A map-matching algorithm dealing with sparse cellular fingerprint observations

The widespread availability of mobile communication makes mobile devices a resource for the collection of data about mobile infrastructures and user mobility. In these contexts, the problem of reconstructing the most likely trajectory of a device on the road network on the basis of the sequence of observed locations (map-matching problem) turns out to be particularly relevant. Different contributions have demonstrated that the reconstruction of the trajectory of a device with good accuracy is technically feasible even when only a sparse set of GNSS positions is available. In this paper, we face the problem of coping with sparse sequences of cellular fingerprints. Compared to GNSS positions, cellular fingerprints provide coarser spatial information, but they work even when a device is missing GNSS positions or is operating in an energy saving mode. We devise a new map-matching algorithm, that exploits the well-known Hidden Markov Model and Random Forests to successfully deal with noisy and sparse cellular observations. The performance of the proposed solution has been tested over a medium-sized Italian city urban environment by varying both the sampling of the observations and the density of the fingerprint map as well as by including some GPS positions into the sequence of fingerprint observations

Non-Intrusive Subscriber Authentication for Next Generation Mobile Communication Systems

Merged with duplicate record 10026.1/753 on 14.03.2017 by CS (TIS)The last decade has witnessed massive growth in both the technological development, and the consumer adoption of mobile devices such as mobile handsets and PDAs. The recent introduction of wideband mobile networks has enabled the deployment of new services with access to traditionally well protected personal data, such as banking details or medical records. Secure user access to this data has however remained a function of the mobile device's authentication system, which is only protected from masquerade abuse by the traditional PIN, originally designed to protect against telephony abuse. This thesis presents novel research in relation to advanced subscriber authentication for mobile devices. The research began by assessing the threat of masquerade attacks on such devices by way of a survey of end users. This revealed that the current methods of mobile authentication remain extensively unused, leaving terminals highly vulnerable to masquerade attack. Further investigation revealed that, in the context of the more advanced wideband enabled services, users are receptive to many advanced authentication techniques and principles, including the discipline of biometrics which naturally lends itself to the area of advanced subscriber based authentication. To address the requirement for a more personal authentication capable of being applied in a continuous context, a novel non-intrusive biometric authentication technique was conceived, drawn from the discrete disciplines of biometrics and Auditory Evoked Responses. The technique forms a hybrid multi-modal biometric where variations in the behavioural stimulus of the human voice (due to the propagation effects of acoustic waves within the human head), are used to verify the identity o f a user. The resulting approach is known as the Head Authentication Technique (HAT). Evaluation of the HAT authentication process is realised in two stages. Firstly, the generic authentication procedures of registration and verification are automated within a prototype implementation. Secondly, a HAT demonstrator is used to evaluate the authentication process through a series of experimental trials involving a representative user community. The results from the trials confirm that multiple HAT samples from the same user exhibit a high degree of correlation, yet samples between users exhibit a high degree of discrepancy. Statistical analysis of the prototypes performance realised early system error rates of; FNMR = 6% and FMR = 0.025%. The results clearly demonstrate the authentication capabilities of this novel biometric approach and the contribution this new work can make to the protection of subscriber data in next generation mobile networks.Orange Personal Communication Services Lt

A Framework for Generic and Energy Efficient Context Recognition for Personal Mobile Devices

