Individual and group dynamic behaviour patterns in bound spaces

The behaviour analysis of individual and group dynamics in closed spaces is a subject of extensive research in both academia and industry. However, despite recent technological advancements the problem of implementing the existing methods for visual behaviour data analysis in production systems remains difficult and the applications are available only in special cases in which the resourcing is not a problem. Most of the approaches concentrate on direct extraction and classification of the visual features from the video footage for recognising the dynamic behaviour directly from the source. The adoption of such an approach allows recognising directly the elementary actions of moving objects, which is a difficult task on its own. The major factor that impacts the performance of the methods for video analytics is the necessity to combine processing of enormous volume of video data with complex analysis of this data using and computationally resourcedemanding analytical algorithms. This is not feasible for many applications, which must work in real time. In this research, an alternative simulation-based approach for behaviour analysis has been adopted. It can potentially reduce the requirements for extracting information from real video footage for the purpose of the analysis of the dynamic behaviour. This can be achieved by combining only limited data extracted from the original video footage with a symbolic data about the events registered on the scene, which is generated by 3D simulation synchronized with the original footage. Additionally, through incorporating some physical laws and the logics of dynamic behaviour directly in the 3D model of the visual scene, this framework allows to capture the behavioural patterns using simple syntactic pattern recognition methods. The extensive experiments with the prototype implementation prove in a convincing manner that the 3D simulation generates sufficiently rich data to allow analysing the dynamic behaviour in real-time with sufficient adequacy without the need to use precise physical data, using only a limited data about the objects on the scene, their location and dynamic characteristics. This research can have a wide applicability in different areas where the video analytics is necessary, ranging from public safety and video surveillance to marketing research to computer games and animation. Its limitations are linked to the dependence on some preliminary processing of the video footage which is still less detailed and computationally demanding than the methods which use directly the video frames of the original footage

Digital Signal Processing for Hydroacoustic System in Biomimetic Underwater Vehicle

Signal processing in hydroacoustic system will be presented in this paper. The research results, depicted in this article, were achieved during realization one of the stages of the project for the development of an biomimetic underwater vehicle (BUV). The hydroacoustic system is installed inside Biomimetic Underwater Vehicle no. 2 (BUV2) and is designed for passive obstacle detection system. The passive measurement system was based on two hydrophones mounted on the upper part of the BUV2. The results of the hydroacoustic module testing were made in a real environment. The signals from the hydrophones were converted from analog to digital form and then filtered and analyzed by using algorithms implemented in the Texas Instruments C2000 series microcontroller

3D Simulation-based Analysis of Individual and Group Dynamic Behaviour in Video Surveillance

The visual behaviour analysis of individual and group dynamics is a subject of extensive research in both academia and industry. However, despite the recent technological advancements, the problem remains difficult. Most of the approaches concentrate on direct extraction and classification of graphical features from the video feed, analysing the behaviour directly from the source. The major obstacle, which impacts the real-time performance, is the necessity of combining processing of enormous volume of video data with complex symbolic data analysis. In this paper, we present the results of the experimental validation of a new method for dynamic behaviour analysis in visual analytics framework, which has as a core an agent-based, event-driven simulator. Our method utilizes only limited data extracted from the live video to analyse the activities monitored by surveillance cameras. Through combining the ontology of the visual scene, which accounts for the logical features of the observed world, with the patterns of dynamic behaviour, approximating the visual dynamics of the world, the framework allows recognizing the behaviour patterns on the basis of logical events rather than on physical appearance. This approach has several advantages. Firstly, the simulation reduces the complexity of data processing by eliminating the need of precise graphic data. Secondly, the granularity and precision of the analysed behaviour patterns can be controlled by parameters of the simulation itself. The experiments prove in a convincing manner that the simulation generates rich enough data to analyse the dynamic behaviour in real time with sufficient precision, completely adequate for many applications of video surveillance

Simulation-based visual analysis of individual and group dynamic behavior

The article presents a new framework for individual and group dynamic behavior analysis with wide applicability to video surveillance and security, accidents and safety management, customer insight and computer games. It combines graphical multi-agent simulation and motion pattern recognition for performing visual data analysis using an object-centric approach. The article describes the simulation model used for modeling the individual and group dynamics which is based on the analytical description of dynamic trajectories in closed micro-worlds and the individual and group behavior patterns exhibited by the agents in the visual scene. The simulator is implemented using 3D graphics tools and supports real-time event log analysis for pattern recognition and classification of the individual and group agent’s behavior

Intelligence graphs for threat intelligence and security policy validation of cyber systems

