A Code Generator for Distributing Sensor Data Models

2010 ◽  
pp. 127-143
Author(s):  
Urs Hunkeler ◽  
Paolo Scotton
Keyword(s):  
Data Models ◽  
Sensor Data ◽  
Code Generator

scholarly journals Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI

2021 ◽  
Author(s):  
Hemant Ghayvat ◽  
Prosanta Gope
Keyword(s):  
Healthy Subject ◽  
Research Work ◽  
Well Being ◽  
Data Models ◽  
Community Dwelling ◽  
Sensor Data ◽  
Elderly Subjects ◽  
Healthy Elderly ◽  
Early Dementia ◽  
The Impact

AbstractReasoning weakening because of dementia degrades the performance in activities of daily living (ADL). Present research work distinguishes care needs, dangers and monitors the effect of dementia on an individual. This research contrasts in ADL design execution between dementia-affected people and other healthy elderly with heterogeneous sensors. More than 300,000 sensors associated activation data were collected from the dementia patients and healthy controls with wellness sensors networks. Generated ADLs were envisioned and understood through the activity maps, diversity and other wellness parameters to categorize wellness healthy, and dementia affected the elderly. Diversity was significant between diseased and healthy subjects. Heterogeneous unobtrusive sensor data evaluate behavioral patterns associated with ADL, helpful to reveal the impact of cognitive degradation, to measure ADL variation throughout dementia. The primary focus of activity recognition in the current research is to transfer dementia subject occupied homes models to generalized age-matched healthy subject data models to utilize new services, label classified datasets and produce limited datasets due to less training. Current research proposes a novel Smart Aging Monitoring and Early Dementia Recognition system that provides the exchange of data models between dementia subject occupied homes (DSOH) to healthy subject occupied homes (HSOH) in a move to resolve the deficiency of training data. At that point, the key attributes are mapped onto each other utilizing a sensor data fusion that assures to retain the diversities between various HSOH & DSOH by diminishing the divergence between them. Moreover, additional tests have been conducted to quantify the excellence of the offered framework: primary, in contradiction of the precision of feature mapping techniques; next, computing the merit of categorizing data at DSOH; and, the last, the aptitude of the projected structure to function thriving due to noise data. The outcomes show encouraging pointers and highlight the boundaries of the projected approach.

Evaluation of Open Data Models for the Exchange of Sensor Data in Cognitive Building

2018 ◽  
Author(s):  
M. Scheffer ◽  
M. Konig ◽  
T. Engelmann ◽  
L. C. Tagliabue ◽  
A. L. C. Ciribini ◽  
...  
Keyword(s):  
Open Data ◽  
Data Models ◽  
Sensor Data

scholarly journals Challenging multi-sensor data models and use of 360 images. The Twelve Months Fountain of Valentino park in Turin.

2020 ◽  
Vol 949 ◽  
pp. 012060
Author(s):  
L Teppati Losè ◽  
G Sammartano ◽  
F Chiabrando ◽  
A Spanò
Keyword(s):  
Data Models ◽  
Sensor Data

Aggregate density-based concept drift identification for dynamic sensor data models

2020 ◽  
Author(s):  
Mohsen Asghari ◽  
Daniel Sierra-Sosa ◽  
Michael Telahun ◽  
Anup Kumar ◽  
Adel S. Elmaghraby
Keyword(s):  
Concept Drift ◽  
Data Models ◽  
Sensor Data

Auditory visualization of landmine detector sensor data

2009 ◽  
Author(s):  
Bradley M. Davis ◽  
Woodrow W. Winchester ◽  
Jason D. Zedlitz
Keyword(s):  
Sensor Data

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

Development of Failure Detection Algorithm for a Slope Using Sensor Data

2018 ◽  
Vol 6 (1) ◽  
pp. 93-101
Author(s):  
Hyun Ki Kim ◽  
Chan Yong Choi ◽  
Ho Suk Bae
Keyword(s):  
Failure Detection ◽  
Detection Algorithm ◽  
Sensor Data
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