The GEO 600 laser interferometer with 600 m armlength is part of a worldwide network of gravitational wave detectors. Due to the use of advanced technologies like multiple pendulum suspensions with a monolithic last stage and signal recycling, the anticipated sensitivity of GEO 600 is close to the initial sensitivity of detectors with several kilometres armlength. This paper describes the subsystems of GEO 600, the status of the detector by September 2001 and the plans towards the first science run.
The Glasgow group is involved in the construction of the GEO600 interferometer as well as in R&D activity on technology for advanced gravitational wave detectors. GEO600 will be the first GW detector using quasimonolithic silica suspensions in order to decrease thermal noise significantly with respect to steel wire suspensions. The results concerning GEO600 suspension mounting and performance will be shown in the first section. Section 2 is devoted to the present results from the direct measurement of thermal noise in mirrors mounted in the 10 m interferometer in Glasgow which has a sensitivity limit of 4 × 10 −19 m Hz −1/2 above 1 kHz. Section 3 presents results on the measurements of coating losses. R&D activity has been carried out to understand better how thermal noise in the suspensions affects the detector sensitivity, and in section 4 a discussion on the non-linear thermoelastic effect is presented.
This paper describes memristor-based neuromorphic circuits for non-linear separable pattern recognition. We initially describe a memristor based neuron circuit and then show how multilayer neural networks can be constructed using this neuron circuit. These neuromorphic circuits are capable of learning both linearly and non-linearly separable logic functions. This paper presents the first study of applying neural network learning algorithms to these circuits in SPICE. Our simulations capture alternate current paths within the memristor crossbars and wire resistances that are essential to properly model in crossbar circuits. Our results show that neural network learning algorithms are able to train around these alternate current paths. Further, it was shown that neural networks can properly train the passive memristor-based crossbars without having to use virtual ground mode operational amplifiers as suggested in previous work. Our circuit requires in-situ training, but reduces the number of transistors required by the circuit by about 3 times and reduced the circuit power consumption almost 2 orders of magnitude compared to a virtual ground approach. The key impact of this study is the demonstration through low level circuit simulations that dense memristor crossbars can be effectively utilized to build neuromorphic processors.
A combined analytical and experimental approach is introduced to estimate the dynamic response of complex systems from a limited number of measurements. The method is based on the concept that modal information is sufficient to extrapolate the complete map of the response from experimental data through the reconstruction of modal loads. The capabilities of the algorithm are first verified via well-controlled lab experiments on a thin-walled aluminium-rotor blade. Numerical results from a comprehensive UH-60 multibody model are then compared with available experimental data. Significant improvements in the accuracy of the predicted results are achieved when simple airloads models are employed as inputs.
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