Abstract: A method of reconstructing an electron microscopy image of size [Mx N] pixels of a first sample, the method implemented by a computer comprising a processor and a memory, the method comprising: providing a set of pre-learned dictionaries, including a first pre-learned dictionary including a set of p1 atoms; acquiring a sparse set of S acquired sub-images, including a first sub-image of size [a×b] pixels wherein a, b?[2, min {M,N}], of the first sample; and reconstructing the electron microscopy image of the first sample using the sparse set of S sub-images of the first sample and the set of pre-learned dictionaries.

Abstract: A method of simulating an electron microscopy image of a sample is described. The method implemented by a computer comprising a processor and a memory. The method comprises: obtaining parameters of the electron microscopy, attributes of the sample and respective thresholds of one or more target properties of the simulated electron microscopy image; and computing the simulated electron microscopy image of size [M×N] pixels of the sample using the obtained parameters of the electron microscope and the obtained attributes of the sample, according to the obtained respective thresholds of the one or more target properties of the simulated electron microscopy image.

Abstract: A 3D nanoprinter electron beam lithography module for a lithography system, such as a scanning electron microscope (SEM) or an environmental SEM (ESEM) with a beam blanker and electron beam lithography attachment, but generally applicable to any electron beam lithography capable system. The module is comprised of an in-situ spin-coating stage that is compatible with a cooling-SEM stage, with a spin-coating motor, a spin-coating sample stub, a liquid waste collector cup, a liquid dispensing arm holding a tube bundle that is connected via tubing to micro-syringe pumps or a pressure driven flow controller or pumps connected to fluid reservoirs, an electron beam scan generator control box, electrical feedthroughs, control electronics, and a computing system responsible for controlling the entire module. The dispensing arm can be controlled by a servo motor.

Abstract: An imaging system includes a focal plane array, readout electronics, and a computing system in which the number of active pixels is either set at a low-fraction of the total pixels thereby reducing the effect of radiation damage, or radiation damage over time is detected and automatically compensated. Machine learning is used to identify radiation damaged pixels and damaged regions which are subsequently eliminated and replaced by the computational system. The machine learning is used to identify changes in the fixed pattern signal/noise and/or noise of the system, and is then computationally corrected.

Abstract: A 3D nanoprinter electron beam lithography module for a lithography system, such as a scanning electron microscope (SEM) or an environmental SEM (ESEM) with a beam blanker and electron beam lithography attachment, but generally applicable to any electron beam lithography capable system. The module is comprised of an in-situ spin-coating stage that is compatible with a cooling-SEM stage, with a spin-coating motor, a spin-coating sample stub, a liquid waste collector cup, a liquid dispensing arm holding a tube bundle that is connected via tubing to micro-syringe pumps or a pressure driven flow controller or pumps connected to fluid reservoirs, an electron beam scan generator control box, electrical feedthroughs, control electronics, and a computing system responsible for controlling the entire module. The dispensing arm can be controlled by a servo motor.

Abstract: An imaging system includes a focal plane array, readout electronics, and a computing system in which the number of active pixels is either set at a low-fraction of the total pixels thereby reducing the effect of radiation damage, or radiation damage over time is detected and automatically compensated. Machine learning is used to identify radiation damaged pixels and damaged regions which are subsequently eliminated and replaced by the computational system. The machine learning is used to identify changes in the fixed pattern signal/noise and/or noise of the system, and is then computationally corrected.

Abstract: A transmitter for use in an aircraft navigation system, the transmitter comprising a body portion (35) having an end (36, 37) adapted for implanting into the ground and an opposite end fitted with an antenna (38,39), and stabilizing means (41) arranged to be rendered operative on impact of the transmitter with the ground.

Abstract: A navigation system for an aircraft fitted with an autopilot, the system comprising at least one ground-based transmitter (2), receiver means (7,8) mounted in the aircraft and including two antenna (5,6) oriented such that their polar diagrams overlap and are inclined with respect to the fore-and-aft axis of the aircraft.