A Utility Method for the Matching Optimization of Ride-Sharing Based on the E-CARGO Model in Internet of Vehicles

The Internet of Vehicles (IoV) is the extension of the Internet of Things (IoT) technology in the field of transportation systems. Ride-sharing is one of intelligent travel applications in IoV. Ride-sharing is aimed at taking passengers with similar itineraries and time arrangements to travel in the same car according to a certain matching rule. To effectively integrate transport capacity resources and reduce the number of cars on the road, ride-sharing has become a popular and economical way of travel. The matching and optimizing of drivers and passengers are the core contents of a ride-sharing application system. This paper mainly studies the dynamic real-time matching of passengers and drivers in IoV, considering the main factors such as travel cost, car capacity, and utility. The matching problem is formulated in a ride-sharing system as a Role-Based Collaboration (RBC). A new utility method for the matching optimization of ride-sharing is present. In this paper, we establish a model for simulating the assignment of ride-sharing with the help of the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. The objective function and formal definitions are proposed. The utility and time of optimal matching are obtained by using the Kuhn-Munkres algorithm on the revenue matrix. The experimental results show that the proposed formal method based on the E-CARGO model and utility theory can be applied in the ride-sharing problem. Numerical experiments show that the matching time cost increases with the increase of the number of drivers and passengers participating in the ride-sharing system. When the number of drivers and passengers is different, one-to-many matching takes the least time, and one-to-one matching takes more time. When the number of drivers and passengers is the same, the time cost of one-to-one matching increases sharply with a certain value (bigger than 800). Compared with other matching methods, the time spent by the one-to-many method is reduced by 30%. The results show that the proposed solution can be applied to the matching and pricing in a ride-sharing system.

AN APERIODIC TILE WITH EDGE-TO-EDGE ORIENTATIONAL MATCHING RULES

We present a single, connected tile which can tile the plane but only nonperiodically. The tile is hexagonal with edge markings, which impose simple rules as to how adjacent tiles are allowed to meet across edges. The first of these rules is a standard matching rule, that certain decorations match across edges. The second condition is a new type of matching rule, which allows tiles to meet only when certain decorations in a particular orientation are given the opposite charge. This forces the tiles to form a hierarchy of triangles, following a central idea of the Socolar–Taylor tilings. However, the new edge-to-edge orientational matching rule forces this structure in a very different way, which allows for a surprisingly simple proof of aperiodicity. We show that the hull of all tilings satisfying our rules is uniquely ergodic and that almost all tilings in the hull belong to a minimal core of tilings generated by substitution. Identifying tilings which are charge-flips of each other, these tilings are shown to have pure point dynamical spectrum and a regular model set structure.

A Simple Contrast Matching Rule for OSEM Reconstructed PET Images with Different Time of Flight Resolution

Background: Time-of-Flight (TOF) is a leading technological development of Positron Emission Tomography (PET) scanners. It reduces noise at the Maximum-Likelihood solution, depending on the coincidence–timing–resolution (CTR). However, in clinical applications, it is still not clear how to best exploit TOF information, as early stopped reconstructions are generally used. Methods: A contrast-recovery (CR) matching rule for systems with different CTRs and non-TOF systems is theoretically derived and validated using (1) digital simulations of objects with different contrasts and background diameters, (2) realistic phantoms of different sizes acquired on two scanners with different CTRs. Results: With TOF, the CR matching rule prescribes modifying the iterations number by the CTRs ratio. Without TOF, the number of iterations depends on the background dimension. CR matching was confirmed by simulated and experimental data. With TOF, image noise followed the square root of the CTR when the rule was applied on simulated data, while a significant reduction was obtained on phantom data. Without TOF, preserving the CR on larger objects significantly increased the noise. Conclusions: TOF makes PET reconstructions less dependent on background dimensions, thus, improving the quantification robustness. Better CTRs allows performing fewer updates, thus, maintaining accuracy while minimizing noise.

HDL Implementation of Scalable Architecture for Packet Classification using Field Split Bit Vector Algorithm

The categorization of incoming packets can be considered as a classification based on the fields of the different headers, such as the source-Internet protocol, the target-Internet protocol, the source-port, destination-port and protocol fields. It requires that each packet is compared with rules and each packet is forwarded to the highest priority matching rule. Packet classification performance also depends on the rule sets. The required storage depends generally on the number of rules and the size of the method. In this paper, we described a Modular Field Split Bit-Vector (FSBV) algorithm, with which the Field Programmable Gate Array (FPGA) classification of packets is performed using Xilinx ISE13.1 software, with a few predefined rules. From the results obtained through EDA tools, it can be concluded that the proposed technique is memory-efficient and latency aware.

