A framework for cloned vehicle detection

Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy.It necessitates an efficient detection mechanism to identify the vehicles with fake license plates accurately,and further explore the motives through discerning the behaviors of cloned vehicles.The ubiquitous inspection spots that deployed in the city have been collecting moving information of passing vehicles,which opens up a new opportunity for cloned vehicle detection.Existing detection methods cannot detect the cloned vehicle effectively due to that they use the fixed speed threshold.In this paper,we propose a two-phase framework,called CVDF,to detect cloned vehicles and discriminate behavior patterns of vehicles that use the same plate number.In the detection phase,cloned vehicles are identified based on speed thresholds extracted from historical trajectory and behavior abnormality analysis within the local neighborhood.In the behavior analysis phase,consider the traces of vehicles that uses the same license plate will be mixed together,we aim to differentiate the trajectories through matching degree-based clustering and then extract frequent temporal behavior patterns.The experimental results on the real-world data show that CVDF framework has high detection precision and could reveal cloned vehicles’behavior effectively.Our proposal provides a scientific basis for traffic management authority to solve the crime of cloned vehicle.

Distributed top-k similarity query on big trajectory streams

Recently, big trajectory data streams are generated in distributed environments with the popularity of smartphones and other mobile devices. Distributed top?k similarity query, which finds k trajectories that are most similar to a given query trajectory from all remote sites, is critical in this field. The key challenge in such a query is how to reduce the communication cost due to the limited network bandwidth resource. Although this query can be solved by sending the query trajectory to all the remote sites, in which the pairwise similarities are computed precisely. However, the overall cost, O(n·m),is huge when nor mis huge, where n is the size of query trajectory and m is the number of remote sites. Fortunately, there are some cheap ways to estimate pairwise similarity, which filter some trajectories in advance without precise computation. In order to overcome the challenge in this query, we devise two general frameworks, into which concrete distance measures can be plugged. The former one uses two bounds (the upper and lower bound), while the latter one only uses the lower bound. Moreover, we introduce detailed implementations of two representative distance measures, Euclidean and DTW distance, after inferring the lower and upper bound for the former framework and the lower bound for the latter one. Theoretical analysis and extensive experiments on real-world datasets evaluate the efficiency of proposed methods.

A Self-Strengthening Epidermis-Like Smart Coating Enabled by Dynamic Iron Sequestration

Living organisms rely on materials sequestration from the environment to strengthen their bodies.Such constant activity provides necessary supplies for critical biological functions,which is difficult by man-made structures due to the lack of a dynamic sequestration mechanism.In this study,it is shown that an epidermis-like smart coating can respond to changes in salinity to allow a spontaneous and progressive strengthening when supported by steel as a sustained iron source.The self-strengthening is enabled by reaction-coupled diffusion,which makes possible the dynamic sequestration of iron from the in situ rusted steel,partially mimicking the iron accumulation process in a mussel.The emergence of iron-phenolic coordination bonds continuously improves modulus,hardness,and adhesion,achieving a strengthening efficiency comparable to mussel byssus.Interestingly,the bilayer design ensures long-term self-healing performance even during strengthening and a mechanically robust anticorrosion coating with an autonomic protection mechanism and gradually enhanced barrier properties during prolonged service.Our results provide a new venue for the design of stimuli-responsive smart materials characteristic of an open biological system and may serve as the basis for creating highly intelligent devices with sophisticated functionality.