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In modern world of today where air traffic is continuously increasing and available space at the airports remains finite, there is a problem of safe docking of aircraft. The problem needs to be solved to ensure safe and smooth movement of aircraft, passengers and crew while making optimum utilization of available ground space. Without such systems having in place, accidents keep occurring due to human judgment errors. These accidents are causing loss of material costs and human injury. The importance of Video-based Docking Systems is continuously increasing due to the challenges of current and upcoming traffic demands of future. This paper evaluates two neural networks architectures for recognition of civil airliners in a Video docking system. The networks compared are feedforward neural network and probabilistic neural network. The results are compared by presenting data to neural networks while deforming the shape, adding noise and partly occluding the shape and presenting those angles for which network was not trained.

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In this research work, performance of 128-quadrature amplitude modulation (QAM) modulation format along with dual polarization is investigated for long-haul communication in chaotic environment at data rate of 112 Gb/s and different fiber lengths. The prime objective of this work is the merger of QAM modulation scheme and optical chaos to acquire higher effective data rate with secure optical communication. The property of semiconductor laser for producing high bandwidth chaos is utilized to encrypt high data rate supported by 128-QAM signal. Chaos produced in this way is then applied to two states i.e., x and y-polarization of signal, at the same time to study results individually through constellation diagrams. The proposed model consisting of 128-QAM transmitter and receiver design, incorporating chaos is tested to investigate and combat the effects of different transmission impairments such as attenuation, dispersion, inline amplifiers effects, and message retrieval.