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Results for “bio-inspired computing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Bio-inspired Computing for Robots

Living creatures may provide algorithms to enable active sensing/control systems in robots. Active sensing could enable planetary rovers to feel their way in unknown environments. The surface of Jupiter's moon Europa consists of fractured ice over a liquid sea that may contain microbes similar to those on Earth. To explore such extreme environments, NASA needs robots that autonomously survive, navigate, and gather scientific data. They will be too far away for guidance from Earth. They must sense their environment and control their own movements to avoid obstacles or investigate a science opportunity. To meet this challenge, CICT's Information Technology Strategic Research (ITSR) Project is funding neurobiologists at NASA's Jet Propulsion Laboratory (JPL) and selected universities to search for biologically inspired algorithms that enable robust active sensing and control for exploratory robots. Sources for these algorithms are living creatures, including rats and electric fish.

Laufenberg, Larry↗
DOE OSTI2025

Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training

Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fashion. In addition, training becomes an even more difficult problem when incorporating biological constraints of excitatory and inhibitory connections. In this work, we identify several key factors, such as low initial firing rates and diverse inhibitory spiking patterns, that determine the overall ability to train in the context of spiking networks with various ratios of excitatory to inhibitory neurons. The results indicate networks with biologically-realistic excitatory:inhibitory ratios can reliably train at low activity levels and in noisy environments. Additionally, the Van Rossum distance, a measure of spike train synchrony, provides insight into the importance of inhibitory neurons to increase network robustness to noise. This work supports further biologically-informed large-scale networks and energy efficient hardware implementations.

bio-inspired computing↗
NASA NTRS2009

NASA Tech Briefs, July 2009

Topics covered include: Dual Cryogenic Capacitive Density Sensor; Hail Monitor Sensor; Miniature Six-Axis Load Sensor for Robotic Fingertip; Improved Blackbody Temperature Sensors for a Vacuum Furnace; Wrap-Around Out-the-Window Sensor Fusion System; Wide-Range Temperature Sensors with High-Level Pulse Train Output; Terminal Descent Sensor Simulation; A Robust Mechanical Sensing System for Unmanned Sea Surface Vehicles; Additive for Low-Temperature Operation of Li-(CF)n Cells; Li/CFx Cells Optimized for Low-Temperature Operation; Number Codes Readable by Magnetic-Field-Response Recorders; Determining Locations by Use of Networks of Passive Beacons; Superconducting Hot-Electron Submillimeter-Wave Detector; Large-Aperture Membrane Active Phased-Array Antennas; Optical Injection Locking of a VCSEL in an OEO; Measuring Multiple Resistances Using Single-Point Excitation; Improved-Bandwidth Transimpedance Amplifier; Inter-Symbol Guard Time for Synchronizing Optical PPM; Novel Materials Containing Single-Wall Carbon Nanotubes Wrapped in Polymer Molecules; Light-Curing Adhesive Repair Tapes; Thin-Film Solid Oxide Fuel Cells; Zinc Alloys for the Fabrication of Semiconductor Devices; Small, Lightweight, Collapsible Glove Box; Radial Halbach Magnetic Bearings; Aerial Deployment and Inflation System for Mars Helium Balloons; Steel Primer Chamber Assemblies for Dual Initiated Pyrovalves; Voice Coil Percussive Mechanism Concept for Hammer Drill; Inherently Ducted Propfans and Bi-Props; Silicon Nanowire Growth at Chosen Positions and Orientations; Detecting Airborne Mercury by Use of Gold Nanowires; Detecting Airborne Mercury by Use of Palladium Chloride; Micro Electron MicroProbe and Sample Analyzer; Nanowire Electron Scattering Spectroscopy; Electron-Spin Filters Would Offer Spin Polarization Greater than 1; Subcritical-Water Extraction of Organics from Solid Matrices; A Model for Predicting Thermoelectric Properties of Bi2Te3; Integrated Miniature Arrays of Optical Biomolecule Detectors; A Software Rejuvenation Framework for Distributed Computing; Kurtosis Approach to Solution of a Nonlinear ICA Problem; Robust Software Architecture for Robots; R4SA for Controlling Robots; Bio-Inspired Neural Model for Learning Dynamic Models; Evolutionary Computing Methods for Spectral Retrieval; Monitoring Disasters by Use of Instrumented Robotic Aircraft; Complexity for Survival of Living Systems; Using Drained Spacecraft Propellant Tanks for Habitation; Connecting Node; and Electrolytes for Low-Temperature Operation of Li-CFx Cells.

Source record↗
NASA NTRS2012

Shape and Color Features for Object Recognition Search

A bio-inspired shape feature of an object of interest emulates the integration of the saccadic eye movement and horizontal layer in vertebrate retina for object recognition search where a single object can be used one at a time. The optimal computational model for shape-extraction-based principal component analysis (PCA) was also developed to reduce processing time and enable the real-time adaptive system capability. A color feature of the object is employed as color segmentation to empower the shape feature recognition to solve the object recognition in the heterogeneous environment where a single technique - shape or color - may expose its difficulties. To enable the effective system, an adaptive architecture and autonomous mechanism were developed to recognize and adapt the shape and color feature of the moving object. The bio-inspired object recognition based on bio-inspired shape and color can be effective to recognize a person of interest in the heterogeneous environment where the single technique exposed its difficulties to perform effective recognition. Moreover, this work also demonstrates the mechanism and architecture of the autonomous adaptive system to enable the realistic system for the practical use in the future.

