Improving SCTP retransmission delays for time-dependent thin streams

A large number of network services rely on IP and reliable transport protocols. For applications that provide abundant data for transmission, loss is usually handled satisfactorily, even if the application is latency-sensitive (Wang et al. 2004). For data streams where small packets are sent intermittently, however, applications can occasionally experience extreme latencies (Griwodz and Halvorsen 2006). As it is not uncommon that such thin-stream applications are time-dependent, any unnecessarily induced delay can have severe consequences for the service provided. Massively Multiplayer Online Games (MMOGs) are a defining example of thin streams. Many MMOGs (like World of Warcraft and Age of Conan) use TCP for the benefits of reliability, in-order delivery and NAT/firewall traversal. It has been shown that TCP has several shortcomings with respect to the latency requirements of thin streams because of the way it handles retransmissions (Griwodz and Halvorsen 2006). As such, an alternative to TCP may be SCTP (Stewart et al. 2000), which was originally developed to meet the requirements of signaling transport. In this paper, we evaluate the Linux-kernel SCTP implementation in the context of thin streams. To address the identified latency challenges, we propose sender-side only enhancements that reduce the application-layer latency in a manner that is compatible with unmodified receivers. These enhancements can be switched on by applications and are used only when the system identifies the stream as thin. To evaluate the latency performance, we have performed several tests over various real networks and over an emulated network, varying parameters like RTT, packet loss and amount of competing cross traffic. When comparing our modifications with SCTP on Linux and FreeBSD and TCP New Reno, our results show great latency improvements and indicate the need for a separate handling of thin and thick streams.     

Directed model checking with distance-preserving abstractions

In directed model checking, the traversal of the state space is guided by an estimate of the distance from the current state to the nearest error state. This paper presents a distance-preserving abstraction for concurrent systems that allows one to compute an interesting estimate of the error distance without hitting the state explosion problem. Our experiments show a dramatic reduction both in the number of states explored by the model checker and in the total runtime.     

Real-time simulation of time-of-flight sensors

Today’s time-of-flight (TOF) sensors measure full-range distance information by estimating the elapsed time between emission and receiving of active light in real-time. Such sensors are inexpensive, compact, and they have a high performance, which especially fits real-time applications, e.g. in the fields of automotive, robotics, 3D imaging, and visualization. The simulation of such sensors is an essential building block for hardware design and application development. Therefore, the simulation data must capture the major sensor characteristics.This paper introduces a simulation approach, which is motivated by physics, for the Photonic Mixing Device (PMD) sensor which is a specific type of time-of-flight sensor. Dynamic motion blurring and resolution artifacts such as flying pixels as well as the typical deviation error are prominent effects of real world systems. Flying pixels arise when an area of inhomogeneous depth is covered by a single PMD-pixel whereas the deviation error is based on the anharmonic properties of the optical signal. The modeling of these artifacts is essential for an authentic simulation approach. We present a detailed comparison between a real PMD-device and the simulation data regarding the sensor characteristics.The proposed algorithms are implemented in a hardware accelerated solution which makes use of the programmability of modern Graphics Processing Units (GPUs). This way, an interactive simulation feedback is provided for applications and further data processing. The simulation takes place in real-time and thus all required control mechanisms are accessible in real-time, too.     

DuD Report

DuD Report

DuD Report     

Supporting casual interactions between board games on public tabletop displays and mobile devices

As more interactive surfaces enter public life, casual interactions from passersby are bound to increase. Most of these users can be expected to carry a mobile phone or PDA, which nowadays offers significant computing capabilities of its own. This offers new possibilities for interaction between these users’ private displays and large public ones. In this paper, we present a system that supports such casual interactions. We first explore a method to track mobile phones that are placed on a horizontal interactive surface by examining the shadows which are cast on the surface. This approach detects the presence of a mobile device, as opposed to any other opaque object, through the signal strength emitted by the built-in Bluetooth transceiver without requiring any modifications to the devices’ software or hardware. We then go on to investigate interaction between a Sudoku game running in parallel on the public display and on mobile devices carried by passing users. Mobile users can join a running game by placing their devices on a designated area. The only requirement is that the device is in discoverable Bluetooth mode. After a specific device has been recognized, a client software is sent to the device which then enables the user to interact with the running game. Finally, we explore the results of a study which we conducted to determine the effectiveness and intrusiveness of interactions between users on the tabletop and users with mobile devices.     

The hierarchical Tobit model: A case study in Bayesian computing

In this paper we discuss the potentials of a new Bayesian inference tool, called the Gibbs sampler, for the analysis of the censored regression or Tobit model. Tobit models have a wide range of applications in empirical sciences, like econometrics and biometrics. The estimation results of the simple Tobit model will be compared to a hierarchical Tobit model, and the Gibbs sampling approach to the related classical algorithm of expectation-maximisation (EM). The underlying botanical example of this paper is concerned with the censoring mechanism in plant reproduction and proposes the Bayesian Tobit model for the growth relationship between the reproductive part and the rest of the plant.     

Effect of Berezinskii-Kosterlitz-Thouless vortex fluctuations on the nuclear spin relaxation rate

In this work we examine the influence of the diffusive motion of vortices in highly anisotropic high-T_c superconductors near the Berezinskii-Kosterlitz-Thouless (BKT) transition temperature T _bkt on the spin-lattice relaxation rate T ₁ ^–1 .We find a jump in T ₁ ^–1 at the temperature T _bkt .     

Smart Workplaces for older adults: coping ‘ethically’ with technology pervasiveness

Universal Access in the Information Society - Pervasive technologies such as Artificial Intelligence, Virtual Reality and the Internet of Things, despite their great potential for improved...     

Online Acoustic System Identification Exploiting Kalman Filtering and an Adaptive Impulse Response Subspace Model

We introduce a novel algorithm for online estimation of Acoustic Impulse Responses (AIRs) which allows for fast convergence by exploiting prior knowledge about the fundamental structure of AIRs. The proposed method assumes that the variability of AIRs of an acoustic scene is confined to a low-dimensional manifold which is embedded in a high-dimensional space of possible AIR estimates. We discuss various approaches which exploit a training data set of AIRs, e.g., high-accuracy AIR estimates from the acoustic scene, to learn a local affine subspace approximation of the AIR manifold. The model is motivated by the idea of describing the generally nonlinear AIR manifold locally by tangential hyperplanes and its validity is verified for simulated data. Subsequently, we describe how the manifold assumption can be used to enhance online AIR estimates by projecting them onto an adaptively estimated subspace. Motivated by the assumption of manifolds being locally Euclidean, the parameters determining the adaptive subspace are learned from the nearest neighbor AIR training samples to the current AIR estimate. To assess the proximity of training data AIRs to the current AIR estimate, we introduce a probabilistic extension of the Euclidean distance which improves the performance for applications with non-white excitation signals. Furthermore, we describe how model imperfections can be tackled by a soft projection of the AIR estimates. The proposed algorithm exhibits significantly faster convergence properties in comparison to a high-performance state-of-the-art algorithm. Furthermore, we show an improved steady-state performance for speech-excited system identification scenarios suffering from high-level interfering noise and nonunique solutions.