Sealing The Cracks in the Wear Layer of the Road Surface by Friction against a Rotary Active Element

The main objective of the research paper is the theoretical and experimental analysis of the method proposed for sealing (clogging) cracks in asphalt, by means of a cylindrical bitumen bar, enriched with plastic and rubber granules (obtained from the use of waste), which melts and infuses into the cracked zone by rotation and friction against it. After analyzing the technical characteristics of the sealed area and the time required to apply the bitumen layer, this method can be chosen in the future to the detriment of the expensive operations of partial milling of the cracked wear layer, making possible the repair of cracks by sealing(clogging), using the friction procedure. The research results highlighted the diminution of road maintenance costs using the method of friction, the decrease of cracks repair time, maintaining the initial characteristics of the repaired area, incorporating a waterproofing material (plastic and rubbber granules from recycled waste), keeping the wear layer in good conditions, possibility of embedding an intelligent system of traffic monitoring at low costs etc.

Design of a VTOL UAV for Anti-poaching Surveillance Missions in the Danube Delta

The main objective of the research paper is the theoretical and experimental analysis of the method proposed for sealing (clogging) cracks in asphalt, by means of a cylindrical bitumen bar, enriched with plastic and rubber granules (obtained from the use of waste), which melts and infuses into the cracked zone by rotation and friction against it. After analyzing the technical characteristics of the sealed area and the time required to apply the bitumen layer, this method can be chosen in the future to the detriment of the expensive operations of partial milling of the cracked wear layer, making possible the repair of cracks by sealing(clogging), using the friction procedure. The research results highlighted the diminution of road maintenance costs using the method of friction, the decrease of cracks repair time, maintaining the initial characteristics of the repaired area, incorporating a waterproofing material (plastic and rubbber granules from recycled waste), keeping the wear layer in good conditions, possibility of embedding an intelligent system of traffic monitoring at low costs etc.

A hierarchical RCNN for vehicle and vehicle license plate detection and recognition

<span>Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced.</span>

A Continuous Terminal Sliding-Mode Observer-Based Anomaly Detection Approach for Industrial Communication Networks

Dynamic traffic monitoring is a critical part of industrial communication network cybersecurity, which can be used to analyze traffic behavior and identify anomalies. In this paper, industrial networks are modeled by a dynamic fluid-flow model of TCP behavior. The model can be described as a class of systems with unmeasurable states. In the system, anomalies and normal variants are represented by the queuing dynamics of additional traffic flow (ATF) and can be considered as a disturbance. The novel contributions are described as follows: (1) a novel continuous terminal sliding-mode observer (TSMO) is proposed for such systems to estimate the disturbance for traffic monitoring; (2) in TSMO, a novel output injection strategy is proposed using the finite-time stability theory to speed up convergence of the internal dynamics; and (3) a full-order sliding-mode-based mechanism is developed to generate a smooth output injection signal for real-time estimations, which is directly used for anomaly detection. To verify the effectiveness of the proposed approach, the real traffic profiles from the Center for Applied Internet Data Analysis (CAIDA) DDoS attack datasets are used.

Citizen Science for Traffic Monitoring: Investigating the Potentials for Complementing Traffic Counters with Crowdsourced Data

Traffic counts are among the most frequently employed data to assess the traffic patterns and key performance indicators of next generation sustainable cities. Automatised counting is often based on conventional traffic monitoring systems such as inductive loop counters (ILCs). These are costly to install, maintain, and support. In this paper, we investigate the possibilities to complement and potentially replace the existing traffic monitoring infrastructure with crowdsourcing solutions. More precisely, we investigate the capabilities to predict the ILC-obtained data using Telraam counters, low-cost camera counters voluntarily employed by citizens and freely accessible by the general public. In this context, we apply different exploratory data analysis approaches and demonstrate a regression procedure with a selected set of regression models. The presented analysis is demonstrated on different urban and highway road segments in Slovenia. Our results show that the data obtained from low-cost and easily accessible counters can be used to replace the existing traffic monitoring infrastructure in different scenarios. These results confirm the prospective to directly apply the citizen engagement in the process of planning and maintaining sustainable future cities.

A Scalable Big Data Framework for Real-Time Traffic Monitoring System

In this paper, we present a scalable and real-time intelligent transportation system based on a big data framework. The proposed system allows for the use of existing data from road sensors to better understand traffic flow, traveler behavior, and increase road network performance. Our transportation system is designed to process large-scale stream data to analyze traffic events such as incidents, crashes and congestion. The experiments performed on the public transportation modes of the city of Casablanca in Morocco reveal that the proposed system achieves a significant gain of time, gathers large-scale data from many road sensors and is not expensive in terms of hardware resource consumption.

Drone to Obstacle Distance Estimation Using YOLO V3 Network and Mathematical Principles

Now a days, drones are very commonly used in various real time applications. Moving towards autonomy, these drones rely on obstacle detection sensors and various collision avoidance algorithms programmed into it. Development of fully autonomous drones provide the fundamental benefits of being able to operate in hazardous environments without a human pilot. Among the various sensors, monocular cameras provide a rich source of information and are one of the main sensing mechanisms in low flying drones. These drones can be used for rescue and search operations, traffic monitoring, infrastructure, and pipeline inspection, and in construction sites. In this paper, we propose an onboard obstacle detection model using deep learning techniques, combined with a mathematical approach to calculate the distance between the detected obstacle and the drone. This when implemented does not need any additional sensor or Global Positioning Systems (GPS) other than the vision sensor.