The Department of Engineering Cybernetics at NTNU: From 1994 Into the Future

A short overview of the developments at the Department of Engineering Cybernetics at NTNU over the last 15 years is given. The vision of the department is to stay among Europe's most well recognized universities in control engineering, both with respect to education and research. It is discussed how this is achieved, and will continue to be strengthened in the future

Validation and Experimental Testing of Observers for Robust GNSS-Aided Inertial Navigation

This chapter is the study of state estimators for robust navigation. Navigation of vehicles is a vast field with multiple decades of research. The main aim is to estimate position, linear velocity, and attitude (PVA) under all dynamics, motions, and conditions via data fusion. The state estimation problem will be considered from two different perspectives using the same kinematic model. First, the extended Kalman filter (EKF) will be reviewed, as an example of a stochastic approach; second, a recent nonlinear observer will be considered as a deterministic case. A comparative study of strapdown inertial navigation methods for estimating PVA of aerial vehicles fusing inertial sensors with global navigation satellite system (GNSS)-based positioning will be presented. The focus will be on the loosely coupled integration methods and performance analysis to compare these methods in terms of their stability, robustness to vibrations, and disturbances in measurements

An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 Satellite

Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding these ground-truth annotations. In this work, we introduce The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, an open dataset with 200 diverse hyperspectral images from the HYPSO-1 mission, available in both raw and calibrated forms for scientific research in Earth observation. Moreover, 38 of these images from different countries include ground-truth labels at pixel-level totaling about 25 million spectral signatures labeled for sea/land/cloud categories. To demonstrate the potential of the dataset and its labeled subset, we have additionally optimized a deep learning model (1D Fully Convolutional Network), achieving superior performance to the current state of the art. The complete dataset, ground-truth labels, deep learning model, and software code are openly accessible for download at the website https://ntnu-smallsat-lab.github.io/hypso1_sea_land_clouds_dataset/ .Comment: Computer Vision, Artificial Intelligence, Remote Sensing, Earth Observation, Hyperspectral Imaging, Classification, Labeled Dat

Nonlinear Observer for Tightly Integrated Inertial Navigation Aided by Pseudo-Range Measurements

A modular nonlinear observer for inertial navigation aided by pseudo-range measurements is designed and analyzed. The attitude observer is based on a recent nonlinear complementary filter that uses magnetometer and accelerometer vector measurements to correct the quaternion attitude estimate driven by gyro measurements, including gyro bias estimation. A tightly integrated translational motion observer is driven by accelerometer measurements, employs the attitude estimates, and makes corrections using the pseudo-range and range-rate measurements. It estimates position, range bias errors, velocity and specific force in an earth-fixed Cartesian coordinate frame, where the specific force estimate is used as a reference vector for the accelerometer measurements in the attitude observer. The exponential stability of the feedback interconnection of the two observers is analyzed and found to have a semiglobal region of attraction with respect to the attitude observer initialization and local region of attraction with respect to translational motion observer initialization. The latter is due to linearization of the range measurement equations that is underlying the selection of injection gains by solving a Riccati equation. In typical applications, the pseudo-range equations admit an explicit algebraic solution that can be easily computed and used to accurately initialize the position and velocity estimates. Hence, the limited region of attraction is not seen as a practical limitation of the approach for many applications. Advantages of the proposed nonlinear observer are low computational complexity and a solid theoretical foundation

Nonlinear Vehicle Velocity Observer with Road-Tire Friction Adaptation

Abstract-A nonlinear observer for lateral velocity of an automotive vehicle is extended for robustness with respect to unknown road surface conditions. The observer uses a friction model parametrized with the maximum road-tire friction coefficient, and an adaptive parameter update law is designed for estimation of this coefficient. The adaptive nonlinear observer is proven to be uniformly globally asymptotically stable under a uniform δ -persistency-of-excitation condition, and a set of additional technical assumptions, using results related to Matrosov's Theorem. The adaptive observer is validated using experimental data from a car

Editorial Board

Source at http://dx.doi.org/10.1186/s12888-017-1345-8 Background: The duration of untreated psychosis is determined by both patient and service related factors. Few studies have considered the geographical accessibility of services in relation to treatment delay in early psychosis. To address this, we investigated whether treatment delay is co-determined by straight-line distance to hospital based specialist services in a mainly rural mental health context. Methods: A naturalistic cross-sectional study was conducted among a sample of recent onset psychosis patients in northern Norway (n = 62). Data on patient and service related determinants were analysed. Results: Half of the cohort had a treatment delay longer than 4.5 months. In a binary logistic regression model, straight-line distance was found to make an independent contribution to delay in which we controlled for other known risk factors. Conclusions: The determinants of treatment delay are complex. This study adds to previous studies on treatment delay by showing that the spatial location of services also makes an independent contribution. In addition, it may be that insidious onset is a more important factor in treatment delay in remote areas, as the logistical implications of specialist referral are much greater than for urban dwellers. The threshold for making a diagnosis in a remote location may therefore be higher. Strategies to reduce the duration of untreated psychosis in rural areas would benefit from improving appropriate referral by crisis services, and the detection of insidious onset of psychosis in community based specialist services

Integrated monitoring of mola mola behaviour in space and time

Over the last decade, ocean sunfish movements have been monitored worldwide using various satellite tracking methods. This study reports the near-real time monitoring of finescale (< 10 m) behaviour of sunfish. The study was conducted in southern Portugal in May 2014 and involved satellite tags and underwater and surface robotic vehicles to measure both the movements and the contextual environment of the fish. A total of four individuals were tracked using custom-made GPS satellite tags providing geolocation estimates of fine-scale resolution. These accurate positions further informed sunfish areas of restricted search (ARS), which were directly correlated to steep thermal frontal zones. Simultaneously, and for two different occasions, an Autonomous Underwater Vehicle (AUV) videorecorded the path of the tracked fish and detected buoyant particles in the water column. Importantly, the densities of these particles were also directly correlated to steep thermal gradients. Thus, both sunfish foraging behaviour (ARS) and possibly prey densities, were found to be influenced by analogous environmental conditions. In addition, the dynamic structure of the water transited by the tracked individuals was described by a Lagrangian modelling approach. The model informed the distribution of zooplankton in the region, both horizontally and in the water column, and the resultant simulated densities positively correlated with sunfish ARS behaviour estimator (r(s) = 0.184, p < 0.001). The model also revealed that tracked fish opportunistically displace with respect to subsurface current flow. Thus, we show how physical forcing and current structure provide a rationale for a predator's finescale behaviour observed over a two weeks in May 2014