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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.
Studies on Pulse Electrodeposition of Pt-Ni binary Alloy For Electrochemical Cell Applications
(2018)
Environmental Protection
(2018)
Neuroscientists want to inspect the data their simulations are producing while these are still running. This will on the one hand save them time waiting for results and therefore insight. On the other, it will allow for more efficient use of CPU time if the simulations are being run on supercomputers. If they had access to the data being generated, neuroscientists could monitor it and take counter-actions, e.g., parameter adjustments, should the simulation deviate too much from in-vivo observations or get stuck.
As a first step toward this goal, we devise an in situ pipeline tailored to the neuroscientific use case. It is capable of recording and transferring simulation data to an analysis/visualization process, while the simulation is still running. The developed libraries are made publicly available as open source projects. We provide a proof-of-concept integration, coupling the neuronal simulator NEST to basic 2D and 3D visualization.
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.