Titlebook: Advances in Smart Technologies Applications and Case Studies; Selected Papers from Ali El Moussati,Kidiyo Kpalma,Sylvain Guégan Conference

絆住

60 GHz RoF System Based on IR-MBOOK Transmitter and Non-coherent Receiver the baseband. The system efficiency is evaluated and analyzed by quality factor (Q factor) performance. Simulation results show the efficiency of monocycle pulse compared with Gaussian pulse shape, and RoF system with transmission rate of 4?Gbps is successfully achieved up?to 45?km.

愛哭

synovium

3D Shape Recognition Based on Uncoded Structured Light Using ANN Classifierhe proposed approach allow 3D object recognition with an accuracy of 99.6% using the ANN classifier on a database formed by 10 objects. We present a comparison between the results obtained by applying our approach to different databases made up of 6, 7 and 10 objects and treat these results accordin

Rotator-Cuff

Machine Vision-Based Cocoa Beans Fermentation Degree Assessment) and well fermented (WF) categories. Experimental results show that 99.17% of UF beans, 97.50% of PF beans and 100% of WF beans were detected successfully. This results revealed that the proposed method could be used as a fast, accurate and a reliable tool to discriminate cocoa beans according to t

legitimate

親密

Conflagration

Computer Graphics and Geometric Modelling the baseband. The system efficiency is evaluated and analyzed by quality factor (Q factor) performance. Simulation results show the efficiency of monocycle pulse compared with Gaussian pulse shape, and RoF system with transmission rate of 4?Gbps is successfully achieved up?to 45?km.

AROMA

慟哭

Computer Graphics for Artists IIhe proposed approach allow 3D object recognition with an accuracy of 99.6% using the ANN classifier on a database formed by 10 objects. We present a comparison between the results obtained by applying our approach to different databases made up of 6, 7 and 10 objects and treat these results accordin

輕推

https://doi.org/10.1007/978-1-84882-470-6) and well fermented (WF) categories. Experimental results show that 99.17% of UF beans, 97.50% of PF beans and 100% of WF beans were detected successfully. This results revealed that the proposed method could be used as a fast, accurate and a reliable tool to discriminate cocoa beans according to t