A Systems-Level Approach to Human Epileptic Seizures

Epileptic seizures are due to the pathological collective activity of large cellular assemblies. A better understanding of this collective activity is integral to the development of novel diagnostic and therapeutic procedures. In contrast to reductionist analyses, which focus solely on small-scale characteristics of ictogenesis, here we follow a systems-level approach, which combines both small-scale and larger-scale analyses. Peri-ictal dynamics of epileptic networks are assessed by studying correlation within and between different spatial scales of intracranial electroencephalographic recordings (iEEG) of a heterogeneous group of patients suffering from pharmaco-resistant epilepsy. Epileptiform activity as recorded by a single iEEG electrode is determined objectively by the signal derivative and then subjected to a multivariate analysis of correlation between all iEEG channels. We find that during seizure, synchrony increases on the smallest and largest spatial scales probed by iEEG. In addition, a dynamic reorganization of spatial correlation is observed on intermediate scales, which persists after seizure termination. It is proposed that this reorganization may indicate a balancing mechanism that decreases high local correlation. Our findings are consistent with the hypothesis that during epileptic seizures hypercorrelated and therefore functionally segregated brain areas are re-integrated into more collective brain dynamics. In addition, except for a special sub-group, a highly significant association is found between the location of ictal iEEG activity and the location of areas of relative decrease of localised EEG correlation. The latter could serve as a clinically important quantitative marker of the seizure onset zone (SOZ).

Towards Self-Learning Radio-Based Localization Systems

Location-awareness indoors will be an inseparable feature of mobile services/applications in future wireless networks. Its current ubiquitous availability is still obstructed by technological challenges and privacy issues. We propose an innovative approach towards the concept of indoor positioning with main goal to develop a system that is self-learning and able to adapt to various radio propagation environments. The approach combines estimation of propagation conditions, subsequent appropriate channel modelling and optimisation feedback to the used positioning algorithm. Main advantages of the proposal are decreased system set-up effort, automatic re-calibration and increased precision.

Privacy-Preserving Optimal Meeting Location Determination on Mobile Devices

Equipped with state-of-the-art smartphones and mobile devices, today's highly interconnected urban population is increasingly dependent on these gadgets to organize and plan their daily lives. These applications often rely on current (or preferred) locations of individual users or a group of users to provide the desired service, which jeopardizes their privacy; users do not necessarily want to reveal their current (or preferred) locations to the service provider or to other, possibly untrusted, users. In this paper, we propose privacy-preserving algorithms for determining an optimal meeting location for a group of users. We perform a thorough privacy evaluation by formally quantifying privacy-loss of the proposed approaches. In order to study the performance of our algorithms in a real deployment, we implement and test their execution efficiency on Nokia smartphones. By means of a targeted user-study, we attempt to get an insight into the privacy-awareness of users in location-based services and the usability of the proposed solutions.

Scheduling in service systems: an application in the event-tourism industry

This paper deals with an event-bus tour booked by Bollywood film fans. During the tour, the participants visit selected locations of famous Bollywood films at various sites in Switzerland. Moreover, the tour includes stops for lunch and shopping. Each day, up to five buses operate the tour; for organizational reasons, two or more buses cannot stay at the same location simultaneously. The planning problem is how to compute a feasible schedule for each bus such that the total waiting time (primary objective) and the total travel time (secondary objective) are minimized. We formulate this problem as a mixed-integer linear program, and we report on computational results obtained with the Gurobi solver.

