CCD imaging instruments for planetary spacecraft applications

The development of new spacecraft camera systems to be used in conjunction with CCD sensors is reported. A brief overview of the science objectives and engineering constraints which influence the design of cameras for deep space is followed by a review of two current development programs, one leading to a line scan imager and the other to an area array frame camera. For each of these, a general description of the imager is given. It is evident that currently available CCDs fall short of requirements in some respects

Low Latency Rendering with Dataflow Architectures

The research presented in this thesis concerns latency in VR and synthetic environments. Latency is the end-to-end delay experienced by the user of an interactive computer system, between their physical actions and the perceived response to these actions. Latency is a product of the various processing, transport and buffering delays present in any current computer system. For many computer mediated applications, latency can be distracting, but it is not critical to the utility of the application. Synthetic environments on the other hand attempt to facilitate direct interaction with a digitised world. Direct interaction here implies the formation of a sensorimotor loop between the user and the digitised world - that is, the user makes predictions about how their actions affect the world, and see these predictions realised. By facilitating the formation of the this loop, the synthetic environment allows users to directly sense the digitised world, rather than the interface, and induce perceptions, such as that of the digital world existing as a distinct physical place. This has many applications for knowledge transfer and efficient interaction through the use of enhanced communication cues. The complication is, the formation of the sensorimotor loop that underpins this is highly dependent on the fidelity of the virtual stimuli, including latency. The main research questions we ask are how can the characteristics of dataflow computing be leveraged to improve the temporal fidelity of the visual stimuli, and what implications does this have on other aspects of the fidelity. Secondarily, we ask what effects latency itself has on user interaction. We test the effects of latency on physical interaction at levels previously hypothesized but unexplored. We also test for a previously unconsidered effect of latency on higher level cognitive functions. To do this, we create prototype image generators for interactive systems and virtual reality, using dataflow computing platforms. We integrate these into real interactive systems to gain practical experience of how the real perceptible benefits of alternative rendering approaches, but also what implications are when they are subject to the constraints of real systems. We quantify the differences of our systems compared with traditional systems using latency and objective image fidelity measures. We use our novel systems to perform user studies into the effects of latency. Our high performance apparatuses allow experimentation at latencies lower than previously tested in comparable studies. The low latency apparatuses are designed to minimise what is currently the largest delay in traditional rendering pipelines and we find that the approach is successful in this respect. Our 3D low latency apparatus achieves lower latencies and higher fidelities than traditional systems. The conditions under which it can do this are highly constrained however. We do not foresee dataflow computing shouldering the bulk of the rendering workload in the future but rather facilitating the augmentation of the traditional pipeline with a very high speed local loop. This may be an image distortion stage or otherwise. Our latency experiments revealed that many predictions about the effects of low latency should be re-evaluated and experimenting in this range requires great care

Accelerating Pattern Matching in Neuromorphic Text Recognition System Using Intel Xeon Phi Coprocessor

Neuromorphic computing systems refer to the computing architecture inspired by the working mechanism of human brains. The rapidly reducing cost and increasing performance of state-of-the-art computing hardware allows large-scale implementation of machine intelligence models with neuromorphic architectures and opens the opportunity for new applications. One such computing hardware is Intel Xeon Phi coprocessor, which delivers over a TeraFLOP of computing power with 61 integrated processing cores. How to efficiently harness such computing power to achieve real time decision and cognition is one of the key design considerations. This work presents an optimized implementation of Brain-State-in-a-Box (BSB) neural network model on the Xeon Phi coprocessor for pattern matching in the context of intelligent text recognition of noisy document images. From a scalability standpoint on a High Performance Computing (HPC) platform we show that efficient workload partitioning and resource management can double the performance of this many-core architecture for neuromorphic applications

There and Back Again: Self-supervised Multispectral Correspondence Estimation

Across a wide range of applications, from autonomous vehicles to medical imaging, multi-spectral images provide an opportunity to extract additional information not present in color images. One of the most important steps in making this information readily available is the accurate estimation of dense correspondences between different spectra. Due to the nature of cross-spectral images, most correspondence solving techniques for the visual domain are simply not applicable. Furthermore, most cross-spectral techniques utilize spectra-specific characteristics to perform the alignment. In this work, we aim to address the dense correspondence estimation problem in a way that generalizes to more than one spectrum. We do this by introducing a novel cycle-consistency metric that allows us to self-supervise. This, combined with our spectra-agnostic loss functions, allows us to train the same network across multiple spectra. We demonstrate our approach on the challenging task of dense RGB-FIR correspondence estimation. We also show the performance of our unmodified network on the cases of RGB-NIR and RGB-RGB, where we achieve higher accuracy than similar self-supervised approaches. Our work shows that cross-spectral correspondence estimation can be solved in a common framework that learns to generalize alignment across spectra

Towards Highly-Integrated Stereovideoscopy for \u3ci\u3ein vivo\u3c/i\u3e Surgical Robots

When compared to traditional surgery, laparoscopic procedures result in better patient outcomes: shorter recovery, reduced post-operative pain, and less trauma to incisioned tissue. Unfortunately, laparoscopic procedures require specialized training for surgeons, as these minimally-invasive procedures provide an operating environment that has limited dexterity and limited vision. Advanced surgical robotics platforms can make minimally-invasive techniques safer and easier for the surgeon to complete successfully. The most common type of surgical robotics platforms -- the laparoscopic robots -- accomplish this with multi-degree-of-freedom manipulators that are capable of a diversified set of movements when compared to traditional laparoscopic instruments. Also, these laparoscopic robots allow for advanced kinematic translation techniques that allow the surgeon to focus on the surgical site, while the robot calculates the best possible joint positions to complete any surgical motion. An important component of these systems is the endoscopic system used to transmit a live view of the surgical environment to the surgeon. Coupled with 3D high-definition endoscopic cameras, the entirety of the platform, in effect, eliminates the peculiarities associated with laparoscopic procedures, which allows less-skilled surgeons to complete minimally-invasive surgical procedures quickly and accurately. A much newer approach to performing minimally-invasive surgery is the idea of using in-vivo surgical robots -- small robots that are inserted directly into the patient through a single, small incision; once inside, an in-vivo robot can perform surgery at arbitrary positions, with a much wider range of motion. While laparoscopic robots can harness traditional endoscopic video solutions, these in-vivo robots require a fundamentally different video solution that is as flexible as possible and free of bulky cables or fiber optics. This requires a miniaturized videoscopy system that incorporates an image sensor with a transceiver; because of severe size constraints, this system should be deeply embedded into the robotics platform. Here, early results are presented from the integration of a miniature stereoscopic camera into an in-vivo surgical robotics platform. A 26mm X 24mm stereo camera was designed and manufactured. The proposed device features USB connectivity and 1280 X 720 resolution at 30 fps. Resolution testing indicates the device performs much better than similarly-priced analog cameras. Suitability of the platform for 3D computer vision tasks -- including stereo reconstruction -- is examined. The platform was also tested in a living porcine model at the University of Nebraska Medical Center. Results from this experiment suggest that while the platform performs well in controlled, static environments, further work is required to obtain usable results in true surgeries. Concluding, several ideas for improvement are presented, along with a discussion of core challenges associated with the platform. Adviser: Lance C. Pérez [Document = 28 Mb