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This paper describe an algorithm able to regulate and control the parameters that manage some common processing algorithms, in order to obtain the best data quality. The processing chain to be regulated consists of an acquisition module and two filtering processes, i.e., edge-preserving and edge-extraction filterings. The system has been implemented as an expert system, which, in addition to the processing units, includes a regulation module. Only a general prototype for the regulation module has been designed, so it is possible to assign a different processor to each module, thus allowing a parallel implementation of the system. Each regulation module has been specialized with knowledge about the parameters regulating the related processing unit. Moreover, criteria to judge the quality of data at each level have been devised in order to have methods to select and regulate the processing parameters to be adjusted. Data can be propagated among the modules more than one time to perform backtracking actions aimed at achieving the best quality of final data, even thought this results in lower quality data at the intermediate levels.Experimental results on indoor images are presented to assess the validity of the proposed approach.*

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Millimeter Wave (mmWave) band can be a solution to serve the vast number of Internet of Things (IoT) and Vehicle to Everything (V2X) devices. In this context, Cognitive Radio (CR) is capable of managing the mmWave spectrum sharing efficiently. However, Cognitive mmWave Radios are vulnerable to malicious users due to the complex dynamic radio environment and the shared access medium. This indicates the necessity to implement techniques able to detect precisely any anomalous behaviour in the spectrum to build secure and efficient radios. In this work, we propose a comparison framework between deep generative models: Conditional Generative Adversarial Network (C-GAN), Auxiliary Classifier Generative Adversarial Network (AC-GAN), and Variational Auto Encoder (VAE) used to detect anomalies inside the dynamic radio spectrum. For the sake of the evaluation, a real mmWave dataset is used, and results show that all of the models achieve high probability in detecting spectrum anomalies. Especially, AC-GAN that outperforms C-GAN and VAE in terms of accuracy and probability of detection.