5 research outputs found
Joint and Competitive Caching Designs in Large-Scale Multi-Tier Wireless Multicasting Networks
Caching and multicasting are two promising methods to support massive content
delivery in multi-tier wireless networks. In this paper, we consider a random
caching and multicasting scheme with caching distributions in the two tiers as
design parameters, to achieve efficient content dissemination in a two-tier
large-scale cache-enabled wireless multicasting network. First, we derive
tractable expressions for the successful transmission probabilities in the
general region as well as the high SNR and high user density region,
respectively, utilizing tools from stochastic geometry. Then, for the case of a
single operator for the two tiers, we formulate the optimal joint caching
design problem to maximize the successful transmission probability in the
asymptotic region, which is nonconvex in general. By using the block successive
approximate optimization technique, we develop an iterative algorithm, which is
shown to converge to a stationary point. Next, for the case of two different
operators, one for each tier, we formulate the competitive caching design game
where each tier maximizes its successful transmission probability in the
asymptotic region. We show that the game has a unique Nash equilibrium (NE) and
develop an iterative algorithm, which is shown to converge to the NE under a
mild condition. Finally, by numerical simulations, we show that the proposed
designs achieve significant gains over existing schemes.Comment: 30 pages, 6 pages, submitted to IEEE GLOBECOM 2017 and IEEE Trans.
Commo
CoNi-MPC: Cooperative Non-inertial Frame Based Model Predictive Control
This paper presents a novel solution for UAV control in cooperative
multi-robot systems, which can be used in various scenarios such as
leader-following, landing on a moving base, or specific relative motion with a
target. Unlike classical methods that tackle UAV control in the world frame, we
directly control the UAV in the target coordinate frame, without making motion
assumptions about the target. In detail, we formulate a non-linear model
predictive controller of a UAV, referred to as the agent, within a non-inertial
frame (i.e., the target frame). The system requires the relative states (pose
and velocity), the angular velocity and the accelerations of the target, which
can be obtained by relative localization methods and ubiquitous MEMS IMU
sensors, respectively. This framework eliminates dependencies that are vital in
classical solutions, such as accurate state estimation for both the agent and
target, prior knowledge of the target motion model, and continuous trajectory
re-planning for some complex tasks. We have performed extensive simulations to
investigate the control performance with varying motion characteristics of the
target. Furthermore, we conducted real robot experiments, employing either
simulated relative pose estimation from motion capture systems indoors or
directly from our previous relative pose estimation devices outdoors, to
validate the applicability and feasibility of the proposed approach
CREPES: Cooperative RElative Pose Estimation System
Mutual localization plays a crucial role in multi-robot cooperation. CREPES,
a novel system that focuses on six degrees of freedom (DOF) relative pose
estimation for multi-robot systems, is proposed in this paper. CREPES has a
compact hardware design using active infrared (IR) LEDs, an IR fish-eye camera,
an ultra-wideband (UWB) module and an inertial measurement unit (IMU). By
leveraging IR light communication, the system solves data association between
visual detection and UWB ranging. Ranging measurements from the UWB and
directional information from the camera offer relative 3-DOF position
estimation. Combining the mutual relative position with neighbors and the
gravity constraints provided by IMUs, we can estimate the 6-DOF relative pose
from a single frame of sensor measurements. In addition, we design an estimator
based on the error-state Kalman filter (ESKF) to enhance system accuracy and
robustness. When multiple neighbors are available, a Pose Graph Optimization
(PGO) algorithm is applied to further improve system accuracy. We conduct
enormous experiments to demonstrate CREPES' accuracy between robot pairs and a
team of robots, as well as performance under challenging conditions
Body Dissatisfaction and Disordered Eating Behaviors: The Mediation Role of Smartphone Addiction and Depression
This study aimed to determine whether smartphone addiction and depression sequentially mediate the relationship between body dissatisfaction and disordered eating behaviors (e.g., restrained eating, emotional eating and external eating). A total of 5986 participants (54.1% females, average age = 19.8 years, age range = 17–32) completed the Satisfaction and Dissatisfaction with Body Parts Scale, the Three-Factor Eating Questionnaire, the Smartphone Addiction Scale and the Patient Health Questionnaire-9. Mediational analysis showed that, after controlling for age, sex and body mass index, body dissatisfaction was related to disordered eating behaviors through (a) the mediating effect of smartphone addiction, (b) the mediating effect of depression, and (c) the serial mediating effect of smartphone addiction and depression. In conclusion, our study showed for the first time that smartphone addiction and depression can be sequential mediator variables in the association between body dissatisfaction and disordered eating. However, this study is a cross-sectional study; future longitudinal studies could further test the causal associations between these study variables