1-2 of 2 results
-
NASA’s Embry-Riddle High-Altitude Science Experiment Rig (ERHASER)
PI Pedro LLanos
CO-I Sathya Gangadharan
The purpose of this study aboard the NASA’s Airborne Science Program WB-57 aircraft was to assess the effect of radiation on murine naïve and activated T lymphocytes (T cells) and to test the effectiveness of thermal, radiation and flight tracking technology in biological scientific payloads. Flight cells were kept under proper environmental conditions by using an active thermal system, whereas the levels of radiation were measured by NASA’s Timepix radiation sensor during ascent, cruise at 60,000 feet, and descent.
Exposure to space radiation may place astronauts at significant health risks. This is an under-investigated area of research and therefore more knowledge is needed to better plan long-term space missions. We cultured cells in specific cytokines known to increase their viability and exposed them to either flight or had them as ground controls. In addition, an Automatic Dependent Surveillance-Broadcast (ADS-B) device was utilized to track the state vector of the aircraft during flight. The aims of this pilot research study were:
Aim 1: The first aim was to study the position of the aircraft using the ADS-B device for subsonic or supersonic flights through triangulation from communication nodes along the Gulf of Mexico, which had never done before. We aimed to get insights into some challenges the Federal Administration Aviation (FAA) is facing with integrating the newly emerging era of suborbital space vehicles into the National Air Space.
Aim 2: The second objective was to test the effects of radiation using the Timepix, a sensor that had flown on NASA’s Exploration Flight Test (EFT)-1 On December 5, 2014, and that had never flown before aboard this aircraft to study the radiation levels at 60,000 ft.
Aim 3: Next, we wanted to assess the radiation levels on the immune cells, also called T cells. We used both naïve and activated murine T cells, which were supplemented with cytokines IL-2 and IL-12, as well as treated with the novel supercritical CO2 extract of neem tree Azadirachta indica (SCNE). Interleukin-2 (IL-2) is a potent T cell growth factor used for T cell expansion and treatment of several types of cancer [23]. IL-12 is involved in the differentiation of naive T cells into the T helper cells. It is also known as a T cell-stimulating factor, and a promising agent in cancer immunotherapy [24]. These two cytokines facilitate their effect by targeting the immune system. We sought to investigate whether exposure to radiation and other flight stressors would have any effect on these cells, especially at the 60,000 feet, where peak galactic cosmic rays (GCR) scattering of secondary particles occurs. Given the cytokines' ability to alter the cellular processes and the role of supercritical extract as the natural compound with pluripotent properties, we wanted to test whether supplementing cells with these additives would rescue the cells of radiation damage. We performed the phenotypic analysis of the cells and assessed their ability to release cytokines.
Aim 4: The fourth objective included the development of an Environment Control Life Support System (ECLSS) NanoLab, which could be used to host and sustain the cells at the desired temperature while other variables are being tested.
Below: Timeline of the airborne research experiment.
Below left: Temperature profile during flight. Below right: Total radiation dose during flight.
Below: Example of effects of flight on the expression of cytokines of T cells and Naïve cells.
Below: ERAU team picture at NASA’s Ellington Field, Houston, Texas, 2019.
Categories: Faculty-Staff
-
ICARUS Drone Net
PI Samuel Siewert
CO-I Iacopo Gentilini
CO-I Mehran Andalibi
CO-I Stephen Bruder
Drone Net is a conceptual architecture to integrate passive sensor nodes in a local sensor network along with traditional active sensing methods for small Unmanned Aerial Systems traffic management. The goal of the proposed research architecture is to evaluate use of multiple passive sensor nodes integrating Electro-Optical/Infrared and acoustic arrays networked around a UAS Traffic Management operating region (Class G uncontrolled airspace). The Drone Net approach will be compared to and/or used in addition to RADAR and Automatic Dependent Surveillance-Broadcast tracking and identification. We hypothesize that this approach can better manage non-compliant small UAS along with compliant UAS and general aviation in sensitive airspace, urban locations, and geo-fenced regions.
Drone-Net: Information Fusion Networks for Reliable Localization
ICARUS Research Group: ERAU Prescott
The challenge and opportunity presented by use of UAS “drones” in the national airspace (NAS) has historic significance not seen since the early days of aviation growth after the First World War. The FAA estimates that by 2020 the drone market will be $98 billion with 7 million drones added annually [1]. Market beneficiaries include industrial inspection, aerial photography, insurance, agricultural and government services [1]. While ADS-B for drones, along with registration, has been proposed as a quick fix, to allow drones into the NAS and to share populated areas, it is not clear how this will work for all types of drones ranging from professional service to hobby. For example, many drones will be fully compliant, but some may be semi-compliant (e.g., low quality position reporting due to degraded GPS), and others perhaps even totally non-compliant or hostile [2].
