no. 3 (February 8, 2017): 266–74. http://dx.doi.org/10.1080/02564602.2017.1281173.

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11

Seidl, Karsten, Tom Torfs, Patrick A. De Mazière, Gert Van Dijck, Richard Csercsa, Balazs Dombovari, Yohanes Nurcahyo, et al. "Control and data acquisition software for high-density CMOS-based microprobe arrays implementing electronic depth control." Biomedizinische Technik/Biomedical Engineering 55, no. 3 (January 1, 2010): 183–91. http://dx.doi.org/10.1515/bmt.2010.014.

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Fahrner, Achim, Michael Geese, and Armin Happel. "Data compression algorithms for EOL flash programming of automotive electronic control units." ATZelektronik worldwide 5, no. 2 (April 2010): 22–26. http://dx.doi.org/10.1007/bf03242260.

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13

Khan, Usman A., Waheed U. Bajwa, Angelia Nedic, Michael G. Rabbat, and Ali H. Sayed. "Optimization for Data-Driven Learning and Control." Proceedings of the IEEE 108, no. 11 (November 2020): 1863–68. http://dx.doi.org/10.1109/jproc.2020.3031225.

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14

Hagiwara, T. "Optimal Sampled-data Control Systems[Book Reviews]." Proceedings of the IEEE 86, no. 4 (April 1998): 741–42. http://dx.doi.org/10.1109/jproc.1998.663553.

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Hagiwara, T. "Optimal Sampled-data Control Systems [Book Reviews]." Proceedings of the IEEE 86, no. 6 (June 1998): 1293–94. http://dx.doi.org/10.1109/jproc.1998.687843.

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16

Djedidi, Oussama, Mohand A. Djeziri, and Nacer K. M’Sirdi. "Data-Driven Approach for Feature Drift Detection in Embedded Electronic Devices." IFAC-PapersOnLine 51, no. 24 (2018): 1024–29. http://dx.doi.org/10.1016/j.ifacol.2018.09.714.

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Li, Yan Qin, and Chun Fu Li. "Data Acquisition System Design and Data Analysis on Automatic Transmission." Applied Mechanics and Materials 239-240 (December 2012): 861–64. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.861.

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An efficient data acquisition system for electronic automatic transmission test, which makes use of American NI’s data acquisition card, is designed based on Virtual Instrument. The data can be collected by the data acquisition system include automatic transmission oil pressure, solenoid duty, as well as engine speed ，turbine speed ，output shaft speed and throttle degree，and so on. The key problems of the system design are measurement method of automatic transmission solenoid duty, signal conditioning circuit design and multi-signal acquisition synchronization. Test platform is North-Benz dump truck 2627K (Its power-train assembly is replaced), of which transmission and engine use respectively HD4070PR electronic automatic transmission and BF6M1015CP electronic diesel engine. The automatic transmission shift control strategy and oil pressure characteristics of clutch or brake etc can be analyzed through test data acquisition.
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18

Men, Hong, Donglin Chen, Xiaoting Zhang, Jingjing Liu, and Ke Ning. "Data Fusion of Electronic Nose and Electronic Tongue for Detection of Mixed Edible-Oil." Journal of Sensors 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/840685.

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For the problem of the waste of the edible-oil in the food processing, on the premise of food security, they often need to add new edible-oil to the old frying oil which had been used in food processing to control the cost of the production. Due to the fact that the different additive proportion of the oil has different material and different volatile gases, we use fusion technology based on the electronic nose and electronic tongue to detect the blending ratio of the old frying oil and the new edible-oil in this paper. Principal component analysis (PCA) is used to distinguish the different proportion of the old frying oil and new edible-oil; on the other hand we use partial least squares (PLS) to predict the blending ratio of the old frying oil and new edible-oil. Two conclusions were proposed: data fusion of electronic nose and electronic tongue can be used to detect the blending ratio of the old frying oil and new edible-oil; in contrast to single used electronic nose or single used electronic tongue, the detection effect has increased by using data fusion of electronic nose and electronic tongue.
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19

Al-Sunni, F. M., and S. H. Al-Amer. "Robust control of sampled data systems." IEE Proceedings - Control Theory and Applications 145, no. 2 (March 1, 1998): 236–40. http://dx.doi.org/10.1049/ip-cta:19981848.

