scholarly journals Look twice: Uncover hidden information in room climate sensor data

Author(s):  
Dominic Worner ◽  
Thomas von Bomhard ◽  
Marc Roschlin ◽  
Felix Wortmann
2009 ◽  
Author(s):  
Bradley M. Davis ◽  
Woodrow W. Winchester ◽  
Jason D. Zedlitz
Keyword(s):  

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

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  

2020 ◽  
Vol 59 (1-4) ◽  
pp. 611-621
Author(s):  
Sára Horváthy

SummaryEgeria, a 4th century pious woman from the south of present-day Spain, retold, after visiting Palestine with the Bible in hand, her observations to her sisters. If the linguistic aspects of her letters are quite well-known, much less is known about its stylistic value, inappropriately called “simple”.What seems to be boringly the same again and again, is in fact a constantly renewed and perfectly mastered “variation on a theme”, just as in a well-composed piece of music. Her apparent objectivity is indeed a wish to focus on what she considers the most important, namely to tell her community, as closely to reality as possible, what she observed during her pilgrimage. However, Egeria’s latin is also a testimony of the christian lexicon in construction and of the social changes that were in progress by that time.Linguistics and stylistics work together here, the choice of a word or a grammatical formula reveals hidden information about the proper style of an author who, despite her supposed objectivity, had real personal purposes.


2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich

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


2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>


1994 ◽  
Author(s):  
Andrew J. Mazzella ◽  
Delorey Jr. ◽  
Dennis E.
Keyword(s):  

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