The advancements in the field of mobile computing over the last decade have enabled the scientific community to expedite the theoretical and experimental work to achieve the vision of ubiquitous computing. As ubiquitous computing aims to provide seamless and distraction free task support to its users, one of the essential pieces of information required by the ubiquitous computing systems to do so is the context of its users. Context of a user can be defined as the information that describes the task the user is performing and the environment in which the user is currently present. Among various platforms that are commonly used to determine user's context, the personal mobile devices like smart phones stand out as one of the most widely used and widely evaluated ones. However, despite numerous advantages that are provided by modern day personal mobile devices, such as high computational and communication capabilities, variety of on-board sensors to capture raw data related to user's motion and environment, high resolution displays to enable interaction with other services and systems, these devices suffer from limited battery resources. In contrast to the advancements in other domains, the advancements in the battery domain have not been up to the mark. Consequently, the context recognition applications developed for these devices suffer from the trade-off between achieving accuracy and longevity of other device's basic operations. As a result, most of the existing context recognition applications for these devices are fine tuned for specific context types and thereby lacks generality. The situation gets worse when a number of context recognition applications are executed simultaneously, thus competing for limited resources and consuming the device's battery additively. To address the aforementioned issues, this thesis provides a generic and energy efficient context recognition framework for personal mobile devices. The main contribution consists of a generic framework to support development of context recognition applications supported by algorithms to achieve their energy efficient execution. The proposed framework consists of two systems namely the component system and the activation system. The component system allows developers to create context recognition applications using a component abstraction. This enables runtime analysis of applications' structures to adopt our novel energy efficiency mechanism. The activation system uses a state machine abstraction to allow context dependent activation of context recognition configurations pertaining to relevant user's tasks such that only needed configurations are executed to determine only the relevant context characteristics, thereby enabling energy efficiency. The activation system also provides generic applicability of four different energy efficiency techniques, already used in different existing systems but mostly for specific context characteristics. To aid rapid prototyping, both systems are equipped with off-line development tools. The tools include graphical editors and a component tool-kit. The graphical editors allow developers to create component configurations used by the component system and state machines used by the activation system. These editors enable developers to create component configurations and state machines by simply dragging, dropping and connecting different models used in our component and state machine abstractions. These tools also provide validation and code generation utilities. In addition to the graphical editor, the framework provides a component tool-kit which consists of a number of already implemented sensing, preprocessing and classification components which can be re-used in new applications. In order to provide the energy efficient execution of context recognition applications, the thesis introduces a novel energy efficiency technique called configuration folding. Configuration folding analyses structures of simultaneously executing context recognition applications to identify redundant functionalities between them and as an output produces a single redundancy free context recognition configuration which holds the structural integrity of all applications. Consequently, the overall energy expenditure is reduced compared to the original expenditure when redundant functionalities are not removed. The experimental evaluation of configuration folding on test applications shows energy savings between 13 and 48 %. The thesis also investigates optimization possibilities in configuration folding in case the redundant functionalities between the applications differ in parametrization. Towards this end, the thesis identifies commonly used parameters in context recognition applications and defines relations between them. Finally, an extended version of configuration folding is introduced to handle the differences in parametrization. The evaluation of the extended version of configuration folding on test scenarios shows energy saving of up to 45%. The contributions in this thesis have been evaluated extensively. The framework has been used in number of European Commission (EC) projects and in student projects and theses at the University of Duisburg-Essen, Germany. Using the component system and the activation system, a number of applications have been developed in those projects. Some of these applications include crowd density estimation in buses, bus ride detections, navigation application for buses in Madrid, user movement detection, user localization, fall detection application etc. Moreover, the component system, the activation system and the configuration folding technique have been published in different prestigious conferences and workshops

Multimodal Content Delivery for Geo-services

This thesis describes a body of work carried out over several research projects in the area of multimodal interaction for location-based services. Research in this area has progressed from using simulated mobile environments to demonstrate the visual modality, to the ubiquitous delivery of rich media using multimodal interfaces (geo- services). To effectively deliver these services, research focused on innovative solutions to real-world problems in a number of disciplines including geo-location, mobile spatial interaction, location-based services, rich media interfaces and auditory user interfaces. My original contributions to knowledge are made in the areas of multimodal interaction underpinned by advances in geo-location technology and supported by the proliferation of mobile device technology into modern life. Accurate positioning is a known problem for location-based services, contributions in the area of mobile positioning demonstrate a hybrid positioning technology for mobile devices that uses terrestrial beacons to trilaterate position. Information overload is an active concern for location-based applications that struggle to manage large amounts of data, contributions in the area of egocentric visibility that filter data based on field-of-view demonstrate novel forms of multimodal input. One of the more pertinent characteristics of these applications is the delivery or output modality employed (auditory, visual or tactile). Further contributions in the area of multimodal content delivery are made, where multiple modalities are used to deliver information using graphical user interfaces, tactile interfaces and more notably auditory user interfaces. It is demonstrated how a combination of these interfaces can be used to synergistically deliver context sensitive rich media to users - in a responsive way - based on usage scenarios that consider the affordance of the device, the geographical position and bearing of the device and also the location of the device

Emotion Recognition from Speech with Acoustic, Non-Linear and Wavelet-based Features Extracted in Different Acoustic Conditions