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20

Poorjam, Amir Hossein, Max A. Little, Jesper Rindom Jensen, and Mads Grasboll Christensen. "Quality Control in Remote Speech Data Collection." IEEE Journal of Selected Topics in Signal Processing 13, no. 2 (May 2019): 236–43. http://dx.doi.org/10.1109/jstsp.2019.2904212.

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21

Moyne, James R., and Dawn M. Tilbury. "The Emergence of Industrial Control Networks for Manufacturing Control, Diagnostics, and Safety Data." Proceedings of the IEEE 95, no. 1 (January 2007): 29–47. http://dx.doi.org/10.1109/jproc.2006.887325.

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Kinoshita, Takuya, and Toru Yamamoto. "Design of a Data-Oriented Cascade Control System." IEEJ Transactions on Electronics, Information and Systems 136, no. 5 (2016): 703–9. http://dx.doi.org/10.1541/ieejeiss.136.703.

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23

Ding, Wenxiu, Zheng Yan, and Robert H. Deng. "Privacy-Preserving Data Processing with Flexible Access Control." IEEE Transactions on Dependable and Secure Computing 17, no. 2 (March 1, 2020): 363–76. http://dx.doi.org/10.1109/tdsc.2017.2786247.

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Nair, Girish N., Fabio Fagnani, Sandro Zampieri, and Robin J. Evans. "Feedback Control Under Data Rate Constraints: An Overview." Proceedings of the IEEE 95, no. 1 (January 2007): 108–37. http://dx.doi.org/10.1109/jproc.2006.887294.

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25

MUKAI, Yuji, Tsuyoshi TERAMOTO, Takashi IGARASHI, and Shigeru OHMORI. "Attempt to Improve Quality Control System of Clinical Trial Data Using Electronic Checklist." Rinsho yakuri/Japanese Journal of Clinical Pharmacology and Therapeutics 43, no. 5 (2012): 353–60. http://dx.doi.org/10.3999/jscpt.43.353.

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Okubo, Yuka, Yoichiro Ashida, Takuya Kinoshita, and Toru Yamamoto. "Experimental Evaluation of a Data-Driven Control System using an Electronic Thermal Regulator." Journal of Robotics, Networking and Artificial Life 5, no. 2 (2018): 89. http://dx.doi.org/10.2991/jrnal.2018.5.2.3.

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27

Timimi, Hazim, Dennis Falzon, Philippe Glaziou, Charalambos Sismanidis, and Katherine Floyd. "WHO guidance on electronic systems to manage data for tuberculosis care and control." Journal of the American Medical Informatics Association 19, no. 6 (November 2012): 939–41. http://dx.doi.org/10.1136/amiajnl-2011-000755.

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Okubo, Yuka, Yoichiro Ashida, Takuya Kinoshita, and Toru Yamamoto. "Experimental Evaluation of a Data-Driven Control System using an Electronic Thermal Regulator." Proceedings of International Conference on Artificial Life and Robotics 23 (February 2, 2018): 314–17. http://dx.doi.org/10.5954/icarob.2018.os3-3.

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29

Huang, Jing-Wen, and Jia-Wen Gao. "How could data integrate with control? A review on data-based control strategy." International Journal of Dynamics and Control 8, no. 4 (September 19, 2020): 1189–99. http://dx.doi.org/10.1007/s40435-020-00688-x.

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30

Woeltje, Keith F., Michael Y. Lin, Michael Klompas, Marc Oliver Wright, Gianna Zuccotti, and William E. Trick. "Data Requirements for Electronic Surveillance of Healthcare-Associated Infections." Infection Control & Hospital Epidemiology 35, no. 9 (September 1, 2014): 1083–91. http://dx.doi.org/10.1086/677623.

