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A Robust Interface for Head Motion based Control of a Robot Arm using MARG and Visual Sensors
(2018)
Head-controlled human machine interfaces have gained popularity over the past years, especially in the restoration of the autonomy of severely disabled people, like tetraplegics. These interfaces need to be reliable and robust regarding the environmental conditions to guarantee safety of the user and enable a direct interaction between a human and a machine. This paper presents a hybrid MARG and visual sensor system for head orientation estimation which is in this case used to teleoperate a robotic arm. The system contains a Magnetic Angular Rate Gravity (MARG)-sensor and a Tobii eye tracker 4C. A MARG sensor consists of tri-axis accelerometer, gyroscope as well as a magnetometer which enable a complete measurement of orientation relative to the direction of gravity and magnetic field of the earth. The tri-axis magnetometer is sensitive to external magnetic fields which result in incorrect orientation estimation from the sensor fusion process. In this work the Tobii eye tracker 4C is used to increase head orientation estimation because it also features head tracking even though it is commonly used for eye tracking. This type of visual sensor does not suffer magnetic drift. However, it computes orientation data only, if a user is detectable. Within this work a state machine is presented which enables data fusion of the MARG and visual sensor to improve orientation estimation. The fusion of the orientation data of MARG and visual sensors enables a robust interface, which is immune against external magnetic fields. Therefore, it increases the safety of the human machine interaction.
Bifacial photovoltaic (PV) modules are able to utilize light from both sides and can therefore significantly increase the electric yield of PV power plants, thus reducing the cost and improving profitability. Bifacial PV technology has a huge potential to reach a major market share, in particular when considering utility scale PV plants. Accordingly, bifacial PV is currently attracting increasing attention from involved engineers, scientists and investors. There is a lack of available, structured information about this topic. A book that focuses exclusively on bifacial PV thus meets an increasing need. Bifacial Photovoltaics: Technology, applications and economics provides an overview of the history, status and future of bifacial PV technology with a focus on crystalline silicon technology, covering the areas of cells, modules, and systems. In addition, topics like energy yield simulations and bankability are addressed. It is a must-read for researchers and manufacturers involved with cutting-edge photovoltaics.
CIP is an open-source high-level function library for (non-linear) curve fitting and data smoothing (with cubic splines), clustering (k-medoids, ART-2a) and machine learning (multiple linear/polynomial regression, feed-forward perceptron-type shallow and deep neural networks and support vector machines). In addition it provides several heuristics for the selection of training and test data or methods to estimate the relevance of data input components. CIP is built on top of the computing platform Mathematica to exploit its algorithmic and graphical capabilities.
CIP is an open-source high-level function library for (non-linear) curve fitting and data smoothing (with cubic splines), clustering (k-medoids, ART-2a) and machine learning (multiple linear/polynomial regression, feed-forward perceptron-type shallow and deep neural networks and support vector machines). In addition it provides several heuristics for the selection of training and test data or methods to estimate the relevance of data input components. CIP is built on top of the computing platform Mathematica to exploit its algorithmic and graphical capabilities.
Radiotherapy (RT) treatment planning is based on computed tomography (CT) images and traditionally uses the conventional Hounsfield unit (CHU) range. This HU range is suited for human tissue but inappropriate for metallic materials. To guarantee safety of patient carrying implants precise HU quantification is beneficial for accurate dose calculations in planning software. Some modern CT systems offer an extended HU range (EHU). This study focuses the suitability of these two HU ranges for the quantification of metallic components of active implantable medical devices (AIMD). CT acquisitions of various metallic and non-metallic materials aligned in a water phantom were investigated. From our acquisitions we calculated that materials with mass-density ρ > 3.0 g/cm3 cannot be represented in the CHU range. For these materials the EHU range could be used for accurate HU quantification. Since the EHU range does not effect the HU values for materials ρ < 3.0 g/cm3, it can be used as a standard for RT treatment planning for patient with and without implants.