This article studies ultra-high-precision positioning with piezoactuators and illustrates the results with an example Scanning Probe Microscopy (SPM) application. Loss of positioning precision in piezoactuators occurs (1) due to hysteresis during long range applications, (2) due to creep effects when positioning is needed over extended periods of time, and (3) due to induced vibrations during h...

Ultra-precision grinding using super abrasives such as diamond and Cubic Boron Nitride (CBN) is ideally suited to the production of artefacts in hard or brittle materials such as ceramics or hard metals. These materials are well suited to the production of moulds. Conventional ultra-precision machining techniques such as diamond turning or micro milling are of limited use in the production of t...

Sir Christopher Wren is widely acknowledged as a key individual in the formation of the Royal Society, a society initially advocated ‘for the promotion of physicomathematical experimental learning’. Many people would not be surprised to learn that England’s most famous architect was a driving force behind the Royal Society. But most people would not know that Wren was at that time an astronomer...

We show that the Big Bang Observer (BBO), a proposed space-based gravitational-wave (GW) detector, would provide ultra-precise measurements of cosmological parameters. By detecting ∼ 3 × 10 compact-star binaries, and utilizing them as standard sirens, BBO would determine the Hubble constant to ∼ 0.1%, and the dark energy parameters w0 and wa to ∼ 0.01 and ∼ 0.1, respectively. BBO’s dark-energy ...

The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches‎. ‎Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data‎. ‎In this paper‎, ‎a methodology has been employed to opt...

Artificial neural networks with millions of adjustable parameters and a similar number of training examples are a potential solution for difficult, large-scale pattern recognition problems in areas such as speech and face recognition, classification of large volumes of web data, and finance. The bottleneck is that neural network training involves iterative gradient descent and is extremely comp...

Artificial neural networks with millions of adjustable parameters and a similar number of training examples are a potential solution for difficult, large-scale pattern recognition problems in areas such as speech and face recognition, classification of large volumes of web data, and finance. The bottleneck is that neural network training involves iterative gradient descent and is extremely comp...