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Electronic surveillance for healthcare-associated infections (HAIs) is increasingly widespread. This is driven by multiple factors: a greater burden on hospitals to provide surveillance data to state and national agencies, financial pressures to be more efficient with HAI surveillance, the desire for more objective comparisons between healthcare facilities, and the increasing amount of patient data available electronically. Optimal implementation of electronic surveillance requires that specific information be available to the surveillance systems. This white paper reviews different approaches to electronic surveillance, discusses the specific data elements required for performing surveillance, and considers important issues of data validation.Infect Control Hosp Epidemiol 2014;35(9):1083-1091
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Zhao, Yongbin, Meng Cui, Lijuan Zheng, Rui Zhang, Lili Meng, Dong Gao, and Yu Zhang. "Research on electronic medical record access control based on blockchain." International Journal of Distributed Sensor Networks 15, no. 11 (November 2019): 155014771988933. http://dx.doi.org/10.1177/1550147719889330.

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For the medical industry, there are problems such as poor sharing of medical data, tampering, and leakage of private data. In view of these problems, a blockchain-based electronic medical record access control research scheme based on the role-based access control model is proposed in this article. First, the appropriate access control strategy is adopted to solve the leakage problem of the user’s medical privacy information during the access process. Then, the information entropy technology is used to quantify the medical data, so that the medical data can be effectively and maximally utilized. Using the distributed general ledger characteristics of blockchain and its inherent security attributes, data islands can be eliminated, data sharing among medical systems can be promoted, access records can be prevented from being tampered with, and medical research and precise medical treatment can be better supported. Through this research, not only can user’s medical privacy information protection be realized during the service process but also patients can manage their own medical data autonomously, which is beneficial to privacy protection under the medical data sharing.
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32

Sunny, Oktavia, Anastasia Lidya Maukar, and Ineu Widaningsih Sosodoro. "Perancangan Kapasitas Produksi Produk Electronic Control Unit (Ecu)." JIE Scientific Journal on Research and Application of Industrial System 4, no. 1 (August 6, 2019): 53. http://dx.doi.org/10.33021/jie.v4i1.748.

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The export demand for Electoric Control Unit (ECU) products, which continued to increase by approximately 25% in December 2014, made the company have to review its production capacity. Based on current production capacity data, it is known that the process cycle time is 64.9 seconds while the takt time needed to meet consumer demand is 39.4 seconds for the first production line. With working time included in working hours overtime, this first-line capacity cannot meet production demands. Work measurement is done to get actual time data for capacity calculation. Man machine chart is one of the tools used to determine the production line capacity with the current number of machines and workers. To be able to fulfill the production demand, a capacity design with 2 proposed improvements is carried out, namely: adding a working time of one shift for the first production line or balancing work time between lines. By calculating the results obtained and the costs incurred, the proposal for balancing work time between lines becomes more effective to do.
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Ab Rahman, Ahmad Faiz, Hazlina Selamat, Ahmad Jais Alimin, Mohd Taufiq Muslim, Muhammad Mazizan Msduki, and Nurulaqilla Khamis. "Automotive Real-Time Data Acquisition Using Wi-Fi Connected Embedded System." ELEKTRIKA- Journal of Electrical Engineering 18, no. 3-2 (December 24, 2019): 7–12. http://dx.doi.org/10.11113/elektrika.v18n3-2.189.

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The advancement in embedded systems, which includes the mass deployment of internet-connected electronics, allows the concept of Internet of Things (IoT), to become a reality. This paper discusses one example of how an internet-connected embedded system is utilized in an automotive system. An Electronic Control Unit (ECU), which functions as a control unit in a fuel injection system, are equipped with Wi-Fi capability and installed on 110cc motorcycle. The ECU is connected to multiple sensors that is used by the ECU as part of control system, as well as giving raw data in real time to the server by using Wi-Fi as the communication medium. The server will accumulate data transmitted from ECU by using MQTT protocol, chosen due to its minimal data profile. The data can be visualized through web portal, or opened by any other web-enabled devices. The data collected may also be used later for any other purposes, such as On-Board Diagnostics (OBD) system, etc.
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Ahmed, Bashar Shahir, Mohamed Larabi Ben Maâti, and Mohammed Al-Sarem. "Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence." International Journal of Business Intelligence Research 11, no. 2 (July 2020): 1–10. http://dx.doi.org/10.4018/ijbir.2020070101.

