ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Emerging and next-generation test instrumentation increasingly relies on network communication to manage complex and dynamic test scenarios, particularly for uninhabited autonomous systems. Adapting wireless communication infrastructure to accommodate challenging testing needs can benefit from reconfigurable radio technology. Frequency agility is one characteristic of reconfigurable radios that to date has seen only limited progress toward programmability. This paper overviews an ongoing project to validate a promising chipset that performs conversion of RF signals directly into digital data for the wireless receiver and, for the transmitter, converts digital data into RF signals. The Software Configurable Multichannel Transceiver (SCMT) enables four transmitters and four receivers in a single unit, programmable for any frequency band between 1 MHz and 6 GHz.

Operation of Unmanned Air Vehicles (UAVs) has increased significantly over the past few years. However, routine operation in non-segregated airspace remains a challenge, primarily due to nature of the environment and restrictions and challenges that accompany this. Currently, tight human control is envisaged as a means to achieve the oft quoted requirements of transparency , equivalence and safety. However, the problems of high cost of human operation, potential communication losses and operator remoteness remain as obstacles. One means of overcoming these obstacles is to devolve authority, from the ground controller to an on-board system able to understand its situation and make appropriate decisions when authorised. Such an on-board system is known as an Autonomous System. The nature of the autonomous system, how it should be designed, when and how authority should be transferred and in what context can they be allowed to control the vehicle are the general motivation for this study. To do this, the system must overcome the negative aspects of differentiators that exist between UASs and manned aircraft and introduce methods to achieve required increases in the levels of versatility, cost, safety and performance. The general thesis of this work is that the role and responsibility of an airborne autonomous system are sufficiently different from those of other conventionally controlled manned and unmanned systems to require a different architectural approach. Such a different architecture will also have additional requirements placed upon it in order to demonstrate acceptable levels of Transparency, Equivalence and Safety. The architecture for the system is developed from an analysis of the basic requirements and adapted from a consideration of other, suitable candidates for effective control of the vehicle under devolved authority. The best practices for airborne systems in general are identified and amalgamated with established principles and approaches of robotics and intelligent agents. From this, a decision architecture, capable of interacting with external human agencies such as the UAS Commander and Air Traffic Controllers, is proposed in detail. This architecture has been implemented and a number of further lessons can be drawn from this. In order to understand in detail the system safety requirements, an analysis of manned and unmanned aircraft accidents is made. Particular interest is given to the type of control moding of current unmanned aircraft in order to make a comparison, and prediction, with accidents likely to be caused by autonomously controlled vehicles. The effect of pilot remoteness on the accident rate is studied and a new classification of this remoteness is identified as a major contributor to accidents A preliminary Bayesian model for unmanned aircraft accidents is developed and results and predictions are made as an output of this model. From the accident analysis and modelling, strategies to improve UAS safety are identified. Detailed implementations within these strategies are analysed and a proposal for more advanced Human-Machine Interaction made. In particular, detailed analysis is given on exemplar scenarios that a UAS may encounter. These are: Sense and Avoid , Mission Management Failure, Take Off/Landing, and Lost Link procedures and Communications Failure. These analyses identify the nature of autonomous, as opposed to automatic, operation and clearly show the benefits to safety of autonomous air vehicle operation, with an identifiable decision architecture, and its relationship with the human controller. From the strategies and detailed analysis of the exemplar scenarios, proposals are made for the improvement of unmanned vehicle safety The incorporation of these proposals into the suggested decision architecture are accompanied by analysis of the levels of benefit that may be expected. These suggest that a level approaching that of conventional manned aircraft is achievable using currently available technologies but with substantial architectural design methodologies than currently fielded.

In this thesis, the development of a stereo camera system for an intelligent tool is presented. The task of the system is to identify and localise objects so that the tool can guide a robot. Different approaches to object detection have been implemented and evaluated and the systems ability to localise objects has been tested. The results show that the system can achieve a localisation accuracy below 5 mm.

Thermal spectrum cameras are gaining interest in many applications due to their long wavelength which allows them to operate under low light and harsh weather conditions. One disadvantage of thermal cameras is their limited visual interpretability for humans, which limits the scope of their applications. In this thesis, we try to address this problem by investigating the possibility of transforming thermal infrared (TIR) images to perceptually realistic visible spectrum (VIS) images by using Convolutional Neural Networks (CNNs). Existing state-of-the-art colorization CNNs fail to provide the desired output as they were trained to map grayscale VIS images to color VIS images. Instead, we utilize an auto-encoder architecture to perform cross-spectral transformation between TIR and VIS images. This architecture was shown to quantitatively perform very well on the problem while producing perceptually realistic images. We show that the quantitative differences are insignificant when training this architecture using different color spaces, while there exist clear qualitative differences depending on the choice of color space. Finally, we found that a CNN trained from day time examples generalizes well on tests from night time.

