Integration of IoT, Edge Computing and Cloud Computing for Monitoring and Controlling Automated External Defibrillator Cabinets in Emergency Medical Service

Namee, Khanista; Panong, Nuttapat; Polpinij, Jantima

doi:10.1109/infoman.2019.8714717

Cited by 7 publications

(1 citation statement)

References 5 publications

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“…On the other hand, the development of edge-computing techniques makes it also possible to monitor the health of patients without the need to hospitalize them [7]. Examples of edge-computing applications in healthcare include areas such as: analysis and therapy for voice disorder [8], coronary heart disease diagnosis [9], detecting behavior of dementia [10], and even management of medical infrastructure [11]. Regardless of the application, all edge processing systems are characterized by computing efficiency suitable for processing a specific type of data close to its source.…”

Section: Introductionmentioning

confidence: 99%

CMOS Perceptron for Vesicle Fusion Classification

Naumowicz¹,

Pietrzak²,

Szczęsny³

et al. 2022

Electronics

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Edge computing (processing data close to its source) is one of the fastest developing areas of modern electronics and hardware information technology. This paper presents the implementation process of an analog CMOS preprocessor for use in a distributed environment for processing medical data close to the source. The task of the circuit is to analyze signals of vesicle fusion, which is the basis of life processes in multicellular organisms. The functionality of the preprocessor is based on a classifier of full and partial fusions. The preprocessor is dedicated to operate in amperometric systems, and the analyzed signals are data from carbon nanotube electrodes. The accuracy of the classifier is at the level of 93.67%. The implementation was performed in the 65 nm CMOS technology with a 0.3 V power supply. The circuit operates in the weak-inversion mode and is dedicated to be powered by thermal cells of the human energy harvesting class. The maximum power consumption of the circuit equals 416 nW, which makes it possible to use it as an implantable chip. The results can be used, among others, in the diagnosis of precancerous conditions.

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