DragonflEYE: a passive approach to aerial collision sensing

"This dissertation describes the design, development and test of a passive wide-field optical aircraft collision sensing instrument titled 'DragonflEYE'. Such a ""sense-and-avoid"" instrument is desired for autonomous unmanned aerial systems operating in civilian airspace. The instrument was configured as a network of smart camera nodes and implemented using commercial, off-the-shelf components. An end-to-end imaging train model was developed and important figures of merit were derived. Transfer functions arising from intermediate mediums were discussed and their impact assessed. Multiple prototypes were developed. The expected performance of the instrument was iteratively evaluated on the prototypes, beginning with modeling activities followed by laboratory tests, ground tests and flight tests. A prototype was mounted on a Bell 205 helicopter for flight tests, with a Bell 206 helicopter acting as the target. Raw imagery was recorded alongside ancillary aircraft data, and stored for the offline assessment of performance. The ""range at first detection"" (R0), is presented as a robust measure of sensor performance, based on a suitably defined signal-to-noise ratio. The analysis treats target radiance fluctuations, ground clutter, atmospheric effects, platform motion and random noise elements. Under the measurement conditions, R0 exceeded flight crew acquisition ranges. Secondary figures of merit are also discussed, including time to impact, target size and growth, and the impact of resolution on detection range. The hardware was structured to facilitate a real-time hierarchical image-processing pipeline, with selected image processing techniques introduced. In particular, the height of an observed event above the horizon compensates for angular motion of the helicopter platform.

Safeguarding DeFi Smart Contracts against Oracle Deviations

This paper presents OVer, a framework designed to automatically analyze the behavior of decentralized finance (DeFi) protocols when subjected to a "skewed" oracle input. OVer firstly performs symbolic analysis on the given contract and constructs a model of constraints. Then, the framework leverages an SMT solver to identify parameters that allow its secure operation. Furthermore, guard statements may be generated for smart contracts that may use the oracle values, thus effectively preventing oracle manipulation attacks. Empirical results show that OVer can successfully analyze all 10 benchmarks collected, which encompass a diverse range of DeFi protocols. Additionally, this paper also illustrates that current parameters utilized in the majority of benchmarks are inadequate to ensure safety when confronted with significant oracle deviations.Comment: 13 pages; extended version of paper accepted in ICSE'2

Non-intrusive flight test instrumentation using video recognition: Reducing the cost and time to market for certified flight simulation devices

The National Research Council of Canada has conducted feasibility studies into the development of non-intrusive flight test instrumentation methods with the goal of reducing the cost and time-to-market for certified aerospace products. Video recognition for the collection of flight test time history data was one such non-intrusive method. The advantages of using machine vision for flight data collection are many. One video camera can be used to extract data for many in-flight parameters, reducing instrumentation time, the airworthiness effort, the overall aircraft schedule and associated costs. This paper details the development of flight test video recognition software, calibration algorithms, hardware, and the accuracy of data collected by video via full flight simulator data benchmarks. Video recognition is a convenient means of collecting cockpit flight test data for model development and certification of full flight simulator devices.Peer reviewed: YesNRC publication: Ye

Range Performance Evaluation from the Flight Tests of a Passive Electro-Optical Aircraft Detection Sensor for Unmanned Aircraft Systems

The range performance evaluation of a multi-camera electro-optical aircraft detection instrument, "Dragon-EYE", was conducted. A "range at first detection" (R0) quantity, evaluated from the temporal signal-to-noise ratio of potential targets on a collision course, is proposed as a generic metric for evaluating electro-optical systems. The methodology and evaluation process are discussed. Efficacy of the approach was tested by flying multiple collision trajectories, with the instrument mounted onto a Bell 205 helicopter acting as a surrogate unmanned aircraft system, while an instrumented Bell 206 Jet-Ranger acted as the intruder. The R0 values were extracted and subsequently compared to visual estimates by the flight crew. An aggregate detection range of R0 = 6.3+-1.7 km was observed to be within the margin of error for flight-crew detection range of 4.8+-2.0 km. Sensitivity analysis was conducted on the choice of threshold and the sensor's angular resolution, with increased resolution yielded diminishing returns due to atmospheric extinction. Robustness was assessed by repeating the experiment on a different day with a secondary camera array, "Cerberus", recording images simultaneously. The observed detection ranges were within the margin of error of prior estimates. In addition, measured ranges from Cerberus aligned with their predicted values.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Physical Unclonable Function (PUF)-Based e-Cash Transaction Protocol (PUF-Cash)

