Residual Mode Filters

The technology relates generally to controlled ecosystems, and more particularly, to a Controlled Closed-Ecosystem Development System (CCEDS) that can be used to develop designs for sustainable, small-scale reproductions of subsets of the Earths biosphere and the Orbiting Modular Artificial-Gravity Spacecraft (OMAGS). The technology encompassing a CCEDS includes one or more a Closed Ecological Systems (CESs), each having one or more Controlled Ecosystem Modules (CESMs). Each CESM can have a biome containing at least one organism, and equipment comprising one or more of sensors, actuators, or components that are associated with the biome. A controller operates the equipment to effect transfer of material among CESMs to optimize one or more CESM biomes with respect to their organism population health, resilience, variety, quantities, biomass, and sustainability. A CES is a community of organisms and their resources that persist in a sealed volume such that mass is not added or removed. The mass (food/air/water) required by the CES organisms is continually recycled from the mass (waste) produced by the organisms. Energy and information may be transferred to and from a CES. CES research promises to become a significant resource for the resolution of global ecology problems which have thus far been experimentally inaccessible and may very well prove an invaluable resource for predicting the probable ecological consequences of anthropogenic materials on regional ecosystems. In order to create CESs that are orders of magnitude smaller than the Earth that can function without the Earth, the desired gravity level and necessary radiation shielding must be provided by other means. Orbiting Modular Artificial-Gravity Spacecraft (OMAGS) is a fractional gravity spacecraft design for CES payloads and is depicted in Figures below. In tandem, the CCEDS and OMAGS systems can be used to foster gravitational ecosystem research for developing sustainable communities in space and on Earth.

Every 12 seconds, the Dynamic Weather Route (DWR) automation system computes and analyzes trajectories for en-route flights. DWR first identifies flights that could save 5 or more flying minutes (wind-corrected) by flying direct to a downstream return fix on their current flight plan. Eligible return fixes are limited so as not to take flights too far off their current route or interfere with arrival routings near the destination airport. Using the direct route as a reference route, DWR inserts up to two auxiliary waypoints as needed to find a minimum-delay reroute that avoids the weather and returns the flight to its planned route at the downstream fix. If a reroute is found that can save 5 minutes or more relative to the current flight plan, the flight is posted to a list displayed to the airline or FAA user. Auxiliary waypoints are defined using fix-radial-distance format, and a snap to nearby named fix option is available for todays voice-based communications. Users may also adjust the alert criteria, nominally set to 5 minutes, based on their workload and desired potential savings for their flights. A graphical user interface enables visualization of proposed routes on a traffic display and modification, if necessary, using point, click, and drag inputs. If needed, users can adjust the reroute parameters including the downstream return fix, any inserted auxiliary waypoints, and the maneuver start point. Reroute metrics, including flying time savings (or delay) relative to the current flight plan, proximity to current and forecast weather, downstream sector congestion, traffic conflicts, and conflicts with special use airspace are all updated dynamically as the user modifies a proposed route.

Some of the current challenges faced by research in artificial intelligence and autonomous control systems include providing self control, resilience, adaptability, and stability for intelligent systems, especially over a long period of time, in changing environments. The Evolvable Neural Software System (ENSS), Formulation for Emotion Embedding in Logic Systems (FEELS), Stability Algorithm for Neural Entities (SANE), and the Logic Expansion for Autonomously Reconfigurable Neural Systems (LEARNS) are foundations for tackling some of these challenges, by providing the basic algorithms evolvable systems could use to manage its own behavior. These algorithms would allow networks to self regulate, noticing unusual behavior and the circumstances that may have caused that behavior, and then correcting to behave more predictably when similar circumstances are encountered. The process is similar to how psychology in organisms evolved iteratively, eventually finding and keeping better responses to given stimuli.

NASA Ames has developed an Autonomous Situational Awareness Platform system for a UAS (ASAP-U), a traffic management system to incorporate Unmanned Aerial Systems (UASs) into the National Airspace System. The Autonomous Situational Awareness Platform (ASAP) is a system that combines existing navigation technology (both aviation and maritime) with new procedures to safely integrate Unmanned Aerial Systems (UASs) with other airspace vehicles. It uses a module called ASAP-U, which includes a transmitter, receivers, and various links to other UAS systems. The module collects global positioning system GPS coordinates and time from a satellite antenna, and this data is fed to the UAS's flight management system for navigation. The ASAP-U module autonomously and continuously sends UAS information via a radio frequency (RF) antenna using Self-Organized Time Division Multiple Access (SOTDMA) to prevent signal overlap. It also receives ASAP data from other aircraft. In case of transmission overload, priority is given to closer aircraft. Additionally, the module can receive weather data, navigational aid data, terrain data, and updates to the UAS flight plan. The collected data is relayed to the flight management system, which includes various databases and a navigation computer to calculate necessary flight plan modifications based on regulations, right-of-way rules, terrain, and geofencing. Conflicts are checked against databases, and if none are found, the flight plan is implemented. If conflicts arise, modifications can be made. The ASAP-U module continuously receives and transmits data, including UAS data and data from other aircraft, to detect conflicts with other aircraft, terrain, weather, and geofencing. Based on this information, the flight management system determines the need for course adjustments and the flight control system executes them for a safe flight route.

The NASA software application developed under the TASAR project is called the Traffic Aware Planner (TAP). TAP automatically monitors for flight optimization opportunities in the form of lateral and/or vertical trajectory changes. Surveillance data of nearby aircraft, using ADS-B IN technology, are processed to evaluate and avoid possible conflicts resulting from requested changes in the trajectory. TAP also leverages real-time connectivity to external information sources, if available, of operational data relating to winds, weather, restricted airspace, etc., to produce the most acceptable and beneficial trajectory-change solutions available at the time. The software application is designed for installation on low-cost Electronic Flight Bags that provide read-only access to avionics data. The user interface is also compatible with the popular iPad. FAA certification and operational approval requirements are expected to be minimal for this non-safety-critical flight-efficiency application, reducing implementation cost and accelerating adoption by the airspace user community. Awarded "2016 NASA Software of the Year"