With its potential to solve the problem of car parking difficulties, smart parking management systems have elicited a significant amount of interest from both academia and industry. Robust and reliable parking detection is a key technology for smart parking management systems. Presently, magnetic sensors are being widely used in parking detection. However, they are prone to produce high false and missed detection rates in environments that have geomagnetic interference. This article proposes a new sensing device based on the combination of a magnetic sensor and a pulsed coherent radar. The sensing device is used for parking detection in the context of Internet of Things. A Dempster–Shafer evidence theory-based collaborative fusion approach is developed to improve the accuracy of vehicle detection. Pulsed coherent radar is triggered by the magnetic sensor to reduce sampling power consumption. The prototype system is tested under various interference conditions. The results show that the combination of magnetic and radar sensors improves vehicle detection accuracy, and the lifetime of battery-powered sensor node is longer than five years.

Wireless magnetic sensor networks (WMSNs) provide a low-power, low-cost solution for traffic information acquisition. Systems used for traffic speed estimation based on WMSNs have been well studied only when the geomagnetic field has not interfered. However, the widely developed electric rail transit systems, such as subways, light rails, and high-speed rail systems, produce magnetic field radiation that interferes with the geomagnetic field. So it is imperative to filter out noise in order to increase the detection accuracy. This article proposes a system for traffic speed estimation which can effectively eliminate the geomagnetic background interference. A morphological filter is designed for removing the interference and extracting the magnetic signatures of vehicles. The speed estimation is implemented based on the signatures reidentification. Experimental results verify that the proposed system is more accurate than existing systems.

A basic problem for the constant dimension subspace coding is to determine the maximal possible size Aq(n, d, k) of a set of k-dimensional subspaces in Fnqsuch that the subspace distance satisfies d(U, V ) = 2k - 2 dim (U ∩ V ) ≥ d for any two different subspaces U and V in this set. We present two new constructions of constant dimension subspace codes using subsets of maximum rank-distance (MRD) codes in several blocks. This method is firstly applied to the linkage construction and secondly to arbitrary number of blocks of lifting MRD codes. In these two constructions, subsets of MRD codes with bounded ranks play an essential role. The Delsarte theorem about the rank distribution of MRD codes is an important ingredient to count codewords in our constructed constant dimension subspace codes. We give many new lower bounds for Aq(n, d, k). More than 110 new constant dimension subspace codes better than previously best known codes are constructed.

Wireless magnetic sensor networks (WMSNs) offer an effective and efficient low-cost alternative for real-time parking space monitoring. Current algorithms used for parking space detection based on the WMSNs have been well studied but not without eliminating the geomagnetic field interference. Electric railway systems, such as subways and light rail systems, produce magnetic field radiation that interferes with the geomagnetic field. The electric railway magnetic field interference has increased and is prominent in urban settings; therefore, it is imperative to filter out the interference in order to increase the detection accuracy. This paper proposes an algorithm for parking space detection, which can effectively eliminate the geomagnetic background interference. We solve the parking detection problem through signal filtering technology. Based on mathematical morphology, we designed two filters for extracting morphological structures from interfered magnetic signals. The experimental results verify that the proposed algorithm is more accurate than the existing algorithms.

Frequent serial episodes within an event sequence describe the behavior of users or systems about the application. Existing mining algorithms calculate the frequency of an episode based on overlapping or non-minimal occurrences, which is prone to over-counting the support of long episodes or poorly characterizing the followed-by-closely relationship over event types. In addition, due to utilizing the Apriori-style level wise approach, these algorithms are computationally expensive. In this paper, we propose an efficient algorithm MANEPI (Minimal And Non-overlapping EPIsode) for mining more interesting frequent episodes within the given event sequence. The proposed frequency measure takes both minimal and non-overlapping occurrences of an episode into consideration and ensures better mining quality. The introduced depth first search strategy with the Apriori Property for performing episode growth greatly improves the efficiency of mining long episodes because of scanning the given sequence only once and not generating candidate episodes. Moreover, an optimization technique is presented to narrow down search space and speed up the mining process. Experimental evaluation on both synthetic and real-world datasets demonstrates that our algorithms are more efficient and effective.