Q-Learning Based Dynamic Routing Protocol with Low Latency and High Reliability for Medical Data Collection System Using Body Area Networks

Author(s):  
Yoo-Jin Choi ◽  
Jeong-Hoon Kwon ◽  
Ho-Jeong Na ◽  
Sang-Jo Yoo
2011 ◽  
Vol 145 ◽  
pp. 374-378
Author(s):  
Yea Dat Chuah ◽  
Ryoichi Komiya ◽  
Bok Min Goi

This paper reports the experimental of human walking style data collection system to know abnormal walking styles. The collection system is composed of 3D accelerator and body area network. Data is stored in a PC for the comparison of the normal and abnormal walking style differences. Based on the differences, we are going to define abnormal walking style in terms of 3D acceleration amplitudes.


2012 ◽  
Vol 249-250 ◽  
pp. 1259-1263
Author(s):  
Shao Zhong Hu

This paper proposes an approach that uses serial communication based on the Windows system to collect the manufacturing data through multi-thread mechanism. The data collection system adopts two levels control architecture. The down streaming level is based on several data collection terminals that use ARM chips as central processing units (CPU). The upper level is consisted of personal computers which are connected to the communication network through RS-422, aiming to transfer the data on one side. On the other side, these computers interact with data server, aiming to transmit the data to the processing center. SQL Server 2000 has been utilized as the data manager within this system which uses multi-thread technology to continuously enquiry the data collection terminals (DCTs). After the practical implementation, this system is qualified in the manufacturing environment with high reliability, real-time feature as well as quick responses to the users when they operating the DCTs.


1976 ◽  
Vol 15 (01) ◽  
pp. 21-28 ◽  
Author(s):  
Carmen A. Scudiero ◽  
Ruth L. Wong

A free text data collection system has been developed at the University of Illinois utilizing single word, syntax free dictionary lookup to process data for retrieval. The source document for the system is the Surgical Pathology Request and Report form. To date 12,653 documents have been entered into the system.The free text data was used to create an IRS (Information Retrieval System) database. A program to interrogate this database has been developed to numerically coded operative procedures. A total of 16,519 procedures records were generated. One and nine tenths percent of the procedures could not be fitted into any procedures category; 6.1% could not be specifically coded, while 92% were coded into specific categories. A system of PL/1 programs has been developed to facilitate manual editing of these records, which can be performed in a reasonable length of time (1 week). This manual check reveals that these 92% were coded with precision = 0.931 and recall = 0.924. Correction of the readily correctable errors could improve these figures to precision = 0.977 and recall = 0.987. Syntax errors were relatively unimportant in the overall coding process, but did introduce significant error in some categories, such as when right-left-bilateral distinction was attempted.The coded file that has been constructed will be used as an input file to a gynecological disease/PAP smear correlation system. The outputs of this system will include retrospective information on the natural history of selected diseases and a patient log providing information to the clinician on patient follow-up.Thus a free text data collection system can be utilized to produce numerically coded files of reasonable accuracy. Further, these files can be used as a source of useful information both for the clinician and for the medical researcher.


Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan

Monitoring data at the Ugandan Salama SHIELD Foundation revealed perfect repayment rates in its microfinance program. But rather than take these data at face value, a diligent program officer set out to determine if the data might be concealing other stories. In his efforts to investigate the truth behind the data, he made a number of decisions about what data to collect—and, importantly, what not to. But, as this case demonstrates, actionable data is only half the story; right-fit resources and systems are necessary to turn data into action. Readers will think critically about what data are necessary to answer key operational questions and will design data collection instruments to deliver these data. They will also consider ways of applying the CART principles to strengthen the data collection system and determine where the organization should focus its monitoring efforts.


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