Since the infectious coronavirus disease (COVID-19) was first reported in Wuhan, it has become a public health problem in China and even around the world. This pandemic is having devastating effects on societies and economies around the world. The increase in the number of COVID-19 tests gives more information about the epidemic spread, which may lead to the possibility of surrounding it to prevent further infections. However, wearing a face mask that prevents the transmission of droplets in the air and maintaining an appropriate physical distance between people, and reducing close contact with each other can still be beneficial in combating this pandemic. Therefore, this research paper focuses on implementing a Face Mask and Social Distancing Detection model as an embedded vision system. The pretrained models such as the MobileNet, ResNet Classifier, and VGG are used in our context. People violating social distancing or not wearing masks were detected. After implementing and deploying the models, the selected one achieved a confidence score of 100%. This paper also provides a comparative study of different face detection and face mask classification models. The system performance is evaluated in terms of precision, recall, F1-score, support, sensitivity, specificity, and accuracy that demonstrate the practical applicability. The system performs with F1-score of 99%, sensitivity of 99%, specificity of 99%, and an accuracy of 100%. Hence, this solution tracks the people with or without masks in a real-time scenario and ensures social distancing by generating an alarm if there is a violation in the scene or in public places. This can be used with the existing embedded camera infrastructure to enable these analytics which can be applied to various verticals, as well as in an office building or at airport terminals/gates.

In the medical sector, the digital image is multimedia data that contain secret information. However, designing an efficient secure cryptosystem to protect the confidential images in sharing is a challenge. In this work, we propose an improved chaos-based cryptosystem to encrypt and decrypt rapidly secret medical images. A complex chaos-based PRNG is suggested to generate a high-quality key that presents high randomness behaviour, high entropy, and high complexity. An improved architecture is proposed to encrypt the secret image that is based on permutation, substitution, and diffusion properties. In the first step, the images pixels are randomly permuted through a matrix generated using the PRNG. Next, pixels bits are permuted using an internal condition. After that, the pixels are substituted using two different S-boxes with an internal condition. In the final step, the image is diffused by XORing pixels with the key stream generated by the PRNG in order to acquire an encrypted image. R rounds of encryption can be performed in a loop to increase the complexity. The cryptosystem is evaluated in depth by his application on several medical images with different types, contents, and sizes. The obtained simulation results demonstrate that the system enables high-level security and performance. The information entropy of the encrypted image has achieved an average of 7.9998 which is the most important feature of randomness. The algorithm can take full advantage of parallelism and pipeline execution in hardware implementation to meet real-time requirements. The PRNG was tested by NIST 800-22 test suit, which indicates that it is suitable for secure image encryption. It provides a large key space of 2¹⁹² which resists the brute-force attack. However, the cryptosystem is appropriate for medical image securing.

The impact of Partial Shading Conditions (PSCs) significantly influences the output of Photovoltaic Systems (PVSs). Under PSCs, the Power-Voltage (P-V) characteristic of the PVS unveils numerous power peaks, inclusive of local maxima and a global maximum. The latter represents the optimum power point. Traditional Maximum Power Point Tracking (MPPT) algorithms struggle to track the Global Maximum Power Point (GMPP). To address this, our study emphasizes the creation of a novel algorithm capable of identifying the GMPP. This approach combines the Cuckoo Search (CS) MPPT algorithm with an Integral Super-Twisting Sliding Mode Controller (STSMC) using their benefits to enhance the PVS performance under PSCs in terms of high efficiency, low power losses, and high-speed convergence towards the GMPP. The STSMC is a second-order Sliding Mode Control strategy that employs a continuous control action that attenuates the “chattering” phenomenon, caused when the first-order SMC technique is employed. Indeed, the proposed CS-STSMC-MPPT algorithm consists of two parts. The first one is based on the CS algorithm used for scanning the power-voltage curve to identify the GMPP, and subsequently generating the associated optimal voltage reference. The second part aims to track the voltage reference by manipulating the duty cycle of the boost converter. The proposed CS-STSMC-MPPT algorithm is featured by its strength against uncertainties and modeling errors. The obtained simulation results underline a high convergence speed and an excellent precision of the proposed method in identifying and tracking the GMPP with high efficiency under varying shading scenarios. For comparative purposes, this method is set against the hybrid CS-Proportional Integral Derivative, the conventional CS, the Particle Swarm Optimization, and the Perturb and Observe algorithms under different PSCs, including zero, weak, and severe shading. Simulation conducted in the Matlab/Simulink environment confirms the superior performance of the proposed CS-STSMC-MPPT algorithm in terms of precision, convergence speed, efficiency, and resilience.

In this paper we propose a novel and efficient hardware implementation of an image watermarking system based on the Haar Discrete Wavelet Transform (DWT). DWT is used in image watermarking to hide secret pieces of information into a digital content with a good robustness. The main advantage of Haar DWT is the frequencies separation into four subbands (LL, LH, HL, and HH) which can be treated independently. This permits ensuring a better compromise between robustness and visibility factors. A Field Programmable Gate Array (FPGA) that is based on a very large scale integration architecture of the watermarking algorithm is developed to accelerate media authentication. A hardware cosimulation strategy using the Matlab-Xilinx system generator (XSG) was applied to prove the validity of the suggested implementation. The hardware cosimulation results show the effectiveness of the developed architecture in terms of visibility and robustness against several attacks. The proposed hardware system presents also a high performance in terms of the operating speed.

This paper presents a hardware implementation upon a Field Programmable Gate Array (FPGA) of improved Vector Control Strategies (VCSs) based on a Fuzzy Logic System (FLS) of a grid-connected wind energy conversion system. Usually, the classical VCS is based on fixed-gain Proportional Integral Derivative (PID) controllers, which are known to exhibit limited performance against the nonlinear behavior of wind systems, such as rapid fluctuations of the wind speed and uncertainties of the system parameters. In order to overcome this limitation, an improved VCS based on Fuzzy Gain Scheduling PID controllers (VCS-FGS-PID) is suggested in this work to guarantee good tracking, high accuracy and good robustness under system parameter variations. Indeed, the PID controller gains are tuned, in real-time, by the FLS. In addition, the proposed VCS-FGS-PID methods are implemented on the FPGA in order to reduce the delays and the period of the system of the control loop, thanks to its parallel processing. In fact, the performance of the suggested VCS-FGS-PID approaches are proved by digital simulation with the Xilinx system generator tool under Matlab/Simulink, in addition to an experimental hardware-in-the-loop implementation using the FPGA. The obtained results demonstrate that the proposed VCS-FGS-PID techniques offer better performance as regards good tracking and high accuracy and good robustness against stator resistance variability compared to VCS-PI.

In this paper, we propose an efficient cryptosystem for digital image encryption and authentication. The cryptosystem is a hybrid scheme that uses symmetric and asymmetric approaches. The first one is used to encrypt the host image by utilizing a chaos-based key generator. The second one is used to encrypt the initial secret key and the owner’s signature that permit authentication. The algorithm is evaluated and validated by its application on several types of standard images and tools such as the statistical analysis, the key, and the performance analysis. The results indicate that the proposed cryptosystem provides high performance and enhanced security. The NIST 800-22 is used for testing the pseudorandom numbers generation (PRNG). The obtained simulation results are better than those cited in the recent works in terms of execution time and security level and low computational complexity.