In this paper, we propose a new chaos-based encryption scheme for medical images. It is based on a combination of chaos and DNA computing under the scenario of two encryption rounds, preceded by a key generation layer, and follows the permutation-substitution-diffusion structure. The SHA-256 hash function alongside the initial secret keys is employed to produce the secret keys of the chaotic systems. Each round of the proposed algorithm involves six steps, i.e., block-based permutation, pixel-based substitution, DNA encoding, bit-level substitution (i.e., DNA complementing), DNA decoding, and bit-level diffusion. A thorough search of the relevant literature yielded only this time the pixel-based substitution and the bit-level substitution are used in cascade for image encryption. The key-streams in the bit-level substitution are based on the logistic-Chebyshev map, while the sine-Chebyshev map allows producing the key-streams in the bit-level diffusion. The final encrypted image is obtained by repeating once the previous steps using new secret keys. Security analyses and computer simulations both confirm that the proposed scheme is robust enough against all kinds of attacks. Its low complexity indicates its high potential for real-time and secure image applications. INDEX TERMS Image encryption, medical images, permutation and diffusion, S-box, chaos, DNA encoding, SHA-256 hash function.
The increasing number of devices together with uncoordinated transmissions result in a major challenge of scalability in the Internet of things. This paper deals with signal detection in the uplink of a LoRa network through a deep learning-based approach. Two strategies are proposed: regression for bit detection based on a deep feedforward neural network and classification for symbol detection based on a convolutional neural network. These receivers can decode a selected user's signals when multiple users simultaneously transmit over the same frequency band with the same spreading factor. Simulation results show that both receivers outperform the classical LoRa one in the presence of interference. The results show that the introduced approach is relevant to deal with the scalability issue.
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