Uzair Ahmad

In-building Localization using Neural Networks

Location awareness is key capability of context-aware ubiquitous environments. Received signal strength (RSS) based localization is increasingly popular choice especially for indoor scenarios after pervasive adoption of IEEE 802.11 wireless LAN. Fundamental requirement of such localization systems is to estimate location from RSS at a particular location. Multi-path propagation effects make RSS to fluctuate in unpredictable manner, introducing uncertainty in location estimation. Moreover, in real life situations RSS values are not available at some locations all the time making the problem more difficult. We employ modular multi-layer perceptron (MMLP) approach to effectively reduce the uncertainty in location estimation system. It provides better location estimation results than other approaches and systematically caters for unavailable signals at estimation time

A modular classification model for received signal strength based location systems

Estimating location of mobile devices based on received signal strength (RSS) patterns is an attractive method to realize indoor positioning systems. Accuracy of RSS based location estimation, particularly in large target sites, is effected by several environmental factors. Especially the temporal or permanent absence of radio signals introduces null values rendering sparsity and redundancy in feature space. We present a visibility matrix based modular classification model which systematically caters for unavailable signals. This model is practically realized using two eminent classification methods: (1) multi-layer perceptron and (2) LVQ. In order to confirm robustness and applicability of this model, we developed two location systems at different sites. Experimental results in real-world environments demonstrate that modular classification model consistently achieves superior location accuracy.

Modular Multilayer Perceptron for WLAN Based Localization

Location awareness is key capability of context-aware Ubiquitous environments. Received signal strength (RSS) based localization is increasingly popular choice especially for in-building scenarios after pervasive adoption of IEEE 802.11 wireless LAN. Fundamental requirement of such localization systems is to estimate location from RSS at a particular location. Multipath propagation effects make RSS to fluctuate in unpredictable manner, introducing uncertainty in location estimation. Moreover, in real life situations RSS values are not available at some locations all the time making the problem more difficult. We employ modular multi layer perceptron (MMLP) approach to effectively reduce the uncertainty in location estimation system. It provides better location estimation results than other approaches and systematically caters for unavailable signals at estimation time.

Context-aware, self-scaling Fuzzy ArtMap for received signal strength based location systems

Location awareness is the key capability of mobile computing applications. Despite high demand, indoor location technologies have not become truly ubiquitous mainly due to their requirements of costly infrastructure and dedicated hardware components. Received signal strength (RSS) based location systems are poised to realize economical ubiquity as well as sufficient accuracy for variety of applications. Nevertheless high resolution RSS based location awareness requires tedious sensor data collection and training of classifier which lengthens location system development life cycle. We present a rapid development approach based on online and incremental learning method which significantly reduces development time while providing competitive accuracy in comparison with other methods. ConSelFAM (Context-aware, Self-scaling Fuzzy ArtMap) extends the Fuzzy ArtMap neural network system. It enables on the fly expansion and reconstruction of location systems which is not possible in previous systems.

On building a reflective middleware service for location-awareness

Location based services are becoming essential feature of context-awareness in ubiquitous computing. Reflective distribute component programming model is proposed to systematically provide location to location based services (LBS). We integrate distributed component technology and reflection to develop localization capability as middleware service. Concept of meta object protocols (Reflection) is used in different way than traditional reflective mechanisms. It deals with the state of the component that is not inside the component rather resides outside of it, namely extrinsic. This component model provides the basis for middleware architecture to support location providing service at design, implementation and run time. We describe the methodology we used to build location-awareness as middleware service based upon our reflective component model.

Reflective middleware for location-aware application adaptation

Today mobile computing is pervasively taking over the traditional desktop computing. Mobile devices are characterized by abrupt and un-announced changes in execution context. The applications running on these devices need to be autonomous and thus dynamically adapt according to the changing context. Existing middleware support for the typical distributed applications is strictly based on component technology. Future mobile applications require highly dynamic and adaptive services from the middleware components i.e. context-aware autonomic adaptation. Traditional middleware do not address this emerging need of wide ranges of mobile applications mainly because of their monolithic and inflexible nature. It is hypothesized that such application adaptation can be achieved through meta-level protocols that can reflectively change the state and behaviors of the system. We integrate Component Technology with our active Meta Object Protocols for enabling mobile applications to become adaptive for different contexts. This paper implements the application adaptation service of this middleware. It specializes the concept of Meta Object Protocols for autonomic adaptation of mobile applications. ActiveMOP provide robust and highly flexible framework for autonomic component development for mobile applications. It proved to be a very simple and powerful way to programmatically develop location-driven applications based on autonomic components.

The election algorithm for semantically meaningful location-awareness

The technology of multimedia content adaptation based upon the location of a target device can become the long expected killer application of ubiquitous computing. Easy to develop, lightweight, and robust location estimation is the core component of this technology. Until now, location estimation technology remains restricted to highly sophisticated hardware and networking infrastructure where semantics of the location information are defined and controlled by service providers. We aim to lower the technical and infrastructure barriers to allow general users to define and develop the semantically meaningful location systems. This paper presents a simple location estimation method to build radio beacon based location systems in the indoor environments. It employs an realtime learning approach which requires zero prior knowledge. The salient features of our method are low memory requirements and simple computations which make it desirable for location-aware multimedia systems functioning in distributed client-server settings as well as privacy sensitive applications residing on stand alone devices.

CompoNet: Programmatically Embedding Neural Networks into AI Applications as Software Components

The provision of embedding neural networks into software applications can enable variety of artificial intelligence systems for individual users as well as organizations. Previously, software implementation of neural networks remained limited to only simulations or application specific solutions. Tightly coupled solutions end up in monolithic systems and non reusable programming efforts. We adapt component based software engineering approach to effortlessly integrate neural network models into AI systems in an application independent way. As proof of concept, this paper presents componentization of three famous neural network models i) multi layer perceptron ii) learning vector quantization and iii) adaptive resonance theory family of networks.Human behavior representation in military simulations is not sufficiently realistic, specially the decision making by synthetic military commanders. In order to address some of these deficiencies, we have developed a computer implementation of Recognition Primed Decision Making (RPD) model using Soar cognitive architecture and it is referred to as RPD-Soar agent in this paper. The proposed implementation is evaluated using prototypical scenarios arising in command decision making in tactical situations. Due to the ability of the RPD-Soar agent to mentally simulate applicable courses of action it is possible to use the agent without further training. These agents can be further enhanced to exhibit various levels of expertise. The preliminary results clearly demonstrate the ability of the model to represent human behavior variability within and across individuals. The results also show the change in decision making strategy with experience.

Indoor location estimation using radio beacons

We present a simple location estimation method for developing radio beacon based location system in the indoor environments. It employs an online learning approach for making large scale location systems in a short time collaboratively. The salient features of our method are low memory requirements and simple computations which make it suitable for both distributed location-aware applications based on client-server model as well as privacy sensitive applications residing on stand alone devices.