A steady laminar flow over a vertical stretching sheet with the existence of viscous dissipation, heat source/sink, and magnetic fields has been numerically inspected through a shooting scheme based Runge—Kutta–Fehlberg-integration algorithm. The governing equation and boundary layer balance are expressed and then converted into a nonlinear normal system of differential equations using suitable transformations. The impact of the physical parameters on the dimensionless velocity, temperature, the local Nusselt, and skin friction coefficient are described. Results show good agreement with recent researches. Findings reveal that the Nusselt number at the sheet surface augments, since the Hartmann number, stretching velocity ratio A, and Hartmann number Ha increase. Nevertheless, it reduces with respect to the heat generation/absorption coefficient δ.

Intelligent surveillance systems are widely used in several critical areas as airports, ATM, and bank agencies to ensure more security and more safety. The need for an intelligent behavior recognition system is still increasing. Traditional approaches based on access to restricted places or suspected actions as theft, scam, and loitering are insufficient to identify suspect behavior. These actions do not represent a real key of suspects. Novel trends attempt to extract behaviors from involuntary actions as face gestures, face characteristics, and feeling features. This paper is motivated not only by the limits of the traditional approaches but also by the complexity of intelligent algorithms. In this context, the present paper uses face features to recognize the feeling of fear like a suspect behavior. Indeed, this feeling represents the main characteristic of a suspicious person under crime as announced by several psychologist scientists. The fear feeling is usually followed by an increase in heart rate beats. This paper describes the recognition of fear feeling using a camera as a contactless sensor. Frequencies associated with face based-video are used to estimate the heart rate according to the fusion of three techniques: bandpass filter, Eulerian transformer, and Lagrangian transformer. The proposed algorithm benefits from the advantages of each technique, but it is challenged by the Real-Time exigence. For this purpose, a Raspberry PI3 board is used in relation to Raspbian Operating System to ensures Real-Time criteria. The proposed is trained according to CK+ dataset. In this paper, contributions attempt to ensure not only a high recognition rate using a non-complex algorithm but also guarantee a real-time computation. Results reveal that the proposed algorithm has the best heart rate estimation in comparison with traditional methods. Hardware results justify the success of the proposed design in terms of resource requirements.

Precision agriculture, also referred to as smart farming, is one of the main pillars of modern society to achieve the Sustainable Development Goals (SDGs). Precision agriculture aims to improve the quality and quantity of production while conserving scarce natural resources. Smart farming has grown in recent years thanks to the adoption of modern technologies, including artificial intelligence (AI) and the Internet of Things (IoT). In this work, we consider an irrigation system for olive orchards based on unmanned aerial vehicles (UAVs). Specifically, UAVs ensure remote sensing (RS), which offers the advantage of collecting vital information on a large temporal and spatial scale (which cannot be achieved with traditional technologies). However, UAV-based irrigation systems face tremendous challenges due to the various requirements of a powerful computing ability, battery capacity, energy efficiency, and spectral efficiency for different connected devices. This paper addresses the energy efficiency and spectral efficiency trade-off problem of UAV-based irrigation systems. We propose to adopt massive multiple input, multiple output (M-MIMO) technology to ensure wireless communication. In fact, this technology plays a significant role in future sixth-generation (6G) wireless mobile networks and has the potential to enhance the energy efficiency as well as the spectral efficiency. We design a network model with a three-layered architecture and analytically compute the achievable spectral efficiency and the energy efficiency of the studied system. Then, we numerically determine the optimal number of ground base station antennas as well as the optimal number of IoT devices that should be used to ensure the maximum energy efficiency while guaranteeing a high spectral efficiency. The numerical results prove that the proposed UAV-based irrigation system outperforms conventional systems and demonstrate that the best spectral and energy efficiency trade-off is obtained by using the M-MMSE combiner.

The revolution in technology affects many fields and among them the Healthcare system. The application-based computer was developed to help specialists to detect diseases, and to perform some basics operations. In this paper, focus is given on the proposed attempts to detect Epilepsy Disease (ED). Several Computer-Aided Diagnosis (CAD) methods were used to provide the brain’s disease status according to signals related to brain activities. These applications achieved acceptable results but still have their limitations. An intelligence CAD based on the Balanced Communication-Avoiding Support Vector Machine (BCA-SVM) is proposed to detect ED using Electroencephalogram (EEG) signals. This attempt is implemented on a Raspberry Pi 4 as a real board to ensure real-time processing. The CAD-based on BCA-SVM achieved an accuracy of 99.8% and the execution time was around 3.2s satisfying the real-time requirement.