Fully-automatic defects classification and restoration for STM images.

The Scanning tunneling microscope (STM) is a micro instrument designed for surface morphology with nanometer precision. The restoration of the STM image defects usually needs human judgements and manual positioning because of the diversity of the morphology and the randomness of the defects. This paper provides a new fully-automatic method that combines deep convolutional neural classification network and unique restoration algorithms corresponding to different defects. Aimed at automatically processing compound defects in STM images, the method first predicts what kinds of defects a raw STM image has by a series of parallel binary classification networks, and then decides the process order according to the predicted labels, and finally restores the defects by corresponding global restoration algorithms in order. Experiment results prove the provided method can restore the STM images by self-judging, self-positioning, self-processing without any manual intervention

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

On-belt Tomosynthesis: 3D Imaging of Baggage for Security Inspection

This thesis describes the design, testing and evaluation of `On-belt Tomosynthesis' (ObT): a cost-e ective baggage screening system based on limited angle digital x-ray tomosynthesis and close-range photogrammetry. It is designed to be retro tted to existing airport conveyor-belt systems and to overcome the limitations of current systems creating a pseudo-3D imaging system by combining x-ray and optical imaging to form digital tomograms. The ObT design and set-up consists of a con guration of two x-ray sources illuminating 12 strip detectors around a conveyor belt curve forming an 180 arc. Investigating the acquired ObT x-ray images' noise sources and distortions, improvements were demonstrated using developed image correction methods. An increase of 45% in image uniformity was shown as a result, in the postcorrection images. Simulation image reconstruction of objects with lower attenuation coe cients showed the potential of ObT to clearly distinguish between them. Reconstruction of real data showed that objects of bigger attenuation di erences (copper versus perspex, rather than air versus perspex) could be observed better. The main conclusion from the reconstruction results was that the current imaging method needed further re nements, regarding the geometry registration and the image reconstruction. The simulation results con rmed that advancing the experimental method could produce better results than the ones which can currently be achieved. For the current state of ObT, a standard deviation of 2 mm in (a) the source coordinates, and 2 in (b) the detector angles does not a ect the image reconstruction results. Therefore, a low-cost single camera coordination and tracking solution was developed to replace the previously used manual measurements. Results obtained by the developed solution showed that the necessary prerequisites for the ObT image reconstruction could be addressed. The resulting standard deviation was of an average of 0.4 mm and 1 degree for (a) and (b) respectively