Real-Time Panoramic Tracking for Event Cameras

Event cameras are a paradigm shift in camera technology. Instead of full frames, the sensor captures a sparse set of events caused by intensity changes. Since only the changes are transferred, those cameras are able to capture quick movements of objects in the scene or of the camera itself. In this work we propose a novel method to perform camera tracking of event cameras in a panoramic setting with three degrees of freedom. We propose a direct camera tracking formulation, similar to state-of-the-art in visual odometry. We show that the minimal information needed for simultaneous tracking and mapping is the spatial position of events, without using the appearance of the imaged scene point. We verify the robustness to fast camera movements and dynamic objects in the scene on a recently proposed dataset and self-recorded sequences.Comment: Accepted to International Conference on Computational Photography 201

Test-Case Generation for Embedded Binary Code Using Abstract Interpretation

This paper describes a framework for test-case generation for microcontroller binary programs using abstract interpretation techniques. The key idea of our approach is to derive program invariants a priori, and then use backward analysis to obtain test vectors that are executed on the target microcontroller. Due to the structure of binary code, the abstract interpretation framework is based on propositional encodings of the program semantics and SAT solving

Towards Real-time, On-board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on the- fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft

Towards Real-Time, On-Board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respond to uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, and hardware components. This system can detect and diagnose failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; and (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software. We call this approach rt-R2U2, a name derived from its requirements. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual flight data from the NASA Swift UAS

Higgs Doublets, Split Multiplets and Heterotic SU(3)_C x SU(2)_L x U(1)_Y Spectra

A methodology for computing the massless spectrum of heterotic vacua with Wilson lines is presented. This is applied to a specific class of vacua with holomorphic SU(5)-bundles over torus-fibered Calabi-Yau threefolds with fundamental group Z_2. These vacua lead to low energy theories with the standard model gauge group SU(3)_C x SU(2)_L x U(1)_Yand three families of quark/leptons. The massless spectrum is computed, including the multiplicity of Higgs doublets.Comment: 11+1 p

Moduli Dependent Spectra of Heterotic Compactifications

Explicit methods are presented for computing the cohomology of stable, holomorphic vector bundles on elliptically fibered Calabi-Yau threefolds. The complete particle spectrum of the low-energy, four-dimensional theory is specified by the dimensions of specific cohomology groups. The spectrum is shown to depend on the choice of vector bundle moduli, jumping up from a generic minimal result to attain many higher values on subspaces of co-dimension one or higher in the moduli space. An explicit example is presented within the context of a heterotic vacuum corresponding to an SU(5) GUT in four-dimensions.Comment: 11+1 pages, 2 figures, comments adde