Dr. Mohammed Amine TABERKIT

We present in this work a dual-channel heterostructure strained structure, introduce the high carrier mobility Awaited in heterostructure devices while using several models which are: CVT, SHIRAHATA, and WATT, we present a two-dimensional simulation of dual strained channel heterostructure P-MOSFETs. This study is accomplished usingSILVACO-TCAD simulation software, the comparison of the effect of using strain technique on P-MOSFET transistors will demonstrate the importance of using strain technique especially in dual channel heterostructure MOSFET. The simulation of fabrication steps and the extraction of the electronic proprieties in terms of transfer and output characteristics, transconductance, and the quasi-static capacitance allow understanding and interpreting these enhancements

Unmanned Aerial Vehicles (UAVs) are used in several applications and they are growing in popularity. Recent progress in unmanned aerial vehicles and artificial intelligence constitutes a new chance for an autonomous operation and flight. Nowadays, artificial intelligence and deep learning are driving the evolution of UAVs and fueling their autonomous future. Computer vision achieved very important progress in image classification and segmentation, and object detection, which make it very attractive research field when it is applied on unmanned aerial vehicle. Artificial intelligence is not only important and benefic, but can be rather, dangerous and serious matter since the UAVs learns through algorithms, and use that for future decision making. This work is a survey, where we present works, challenges and dangerous part of using artificial intelligence on UAVs.

The different crop diseases are a serious threatresulting in significant yield losses, where their effective monitoring and accurate early identification techniques are consideredcrucial to ensure stable and reliable crop productivity andfood security. The traditional methods often rely on humanexpert-based inspection of disease symptoms, which could beeffective for small crop fields. However, they require a very longtime and great physical effort to cover large crops resultingin very high miss detection rates. Recent innovative advancesin remote sensing technologies and computer vision techniquesare considered an effective way to solve such problems. Tothis end, in this paper, we focus on the recent advances inUnmanned Aerial Vehicle platforms and deep learning basedcomputer vision algorithms to identify crop diseases at their earlystage to improve food production.