MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

Surveys, Astrometric Follow-up & Population Statistics

Asteroid surveys are the backbone of asteroid science, and with this in mind we begin with a broad review of the impact of asteroid surveys on our field. We then provide a brief history of asteroid discoveries so as to place contemporary and future surveys in perspective. Surveys in the United States have discovered the vast majority of the asteroids and this dominance has been consolidated since the publication of Asteroids III. Our descriptions of the asteroid surveys that have been operational since that time are focussed upon those that have contributed the vast majority of asteroid observations and discoveries. We also provide some insight into upcoming next-generation surveys that are sure to alter our understanding of the small bodies in the inner solar system and provide evidence to untangle their complicated dynamical and physical histories. The Minor Planet Center, the nerve center of the asteroid discovery effort, has improved its operations significantly in the past decade so that it can manage the increasing discovery rate, and ensure that it is well-placed to handle the data rates expected in the next decade. We also consider the difficulties associated with astrometric follow-up of newly identified objects. It seems clear that both of these efforts must operate in new modes in order to keep pace with expected discovery rates of next-generation ground- and space-based surveys.Comment: Chapter to appear in the book ASTEROIDS IV, (University of Arizona Press) Space Science Series, edited by P. Michel, F. DeMeo and W. Bottk

Understanding Vehicular Traffic Behavior from Video: A Survey of Unsupervised Approaches

Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed. Research has shifted from high-resolution estimation of vehicle state and instead, pushed machine learning approaches to extract meaningful patterns in aggregates in an unsupervised fashion. These patterns represent priors on observable motion, which can be utilized to describe a scene, answer behavior questions such as where is a vehicle going, how many vehicles are performing the same action, and to detect an abnormal event. The review focuses on two main methods for scene description, trajectory clustering and topic modeling. Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented. Discussion and comment on future directions in the field are also provide

Video surveillance systems-current status and future trends

Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance

Air Force Institute of Technology Research Report 2020

This Research Report presents the FY20 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

Traffic Characterization of an Internet of Things(IOT) Network Architecture

Internet of things (IoT) is an evolving paradigm that is currently getting more attention and rapidly gaining importance. The basic idea of IoT is to connect everyone and everything to the Internet for information exchange. It is essential to develop a clear understanding of characteristics of IoT traffic sources as well as to find a traffic model that efficiently characterizes the statistical behavior of IoT traffic. Since many IoT devices generate relatively small sized data, we are particularly interested in an IoT network architecture where data from a number of different IoT devices are aggregated at an IoT gateway. We focus on characterizing the IoT aggregated traffic pattern for three common IoT applications with real-time and non-real-time quality of service (QoS) requirements. These applications include healthcare, smart cities, and video surveillance. Our study is based on generating a real IoT traffic trace in a lab by using various sensors and devices in the aforementioned applications. The generated traffic trace is transmitted wirelessly over the air using Wi-Fi technology to an IoT gateway. The input network traffic to this gateway is characterized. In the experiments, the amount of input traffic to the gateway is varied and different traffic patterns for each of the selected applications are examined. Statistical tests and parameters are used to determine the best matching packet inter-arrival time distribution for different traffic penetrations. Moreover, we also examine packet size distributions. Based on our empirical data, the experimental results indicate that IoT packet inter-arrival time follows a Pareto distribution. However, it can be better modeled as a Weibull distribution in some traffic patterns. Our experimental results also reveal that the packet size distribution of different penetrations of the studied IoT applications is not in a good match with the commonly used Geometric distribution. Furthermore, we investigate the impact of traffic characterization on the performance of the considered IoT network architecture for a certain availability of network resources using computer simulations