Many applications that could benefit from the underwater optical wireless communication technique face challenges in using this technology due to the substantial, varying attenuation that affects optical signal transmission through waterbodies. This research demonstrated that convolutional neural networks (CNNs) could readily address these problems. A modified CNN model was proposed to recover the original data of a non-return to zero on–off keying modulated signal transmitted optically through a tank full of Gulf seawater. A comparison between the proposed CNN model and a conventional fixed-threshold decoder (FTD) demonstrates the excellent performance of the proposed CNN model, which improved the bit error ratio (BER), signal-to-noise ratio (SNR), and effective channel length. The BER of the optical signals that are transmitted at powers of 24, 26, and 27 dBm and a bit rate of 10 Mbit/s at a distance of 3 m from the transmitter when FTD is used is 7.826 × 10⁻⁷, 5.049 × 10⁻⁸, and 8.38 × 10⁻¹⁰, respectively. When the CNN decoder is used at the same distance and powers, the BER is 6.23 × 10⁻¹⁴, 1.44 × 10⁻¹⁶, and 2.69 × 10⁻¹⁸, respectively. In conclusion, the BER decreased by about seven orders of magnitude, the effective channel length increased by four times, and the SNR decreased by about 20 dB. The simplicity of the proposed CNN decoder is independent of the prior knowledge of the channel conditions. Furthermore, the magnificent obtained results make the proposed CNN decoder an ideal substitute for ordinary underwater optical wireless communication decoders.

The World Health Organization has declared the COVID-19 pandemic, with most countries being affected by this virus both socially and economically. It thus became necessary to develop solutions to help monitor and control disease spread by controlling medical workers’ movements and warning them against approaching infected individuals in isolation rooms. This paper introduces a control system that uses improved particle swarm optimization (PSO), and artificial neural network (ANN) approaches to achieve social distancing. The distance between medical workers carrying mobile nodes and the beacon node (isolation room) was determined using the ZigBee wireless protocol’s received signal strength indicator (RSSI). Two path loss models were developed to determine the distance from patients with COVID-19: the first is a log-normal shading model (LNSM), and the second is a polynomial function (POL). The coefficient values of the POL model were controlled based on PSO to improve model performance. A random-nonlinear time variation controller-PSO (RNT-PSO) approach was developed to avoid the local minima of the conventional PSO. As a result, social distancing for COVID-19 can be accurately determined. The measured RSSI and the distance were used as ANN inputs, while three control signals (alarming, warning, and closing) were used as ANN outputs. The results revealed that the hybrid model between POL and RNT-PSO, called RNT-PSO-POL, improved the system’s performance by reducing the mean absolute error of distance to 1.433 m, compared to 1.777 m for the LNSM. The results show that the ANN achieves robust performance in terms of mean squared error.

Recently, Wireless systems for indoor environments have become very important and popular. With increasing the need for mobile devices like laptops, tabs and smartphones, the optimization of WLAN coverage became very necessary to provide efficient communication with less cost. This optimization is verified by decreasing number of WiFi Access Point coverage. A multi objectives algorithm is proposed in this paper to optimize WiFi coverage in indoor environment using MatLab software. The proposed algorithm employs (BPSO) to determine the optimal number and locations for WiFi APs distribution based on predefined received power thresholds value. it has been found that the proposed algorithm is flexible using weighted multi-fitness functions. In addition, conducting site survey in real environment verifies the reliability of the proposed algorithm. Applying APD-CS algorithm in the adopted real environment increase the coverage by 64.6% and the average received power 7 dBm at Pth equal to-55 dBm.

In recent years, intelligent controllers have become widespread in several applications, including vehicle airbags. Airbags are crucial in vehicles, reducing the rate of deaths due to car collisions. Therefore, this paper aims to design a system to control the vehicle airbag using artificial intelligence (AI) techniques, particularly the adaptive neuro-fuzzy inference system (ANFIS). Firstly, the airbag control system was analyzed, and significant factors affecting the control system were extracted. Three controllers were developed: the ANFIS-based generalized-bell membership function (ANFIS-B), ANFIS-based triangular membership function (ANFIS-T), and ANFIS-based Gaussian membership function (ANFIS-G). All controllers used three membership functions in each input. The results showed that the mean absolute errors were found to be 0.0006309, 0.001435, and 0.006127 based on ANFIS-B, ANFIS-T, and ANFIS-G, respectively. The proposed ANFIS-B controller achieved satisfactory results and outperformed other controllers.

In this paper, the gravitational search algorithm (GSA) is proposed as a method for controlling the opening and closing of airplane wing tires. The GSA is used to find the optimum proportional-integral-derivative (PID) controller, which controls the wing tires during take-off and landing. In addition, the GSA is suggested as an approach for overcoming the absence of the transfer function, which is usually required to design the optimum PID. The use of the GSA is expected to improve the system. Two of the most popular optimisation algorithms-the harmony search algorithm (HSA) and the particle swarm optimisation (PSO)-were used for the sake of comparison. Moreover, the GSA-, HSA- and PSO-based optimum PID controllers were compared with one of the most important PID tuning methods, the Ziegler-Nichols (ZN) method. In this study, the integral time absolute error (ITAE) was used as a fitness function. First, four transfer functions for different applications were used to compare the performance of the GSA-based PID (PID-GSA), HSA-based PID (PID-HSA), PSO-based PID (PID-PSO) and Ziegler-Nichols-based PID (PID-ZN). Next, the GSA was used to design the optimum PID controller for the opening and closing systems of the airplane wing tires. The results reveal that the GSA provides better outcomes in terms of ITAE when compared with the other adopted algorithms. Furthermore, the GSA demonstrates a fast and robust response to reference variation.