Probability of Error Analysis for NOMA Systems in Rayleigh Fading Channels: Enabling IoT in Civil Engineering

In the realm of digital communication, understanding and mitigating the probability of error is crucial, particularly in Rayleigh fading channels where signal impairments are common. This paper presents a unified approach to derive the probability of error formulations for two-users NOMA systems operating in Rayleigh fading channels. The methodologies and findings outlined in this study are essential for IoT applications in construction and civil engineering. Specifically, the derived error probability formulations can be employed to enhance the reliability and efficiency of IoT-based monitoring systems in these sectors. By optimizing communication protocols, the proposed approach ensures accurate data transmission, thereby facilitating real-time monitoring and decision-making processes in construction sites and civil infrastructure projects.

Light Communication for Controlling Industrial Robots

Optical Wireless Communication (OWC) is regarded as an auspicious communication approach that can outperform the existing wireless technology. It utilizes LED lights, whose subtle variation in radiant intensity generate a binary data stream. This is perceived by a photodiode, that converts it to electric signals for further interpretation. This article aims at exploring the use of this emerging technology in order to control wirelessly industrial robots, overcoming the need for wires, especially in environments where radio waves are not working due to environmental factors or not allowed for safety reasons. We performed experiments to ensure the suitability and efficiency of OWC based technology for the aforementioned scope and "in vitro" tests in various Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) configurations to observe the system throughput and reliability. The technology performance in the "clear LoS" and in the presence of a transparent barrier, were also analyzed.

CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection

Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN, the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.