High accuracy context recovery using clustering mechanisms

This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point id and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing state-of-the-art probabilistic clustering technique, the Latent Dirichiet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.<br /

Design and Simulation of Single Phase Intelligent Prepaid Energy Meter

In this paper, the design and simulation of Intelligent Prepaid Energy Meter (IPEM) has been presented. The objectives of this work  are :( i) to model an IPEM,( ii) to show its reliability on  load measurement ; and (iii) to show graphical behavior of energy consumption pattern of different loads connected to power supply.  The design methodology is Artificial Intelligent (AI) based-using “ knowledge-based” and “cognitive simulation” approach.  The intelligentce properties and expected results of the proposed digital meter was modeled into the system; and was simulated using Matlab /Simulation tool. Results obtained were very satisfactory. If  fully implemented, on one hand, the estimated bills or irregular billing imposed by Power Holding Company of Nigeria(PHCN) on her customers will stop; and on the other hand ,revenue loss through unpaid bills suffered by PHCN will greatly reduce. This will have an overall effects on the nation’s economy as revenue collection will increase. Keyword:Artificial Intelligence, Prepaid Energy Meter, Model, Simulation, Matlab/Simulin

Sensing and Visualizing Social Context from Spatial Proximity

The concept of pervasive computing, as introduced by Marc Weiser under the name ubiquitous computing in the early 90s, spurred research into various kinds of context-aware systems and applications. There is a wide range of contextual parameters, including location, time, temperature, devices and people in proximity, which have been part of the initial ideas about context-aware computing. While locational context is already a well understood concept, social context---based on the people around us---proves to be harder to grasp and to operationalize. This work continues the line of research into social context, which is based on the proximity and meeting patterns of people in the physical space. It takes this research out of the lab and out of well controlled situations into our urban environments, which are full of ambiguity and opportunities. The key to this research is the tool that caused dramatic change in individual and collective behavior during the last 20 years and which is a manifestation of many of the ideas of the pervasive computing paradigm: the mobile phone. In this work, the mobile is regarded as a proxy for people. Through it, the social environment becomes accessible to digital measurement and processing. To understand the large amount of data that now becomes available to automatic measurement, we will turn to the discipline of social network analysis. It provides powerful methods, that are able to condense data and extract relevant meaning. Visualization helps to understand and interpret the results. This thesis contains a number of experiments, that demonstrate how the automatic measurement of social proximity data through Bluetooth can be used to measure variables of personal behavior, group behavior and the behavior of groups in relation to places. The principal contributions are: * A methodology to visualize personal social context by using an ego proximity network. Specific episodes can be localized and compared. * method to compare different days in terms of social context, e.g. to support automatic diary applications. * A method to compose social geographic maps. Locations of similar social context are detected and combined. * Functions to measure short-term changes in social activity, based on the distinction between strange and familiar devices. * The characterization of Bluetooth inquiries for social proximity sensing. * A dataset of Bluetooth sightings from an ego perspective in seven different settings. Additionally, some settings feature multiple stationary scanners and Cell-ID measurements. * Soft- and hardware to capture, collect, store and analyze Bluetooth proximity data

Learning significant user locations with GPS and GSM

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (leaves 57-59).This thesis addresses the tasks of place discovery and place recognition - learning and recognizing places significant to a user - by analyzing GPS location and GSM cell tower data collected from the user's mobile phone. Location provides valuable context into the user's environment, and place-discovery and recognition algorithms enable human-centric systems to communicate with the user in human terms. In this thesis, we introduce a novel two-phased approach to place-discovery and recognition that combines the advantages of GPS and GSM cell data. We design and implement a system that produces a compact travel summary from the user's daily GPS logs. We then use computational geometry to investigate the aspect ratios of GSM cell coverage polygons as an optimization to place recognition. Finally, we conclude by presenting a one-month empirical study to demonstrate the effectiveness of our two-phased approach, and identify a set of anomalies in our experiment that can direct further development of place-discovery systems.by Xiao Yu.M.Eng

Blind guide: anytime, anywhere

Sight dominates our mental life, more than any other sense. Even when we are just thinking about something the world, we end imagining what looks like. This rich visual experience is part of our lives. People need the vision for two complementary reasons. One of them is vision give us the knowledge to recognize objects in real time. The other reason is vision provides us the control one need to move around and interact with objects. Eyesight helps people to avoid dangers and navigate in our world. Blind people usually have enhanced accuracy and sensibility of their other natural senses to sense their surroundings. But sometimes this is not enough because the human senses can be affected by external sources of noise or disease. Without any foreign aid or device, sightless cannot navigate in the world. Many assistive tools have been developed to help blind people. White canes or guide dogs help blind in their navigation. Each device has their limitation. White canes cannot detect head level obstacles, drop-offs, and obstructions over a meter away. The training of a guide dog takes a long time, almost five years in some cases. The sightless also needs training and is not a solution for everybody. Taking care of a guide dog can be expensive and time consuming. Humans have developed technology for helping us in every aspect of our lives. The primary goal of technology is helping people to improve their quality of life. Technology can assist us with our limitations. Wireless sensor networks is a technology that has been used to help people with disabilities. In this dissertation, the author proposes a system based on this technology called Blind Guide. Blind Guide is an artifact that helps blind people to navigate in indoors or outdoors scenarios. The prototype is portable assuring that can be used anytime and anywhere. The system is composed of wireless sensors that can be used in different parts of the body. The sensors detect an obstacle and inform the user with an audible warning providing a safety walk to the users. A great feature about Blind Guide is its modularity. The system can adapt to the needs of the user and can be used in a combination with other solution. For example, Blind Guide can be used in conjunction with the white cane. The white cane detects obstacles below waist level and a Blind Guide wireless sensor in the forehead can detect obstacles at the head level. This feature is important because some sightless people feel uncomfortable without the white cane. The system is scalable giving us the opportunity to create a network of interconnected Blind Guide users. This network can store the exact location and description of the obstacles found by the users. This information is public for all users of this system. This feature reduces the time required for obstacle detection and consequent energy savings, thus increasing the autonomy of the solution. One of the main requirements for the development of this prototype was to design a low-cost solution that can be accessible for anyone around the world. All the components of the solution can provide a low-cost solution, easily obtainable and at a low cost. Technology makes our life easier and it must be available for anyone. Modularity, portability, scalability, the possibility to work in conjunction with other solutions, detecting objects that other solutions cannot, obstacle labeling, a network of identified obstacles and audible warnings are the main aspects of the Blind Guide system. All these aspects makes Blind Guide an anytime, anywhere solution for blind people. Blind Guide was tested with a group of volunteers. The volunteers were sightless and from different ages. The trials performed to the system show us positive results. The system successfully detected incoming obstacles and informed in real time to its users. The volunteers gave us a positive feedback telling that they felt comfortable using the prototype and they believe that the system can help them with their daily routine

Advances in crowd analysis for urban applications through urban event detection

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined