Effective role of optical duobinary transmitter with optical coherent quadrature amplitude modulation (QAM) receiver based on light amplifiers measured is stimulated by using optisystem simulation software version 13. Signal, noise power levels are stimulated after long haul fiber optic range up to 350 km distance. Measured light amplifiers, optical duobinary transmitters and optical QAM receiver are employed to measure the peak signal amplitude power (SAP) and noise signal power for bit rate transmission with 100 Gb/s based 350 km length. Maximum signal power level margin is enhanced with high data rates transmission.

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The speckle noise present in synthetic‐aperture radar (SAR) images is responsible for hindering the extraction of the exact information that needs to be utilized for potential remote sensing applications. Thus the quality of SAR images needs to be enhanced by removing speckle noise in an effective manner. In this paper, A Deep Neural Network‐based Speckle Noise Removal Technique (DNN‐SNRT) is proposed that utilizes the benefits of convolution and Long Short Term Memory‐based neural networks to enhance the quality of SAR images. The proposed DNN‐SNRT uses multiple radar intensity images that are archived from the specific area of interest to facilitate the self‐learning of the intensity features derived from the image patches. The proposed DNN‐SNRT incorporates a dual neural network to remove speckle noise and flexibly estimates the thresholds and weights to achieve an effective SAR image quality improvement. The proposed DNN‐SNRT is capable of automatically updating the intensity features of SAR images during the training process. Experimental investigation of the proposed DNN‐SNRT conducted based on TerraSAR‐X images confirmed the superior enhancement of image quality over comparable recent filters. The results of the DNN‐SNRT scheme were also proved that it is able to reduce noise and preserve edges during the image quality enhancement process.

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The study aims to assess the detection performance of a rapid primary screening technique for COVID‐19 that is purely based on the cough sound extracted from 2200 clinically validated samples using laboratory molecular testing (1100 COVID‐19 negative and 1100 COVID‐19 positive). Results and severity of samples based on quantitative RT‐PCR (qRT‐PCR), cycle threshold, and patient lymphocyte numbers were clinically labeled. Our suggested general methods consist of a tensor based on audio characteristics and deep‐artificial neural network classification with deep cough convolutional layers, based on the dilated temporal convolution neural network (DTCN). DTCN has approximately 76% accuracy, 73.12% in TCN, and 72.11% in CNN‐LSTM which have been trained at a learning rate of 0.2%, respectively. In our scenario, CNN‐LSTM can no longer be employed for COVID‐19 predictions, as they would generally offer questionable forecasts. In the previous stage, we discussed the exactness of the total cases of TCN, dilated TCN, and CNN‐LSTM models which were truly predicted. Our proposed technique to identify COVID‐19 can be considered as a robust and in‐demand technique to rapidly detect the infection. We believe it can considerably hinder the COVID‐19 pandemic worldwide.

Summary Wireless Sensor Network (WSN) plays an essential role in consumer electronics, remote monitoring, an electromagnetic signal, and so forth. The functional capacity of WSN gets enhanced everyday with different technologies. The rapid development of wireless communication, as well as digital electronics, provides automatic sensor networks with low cost and power in various functions, but the challenge faced in WSN is to forward a huge amount of data between the nodes, which is a highly complex task to provide superior delay and energy loss. To overcome these issues, the development of a routing protocol is used for the optimal selection of multipath to perform efficient routing in WSN. This paper developed an energy‐efficient routing in WSNs utilizing the hybrid meta‐heuristic algorithm with the help of Hybrid African Vultures‐Cuckoo Search Optimization (HAV‐CSO). Here, the designed method is utilized for choosing the optimal cluster heads for progressing the routing. The developed HAV‐CSO method is used to enhance the network lifetime in WSN. Hence, the hybrid algorithm also helps select the cluster heads by solving the multi‐objective function in terms of distance, intra‐cluster distance, delay, inter‐cluster distance, throughput, path loss, energy, transmission load, temperature, and fault tolerance. The developed model achieved 7.8% higher than C‐SSA, 25.45% better than BSO‐MTLBO, 23.21% enhanced than AVOA, and 1.29% improved than CSO. The performance of the suggested model is validated, and the efficacy of the developed work is proved over other existing works.

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