Enhanced lion swarm optimization and elliptic curve cryptography scheme for secure cluster head selection and malware detection in IoT-WSN.

Wireless Sensor Networks present a significant issue for data routing because of the potential use of obtaining data from far locations with greater energy efficiency. Networks have become essential to modern concepts of the Internet of Things. The primary foundation for supporting diverse service-centric applications has continued to be the sensor node activity of both sensing phenomena in their local environs and relaying their results to centralized Base Stations.

A Monte Carlo based scatter removal method for non-isocentric cone-beam CT acquisitions using a deep convolutional autoencoder.

The primary cone-beam computed tomography (CBCT) imaging beam scatters inside the patient and produces a contaminating photon fluence that is registered by the detector. Scattered photons cause artifacts in the image reconstruction, and are partially responsible for the inferior image quality compared to diagnostic fan-beam CT. In this work, a deep convolutional autoencoder (DCAE) and projection-based scatter removal algorithm were constructed for the ImagingRing system on rails (IRr), which allows for non-isocentric acquisitions around virtual rotation centers with its independently rotatable source and detector arms.

Combining dual-tree complex wavelets and multiresolution in iterative CT reconstruction with application to metal artifact reduction.

Background: This paper investigates the benefits of data filtering via complex dual wavelet transform for metal artifact reduction (MAR). The advantage of using complex dual wavelet basis for MAR was studied on simulated dental computed tomography (CT) data for its efficiency in terms of noise suppression and removal of secondary artifacts. Dual-tree complex wavelet transform (DT-CWT) was selected due to its enhanced directional analysis of image details compared to the ordinary wavelet transform.

A realistic, accurate and fast source modeling approach for the EEG forward problem.

The aim of this paper is to advance electroencephalography (EEG) source analysis using finite element method (FEM) head volume conductor models that go beyond the standard three compartment (skin, skull, brain) approach and take brain tissue inhomogeneity (gray and white matter and cerebrospinal fluid) into account. The new approach should enable accurate EEG forward modeling in the thin human cortical structures and, more specifically, in the especially thin cortices in children brain research or in pathological applications. The source model should thus be focal enough to be usable in the thin cortices, but should on the other side be more realistic than the current standard mathematical point dipole.

[Viral superantigens].

Superantigens (SAgs) are microbial proteins produced by various microorganisms that elicit excessive and strong stimulation of T cells via an unconventional mechanism. They cause polyclonal activation of T cells in a non-specific manner, by binding to a particular variable-beta (Vβ) chain of T-cell receptor (TCR) and MHC class II molecule, in unprocessed form and outside of peptide-binding cleft, forming a bridge between the antigen presenting cell and the T cell. SAgs are classified into three groups, namely 1) exogenous (soluble proteins and exotoxins secreted by microorganisms), 2) endogenous (transmembrane proteins encoded by viruses which are integrated into the genome) and 3) B-cell SAgs (proteins which stimulate predominantly B cells).