Distributed localization of a RF target in NLOS environments

We propose a novel distributed expectation maximization (EM) method for non-cooperative RF device localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. In the case of non-line-of-sight signals, the signal path consists of a single reflection between the transmitter and receiver. Each sensor is able to measure the time difference of arrival of the target's signal with respect to a reference sensor, as well as the angle of arrival of the target's signal. We derive a distributed EM algorithm where each node makes use of its local information to compute summary statistics, and then shares these statistics with its neighbors to improve its estimate of the target localization. Since all the measurements need not be centralized at a single location, the spectrum usage can be significantly reduced. The distributed algorithm also allows for increased robustness of the sensor network in the case of node failures. We show that our distributed algorithm converges, and simulation results suggest that our method achieves an accuracy close to the centralized EM algorithm. We apply the distributed EM algorithm to a set of experimental measurements with a network of four nodes, which confirm that the algorithm is able to localize a RF target in a realistic non-line-of-sight scenario.Comment: 30 pages, 11 figure

Static/Dynamic Filtering for Mesh Geometry

The joint bilateral filter, which enables feature-preserving signal smoothing according to the structural information from a guidance, has been applied for various tasks in geometry processing. Existing methods either rely on a static guidance that may be inconsistent with the input and lead to unsatisfactory results, or a dynamic guidance that is automatically updated but sensitive to noises and outliers. Inspired by recent advances in image filtering, we propose a new geometry filtering technique called static/dynamic filter, which utilizes both static and dynamic guidances to achieve state-of-the-art results. The proposed filter is based on a nonlinear optimization that enforces smoothness of the signal while preserving variations that correspond to features of certain scales. We develop an efficient iterative solver for the problem, which unifies existing filters that are based on static or dynamic guidances. The filter can be applied to mesh face normals followed by vertex position update, to achieve scale-aware and feature-preserving filtering of mesh geometry. It also works well for other types of signals defined on mesh surfaces, such as texture colors. Extensive experimental results demonstrate the effectiveness of the proposed filter for various geometry processing applications such as mesh denoising, geometry feature enhancement, and texture color filtering

Are there interactive effects of physiological and radiative forcing produced by increased CO2 concentration on changes of land hydrological cycle?

AbstractThree coupled climate–carbon cycle models including CESM (Community Earth System Model), CanEsm (the Canadian Centre for Climate Modelling and Analysis Earth System Model) and BCC (Beijing Climate Center Climate System Model) were used to estimate whether changes in land hydrological cycle responded to the interactive effects of CO2-physiological forcing and CO2-radiative forcing. No signs could be indicated that the interactive effects of CO2-physiological forcing and CO2-radiative forcing on the hydrological variables (e.g. precipitation, evapotranspiration and runoff) were detected at global and regional scales. For each model, increases in precipitation, evapotranspiration and runoff (e.g. 0.37, 0.18 and 0.25mm/year2) were simulated in response to CO2-radiative forcing (experiment M3). Decreases in precipitation and evapotranspiration (about −0.02 and −0.09mm/year2) were captured if the CO2 physiological effect was only accounted for (experiment M2). In this experiment, a reverse sign in runoff (the increase of 0.08mm/year2) in contrast to M3 is presented. All models simulated the same signs across Eastern Asia in response to the CO2 physiological forcing and radiative forcing: increases in precipitation and evapotranspiration only considering greenhouse effect; reductions in precipitation and evapotranspiration in response to CO2-physiological effect; and enhanced trends in runoff from all experiments. However, there was still a large uncertainty on the magnitude of the effect of transpiration on runoff (decreased transpiration accounting for 8% to 250% of the increased runoff) from the three models. Two models (CanEsm and BCC) attributed most of the increase in runoff to the decrease in transpiration if the CO2-physiological effect was only accounted for, whereas CESM exhibited that the decrease in transpiration could not totally explain the increase in runoff. The attribution of the CO2-physiological forcing to changes in stomatal conductance versus changes in vegetation structure (e.g. increased Leaf Area Index) is an issue to discuss, and among the three models, no agreement appeared

Efficient Non-Learning Similar Subtrajectory Search

Similar subtrajectory search is a finer-grained operator that can better capture the similarities between one query trajectory and a portion of a data trajectory than the traditional similar trajectory search, which requires the two checked trajectories are similar to each other in whole. Many real applications (e.g., trajectory clustering and trajectory join) utilize similar subtrajectory search as a basic operator. It is considered that the time complexity is O(mn^2) for exact algorithms to solve the similar subtrajectory search problem under most trajectory distance functions in the existing studies, where m is the length of the query trajectory and n is the length of the data trajectory. In this paper, to the best of our knowledge, we are the first to propose an exact algorithm to solve the similar subtrajectory search problem in O(mn) time for most of widely used trajectory distance functions (e.g., WED, DTW, ERP, EDR and Frechet distance). Through extensive experiments on three real datasets, we demonstrate the efficiency and effectiveness of our proposed algorithms.Comment: VLDB 202

Online Ridesharing with Meeting Points [Technical Report]

Nowadays, ridesharing becomes a popular commuting mode. Dynamically arriving riders post their origins and destinations, then the platform assigns drivers to serve them. In ridesharing, different groups of riders can be served by one driver if their trips can share common routes. Recently, many ridesharing companies (e.g., Didi and Uber) further propose a new mode, namely "ridesharing with meeting points". Specifically, with a short walking distance but less payment, riders can be picked up and dropped off around their origins and destinations, respectively. In addition, meeting points enables more flexible routing for drivers, which can potentially improve the global profit of the system. In this paper, we first formally define the Meeting-Point-based Online Ridesharing Problem (MORP). We prove that MORP is NP-hard and there is no polynomial-time deterministic algorithm with a constant competitive ratio for it. We notice that a structure of vertex set, $k$-skip cover, fits well to the MORP. $k$-skip cover tends to find the vertices (meeting points) that are convenient for riders and drivers to come and go. With meeting points, MORP tends to serve more riders with these convenient vertices. Based on the idea, we introduce a convenience-based meeting point candidates selection algorithm. We further propose a hierarchical meeting-point oriented graph (HMPO graph), which ranks vertices for assignment effectiveness and constructs $k$-skip cover to accelerate the whole assignment process. Finally, we utilize the merits of $k$-skip cover points for ridesharing and propose a novel algorithm, namely SMDB, to solve MORP. Extensive experiments on real and synthetic datasets validate the effectiveness and efficiency of our algorithms.Comment: 18 page