Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/519225
Title: | Towards reconstruction quality of biosignals from its compressive measurements for wban based health monitoring environment a deep learning approach |
Researcher: | Evangelin,D |
Guide(s): | Rajakumar,G |
Keywords: | Autoencoder Compressed Sensing Information And Communication Engineering |
University: | Anna University |
Completed Date: | 2023 |
Abstract: | iii newlineABSTRACT newlineMiniaturized sensors equipped with wireless communication and newlinesensing technologies are becoming more common due to the rapid newlineimprovements in wireless communication and micro-electromechanical newlinesystem-based sensor technology. Due to this mechanism, health care newlineorganization is highly benefitted from this technology. The electrical impulses newlineof a specific human organ can be detected by this miniature sensor, which can newlinethen communicate the information to another device. So, the elderly and newlinechronically ill can be treated instantly and can get health tip at their doorsteps newlinethrough sensor based wearable system. Wireless wearable device plays an newlineimportant role in sensor based wearable technology as it is responsible for newlinecollecting, processing and transmitting to the remote clinician throughout the newlineday. Since the wearables are miniaturized in nature, its battery has been newlineconstrained to the energy level. So, this prolonged monitoring may decline newlinebattery life of wearable device. newline |
Pagination: | xxii, 133p. |
URI: | http://hdl.handle.net/10603/519225 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 24.98 kB | Adobe PDF | View/Open |
02_prelim_pages.pdf | 690.65 kB | Adobe PDF | View/Open | |
03_content.pdf | 19.29 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 129.88 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 787.88 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 1.02 MB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 1.75 MB | Adobe PDF | View/Open | |
08_chapter4.pdf | 1.18 MB | Adobe PDF | View/Open | |
10_annexures.pdf | 371.62 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 183.04 kB | Adobe PDF | View/Open |
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