Robert Buchholz

Natural throw and tilt interaction between mobile phones and distant displays

To provide intuitive ways of interacting with media data, this research work addresses the seamless combination of sensor-enabled phones with large displays. An intuitive basic set of tilt gestures is introduced for a stepwise or continuous interaction with both mobile applications and distant user interfaces by utilizing the handheld as a remote control. In addition, we introduce throwing gestures to transfer media documents and even running interfaces to a large display. To improve usability, data and interfaces can be thrown from a mobile phone to a distant screen and also fetched back to achieve mobility. We demonstrate the feasibility of the interaction methods with several advanced application prototypes facilitating a natural flow of interaction.

Microphone Classification Using Fourier Coefficients

Media forensics tries to determine the originating device of a signal. We apply this paradigm to microphone forensics, determining the microphone model used to record a given audio sample. Our approach is to extract a Fourier coefficient histogram of near-silence segments of the recording as the feature vector and to use machine learning techniques for the classification. Our test goals are to determine whether attempting microphone forensics is indeed a sensible approach and which one of the six different classification techniques tested is the most suitable one for that task. The experimental results, achieved using two different FFT window sizes (256 and 2048 frequency coefficients) and nine different thresholds for near-silence detection, show a high accuracy of up to 93.5% correct classifications for the case of 2048 frequency coefficients in a test set of seven microphones classified with linear logistic regression models. This positive tendency motivates further experiments with larger test sets and further studies for microphone identification.

Model-Based Local Path Planning for UAVs

Autonomous aviation continuously becomes more and more important. Algorithms that enable this autonomy have developed quickly in the last years. This paper describes a concept for a reactive path planning algorithm. The aim is to develop a method for static obstacle avoidance of an unmanned aerial vehicle (UAV) by calculating collision-free paths within the field of view of a UAV’s obstacle detection sensor. In contrast to other algorithms, this method considers the properties of the obstacle detection sensors, plans paths that the UAV is able to track, and is applied in three-dimensional space without access to an inner loop controller. In this work we represent the field of view of a UAV as a grid map and apply the wavefront algorithm as the local path planning algorithm. We reduce the configuration space of UAVs within the field of view by calculating an approximated worst-case reachable set based on a linearized reference model. We evaluate the method with approximated specifications for the unmanned helicopters ARTIS and Yamaha RMAX, and with specifications for the obstacle detection sensors LIDAR – and stereo camera. Experiments show that this method is able to generate collision-free paths in a region constricted by obstacles.

Virtual Stochastic Sensors: How to Gain Insight into Partially Observable Discrete Stochastic Systems

This paper introduces the idea of a Virtual Stochastic Sensor. This paradigm enables the analysis of unobservable processes in discrete stochastic systems. Just like a virtual sensor, we use physical sensor readings to deduce the value of the quantity of interest. However, both the physical sensor readings and their relationship with the quantity of interest are stochastic. Therefore the measurement of our virtual stochastic sensor is a statistical estimate of the true value. We describe a method to compute the result of the virtual stochastic sensor and show its validity and real-time capability for two example models. We also give system properties that must apply in order for the feasibility of virtual stochastic sensors, such as the sensitivity of the physical sensor output to changes in the quantity of interest. The future potential of virtual stochastic sensors is their variability. They can be used to gain insight into hidden processes of partially observable systems, using readily available data. They enable online monitoring of production lines using already recorded data to ensure optimal control and maximum production efficiency.

An Optimal Algorithm for Raw Idea Selection under Uncertainty

At the first gate of an innovation process, a large number of raw ideas must be evaluated and those good enough to continue to the next phase be selected. No information about these ideas is available, so they have a high level of uncertainty. We present an algorithm that selects and ranks a set of alternatives in optimal time. The algorithm addresses uncertainty by allowing decision-makers to specify missing information that affect the outcome of their judgments. It generates multiple partial rankings efficiently according to the various possible combinations of missing items of information and identifies the set of items that are needed to obtain a unique result. In this manner, we can reduce the uncertainty in the selection procedure and make explicit expert knowledge that is relevant to the evaluation process. The algorithm is intended for use in a collaborative tool for corporations who utilize a structured innovation process.

