Purpose One of the fundamental tasks in the field of image processing is image denoising. Images are often corrupted by different types of noise and the restoration of images degraded with random-valued impulse noise is still a challenging task. The paper aims to discuss these issues. Design/methodology/approach This paper presents an adaptive threshold-based impulse noise detection following by a novel selective window median filter for restoration of RVIN pixels. Findings The proposed method emphasis a local image statistics using an exponential nonlinear function with an adaptive threshold is derived from the rank-ordered trimmed median absolute difference (ROTMAD) are deliberated to detect the noisy pixels. In the filtering stage, a selective 3×3 moving window median filter is applied to restore the detected noisy pixel. Originality/value Experimental result shows that the proposed algorithm outperforms the existing state-of-art techniques in terms of noise removal and quantitative metrics such as peak signal to noise ratio (PSNR), mean absolute error (MAE), structural similarity index metric (SSIM) and miss and false detection rate.

The use of MIMO networks in wireless communication results in increased data rate but leads to severe interference issues and power losses. Promising beamforming (BF) architecture with Conditional Time split-Energy Extraction (CT-EE) is proposed to maximize the Energy Efficiency while optimizing the Mean Square Error (MSE) and Achievable Sum Rate (ASR). A distributed BF system using Minimum Mean Square Error algorithm is jointly implemented at all cooperating nodes. Energy Extraction (EE) from RF signal at the relay nodes facilitates full connectivity among the users. The signal transmission and EE phases are jointly implemented using time-split architecture, without using fixed pre-assigned time slots. Time split from information decoding to EE phase is done only if the battery life is found to be critical. The paper investigates the scope of bypassing the EE phase after ensuring the required power level. The performance of the proposed architecture is compared with the conventional BF and existing EE methods in terms of MSE, ASR and energy efficiency. The proposed architecture is well suitable to establish uninterrupted connectivity between user nodes which are frequently used and have severe power drain-off issues as they can be a part of natural disaster like flood affected wireless networks.

The internet, like automated tools, has grown to better our daily lives. Interacting IoT products and cyber-physical systems. Generative Adversarial Network's (GANs') generator and discriminator may have different inputs, allowing feedback in supervised models. AI systems use neural networks, and adversarial networks analyse neural network feedback. Cyber-physical production systems (CPPS) herald intelligent manufacturing . CPPS may launch cross-domain attacks since the virtual and real worlds are interwoven. This project addresses enhanced Cyber-Physical System(CPS) feedback structure for Denial-of-Service (DoS) defence . Comparing sensor-controller and controller-to-actuator DoS attack channels shows a swapping system modelling solution for the CPS's complex response feedback. Because of the differential in bandwidth between the two channels and the suspects' limited energy, one person can only launch so many DoS assaults. DoS attacks are old and widespread. Create a layered switching paradigm that employs packet-based transfer techniques to prevent assaults. The discriminator's probability may be used to assess whether feedback samples came from real or fictional data. Cognitive feedback can assess GA feedback data.

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