Dual Graphene Patch Antenna For Ka Band Satellite Applications

Currently; grapheme offers a new opportunity to use in space technology and this is due to its amazing properties like conductivity, strength, flexibility and transparency which allows us to exploit new generation of ultra-fast nanoscale components; Since future wireless communication techniques are geared towards the use of the high frequency spectrum and many recent research prove this trend. This letter presents a proposal for design of a dual graphene-based antenna to use in new communication techniques in Ka band, where the proposed antenna can work for uplink and dowlink frequencies at same time since it has return loss less then -10 dB at this frequencies

Convolutional neural network-based real-time object detection and tracking for parrot AR drone 2.

Recent advancements in the field of Artificial Intelligence (AI) have provided an opportunity to create autonomous devices, robots, and machines characterized particularly with the ability to make decisions and perform tasks without human mediation. One of these devices, Unmanned Aerial Vehicles (UAVs) or drones are widely used to perform tasks like surveillance, search and rescue, object detection and target tracking, parcel delivery (recently started by Amazon), and many more. The sensitivity in performing said tasks demands that drones must be efficient and reliable. For this, in this paper, an approach to detect and track the target object, moving or still, for a drone is presented. The Parrot AR Drone 2 is used for this application. Convolutional Neural Network (CNN) is used for object detection and target tracking. The object detection results show that CNN detects and classifies object with a high level of accuracy (98%). For real-time tracking, the tracking algorithm responds faster than conventionally used approaches, efficiently tracking the detected object without losing it from sight. The calculations based on several iterations exhibit that the efficiency achieved for target tracking is 96.5%

Development of intelligent drone battery charging system based on wireless power transmission using hill climbing algorithm.

In this work, an advanced drone battery charging system is developed. The system is composed of a drone charging station with multiple power transmitters and a receiver to charge the battery of a drone. A resonance inductive coupling-based wireless power transmission technique is used. With limits of wireless power transmission in inductive coupling, it is necessary that the coupling between a transmitter and receiver be strong for efficient power transmission; however, for a drone, it is normally hard to land it properly on a charging station or a charging device to get maximum coupling for efficient wireless power transmission. Normally, some physical sensors such as ultrasonic sensors and infrared sensors are used to align the transmitter and receiver for proper coupling and wireless power transmission; however, in this system, a novel method based on the hill climbing algorithm is proposed to control the coupling between the transmitter and a receiver without using any physical sensor. The feasibility of the proposed algorithm was checked using MATLAB. A practical test bench was developed for the system and several experiments were conducted under different scenarios. The system is fully automatic and gives 98.8% accuracy (achieved under different test scenarios) for mitigating the poor landing effect. Also, the efficiency η of 85% is achieved for wireless power transmission. The test results show that the proposed drone battery charging system is efficient enough to mitigate the coupling effect caused by the poor landing of the drone, with the possibility to land freely on the charging station without the worry of power transmission loss

Human pose estimation-based real-time gait analysis using convolutional neural network.

Gait analysis is widely used in clinical practice to help in understanding the gait abnormalities and its association with a certain underlying medical condition for better diagnosis and prognosis. Several technologies embedded in the specialized devices such as computer-interfaced video cameras to measure patient motion, electrodes placed on the surface of the skin to appreciate muscle activity, force platforms embedded in a walkway to monitor the forces and torques produced between the ambulatory patient and the ground, Inertial Measurement Unit (IMU) sensors, and wearable devices are being used for this purpose. All of these technologies require an expert to translate the data recorded by the said embedded specialized devices, which is typically done by a medical expert but with the recent improvements in the field of Artificial Intelligence (AI), especially in deep learning, it is possible now to create a mechanism where the translation of the data can be performed by a deep learning tool such as Convolutional Neural Network (CNN). Therefore, this work presents an approach where human pose estimation is combined with a CNN for classification between normal and abnormal gait of a human with an ability to provide information about the detected abnormalities form an extracted skeletal image in real-time

Autonomous moving target-tracking for a UAV quadcopter based on fuzzy-PI.

Moving target-tracking is an attractive application for quadcopters and a very challenging, complicated field of research due to the complex dynamics of a quadcopter and the varying speed of the moving target with time. For this reason, various control algorithms have been developed to track a moving target using a camera. In this paper, a Fuzzy-PI controller is developed to adjust the parameters of the PI controller using the position and change of position data as input. The proposed controller is compared to a gain-scheduled PID controller instead of the typical PID controller. To verify the performance of the developed system and distinguish which one has better performance, several experiments of a quadcopter tracking a moving target are conducted under the varying speed of the moving target, indoor and outdoor and during day and night. The obtained results indicate that the proposed controller works well for tracking a moving target under different scenarios, especially during night

VERS UNE ÉVALUATION ADAPTATIVE, INDIVIDUALISÉE ET ÉQUITABLE

L’adaptation et l’individualisation de l’enseignement sont au cœur des recherches actuelles dans les Environnements Informatiques pour l’Apprentissage Humain (EIAH). Ainsi des progressions cruciales ont été réalisées au cours de ces dernières années, ce qui assure l’adaptation du déroulement des apprentissages. Néanmoins, certains aspects concernant l’évaluation en ligne des apprentissages (e-Testing), sont toujours en phase de développement.L’objectif de notre travail est de valoriser les acquis de l’apprenant, dans une perspective de créer un nouvel environnement intelligent qui garantit une évaluation adaptative de l’apprenant selon son état cognitif, en mettant à profit des techniques d’intelligence artificielle, des théories en psychologie cognitive, les sciences de l’éducation, la pédagogie et la didactique