Passive wireless sensor systems can recognize activites of daily living

The ability to determine what activity of daily living a person performs is of interest in many application domains. It is possible to determine the physical and cognitive capabilities of the elderly by inferring what activities they perform in their houses. Our primary aim was to establish a proof of concept that a wireless sensor system can monitor and record physical activity and these data can be modeled to predict activities of daily living. The secondary aim was to determine the optimal placement of the sensor boxes for detecting activities in a room. A wireless sensor system was set up in a laboratory kitchen. The ten healthy participants were requested to make tea following a defined sequence of tasks. Data were collected from the eight wireless sensor boxes placed in specific places in the test kitchen and analyzed to detect the sequences of tasks performed by the participants. These sequence of tasks were trained and tested using the Markov Model. Data analysis focused on the reliability of the system and the integrity of the collected data. The sequence of tasks were successfully recognized for all subjects and the averaged data pattern of tasks sequences between the subjects had a high correlation. Analysis of the data collected indicates that sensors placed in different locations are capable of recognizing activities, with the movement detection sensor contributing the most to detection of tasks. The central top of the room with no obstruction of view was considered to be the best location to record data for activity detection. Wireless sensor systems show much promise as easily deployable to monitor and recognize activities of daily living.

Fine-grained indoor positioning and tracking systems

Indoor positioning has attracted considerable attention for decades due to the increasing demands for location based services. In the past years, although numerous methods have been proposed for indoor positioning, it is still challenging to find a convincing solution that combines high positioning accuracy and ease of deployment. Radio-based indoor positioning has emerged as a dominant method due to its ubiquitousness, especially for WiFi. RSSI (Received Signal Strength Indicator) has been investigated in the area of indoor positioning for decades. However, it is prone to multipath propagation and hence fingerprinting has become the most commonly used method for indoor positioning using RSSI. The drawback of fingerprinting is that it requires intensive labour efforts to calibrate the radio map prior to experiments, which makes the deployment of the positioning system very time consuming. Using time information as another way for radio-based indoor positioning is challenged by time synchronization among anchor nodes and timestamp accuracy. Besides radio-based positioning methods, intensive research has been conducted to make use of inertial sensors for indoor tracking due to the fast developments of smartphones. However, these methods are normally prone to accumulative errors and might not be available for some applications, such as passive positioning. This thesis focuses on network-based indoor positioning and tracking systems, mainly for passive positioning, which does not require the participation of targets in the positioning process. To achieve high positioning accuracy, we work on some information of radio signals from physical-layer processing, such as timestamps and channel information. The contributions in this thesis can be divided into two parts: time-based positioning and channel information based positioning. First, for time-based indoor positioning (especially for narrow-band signals), we address challenges for compensating synchronization offsets among anchor nodes, designing timestamps with high resolution, and developing accurate positioning methods. Second, we work on range-based positioning methods with channel information to passively locate and track WiFi targets. Targeting less efforts for deployment, we work on range-based methods, which require much less calibration efforts than fingerprinting. By designing some novel enhanced methods for both ranging and positioning (including trilateration for stationary targets and particle filter for mobile targets), we are able to locate WiFi targets with high accuracy solely relying on radio signals and our proposed enhanced particle filter significantly outperforms the other commonly used range-based positioning algorithms, e.g., a traditional particle filter, extended Kalman filter and trilateration algorithms. In addition to using radio signals for passive positioning, we propose a second enhanced particle filter for active positioning to fuse inertial sensor and channel information to track indoor targets, which achieves higher tracking accuracy than tracking methods solely relying on either radio signals or inertial sensors.

Assessing Activity and Location of Individual Laying Hens in Large Groups Using Modern Technology

Tracking individual animals within large groups is increasingly possible, offering an exciting opportunity to researchers. Whereas previously only relatively indistinguishable groups of individual animals could be observed and combined into pen level data, we can now focus on individual actors within these large groups and track their activities across time and space with minimal intervention and disturbance. The development is particularly relevant to the poultry industry as, due to a shift away from battery cages, flock sizes are increasingly becoming larger and environments more complex. Many efforts have been made to track individual bird behavior and activity in large groups using a variety of methodologies with variable success. Of the technologies in use, each has associated benefits and detriments, which can make the approach more or less suitable for certain environments and experiments. Within this article, we have divided several tracking systems that are currently available into two major categories (radio frequency identification and radio signal strength) and review the strengths and weaknesses of each, as well as environments or conditions for which they may be most suitable. We also describe related topics including types of analysis for the data and concerns with selecting focal birds.