While the recent advances in Data Science and Machine Learning attract lots of attention in Cyber Security because of their promise for effective security analytics, Vulnerability Analysis, Risk Assessment and Security Policy Validation remain slightly aside. This is mainly due to the relatively slow progress in the theoretical formulation and the technologi-cal foundation of the cyber security concepts such as logical vulnerability, threats and risks. In this article we are proposing a framework for logical analysis, threat intelligence and validation of security policies in cyber systems. It is based on multi-level model, consisting of ontology of situations and actions under security threats, security policies governing the security-related activities, and graph of the transactions. The framework is validated using a set of scenarios describing the most common security threats in digital banking and a proto-type of an event-driven engine for navigation through the intelligence graphs has been im-plemented. Although the framework was developed specifically for application in digital banking, the authors believe that it has much wider applicability to security policy analysis, threat intelligence and security by design of cyber systems for financial, commercial and business operations

Building a big data platform using software without licence costs

This chapter presents the experience in developing and utilizing Big Data platforms using software without license costs, acquired while working on several projects at two research institutions – the Cyber Security Research Centre of London Metropolitan University in the United Kingdom and the GATE Institute of Sofia University in Bulgaria. Unlike the universal computational infrastructures available from large cloud service providers such as Amazon, Google, Microsoft and others, which provide only a wide range of universal tools, we implemented a more specialized solution for Big Data processing on a private cloud, tailored to the needs of academic institutions, public organizations and smaller enterprises which cannot afford high running costs, or do significant in-house development. Since most of the currently available commercial platforms for Big Data are based on open-source software, such a solution is fully compatible with enterprise solutions from leading vendors like Cloudera, HP, IBM, Oracle and others. Although such an approach may be considered less reliable due to the limited support, it also has many advantages, making it attractive for small institutions with limited budgets, research institutions working on innovative solutions and software houses developing new platforms and applications. It can be implemented entirely on the premises, avoiding cloud service costs and can be tailored to meet the specific needs of the organizations. At the same time, it retains the opportunity for scaling up and migrating the developed solutions as the situations evolve

3D reconstruction of building models based on 2D floorplans using statistical analysis

The study of buildings interiors has been in the center of attention of estates industry. Floor plans are the fundamental way of presenting the spatial arrangement of components found in a building such as rooms, corridors, doors, and windows. With the rapid advancement of image processing, computer vision and graphics technologies over the past few years we are at the point where it is feasible to reconstruct and visualize the interiors of a building in three-dimensional space for several case scenarios. In this paper we focus on approximated recreation of internal structure of a kindergarten building from the suburbs of Sofia, Bulgaria in 3D for future visualization of indoor pollution levels on a basis of data produced by sensors installed on the premises. We use well-established image processing algorithms for extracting features from a 2D floor plan, use statistical methods for their classification and convert them into GEOjson format for subsequent 3D reconstruction in Unity game engin

Optimal Capital Structure of Public-Private Partnerships

This paper presents a model to assess the efficiency of the capital structure in public-private partnerships (PPP). A main argument supporting the PPP approach for investment projects is the transfer of know-how from the private partner to the public entity. The paper shows how different knowledge transfer schemes determine an optimal shareholding structure of the PPP. Under the assumption of lower capital cost of the public partner and lower development outlays when the investment is carried out by a private investor, an optimal capital structure is achieved with both the public and the private parties as shareholders.Infrastructure;Investment policy;private capital, capital structure, cost of capital, private financing, private investor, joint ventures, shareholding structure, public investment, investment projects, fixed costs, private finance, capital expenditures, discount rates

Data integration and visualization of air quality in urban environment

Air quality visualization importance cannot be overestimated for several reasons, ranging from public health and environmental awareness to policy-making and urban planning. The tool described here is meant to bridge the gap between scientific data and general audience, promoting greater understanding and prevention of air pollution through taking the hourly readings from 23 Develiot stations in the city of Sofia and overlaying them onto terrain map and 3d buildings layer (coming from Open Street Maps). Various open source / non-commercial tools are used for data preprocessing, analysis, and integration. The languages and data formats are explained. Implementation of the platform, transformation, integration, and visualisation of sensor, historical, and geolocation data are discussed in detail. Integrating 3d building reconstruction and triangulation of measurements for arbitrary points in the map are explained. Finally, the data flow from the sensors through the API, MongoDB database, Flask framework to the frontend web application is shown. Users can interact with the data, zoom in/out, rotate the view, and access additional information about specific data points. All elements of the map are interactive – clicking on the air quality stations domes gives the station details and measurements, selecting an OSM 3d building shows the semantic context (address, type of building, name in Bulgarian and English, owner, etc.). Double clicking on any point in map creates a triangulated marker