Embryonic research at ERAU Prescott to develop a drone detector, which can be placed on roof-tops and networked with other detectors and information services, has shown that multi-spectral EO/IR detection is quite effective. The feasibility of passive methods for civil aviation detection of aircraft not using ADS-B and not registered on flilghtradar24 [3] (via primary and secondary RADAR) has been experimentally observed.
The ICARUS group at ERAU Prescott is working to fly and test ADS-B on drones [9] that are compliant, semi-compliant, and non-compliant in order to evaluate methods of detection, classification and identification. Research by Sandia National Labs has shown that drones typically have very low RADAR cross-section area, similar to stealth aircraft [4] and can present a significant security and safety threat. The risk is that even one or two national security incidents involving service drones, hobby, or terrorism could result in the grounding of all drones in the NAS.
Multiple studies [4, 5] have substantiated the conclusion that no single sensing modality will suffice to reliably detect and localize a wide variety of drones. To this end, the ICARUS group proposes to pursue a heterogeneous information fusion approach with passive EO/IR and progressing to a “richer” passive/active sensor suite. Prior ICARUS research partially funded by DHS ADAC Center of Excellence (CoE) led to development of the SMDSI (Software Defined Multi-Spectral Imager) to detect and track marine traffic [6, 7]. This existing hardware will be adapted to accommodate additional sensors including acoustic, ADS-B, primary/secondary RADAR, and LIDAR in order to accelerate the development of optimal methods of drone detection, classification, and identification.
The first year of this proposed effort, will involve basic research in machine learning, machine vision, real-time systems, and the development of suitable information fusion algorithms, continuing through the duration. The test configuration will be documented to facilitate replication at other participating academic research organizations (ERAU Daytona ASSURE [11], U. of Alaska ACUASI [12], U. of Alaska DHS ADAC, and U. of Colorado Boulder Embedded Systems Engineering) in year two of the proposed research. Each collaborator can provide unique synergy to enhance research objectives and are potential future external funding partners. The team will include ERAU Prescott faculty, undergraduate students, CU Boulder graduate ESE students, and collaboration with faculty at U. of Alaska and Colorado. In the third year of work, passive capabilities can be enhanced with active LIDAR and existing RADAR at ERAU Prescott. The goal is to develop unique sensor fusion algorithms as well as machine learning and traditional salient object detection and classification methods.
The images collected over the lifetime of the project can further be saved in a distributed database of observed compliant, compliant-low-quality, non-compliant and perhaps hostile drones and shared between “Drone net” nodes. The overall vision is to create a network of passive/active drone detection, classification, and identification nodes to enhance security and safety for drone operations that surpasses ADS-B and registration alone [10]. The capability can be used to test and evaluate commercial drone detection systems being evaluated by the FBI at airports [8] and to make policy and technology recommendations to key agencies (FAA, DOT, DHS) for drone integration into the national air space. Drone net will enable ICARUS to pursue external funding from DHS CoEs, ONR (Office of Naval Research), NASA AIST (Advanced Information Systems Technology), FAA ASSURE, SBIR/STTR, and industry.
References
[1] FAA Aerospace Forecast, Fiscal Years 2016-2036, Federal Aviation Administration.
[2] How consumer drones wind up in the hands of ISIS fighters, Techcrunch, October 13, 2016.
[3] flightradar24.com, ADS-B, primary/secondary RADAR flight localization and aggregation services.
[4] Birch, Gabriel Carisle, John Clark Griffin, and Matthew Kelly Erdman. UAS Detection Classification and Neutralization: Market Survey 2015. No. SAND2015-6365. Sandia National Laboratories (SNL-NM), Albuquerque, NM, 2015.
[5] McNeal, Gregory S. "Unmanned Aerial System Threats: Exploring Security Implications and Mitigation Technologies." Available at SSRN (2015).
[6] Arctic Domain Awareness Research Center, U. of Alaska Anchorage, Theme 3, Task 2, SmartCam.
[7] S. Siewert, M. Vis, R. Claus, R. Krishnamurthy, S. B. Singh, A. K. Singh, S. Gunasekaran, “Image and Information Fusion Experiments with a Software-Defined Multi-Spectral Imaging System for Aviation and Marine Sensor Networks”, (in preparation) AIAA SciTech, Grapevine, Texas, January 2017.
[8] FAA Tests FBI Drone Detection System at JFK, Federal Aviation Administration, July, 2016.
[9] Micro-Avionix, Ping2020 ADS-B transponder for UAS.
[10] Rhode & Schwarz, Protecting the Sky - Signal Monitoring of Radio Controlled Civilian Unmanned Aerial Vehicles and Possible Countermeasures, October 2015.
[11] ACUASI, Alaska Center for Unmanned Aircraft Systems Integration, http://acuasi.alaska.edu/, Dr. Mike Hatfield.
[12] ASSURE, Alliance of System Safety for UAS through Research Excellence, http://assureuas.erau.edu/, Dr. Richard Stansbury.
Categories: Faculty-Staff
1-2 of 2 results