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The rising adoption of e-CRM strategies in marketing and customer relationship management has necessitated to more needs especially where a specific customer segment is targeted and the services are personalized. This paper presents a distributed data mining model using access-control architecture in a bid to realize the needs for an online CRM that intends to deliver web content to a specific group of customers. This hybrid model utilizes the integration of the mobile agent and client server technologies that could easily be updated from the already existing web platforms. The model allows the management team to derive insights from the operations of the system since it focuses on e-personalization and web intelligence hence presenting a better approach for decision support among organizations. To achieve this, a software approach made of access-control functions, data mining algorithms, customer-profiling capability, dynamic web page creation, and a rule-based system is utilized.
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35

Rhee, Chanu, Sameer Kadri, Susan S. Huang, Michael V. Murphy, Lingling Li, Richard Platt, and Michael Klompas. "Objective Sepsis Surveillance Using Electronic Clinical Data." Infection Control & Hospital Epidemiology 37, no. 2 (November 3, 2015): 163–71. http://dx.doi.org/10.1017/ice.2015.264.

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OBJECTIVETo compare the accuracy of surveillance of severe sepsis using electronic health record clinical data vs claims and to compare incidence and mortality trends using both methods.DESIGNWe created an electronic health record–based surveillance definition for severe sepsis using clinical indicators of infection (blood culture and antibiotic orders) and concurrent organ dysfunction (vasopressors, mechanical ventilation, and/or abnormal laboratory values). We reviewed 1,000 randomly selected medical charts to characterize the definition’s accuracy and stability over time compared with a claims-based definition requiring infection and organ dysfunction codes. We compared incidence and mortality trends from 2003–2012 using both methods.SETTINGTwo US academic hospitals.PATIENTSAdult inpatients.RESULTSThe electronic health record–based clinical surveillance definition had stable and high sensitivity over time (77% in 2003–2009 vs 80% in 2012, P=.58) whereas the sensitivity of claims increased (52% in 2003–2009 vs 67% in 2012, P=.02). Positive predictive values for claims and clinical surveillance definitions were comparable (55% vs 53%, P=.65) and stable over time. From 2003 to 2012, severe sepsis incidence imputed from claims rose by 72% (95% CI, 57%–88%) and absolute mortality declined by 5.4% (95% CI, 4.6%–6.7%). In contrast, incidence using the clinical surveillance definition increased by 7.7% (95% CI, −1.1% to 17%) and mortality declined by 1.7% (95% CI, 1.1%–2.3%).CONCLUSIONSSepsis surveillance using clinical data is more sensitive and more stable over time compared with claims and can be done electronically. This may enable more reliable estimates of sepsis burden and trends.Infect. Control Hosp. Epidemiol. 2016;37(2):163–171
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Han, Kyeong-Eun, Kwangjoon Kim, SunMe Kim, and Jonghyun Lee. "Adaptive Data Transmission Control for Multilane-Based Ethernet." ETRI Journal 35, no. 1 (February 1, 2013): 146–49. http://dx.doi.org/10.4218/etrij.13.0212.0169.

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37

Pérez Fernández, Javier, Manuel Alcázar Vargas, Juan M. Velasco García, Juan A. Cabrera Carrillo, and Juan J. Castillo Aguilar. "Low-Cost FPGA-Based Electronic Control Unit for Vehicle Control Systems." Sensors 19, no. 8 (April 17, 2019): 1834. http://dx.doi.org/10.3390/s19081834.

