Multi resonance patch antenna with multiple slits

In this paper, we have presented a new design of a multi resonance patch antenna with multiple slits. Slits are located on the three sides of the designed antenna. It is simulated in a planar 3D electromagnetic simulation program, called Sonnet Software, designed on the Aluminum (96%) substrate and operates at three frequencies with reflection coefficient (S11) values lower than -10 dB. Values for the operating frequencies are 4.14, 5.52, 9.24 GHz. Electric field theta polarized gains for these three frequencies are; 8.09, 8.35, and 8.39 dBs respectively. Cross polarization levels are well below -10 dB. A parametric study was conducted by changing the gap size and the dielectric thickness. As a result of the parametric study, it is seen that fabrication tolerances of the antenna are good enough

Deep Transfer Learning for Food Recognition

Food Recognition is an essential topic in the area of computer of its target applications is to avoid achieving a cashier at the dining place. In this paper, we investigate the application of Deep Transfer Learning for food recognition. We fine-tune three well learning models namely; AlexNet, GoogleNet, and Vgg16. The fine tuning procedure depends on removing the last three layers of each model and adds another five new layers. The training and validation of each model conducted through food a dataset collected from our university's canteen. The dataset contains 39 food types, 20 images for each type. The fine-tuned models show similar training and validation performance and achieved 100% accuracy over the small-scale dataset