Proposal and preliminary design for a high speed civil transport aircraft. Swift: A high speed civil transport for the year 2000

To meet the needs of the growing passenger traffic market in light of an aging subsonic fleet, a new breed of aircraft must be developed. The Swift is an aircraft that will economically meet these needs by the year 2000. Swift is a 246 passenger, Mach 2.5, luxury airliner. It has been designed to provide the benefit of comfortable, high speed transportation in a safe manner with minimal environmental impact. This report will discuss the features of the Swift aircraft and establish a solid, foundation for this supersonic transport of tomorrow

Detecting trash and valuables with machine vision in passenger vehicles

The research conducted here will determine the possibility of implementing a machine vision based detection system to identify the presence of trash or valuables in passenger vehicles using a custom designed in-car camera module. The detection system was implemented to capture images of the rear seating compartment of a car intended to be used in shared vehicle fleets. Onboard processing of the image was done by a Raspberry Pi computer while the image classification was done by a remote server. Two vision based algorithmic models were created for the purpose of classifying the images: a convolutional neural network (CNN) and a background subtraction model. The CNN was a fine-tuned VGG16 model and it produced a final prediction accuracy of 91.43% on a batch of 140 test images. For the output analysis, a confusion matrix was used to identify the correlation between correct and false predictions, and the certainties of the three classes for each classified image were examined as well. The estimated execution time of the system from image capture to displaying the results ranged between 5.7 seconds and 11.5 seconds. The background subtraction model failed for the application here due to its inability to form a stable background estimate. The incorrect classifications of the CNN were evident due to the external sources of variation in the images such as extreme shadows and lack of contrast between the objects and its neighbouring background. Improvements in changing the camera location and expanding the training image set were proposed as possible future research

Domain Adaptation with Joint Learning for Generic, Optical Car Part Recognition and Detection Systems (Go-CaRD)

Systems for the automatic recognition and detection of automotive parts are crucial in several emerging research areas in the development of intelligent vehicles. They enable, for example, the detection and modelling of interactions between human and the vehicle. In this paper, we quantitatively and qualitatively explore the efficacy of deep learning architectures for the classification and localisation of 29 interior and exterior vehicle regions on three novel datasets. Furthermore, we experiment with joint and transfer learning approaches across datasets and point out potential applications of our systems. Our best network architecture achieves an F1 score of 93.67 % for recognition, while our best localisation approach utilising state-of-the-art backbone networks achieve a mAP of 63.01 % for detection. The MuSe-CAR-Part dataset, which is based on a large variety of human-car interactions in videos, the weights of the best models, and the code is publicly available to academic parties for benchmarking and future research.Comment: Demonstration and instructions to obtain data and models: https://github.com/lstappen/GoCar

An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles

Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intelligent Transportation Systems (ITS). The main goal of this study is to prevent accidents caused by fatigue, drowsiness, and driver distraction. To avoid these incidents, this paper proposes an integrated safety system that continuously monitors the driver's attention and vehicle surroundings, and finally decides whether the actual steering control status is safe or not. For this purpose, we equipped an ordinary car called FARAZ with a vision system consisting of four mounted cameras along with a universal car tool for communicating with surrounding factory-installed sensors and other car systems, and sending commands to actuators. The proposed system leverages a scene understanding pipeline using deep convolutional encoder-decoder networks and a driver state detection pipeline. We have been identifying and assessing domestic capabilities for the development of technologies specifically of the ordinary vehicles in order to manufacture smart cars and eke providing an intelligent system to increase safety and to assist the driver in various conditions/situations.Comment: 15 pages and 5 figures, Submitted to the international conference on Contemporary issues in Data Science (CiDaS 2019), Learn more about this project at https://iasbs.ac.ir/~ansari/fara

TBD(exp 3)

When asked by the Aeronautical Engineering staff to design a viable supersonic commercial transport, most of the students were well aware that Boeing, McDonnell Douglas, and other aircraft companies had been studying a cadre of transports for more than 30 years and had yet to present a viable aircraft. In the spirit of aviation progress and with much creative license, the TBD design team spearheaded the problem with the full intention of presenting a marketable high speed civil transport in spring of 1992. The project commenced with various studies of future market demands. With the market expansion of American business overseas, the airline industry projects a boom of over 200 million passengers by the year 2000. This will create a much higher demand for time efficient and cost effective inter-continental travel; this is the challenge of the high speed civil transport. The TBD(exp 3), a 269 passenger, long-range civil transport was designed to cruise at Mach 3.0 utilizing technology predicted to be available in 2005. Unlike other contemporary commercial airplane designs, the TBD(exp 3) incorporates a variable geometry wing for optimum performance. This design characteristic enabled the TBD(exp 3) to be efficient in both subsonic and supersonic flight. The TBD(exp 3) was designed to be economically viable for commercial airline purchase, be comfortable for passengers, meet FAR Part 25, and the current FAR 36 Stage 3 noise requirements. The TBD(exp 3) was designed to exhibit a long service life, maximize safety, ease of maintenance, as well as be fully compatible with all current high-traffic density airport facilities

Advanced flight control system study

The architecture, requirements, and system elements of an ultrareliable, advanced flight control system are described. The basic criteria are functional reliability of 10 to the minus 10 power/hour of flight and only 6 month scheduled maintenance. A distributed system architecture is described, including a multiplexed communication system, reliable bus controller, the use of skewed sensor arrays, and actuator interfaces. Test bed and flight evaluation program are proposed

“AccessBIM” - A Model of Environmental Characteristics for Vision Impaired Indoor Navigation and Way Finding

The complexity of modern indoor environments has made navigation difficult for individuals with vision impairment. Hence, this thesis presents the AccessBIM framework, which is an optimized database that’s facilitates generation of a real-time floor plan with path determination. The AccessBIM framework has the potential to play an integral role in improving the independence and quality of life for people with vision impairment whilst also decreasing the cost to the community related to caretakers