Robust Distributed Fusion with Labeled Random Finite Sets

This paper considers the problem of the distributed fusion of multi-object posteriors in the labeled random finite set filtering framework, using Generalized Covariance Intersection (GCI) method. Our analysis shows that GCI fusion with labeled multi-object densities strongly relies on label consistencies between local multi-object posteriors at different sensor nodes, and hence suffers from a severe performance degradation when perfect label consistencies are violated. Moreover, we mathematically analyze this phenomenon from the perspective of Principle of Minimum Discrimination Information and the so called yes-object probability. Inspired by the analysis, we propose a novel and general solution for the distributed fusion with labeled multi-object densities that is robust to label inconsistencies between sensors. Specifically, the labeled multi-object posteriors are firstly marginalized to their unlabeled posteriors which are then fused using GCI method. We also introduce a principled method to construct the labeled fused density and produce tracks formally. Based on the developed theoretical framework, we present tractable algorithms for the family of generalized labeled multi-Bernoulli (GLMB) filters including $\delta$-GLMB, marginalized $\delta$-GLMB and labeled multi-Bernoulli filters. The robustness and efficiency of the proposed distributed fusion algorithm are demonstrated in challenging tracking scenarios via numerical experiments.Comment: 17pages, 23 figure

Modeling Holocene Peatland Carbon Accumulation in North America

Peatlands are a large carbon reservoir. Yet the quantification of their carbon stock still has a large uncertainty due to lacking observational data and well‐tested peatland biogeochemistry models. Here, a process‐based peatland model was calibrated using long‐term peat carbon accumulation data at multiple sites in North America. The model was then applied to quantify the peat carbon accumulation rates and stocks within North America over the last 12,000 years. We estimated that 85–174 Pg carbon was accumulated in North American peatlands over the study period including 0.37–0.76 Pg carbon in subtropical peatlands. During the period from 10,000 to 8,000 years ago, the warmer and wetter conditions might have played an important role in stimulating peat carbon accumulation by enhancing plant photosynthesis. Enhanced peat decomposition due to warming slowed the carbon accumulation through the rest of the Holocene. While recent modeling studies indicate that the northern peatlands will continue to act as a carbon sink in this century, our studies suggest that future enhanced peat decomposition accompanied by peatland areal changes induced by permafrost degradation and other disturbances shall confound the sink and source analysis

Incorporating D2D to Current Cellular Communication System

A device-to-device (D2D) group works as relay nodes to aid the information delivery from a source to a destination in cellular communication network. Within this system, we propose a communication mechanism to aid traditional cellular communication and correspondingly borrow some channel resource from traditional cellular communication system for D2D communication. On one side, to aid cellular communication, we propose a modified Alamouti scheme which does not modify the operation at the base station. This makes our proposed scheme consistent with previous cellular communication system. On the other side, there are many competitive D2D groups that want to potentially utilize the borrowed channel resource from traditional cellular system for delivering their own information. We model this competition as a game and utilize game theory technique to solve this competition problem

Distributed Joint Attack Detection and Secure State Estimation

The joint task of detecting attacks and securely monitoring the state of a cyber-physical system is addressed over a cluster-based network wherein multiple fusion nodes collect data from sensors and cooperate in a neighborwise fashion in order to accomplish the task. The attack detection–state estimation problem is formulated in the context of random set theory by representing joint information on the attack presence/absence, on the system state, and on the attack signal in terms of a hybrid Bernoulli random set (HBRS) density. Then, combining previous results on HBRS recursive Bayesian filtering with novel results on Kullback–Leibler averaging of HBRSs, a novel distributed HBRS filter is developed and its effectiveness is tested on a case study concerning wide-area monitoring of a power network

Baicalin Protects Mice Brain From Apoptosis in Traumatic Brain Injury Model Through Activation of Autophagy

Autophagy is associated with secondary injury following traumatic brain injury (TBI) and is expected to be a therapeutic target. Baicalin, a neuroprotective agent, has been proven to exert multi-functional bioactive effects in brain injury diseases. However, it is unknown if Baicalin influences autophagy after TBI. In the present study, we aimed to explore the effects that Baicalin had on TBI in a mice model, focusing on autophagy as a potential mechanism. We found that Baicalin administration significantly improved motor function, reduced cerebral edema, and alleviated disruption of the blood-brain barrier (BBB) after TBI in mice. Besides, TBI-induced apoptosis was reversed by Baicalin evidenced by Nissl staining, terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, and the level of cleaved caspase-3. More importantly, Baicalin enhanced autophagy by detecting the autophagy markers (LC3, Beclin 1, and p62) using western blot and LC3 immunofluorescence staining, ameliorating mitochondrial apoptotic pathway evidenced by restoration of the TBI-induced translocation of Bax and cytochrome C. However, simultaneous treatment with 3-MA inhibited Baicalin-induced autophagy and abolished its protective effects on mitochondrial apoptotic pathway. In conclusion, we demonstrated that Baicalin enhanced autophagy, ameliorated mitochondrial apoptosis and protected mice brain in TBI mice model

Move-stop-move Target Tracking with Low-altitude Surveillance Radars

Low-altitude small targets, represented by rotor unmanned aerial vehicles, always adopt slow move-and-stop strategy or employ an obstacle blocking strategy to avoid radar detection and conduct point-and-point strikes or interference on important information equipment and strategic bases. This type of target can appear and disappear from the radar Field of View (FoV) multiple times, thus, it is referred to as move-stop-move targets. Dealing with this type of target using traditional tracking models and algorithms can lead to discontinuities in target identity and track fragmentation. To this end, this study investigates the tracking problem of move-stop-move targets with the Labeled Multi-Bernoulli (LMB) filter based on random finite set statistics. To describe the evolution characteristics of multiple entries to the radar FoV, first, we introduce the third type of birth procedure, that is, the Re-Birth (RB) procedure. Specifically, based on the spatial and kinematic relationships between target states before and after returning to the radar FoV, a Spatial Correlation-based RB (SC-RB) procedure is proposed. Then, in the framework of Bayesian filtering, we derive the SC-RB-LMB filter with the proposed SC-RB model, which is capable of tracking move-stop-move targets continuously with its identity unchanged. In typical low-altitude surveillance scenarios, the effectiveness of the proposed model and algorithm is highlighted