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Modeling a location-unaware sensor network as a simplicial complex, where simplices correspond to cliques in the communication graph, has proven useful for solving a number of coverage problems under certain conditions using algebraic topology. Several approaches to finding a sparse cover for a fenced sensor network are considered, including calculating homology changes locally, strong collapsing, and Euler characteristic collapsing.

Authentication by facial recognition is actually one of the solutions to reinforce the security level
of information systems. However, face recognition systems are proven to be vulnerable to spoofing
attack. In fact, an attacker can bypass the authentification process easily by presenting in front of the
camera a copy version of a legitimate user’s face.
To make your face as your password, it is of vital importance to identify and reject the falsified faces

This paper presents a novel method to track the hierarchical structure of Web video groups on the basis of salient keyword matching including semantic broadness estimation. To the best of our knowledge, this paper is the first work to perform extraction and tracking of the hierarchical structure simultaneously. Specifically, the proposed method first extracts the hierarchical structure of Web video groups and salient keywords of them on the basis of an improved scheme of our previously reported method.

Different transforms are compared to extract bit sequences used in secret-key binding for highly-correlated physical-identifier outputs. A set of transforms that perform well in terms of decorrelation efficiency is applied to ring oscillator (RO) outputs to improve the reliability and uniqueness of the extracted sequence, information leakage to an eavesdropper about the secret key and RO outputs, secret-key length, and hardware cost.

We propose a simple protocol which allows two legitimate parties to exchange some confidential message over a wireless
channel with some chosen level of semantic security against passive eavesdroppers, and without needing either secret or public keys. The proposed method leverages the noisy and fading nature of the channel and exploits coding and all-or-nothing transforms to achieve the desired level of semantic security. We also define suitable metrics to estimate the semantic security level in the considered setting.

Automatic Modulation Classification (AMC) has received a major attention last decades, as a required step between signal detection and demodulation. In the fully-blind scenario, this task turns out to be quite challenging, especially when the computational complexity and the robustness to uncertainty matter. AMC commonly relies on a preprocessor whose function is to estimate unknown parameters, filter the received signal and sample it in a suitable way. Any preprocessing error inherently leads to a performance loss.

We study the problem of energy efficiency maximization (EEmax) with joint beamforming and subarray selection, by taking into account the non-linear power amplifier efficiency in a multi-user multiple-input single-output system. The subarray selection problem is formulated using the concept of perspective formulation with additional penalty term in the objective function. To tackle the resulting challenging mixed-Boolean non-convex optimization problem, we rely on