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Abstract: The congestion on the freeways as well as on the city roads is increasing day by day due to a continuous rise in the traffic. Better traffic systems using advanced intelligent components are urgently needed to manage this continuous growth of traffic. Currently, traffic departments in the United States utilize inductive loop technologies to provide a solution to this problem by detecting the cars to control the traffic lights. These inductive loops are placed under the road during the construction of the road, or they are placed in the road at a later point by sawing through the surface. As stated earlier, these inductive loops are primarily used for the detection of vehicles and they consume a large amount of power. What if vehicles could be classified as well as identified instead of just being detected? This would make the traffic control system much more advanced and intelligent. To achieve this, an in-node microprocessor-based vehicle classification approach is proposed in this thesis. It can analyze the vehicles’ magnetic data and determine the types of vehicles passing over a 3-axis magnetometer sensor. This is done using the J48 classification algorithm, which is implemented in Waikato Environment for Knowledge Analysis (WEKA), a machine learning software suite, and the Naïve Bayes model implemented in MATLAB (MATrix LABoratory) as well. The J48 is a decision tree machine learning algorithm derived from Quinlan's C4.5 algorithm which is based on the ID3 (Iterative Dichotomiser 3) algorithm. Features are extracted from the data collected from vehicles passing over the sensor and a decision tree model is generated based on those features. The decision tree iii model that is generated is then implemented using decision commands such as if-else in any language and on any microprocessor platform. Also, to make the sensor node reusable at different locations and in different environments, an adaptive baseline is set up to eliminate the background magnetic field from the raw data. Binary Naïve Bayes model is also used in addition to the J48 algorithm as only two major classes of vehicles are being analyzed which are Sedan and Hatchback.
Abstract: Traffic in major metropolitan areas is only increasing, requiring transportation authorities to mitigate traffic congestion. Intelligent Transportation Systems (ITS) offer transportation authorities the data needed to accurately measure current traffic patterns and to make accurate predictions of future patterns.
Abstract: Over the last few decades, our nation has experienced a significant increase in the amount of traffic congestion on freeways and intra-city roads. The current technology designed to control and improve this national problem has met with some real success. Based on inductive loops, that system’s ability to detect the presence of cars and, therefore, the frequency of use of roads has been a great boon. However, the system is not perfect; moreover, based on projections for future use of road infrastructure, the inductive loops solution will not be adequate for long. Continued smooth operations of our road infrastructure require that some more effective solution be developed.
This report presents algorithms for vision-based detection and classification of vehicles in modeled at rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences among blobs and vehicles, as the vehicles move through the image sequence. The system can classify vehicles into two categories, trucks and non-tucks, based on the dimensions of the vehicles. In addition to the category of each vehicle, the system calculates the velocities of the vehicles and generates counts of vehicles in each lane over a user-specified time interval, the total count of each type of vehicle, and the average velocity of each lane during this interval.
Vehicle classification is currently a widely implemented component in intelligent vehicles, surveillance systems, and traffic monitoring. The major component of vehicle classification is to learn what feature in the images of vehicle provides the most valuable information, which distinguishes different models. In this work, the study of two different famous feature extraction mechanisms and three classifiers is carefully conducted to provide comparison and analysis. The next important component of this work is the investigation of the effect of viewing angle and lighting conditions on the performance of the classifier. The latter is inspired by previous studies on face-recognition systems with different lighting conditions and poses [1].
"Java P2P Unleashed" provides a single source for Java developers who want to develop P2P systems. The book explains the benefits of each technology and shows how to fit the P2P "pieces" together - both in building new systems and integrating with existing ones. starts with a discussion of the P2P architecture, referencing similarities with existing, familiar systems while previewing several types of P2P applications. It explains how to plan ahead for security, routing, performance and other issues when developing a P2P application. Each technology included in the book - JXTA, Jini, JavaSpaces, J2EE, Web services - is approached from a P2P perspective, focusing on implementation concerns Java developers will face while using them. The last section includes several large-scale examples of different P2P applications - managing content, building communities, integrating services, routing messages, and using intelligent agents to gather information. The final chapter looks ahead to future developments in Java P2P technologies.
Abstract: Increasing traffic on roadways requires some real-time system that can collect traffic data and helps us to manage existing road infrastructure. For this purpose, we need a state of art system that can detect and classify vehicles into different categories. We developed an in-node microprocessor-based vehicle classification system to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Our approach for vehicle classification utilizes J48 classification algorithm, which is implemented in machine learning software Weka. J48 is a Quinlan's C4.5 algorithm, an extension of decision tree machine learning based on ID3 algorithm. The decision tree model is generated from a set of features extracted from vehicles passing over the 3-axis sensor. The generated tree model can then be easily implemented on microprocessors. The result of our experiment shows that the vehicle classification system is effective and efficient with the very high accuracy at ~98%.
This book features best selected research papers presented at the International Conference on Machine Learning, Internet of Things and Big Data (ICMIB 2020) held at Indira Gandhi Institute of Technology, Sarang, India, during September 2020. It comprises high-quality research work by academicians and industrial experts in the field of machine learning, mobile computing, natural language processing, fuzzy computing, green computing, human–computer interaction, information retrieval, intelligent control, data mining and knowledge discovery, evolutionary computing, IoT and applications in smart environments, smart health, smart city, wireless networks, big data, cloud computing, business intelligence, internet security, pattern recognition, predictive analytics applications in healthcare, sensor networks and social sensing and statistical analysis of search techniques.