Hamid Esmaeili Najafabadi

TL;DR: A three-module framework, named “Ontology-Based Privacy-Preserving” (OBPP) is proposed to address the heterogeneity issue while keeping the privacy information of IoT devices, and can be widely applied to smart cities.

TL;DR: In this study, the use of a family of Chebyshev chaotic maps for pulse compression in multiple input–multiple output (MIMO) radars is studied and it is shown that, these one-dimensional chaotic maps, which are amongst the simplest chaotic systems, can produce arbitrary number of sequences with arbitrary length, low peak-to-average power ratio (PAPR), high merit factor (MF) or equivalently low integrated side-lobe ratio (ISLR).

TL;DR: A time delay estimation model is designed based on adaptive iterative local searching orthogonal matching pursuit (AILSOMP) algorithm, and an improved three-stage weighted least squares localization algorithm is proposed using the time delay values.

TL;DR: The results show that the proposed algorithms have improved localization and communication performance after efficiently allocating the array resource in the RCI network.

TL;DR: In this paper, the authors proposed a novel framework for transferred deep learning-based anomaly detection in hyperspectral images, which includes four main steps: the image2019s spectral dimension is reduced by applying the principal component analysis (PCA) to decrease computational time.