Real-time face & eye tracking and blink detection using event cameras.

Event cameras contain emerging, neuromorphic vision sensors that capture local-light​ intensity changes at each pixel, generating a stream of asynchronous events. This way of acquiring visual information constitutes a departure from traditional frame-based cameras and offers several significant advantages - low energy consumption, high temporal resolution, high dynamic range and low latency. Driver monitoring systems (DMS) are in-cabin safety systems designed to sense and understand a drivers physical and cognitive state.

Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train.

Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds).

Investigation of cell line specific responses to pH inhomogeneity and consequences for process design.

With increasing bioreactor volumes, the mixing time of the reactor increases as well, which creates an inhomogeneous environment for the cells. This can result in impaired process performance in large-scale production reactors. Particularly the addition of base through the reactor headspace can be problematic, since it creates an area, where cells are repeatedly exposed to an increased pH.

Development, characterization, and application of a 2-Compartment system to investigate the impact of pH inhomogeneities in large-scale CHO-based processes.

Large-scale bioreactors for the production of monoclonal antibodies reach volumes of up to 25 000 L. With increasing bioreactor size, mixing is however affected negatively, resulting in the formation of gradients throughout the reactor. These gradients can adversely affect process performance at large scale.

Predicting industrial-scale cell culture seed trains-A Bayesian framework for model fitting and parameter estimation, dealing with uncertainty in measurements and model parameters, applied to a nonlinear kinetic cell culture model, using an MCMC method.

For production of biopharmaceuticals in suspension cell culture, seed trains are required to increase cell number from cell thawing up to production scale. Because cultivation conditions during the seed train have a significant impact on cell performance in production scale, seed train design, monitoring, and development of optimization strategies is important. This can be facilitated by model-assisted prediction methods, whereby the performance depends on the prediction accuracy, which can be improved by inclusion of prior process knowledge, especially when only few high-quality data is available, and description of inference uncertainty, providing, apart from a "best fit"-prediction, information about the probable deviation in form of a prediction interval.

The impact of pH inhomogeneities on CHO cell physiology and fed-batch process performance - two-compartment scale-down modelling and intracellular pH excursion.

Due to high mixing times and base addition from top of the vessel, pH inhomogeneities are most likely to occur during large-scale mammalian processes. The goal of this study was to set-up a scale-down model of a 10-12 m stirred tank bioreactor and to investigate the effect of pH perturbations on CHO cell physiology and process performance. Short-term changes in extracellular pH are hypothesized to affect intracellular pH and thus cell physiology.

Event-Based Tone Mapping for Asynchronous Time-Based Image Sensor.

The asynchronous time-based neuromorphic image sensor ATIS is an array of autonomously operating pixels able to encode luminance information with an exceptionally high dynamic range (>143 dB). This paper introduces an event-based methodology to display data from this type of event-based imagers, taking into account the large dynamic range and high temporal accuracy that go beyond available mainstream display technologies. We introduce an event-based tone mapping methodology for asynchronously acquired time encoded gray-level data.

HFirst: A Temporal Approach to Object Recognition.

This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous address event representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems. Freedom from rigid timing constraints opens the possibility of using true timing to our advantage in computation.

Progress toward forecasting product quality and quantity of mammalian cell culture processes by performance-based modeling.

The production of biopharmaceuticals requires highly sophisticated, complex cell based processes. Once a process has been developed, acceptable ranges for various control parameters are typically defined based on process characterization studies often comprising several dozens of small scale bioreactor cultivations. A lot of data is generated during these studies and usually only the information needed to define acceptable ranges is processed in more detail.

Visual tracking using neuromorphic asynchronous event-based cameras.

This letter presents a novel computationally efficient and robust pattern tracking method based on a time-encoded, frame-free visual data. Recent interdisciplinary developments, combining inputs from engineering and biology, have yielded a novel type of camera that encodes visual information into a continuous stream of asynchronous, temporal events. These events encode temporal contrast and intensity locally in space and time.

Event-based 3D reconstruction from neuromorphic retinas.

This paper presents a novel N-ocular 3D reconstruction algorithm for event-based vision data from bio-inspired artificial retina sensors. Artificial retinas capture visual information asynchronously and encode it into streams of asynchronous spike-like pulse signals carrying information on, e.g.

Asynchronous event-based hebbian epipolar geometry.

Epipolar geometry, the cornerstone of perspective stereo vision, has been studied extensively since the advent of computer vision. Establishing such a geometric constraint is of primary importance, as it allows the recovery of the 3-D structure of scenes. Estimating the epipolar constraints of nonperspective stereo is difficult, they can no longer be defined because of the complexity of the sensor geometry.