User Pairing and Power Allocation for FTN-based SC-NOMA and MIMO-NOMA Systems Considering User Fairness

Faster than Nyquist (FTN) and non-orthogonal multiple access (NOMA) are two promising paradigms to improve the spectrum efficiency (SE) of communication systems without any extra spectrum resources required. The combination of FTN signaling and NOMA technology is an interesting attempt and has recently been focused on by researchers. In the NOMA technology, user pairing and power allocation are key algorithms that can determine the system capacity. This paper proposes a joint optimal user pairing and power allocation algorithm for the FTN-based single-carrier (SC) NOMA system, considering user fairness. Then, the FTN-based multiple-input-multiple-output (MIMO) NOMA system is studied and a dynamic user pairing and power allocation scheme is presented. In both scenarios, the constraint for user fairness guarantees that the user's SE in the NOMA system is no less than that in the OMA system. Afterward, the performance of the proposed scheme in achievable sum rate (ASR) and quality of service (QoS) is derived and verified. Simulation results show that the proposed user pairing and power allocation can achieve significantly higher ASRs and lower outage probabilities beyond the conventional orthogonal multiple access (OMA) system and the NOMA system with Nyquist-criterion transmission and random user pairing. As far as we know, this paper is the first solution to the issue of user pairing and power allocation for FTN-based NOMA systems.

I Can Read Your Mind: Control Mechanism Secrecy of Networked Dynamical Systems under Inference Attacks

Recent years have witnessed the fast advance of security research for networked dynamical system (NDS). Considering the latest inference attacks that enable stealthy and precise attacks into NDSs with observation-based learning, this article focuses on a new security aspect, i.e., how to protect control mechanism secrets from inference attacks, including state information, interaction structure and control laws. We call this security property as control mechanism secrecy, which provides protection of the vulnerabilities in the control process and fills the defense gap that traditional cyber security cannot handle. Since the knowledge of control mechanism defines the capabilities to implement attacks, ensuring control mechanism secrecy needs to go beyond the conventional data privacy to cover both transmissible data and intrinsic models in NDSs. The prime goal of this article is to summarize recent results of both inference attacks on control mechanism secrets and countermeasures. We first introduce the basic inference attack methods on the state and structure of NDSs, respectively, along with their inference performance bounds. Then, the corresponding countermeasures and performance metrics are given to illustrate how to preserve the control mechanism secrecy. Necessary conditions are derived to guide the secrecy design. Finally, thorough discussions on the control laws and open issues are presented, beckoning future investigation on reliable countermeasure design and tradeoffs between the secrecy and control performance.

Local Topology Inference of Mobile Robotic Networks under Formation Control

The interaction topology is critical for efficient cooperation of mobile robotic networks (MRNs). We focus on the local topology inference problem of MRNs under formation control, where an inference robot with limited observation range can manoeuvre among the formation robots. This problem faces new challenges brought by the highly coupled influence of unobservable formation robots, inaccessible formation inputs, and unknown interaction range. The novel idea here is to advocate a range-shrink strategy to perfectly avoid the influence of unobservable robots while filtering the input. To that end, we develop consecutive algorithms to determine a feasible constant robot subset from the changing robot set within the observation range, and estimate the formation input and the interaction range. Then, an ordinary least squares based local topology estimator is designed with the previously inferred information. Resorting to the concentration measure, we prove the convergence rate and accuracy of the proposed estimator, taking the estimation errors of previous steps into account. Extensions on nonidentical observation slots and more complicated scenarios are also analyzed. Comprehensive simulation tests and method comparisons corroborate the theoretical findings.

3D Magic Mirror: Automatic Video to 3D Caricature Translation

Caricature is an abstraction of a real person which distorts or exaggerates certain features, but still retains a likeness. While most existing works focus on 3D caricature reconstruction from 2D caricatures or translating 2D photos to 2D caricatures, this paper presents a real-time and automatic algorithm for creating expressive 3D caricatures with caricature style texture map from 2D photos or videos. To solve this challenging ill-posed reconstruction problem and cross-domain translation problem, we first reconstruct the 3D face shape for each frame, and then translate 3D face shape from normal style to caricature style by a novel identity and expression preserving VAE-CycleGAN. Based on a labeling formulation, the caricature texture map is constructed from a set of multi-view caricature images generated by CariGANs. The effectiveness and efficiency of our method are demonstrated by comparison with baseline implementations. The perceptual study shows that the 3D caricatures generated by our method meet people's expectations of 3D caricature style.

Self-supervised CNN for Unconstrained 3D Facial Performance Capture from an RGB-D Camera

We present a novel method for real-time 3D facial performance capture with consumer-level RGB-D sensors. Our capturing system is targeted at robust and stable 3D face capturing in the wild, in which the RGB-D facial data contain noise, imperfection and occlusion, and often exhibit high variability in motion, pose, expression and lighting conditions, thus posing great challenges. The technical contribution is a self-supervised deep learning framework, which is trained directly from raw RGB-D data. The key novelties include: (1) learning both the core tensor and the parameters for refining our parametric face model; (2) using vertex displacement and UV map for learning surface detail; (3) designing the loss function by incorporating temporal coherence and same identity constraints based on pairs of RGB-D images and utilizing sparse norms, in addition to the conventional terms for photo-consistency, feature similarity, regularization as well as geometry consistency; and (4) augmenting the training data set in new ways. The method is demonstrated in a live setup that runs in real-time on a smartphone and an RGB-D sensor. Extensive experiments show that our method is robust to severe occlusion, fast motion, large rotation, exaggerated facial expressions and diverse lighting.