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Found 7 papers, 2 papers with code
Multi-Beam Symbol-Level Secure Communication for Hybrid Near- and Far-Field Communications
This paper introduces a multi-beam secure communication scheme for mixed near-field and far-field (hNF) scenarios, primarily designed to address the challenges faced by sixth-generation (6G) networks in simultaneously managing near-field and far-field communications.
Joint Information and Jamming Beamforming for Securing IoT Networks With Rate-Splitting
The goal of this paper is to address the physical layer (PHY) security problem for multi-user multi-input single-output (MU-MISO) Internet of Things (IoT) systems in the presence of passive eavesdroppers (Eves).
Robust Multi-Beam Secure mmWave Wireless Communication for Hybrid Wiretapping Systems
In this paper, we consider the physical layer (PHY) security problem for hybrid wiretapping wireless systems in millimeter wave transmission, where active eavesdroppers (AEs) and passive eavesdroppers (PEs) coexist to intercept the confidential messages and emit jamming signals.
Decomposed and Distributed Directional Modulation for Secure Wireless Communication
This paper presents an AN-aided decomposed and distributed directional modulation (D3M) scheme for secure wireless communications, which takes advantage of the spatial signatures to achieve an extra range-dimension security apart from the angles.
Content-Noise Complementary Learning for Medical Image Denoising
In this study, we propose a simple yet effective strategy, the content-noise complementary learning (CNCL) strategy, in which two deep learning predictors are used to learn the respective content and noise of the image dataset complementarily.
Background-aware Classification Activation Map for Weakly Supervised Object Localization
In our B-CAM, two image-level features, aggregated by pixel-level features of potential background and object locations, are used to purify the object feature from the object-related background and to represent the feature of the pure-background sample, respectively.
Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians
This paper proposes a novel and efficient method to build a Computer-Aided Diagnoses (CAD) system for lung nodule detection based on Computed Tomography (CT).