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The development of new control algorithms in vehicles requires high economic resources, mainly due to the use of generic real-time instrumentation and control systems. In this work, we proposed a low-cost electronic control unit (ECU) that could be used for both development and implementation. The proposed electronic system used a hybrid system on chip (SoC) between a field-programmable gate array (FPGA) and an Advanced RISC (reduced instruction set computer) Machine (ARM) processor that allowed the execution of parallel tasks, fulfilling the real-time requirements that vehicle controls demand. Another feature of the proposed electronic system was the recording of measured data, allowing the performance of the implemented algorithm to be evaluated. All this was achieved by using modular programming that, without the need for a real-time operating system, executed the different tasks to be performed, exploiting the parallelism offered by the FPGA as well as the dual core of the ARM processor. This methodology facilitates the transition between the designing, testing, and implementation stages in the vehicle. In addition, our system is programmed with a single binary file that integrates the code of all processors as well as the hardware description of the FPGA, which speeds up the updating process. In order to validate and demonstrate the performance of the proposed electronic system as a tool for the development and implementation of control algorithms in vehicles, a series of tests was carried out on a test bench. Different traction control system (TCS) algorithms were implemented and the results were compared.
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38

Hirata, M., and F. Ueno. "Final-State Control Using Polynomial and Time-Series Data." IEEE Transactions on Magnetics 47, no. 7 (July 2011): 1944–50. http://dx.doi.org/10.1109/tmag.2011.2127462.

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Mu, Chaoxu, and Changyin Sun. "Data-based intelligent modeling and control for nonlinear systems." Frontiers of Electrical and Electronic Engineering in China 6, no. 2 (June 2011): 291–99. http://dx.doi.org/10.1007/s11460-011-0143-1.

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Golab, Wojciech, and Raouf Boutaba. "Admission control in data transfers over lightpaths." IEEE Journal on Selected Areas in Communications 25, no. 6 (August 2007): 102–10. http://dx.doi.org/10.1109/jsac-ocn.2007.024806.

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Leung, G. M. H., T. P. Perry, and B. A. Francis. "Performance analysis of sampled-data control systems." Automatica 27, no. 4 (July 1991): 699–704. http://dx.doi.org/10.1016/0005-1098(91)90060-f.

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Maolanon, Rungroj, Winadda Wongwiriyapan, and Sirapat Pratontep. "Quality Control of Orange Juice Using Electronic Nose." Advanced Materials Research 1131 (December 2015): 242–45. http://dx.doi.org/10.4028/www.scientific.net/amr.1131.242.

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Applications of electronic noses to classify the freshness of food and beverages by mimicking the olfactory perception are becoming widely recognized in food industries. For pasteurized orange juice, packaging and shelf-life are key factors for the quality control, which are generally inspected by the sensory stability and quality (odor, color, texture and taste) of the orange juice. An electronic nose based on five different commercial metal oxide gas sensors, a temperature sensor and a humidity sensor has been designed and constructed to examine the quality of orange juice as subjected to the fermentation process. The duration for a single measurement from an orange juice sample was approximately two minutes. The data acquisition of the voltage responses of the gas sensors were achieved via a microcontroller unit. The data classification was statistically analyzed by the “Principal Component Analysis (PCA)”. The Euclidean distance between two PCA groups was used as an indicator of ethanol concentration. The orange juice was laced with various concentrations of ethanol from 0.1 to 1.0% ethanol to simulate fermented orange juice at different stages. The objective was to characterize the freshness of orange juice by means of the ethanol level from the fermentation process. The results show a distinctive classification of the orange juice for an alcohol concentration lower than 0.1%. Thus the electronic nose offers a rapid, highly sensitive alternative for the quality control process.
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Ceseracciu, A., M. Piemontese, F. S. Tehrani, T. M. Pulliam, and F. Galeazzi. "The BaBar data reconstruction control system." IEEE Transactions on Nuclear Science 52, no. 4 (August 2005): 919–26. http://dx.doi.org/10.1109/tns.2005.852701.