Breakdown of the ideal free distribution under conditions of severe and low competition

Under the ideal free distribution (IFD), the number of organisms competing for a resource at different sites is proportional to the resource distribution among sites. The ideal free distribution of competitors in a heterogeneous environment often predicts habitat matching, where the relative number of individuals using any two patches matches the relative availability of resources in those same two patches. If a resource is scarce, access might be restricted to individuals with high resource holding potential, resulting in deviation from the IFD. The distribution of animals may also deviate from the IFD in the case of resource abundance, when social attraction or preference for specific locations rather than competition may determine distribution. While it was originally developed to explain habitat choice, we apply the habitat matching rule to microscale foraging decisions. We show that chickens feeding from two nondepleting feeders distribute proportionally to feeder space under intermediate levels of competition. However, chicken distribution between the feeders deviates from the IFD when feeder space is limited and competition high. Further, despite decreasing aggression with increasing feeder space, deviation from IFD is also observed under an excess supply of feeder space, indicating different mechanisms responsible for deviations from the IFD. Besides demonstrating IFD sensitivity to competition, these findings highlight IFD’s potential as a biological basis for determining minimal resource requirements in animal housing. Significance statement The ideal free distribution (IFD) predicts how animals ought to distribute themselves within a habitat in order to maximize their payoff. Recent studies, however, have questioned the validity of the IFD concept following anomalous results. We studied the IFD in chickens by systematically varying the amount and distribution of space at two feed troughs. We show that when tested over a sufficiently large range, the distribution of birds depends on the overall resource availability. Furthermore, behavioral data suggest that distinctly different mechanisms account for deviations from the IFD at shortage and excess supply of feeder space, respectively.

Vacant parking slot detection and tracking during driving and parking with a standalone around view monitor

Due to distortion, limitations of vision, and occlusion, most of the existing vacant parking slot detection methods with a standalone around view monitor (AVM) are prone to miss some parking slots and incorrectly identify whether the parking slot is vacant. To overcome this problem, we propose a complete method for vacant parking slot detection and tracking during driving and parking. Considering the different conditions of driving and parking, two different deep convolutional neural networks (DCNNs) are used to detect parking slots, of which the vacant parking slot detection network (VPS-Net) is used to detect vacant parking slots during driving, and the directional marking point detection network (DMPR-PS) is used to detect the directional marking points of the target parking slot during parking. Furthermore, in the driving process, we design a new matching rule and tracking management rule based on the Kernelized Correlation Filter (KCF) to track the parking slots, and fuse classification results of multiple frames to determine the occupancy status. In the parking process, since the parking slot is easily blocked by the vehicle, we design another new tracker to track the directional marking points and infer the complete parking slot using tracking results and prior geometric information. To evaluate the proposed method, a labeled video sequence dataset is established. Experiments show that the proposed method has improved the accuracy and continuity of vacant parking slots detection and positioning whether in the driving process or parking process.

An image inpainting method for object removal based on difference degree constraint

In the inpainting method for object removal, SSD (Sum of Squared Differences) is commonly used to measure the degree of similarity between the exemplar patch and the target patch, which has a very important impact on the restoration results. Although the matching rule is relatively simple, it is likely to lead to the occurrence of mismatch error. Even worse, the error may be accumulated along with the process continues. Finally some unexpected objects may be introduced into the target region, making the result unable to meet the requirements of visual consistency. In view of these problems, we propose an inpainting method for object removal based on difference degree constraint. Firstly, we define the MSD (Mean of Squared Differences) and use it to measure the degree of differences between corresponding pixels at known positions in the target patch and the exemplar patch. Secondly, we define the SMD (Square of Mean Differences) and use it to measure the degree of differences between the pixels at known positions in the target patch and the pixels at unknown positions in the exemplar patch. Thirdly, based on MSD and SMD, we define a new matching rule and use it to find the most similar exemplar patch in the source region. Finally, we use the exemplar patch to restore the target patch. Experimental results show that the proposed method can effectively prevent the occurrence of mismatch error and improve the restoration effect.

GENis, an open-source multi-tier forensic DNA information system

GENis is an open source multi-tier information system developed to run a forensic DNA database at local, regional and national levels.1 It was conceived as a highly customizable system, enforcing several security policies including: data encryption, double factor identification, structure of user’s roles and permissions, system-wide secure log auditing, non-repudiation protocols and a blockchain-based option to reinforce genetic profile’s integrity. GENis is able to perform genetic profile queries of autosomal STR’s and its design follows ENFSI2 and ISFG3 standards and recommendations. In this work, we present a summary of GENis architecture, the implemented matching rule definitions, and the Bayesian framework used to provide statistical significance of cold hits, that includes a new strategy to identify common contributors in mixture pairs.