Using hidden non-Markovian Models to reconstruct system behavior in partially-observable systems

Many complex technical systems today have some basic protocol capability, which is used for example to monitor the quality of production output or to keep track of oil pressure in a modern car. The recorded protocols are usually used to detect deviations from some predefined standards and issue warnings. However, the information in such a protocol is not sufficient to determine the source or cause of the problem, since only part of the system is being observed. In this paper we present an approach to reconstruct missing information in only partially-observable stochastic systems based only on recorded system output. The approach uses Hidden non-Markovian Models to model the partially-observable system and Proxel-based simulation to analyze the recorded system output. Experiments were conducted using a production line example. The result of the analysis is a set of possible system behaviors that could have caused the recorded protocol, including their probabilities. We will show that our approach is able to reconstruct the relevant information to determine the source of non-standard system behavior. The combination of Hidden non-Markovian Models and Proxel-based simulation holds the potential to reconstruct unobserved information from partial or even noisy output protocols of a system. It adds value to the information already recorded in many production systems today and opens new possibilities in the analysis of inherently only partially-observable systems.

Security-relevant challenges of selected systems for multi-user interaction

One important goal in the field of multi-user interaction is to support collaborative work of several users as ergonomic as possible. Unfortunately, security-relevant aspects were neglected in the past. Therefore, we study in this contribution the risks and challenges for security of such collaborative working environments on the basis of five selected pen and gesture-based input techniques. We show that the underlying technologies (Anoto pens, Wii Remotes, DiamondTouch, FTIR Table tops, Microsoft Surface) do have deficits, in particular regarding the insurance of user authenticity and data integrity, and that collaborative working brings new challenges for formal security models. We discuss some of the major challenges on situation and context recognition for dynamic role assignment based on a scenario from the field of energy engineering and point out that several of the underlying problems are of special importance for the development of reliable collaborative multimedia applications for object organization and exchange.

Reconstructing model parameters in partially-observable discrete stochastic systems

The analysis of partially-observable discrete stochastic systems reconstructs the unobserved behavior of real-world systems. An example for such a system is a production facility where indistinguishable items are produced by two machines in stochastically distributed time intervals and are then tested by a single quality tester. Here, the source of each defective item can be reconstructed later based solely on the time-stamped test protocol. While existing algorithms can reconstruct various characteristics of the unobserved behavior, a fully specified discrete stochastic model needs to exist. So far, model parameters themselves cannot be reconstructed. In this paper, we present two new approaches that enable the reconstruction of some unknown parameter values in the model specification, namely constant probabilities. Both approaches are shown to work correctly and with acceptable computational effort. They are a first step towards general model parameter inference for partially-observable discrete stochastic systems.

Avoiding redundancies in the Proxel method

The simulation of discrete stochastic systems is used to make predictions on system behaviour. Its most widely used technique, discrete event simulation, computes possible simulation results by using random numbers. Consequently, these results are also only random numbers. Alternative state space–based simulation techniques can directly compute the actual system behaviour, but are computationally infeasible for bigger models. In this work, we improve the state space–based Proxel simulation method by avoiding some of its redundancies through clustering of discrete states. Our experiments demonstrate a speedup by a factor of two to five for realistic models, without any loss in accuracy. If no redundancies in the model can be exploited, the method only incurs a small computational overhead. Our approach thus has the potential of making deterministic state space–based analysis of existing models more efficient, and of enabling the analysis of bigger models that more accurately reflect real systems.

Improving the Efficiency of the Proxel Method by Using Individual Time Steps

Discrete stochastic models (DSM) are widely used in various application fields today. Proxel-based simulation can outperform discrete event-based approaches in the analysis of small stiff DSM, which can occur for example in reliability modeling. However, when parallel processes with largely differing speed are involved, the faster process determines the small discretization time step, investing far too much effort into the approximation of the slower process. This paper relieves that problem by using individual time steps for each transition and situation. The key problem is to keep semantic consistency when using different time steps for parallel transitions. However, the preservation of the probability mass in every single simulation time step could be achieved. Experiments show that binary step division in conjunction with appropriate subdivision criteria can outperform the original Proxel method significantly. This increases the applicability of Proxels, by enabling the analysis of larger and therefore more realistic models.