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44

Rose, G. W., V. R. Roth, K. N. Suh, M. Taljaard, C. Van Walraven, and A. J. Forster. "USE OF AN ELECTRONIC DATA WAREHOUSE TO ENHANCE CARDIAC SURGICAL SITE SURVEILLANCE AT A LARGE CANADIAN CENTRE." Clinical & Investigative Medicine 31, no. 4 (August 1, 2008): 21. http://dx.doi.org/10.25011/cim.v31i4.4824.

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Background/Purpose: Surgical site infection surveillance to determineincidence is a key infection control activity. Case detection is labour-intensive, therefore most infection control programs use manual or simple electronic mechanisms to “trigger” chart review. However, such “trigger” mechanisms are also labour-intensive, and often of poor specificity. Our objective is to develop a complex trigger mechanism using data from an electronic data warehouse, to improve specificity of surveillance of surgical site infection compared to current trigger mechanisms. Methods: We will derive an electronic trigger tool for cardiac surgical site infection surveillance using a nested case-control design, among a cohort of all patients undergoing coronary artery bypass grafting, cardiac valve repairor replacement, or heart transplant at the University of Ottawa Heart Institute, from July 1 2004 to June 30 2007. We will perform a systematic literature review to identify potential trigger factors to include in the model, then construct the trigger tool by backwards stepwise logistic regression. The best-fit model will be used to calculate the probability of surgical site infection. We will select the threshold probability to use in surveillance by visual inspection of receiver-operator-characteristic curves. The accuracy of this electronic trigger mechanism will be compared to pre-existing manual and simple electronic mechanisms using relative true positive ratios and relative false positive ratios. Results/Conclusions: We have selected 200 cases of surgical site infection and 541 controls from among 3744 procedures performed during the study period. As of the date ofthis abstract we are still undertaking the systematic review.
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Kabalan, K. Y., A. El-Hajj, and W. Smari. "Nonlinear and sampled data control systems analysis using spreadsheets." IEE Proceedings - Science, Measurement and Technology 143, no. 1 (January 1, 1996): 52–56. http://dx.doi.org/10.1049/ip-smt:19960020.

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Sato, Takao, Takuma Kusakabe, Natsuki Kawaguchi, Nozomu Araki, and Yasuo Konishi. "Data-driven Control for Output-multiple Dual-rate Systems." IEEJ Transactions on Electronics, Information and Systems 139, no. 4 (April 1, 2019): 484–85. http://dx.doi.org/10.1541/ieejeiss.139.484.

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47

Hirner, Tomáš, Peter Farkaš, and Srečko Krile. "One Unequal Error Control Method for Telemetric Data Transmission." Journal of Electrical Engineering 62, no. 3 (May 1, 2011): 158–62. http://dx.doi.org/10.2478/v10187-011-0026-y.

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Abstract:
One Unequal Error Control Method for Telemetric Data TransmissionIn wireless sensor networks (WSN) it is necessary to use very simple codes for transmission of information since the nodes in these networks have usually only limited energy available not only for transmission but also for processing. On the other hand, common codes do not usually take into account the fact that in case of telemetric information the weights of individual orders are not equal and errors in different orders cause different deviations from correct value. In this contribution, new very simple codes for transmission of telemetric information on WSN will be presented, which take into account the above-mentioned requirements. Resulting square deviation will be used as a quality evaluation criterion.
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48

Chen, Qingxia, Renchao Xie, F. Richard Yu, Jiang Liu, Tao Huang, and Yunjie Liu. "Transport Control Strategies in Named Data Networking: A Survey." IEEE Communications Surveys & Tutorials 18, no. 3 (2016): 2052–83. http://dx.doi.org/10.1109/comst.2016.2528164.

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49

Chen, Xiao, Thomas Navidi, and Ram Rajagopal. "Energy resource control via privacy preserving data." Electric Power Systems Research 189 (December 2020): 106719. http://dx.doi.org/10.1016/j.epsr.2020.106719.

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50

Hassan, Ali, Deepjyoti Deka, Michael Chertkov, and Yury Dvorkin. "Data-Driven Learning and Load Ensemble Control." Electric Power Systems Research 189 (December 2020): 106780. http://dx.doi.org/10.1016/j.epsr.2020.106780.

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