Electronic money (e-money or e-Cash) is the digital representation of physical banknotes augmented by added use cases of online and remote payments. This paper presents a novel, anonymous e-money transaction protocol, built based on physical unclonable functions (PUFs), titled PUF-Cash. PUF-Cash preserves user anonymity while enabling both offline and online transaction capability. The PUF’s privacy-preserving property is leveraged to create blinded tokens for transaction anonymity while its hardware-based challenge–response pair authentication scheme provides a secure solution that is impervious to typical protocol attacks. The scheme is inspired from Chaum’s Digicash work in the 1980s and subsequent improvements. Unlike Chaum’s scheme, which relies on Rivest, Shamir and Adlemans’s (RSA’s) multiplicative homomorphic property to provide anonymity, the anonymity scheme proposed in this paper leverages the random and unique statistical properties of synthesized integrated circuits. PUF-Cash is implemented and demonstrated using a set of Xilinx Zynq Field Programmable Gate Arrays (FPGAs). Experimental results suggest that the hardware footprint of the solution is small, and the transaction rate is suitable for large-scale applications. An in-depth security analysis suggests that the solution possesses excellent statistical qualities in the generated authentication and encryption keys, and it is robust against a variety of attack vectors including model-building, impersonation, and side-channel variants

Artifact for OVer: Safeguarding DeFi Smart Contracts against Oracle Deviations

<p>This work presents Over, a framework designed to automatically analyze the behavior of decentralized finance (DeFi) protocols when subjected to a "skewed" oracle input. <br>Over firstly performs symbolic analysis on the given contract and constructs a model of constraints. Then, the framework leverages an SMT solver to identify parameters that allow its secure operation. Furthermore, guard statements may be generated for smart contracts that may use the oracle values, thus effectively preventing oracle manipulation attacks.</p> <p>We implement Over based on the *Slither* static analysis tool in *Python* for Solidity based smart contracts. To solve the optimization problems, we leverage the SMT solver *Z3*. </p> <p>We answer three research questions. <br>- Are current control parameters of Defi protocols safe under large oracle deviations?<br>- Can Over efficiently analyze various Defi protocols that use oracles?<br>- Can Over assist developers to design safe Defi protocols that use oracles?</p> <p>Empirical results show current parameters utilized in the majority of benchmarks are inadequate to ensure safety when confronted with significant oracle deviations. Over can successfully analyze all 10 benchmarks collected, which encompass a diverse range of DeFi protocols. The results also demonstrate how Over can help developers to design safe protocols.</p&gt

Monocular Ranging for Small Unmanned Aerial Systems in the Far-Field

Recent proliferation of small Unmanned Aerial Systems (sUAS) applications requires onboard collision avoidance systems to mitigate the risk of collision with non-cooperative aircraft and manned aircraft, which may not see sUAS in time to perform an avoidance maneuver. An attractive avenue for onboard collision avoidance is the utilization of machine vision cameras due to their low size, weight and power (SWaP) requirements. In this paper, we characterize the range performance of a machine vision system developed in-house and mounted onto an sUAS. The technique was designed to estimate the performance of a sense-and-avoid system to ensure that the sensing components meet the well-clear requirements for the chosen platform and avoidance strategy. Experimental flight-test data was acquired from test-flights flown along multiple collision geometries for two intruders: a general Aviation (GA) aircraft and a fixed-wing sUAS. The ownship and both intruders were instrumented with inertial navigation systems (INS) recording position and attitude information. The range at first detection, R_0, was extracted from in-flight imagery of head-on collision course geometry synchronized with INS data from both aircraft and ground-truth values extracted from the raw imagery. This initial detection distance, R_0, scales with atmospheric attenuation. Therefore, under clear sky conditions, the derived R_0 value represents the upper bound on the detection range achievable by the test configuration of the detector. Results indicate that the maximum initial detection distance for a 4k resolution action camera fitted with a 41º Field of View (FOV) lens is 2.763 ± 0.037 km for a GA aircraft and 0.881 ± 0.061 km for a fixed-wing sUAS, respectively. The in results this study suggest that a vision-based detect and track system may be analyzed using the sensor characterization and contextualized within aircraft well-clear volumes