Training data is the bottleneck for training Convolutional Neural Networks. A larger dataset gives better accuracy though also needs longer training time. It is shown by finetuning neural networks on synthetic rendered images, that the mean average precision increases. This method was applied to two different datasets with five distinctive objects in each. The first dataset consisted of random objects with different geometric shapes. The second dataset contained objects used to assemble IKEA furniture. The neural network with the best performance, trained on 5400 images, achieved a mean average precision of 0.81 on a test which was a sample of a video sequence. Analysis of the impact of the factors dataset size, batch size, and numbers of epochs used in training and different network architectures were done. Using synthetic images to train CNN’s is a promising path to take for object detection where access to large amount of annotated image data is hard to come by.

The Internet at the interdomain level is a complex network of approximately 50,000 Autonomous Systems (ASes). ASes interconnect through two types of links: (a) transit (customer-provider) and (b) peering links. Recent studies have shown that despite being optional for most ASes, a rich and dynamic peering fabric exists among ASes. Peering has also grown as one of the main instruments for catching up with asymmetric traffic due to CDNs, online video traffic, performance requirements, etc. Moreover, peering has been in the spotlight recently because of peering conflicts between major ISPs and Content Providers. Such conflicts have led to calls for intervention by communication regulators and legislation at the highest levels of government. Peering disputes have also sometimes resulted in partitioning of the Internet. Despite the broad interest and intense debate about peering, several fundamental questions remain elusive. The objective of this thesis is to study peering from a techno-economics perspective. We explore the following questions: 1- What are the main sources of complexity in Internet peering that defy the development of an automated approach to assess peering relationships? 2- What is the current state of the peering ecosystem, e.g., which categories of ASes are more inclined towards peering? What are the most popular peering strategies among ASes in the Internet? 3- What can we say about the economics of contemporary peering practices, e.g., what is the impact of using different peering traffic ratios as a strategy to choose peers? Is the general notion that peering saves network costs, always valid? 4- Can we propose novel methods for peering that result in more stable and fair peering interconnections? We have used game-theoretic modeling, large-scale computational agent-based modeling, and analysis of publicly available peering data to answer the above questions. The main contributions of this thesis include: 1- Identification of fundamental complexities underlying the evaluation of peers and formation of stable peering links in the interdomain network. 2- An empirical study of the state of the peering ecosystem from August 2010 to August 2013. 3- Development of a large-scale agent-based computational model to study the formation and evolution of Internet peering interconnections. 4- A plausible explanation for the gravitation of Internet transit providers towards Open peering and a prediction of its future consequences. 5- We propose a variant of the Open peering policy and a new policy based on cost-benefit analysis to replace the contemporary simplistic policies.

Thermal spectrum cameras are gaining interest in many applications due to their long wavelength which allows them to operate under low light and harsh weather conditions. One disadvantage of thermal cameras is their limited visual interpretability for humans, which limits the scope of their applications. In this thesis, we try to address this problem by investigating the possibility of transforming thermal infrared (TIR) images to perceptually realistic visible spectrum (VIS) images by using Convolutional Neural Networks (CNNs). Existing state-of-the-art colorization CNNs fail to provide the desired output as they were trained to map grayscale VIS images to color VIS images. Instead, we utilize an auto-encoder architecture to perform cross-spectral transformation between TIR and VIS images. This architecture was shown to quantitatively perform very well on the problem while producing perceptually realistic images. We show that the quantitative differences are insignificant when training this architecture using different color spaces, while there exist clear qualitative differences depending on the choice of color space. Finally, we found that a CNN trained from daytime examples generalizes well on tests from night time.

This master thesis explores the possibility of using generative Adversarial Networks (GANs) to refine labeled synthetic code images to resemble real code images while preserving label information. The GAN used in this thesis consists of a refiner and a discriminator. The discriminator tries to distinguish between real images and refined synthetic images. The refiner tries to fool the discriminator by producing refined synthetic images such that the discriminator classify them as real. By updating these two networks iteratively, the idea is that they will push each other to get better, resulting in refined synthetic images with real image characteristics. The aspiration, if the exploration of GANs turns out successful, is to be able to use refined synthetic images as training data in Semantic Segmentation (SS) tasks and thereby eliminate the laborious task of gathering and labeling real data. Starting off from a foundational GAN-model, different network architectures, hyperparameters and other design choices are explored to find the best performing GAN-model. As is widely acknowledged in the relevant literature, GANs can be difficult to train and the results in this thesis are varying and sometimes ambiguous. Based on the results from this study, the best performing models do however perform better in SS tasks than the unrefined synthetic set they are based on and benchmarked against, with regards to Intersection over Union.

The automotive industry is heading towards more automation. This puts high demands on many systems like Pedestrian Detection Systems. Such systems need to operate in real time with high accuracy and in embedded systems with limited power, memory resources and compute power. This in turn puts high demands on model size and model design. Lately Convolutional Neural Networks (ConvNets) have dominated the field of object detection and therefore it is reasonable to believe that they are suited for pedestrian detection as well. Therefore, this thesis investigates how ConvNets have been used for pedestrian detection and how such solutions can be implemented in embedded systems on FPGAs (Field Programmable Gate Arrays). The conclusions drawn are that ConvNets indeed perform well on pedestrian detection in terms of accuracy but to a cost of large model sizes and heavy computations. This thesis also comes up with a design proposal of a ConvNet for pedestrian detection with the implementation in an embedded system in mind. The proposed network performs well on pedestrian classification and the performance looks promising for detection as well, but further development is required.

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