ABSTRACT: In the last years, there has a great progress in automatic speech recognition. The challenge now it is not only recognize the semantic content in the speech but also the called "paralinguistic" aspects of the speech, including the emotions, and the personality of the speaker. This research work aims in the development of a methodology for the automatic emotion recognition from speech signals in non-controlled noise conditions. For that purpose, different sets of acoustic, non-linear, and wavelet based features are used to characterize emotions in different databases created for such purpose

3G migration in Pakistan

The telecommunication industry in Pakistan has come a long way since the country\u27s independence in 1947. The initial era could be fairly termed as the PTCL (Pakistan Telecommunication Company Limited) monopoly, for it was the sole provider of all telecommunication services across the country. It was not until four decades later that the region embarked into the new world of wireless communication, hence ending the decades old PTCL monopoly. By the end of the late 1990\u27s, government support and international investment in the region opened new doors to innovation and better quality, low cost, healthy competition. Wireless licenses for the private sector in the telecommunication industry triggered a promising chain of events that resulted in a drastic change in the telecommunication infrastructure and service profile. The newly introduced wireless (GSM) technology received enormous support from all stakeholders (consumers, regulatory body, and market) and caused a vital boost in Pakistan\u27s economy. Numerous tangential elements had triggered this vital move in the history of telecommunications in Pakistan. Entrepreneurs intended to test the idea of global joint ventures in the East and hence the idea of international business became a reality. The technology had proven to be a great success in the West, while Pakistan\u27s telecom consumer had lived under the shadow of PTCL dominance for decades and needed more flexibility. At last the world was moving from wired to wireless! Analysts termed this move as the beginning of a new era. The investors, telecommunication businesses, and Pakistani treasury prospered. It was a win-win situation for all involved. The learning curve was steep for both operators and consumers but certainly improved over time. In essence, the principle of deploying the right technology in the right market at the right time led to this remarkable success. The industry today stands on the brink of a similar crossroads via transition from second generation to something beyond. With the partial success of 3G in Europe and the USA, the government has announced the release of three 3G licenses by mid 2009. This decision is not yet fully supported by all but still initiated parallel efforts by the operators and the vendors to integrate this next move into their existing infrastructure

Advancement in infotainment system in automotive sector with vehicular cloud network and current state of art

The automotive industry has been incorporating various technological advancement on top-end versions of the vehicle order to improvise the degree of comfortability as well as enhancing the safer driving system. Infotainment system is one such pivotal system which not only makes the vehicle smart but also offers abundance of information as well as entertainment to the driver and passenger. The capability to offer extensive relay of service through infotainment system is highly dependent on vehicular adhoc network as well as back end support of cloud environment. However, it is know that such legacy system of vehicular adhoc network is also characterized by various problems associated with channel capacity, latency, heterogeneous network processing, and many more. Therefore, this paper offers a comprehensive insight to the research work being carried out towards leveraging the infotainment system in order to obtain the true picture of strength, limitation, and open end problems associated with infotainment system

Minimal Infrastructure Radio Frequency Home Localisation Systems

The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls. Such pervasive monitoring functionality offers the potential for elders to live at home for longer periods of their lives with minimal human supervision. The focus of this thesis is on the investigation and development of a home roomlevel localisation technique which can be readily deployed in a realistic home environment with minimal hardware requirements. A conveniently deployed Bluetooth ® localisation platform is designed and experimentally validated throughout the thesis. The platform adopts the convenience of a mobile phone and the processing power of a remote location calculation computer. The use of Bluetooth ® also ensures the extensibility of the platform to other home health supervision scenarios such as wireless body sensor monitoring. Central contributions of this work include the comparison of probabilistic and nonprobabilistic classifiers for location prediction accuracy and the extension of probabilistic classifiers to a Hidden Markov Model Bayesian filtering framework. New location prediction performance metrics are developed and signicant performance improvements are demonstrated with the novel extension of Hidden Markov Models to higher-order Markov movement models. With the simple probabilistic classifiers, location is correctly predicted 80% of the time. This increases to 86% with the application of the Hidden Markov Models and 88% when high-order Hidden Markov Models are employed. Further novelty is exhibited in the derivation of a real-time Hidden Markov Model Viterbi decoding algorithm which presents all the advantages of the original algorithm, while producing location estimates in real-time. Significant contributions are also made to the field of human gait-recognition by applying Bayesian filtering to the task of motion detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even enables a floor recognition rate of 68% using only accelerometers. The unique application of time-varying Hidden Markov Models demonstrates the effect of integrating these freely available motion predictions on long-term location predictions

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity