A novel approach to creating artificial training and test data for an HMM based posture recognition system

Author(s):  
Andreas Kitzig ◽  
Edwin Naroska ◽  
Gudrun Stockmanns ◽  
Reinhard Viga ◽  
Anton Grabmaier
Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 374 ◽  
Author(s):  
Chi-Hua Chen ◽  
Eyhab Al-Masri ◽  
Feng-Jang Hwang ◽  
Despo Ktoridou ◽  
Kuen-Rong Lo

This editorial introduces the special issue, entitled “Applications of Internet of Things”, of Symmetry. The topics covered in this issue fall under four main parts: (I) communication techniques and applications, (II) data science techniques and applications, (III) smart transportation, and (IV) smart homes. Four papers on sensing techniques and applications are included as follows: (1) “Reliability of improved cooperative communication over wireless sensor networks”, by Chen et al.; (2) “User classification in crowdsourcing-based cooperative spectrum sensing”, by Zhai and Wang; (3) “IoT’s tiny steps towards 5G: Telco’s perspective”, by Cero et al.; and (4) “An Internet of things area coverage analyzer (ITHACA) for complex topographical scenarios”, by Parada et al. One paper on data science techniques and applications is as follows: “Internet of things: a scientometric review”, by Ruiz-Rosero et al. Two papers on smart transportation are as follows: (1) “An Internet of things approach for extracting featured data using an AIS database: an application based on the viewpoint of connected ships”, by He et al.; and (2) “The development of key technologies in applications of vessels connected to the Internet”, by Tian et al. Two papers on smart home are as follows: (1) “A novel approach based on time cluster for activity recognition of daily living in smart homes”, by Liu et al.; and (2) “IoT-based image recognition system for smart home-delivered meal services”, by Tseng et al.


Author(s):  
Shogo Sekiguchi ◽  
Liang Li ◽  
Nak Yong Ko ◽  
Woong Choi

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1069
Author(s):  
Bi-Min Hsu

Assistive braille technology has existed for many years with the purpose of aiding the blind in performing common tasks such as reading, writing, and communicating with others. Such technologies are aimed towards helping those who are visually impaired to better adapt to the visual world. However, an obvious gap exists in current technology when it comes to symmetric two-way communication between the blind and non-blind, as little technology allows non-blind individuals to understand the braille system. This research presents a novel approach to convert images of braille into English text by employing a convolutional neural network (CNN) model and a ratio character segmentation algorithm (RCSA). Further, a new dataset was constructed, containing a total of 26,724 labeled braille images, which consists of 37 braille symbols that correspond to 71 different English characters, including the alphabet, punctuation, and numbers. The performance of the CNN model yielded a prediction accuracy of 98.73% on the test set. The functionality performance of this artificial intelligence (AI) based recognition system could be tested through accessible user interfaces in the future.


Sign in / Sign up

Export Citation Format

Share Document