Duong, Tuan A.↗
DOE OSTI2023

A decision support tool for e-waste recycling operations using the hen-and-chicks bio-inspired optimization metaheuristic

E-waste from end-of-life electrical and electronic devices is one of the fastest growing waste streams from households and businesses. E-waste recycling yields environmental sustainability and economic benefits. Due to continuous changes in e-waste types and compositions, recycling businesses face challenges to optimize their operational configuration to achieve better economic and environmental performance. To help e-waste recyclers mitigate this problem, we have developed a modular decision support tool called the Comprehensive Manufacturing Assessment Tool (CMAT) that can simulate both e-waste recycling operations and economics. This tool can give valuable insights regarding the profitability of the entire operation and different e-waste types. In addition, a new bio-inspired metaheuristic optimization algorithm, hen-and-chicks optimization (HACO), was developed to assign manpower to different workstations to maximize operational efficiencies. According to the results of our case study, laptops, desktops, and computer peripherals are the three electronic waste products that produce the most profit. Our examination of the sensitivity of material prices shows that the price of steel has the most significant influence on total profit, because it is the most widely used material in the majority of electronic devices. We have released the decision support tool as open-source software under a general public license. It could be customized for other recycling industries beyond e-waste to achieve business sustainability by making their operations more efficient.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2004

Adaptive Bio-Inspired Wireless Network Routing for Planetary Surface Exploration

Wireless mobile networks suffer connectivity loss when used in a terrain that has hills, and valleys when line of sight is interrupted or range is exceeded. To resolve this problem and achieve acceptable network performance, we have designed an adaptive, configurable, hybrid system to automatically route network packets along the best path between multiple geographically dispersed modules. This is very useful in planetary surface exploration, especially for ad-hoc mobile networks, where computational devices take an active part in creating a network infrastructure, and can actually be used to route data dynamically and even store data for later transmission between networks. Using inspiration from biological systems, this research proposes to use ant trail algorithms with multi-layered information maps (topographic maps, RF coverage maps) to determine the best route through ad-hoc network at real time. The determination of best route is a complex one, and requires research into the appropriate metrics, best method to identify the best path, optimizing traffic capacity, network performance, reliability, processing capabilities and cost. Real ants are capable of finding the shortest path from their nest to a food source without visual sensing through the use of pheromones. They are also able to adapt to changes in the environment using subtle clues. To use ant trail algorithms, we need to define the probability function. The artificial ant is, in this case, a software agent that moves from node to node on a network graph. The function to calculate the fitness (evaluate the better path) includes: length of the network edge, the coverage index, topology graph index, and pheromone trail left behind by other ant agents. Each agent modifies the environment in two different ways: 1) Local trail updating: As the ant moves between nodes it updates the amount of pheromone on the edge; and 2) Global trail updating: When all ants have completed a tour the ant that found the shortest route updates the edges in its path.

Alena, Richard I.↗
DOE OSTI2023

A Fourth-Order Embedded Boundary Finite Volume Method for the Unsteady Stokes Equations with Complex Geometries

A fourth-order finite volume embedded boundary (EB) method is presented for the unsteady Stokes equations. The algorithm represents complex geometries on a Cartesian grid using EB, employing a technique to mitigate the ``small cut-cell"" problem without mesh modifications, cell merging, or state redistribution. Spatial discretizations are based on a weighted least-squares technique that has been extended to fourth-order operators and boundary conditions, including an approximate projection to enforce the divergence-free constraint. Solutions are advanced in time using a fourth-order additive implicit-explicit Runge-Kutta method, with the viscous and source terms treated implicitly and explicitly, respectively. Formal accuracy of the method is demonstrated with several grid convergence studies, and results are shown for an application with a complex bio-inspired material. In conclusion, the developed method achieves fourth-order accuracy and is stable despite the pervasive small cells arising from complex geometries.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Real-Time Cognitive Computing Architecture for Data Fusion in a Dynamic Environment

A novel cognitive computing architecture is conceptualized for processing multiple channels of multi-modal sensory data streams simultaneously, and fusing the information in real time to generate intelligent reaction sequences. This unique architecture is capable of assimilating parallel data streams that could be analog, digital, synchronous/asynchronous, and could be programmed to act as a knowledge synthesizer and/or an "intelligent perception" processor. In this architecture, the bio-inspired models of visual pathway and olfactory receptor processing are combined as processing components, to achieve the composite function of "searching for a source of food while avoiding the predator." The architecture is particularly suited for scene analysis from visual data and odorant.

Duong, Tuan A.↗
NASA NTRS2009

Bio-Inspired Neural Model for Learning Dynamic Models

A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

Duong, Tuan↗
DOE OSTI2024

Neutrons in Structural Biology: Challenges and Opportunities (Workshop Report)

Gaining a thorough understanding of biological systems requires building our knowledge about biological processes from the level of atoms and electrons, and up to whole organisms. Such comprehensive knowledge will allow for a predictive understanding of complex biological systems behavior. It will guide us in the design and development of novel therapeutics and vaccines to tackle existing health threats and to prepare for future pandemics, and it will provide information necessary to create new biomaterials and bio-inspired technologies through manipulation of biological macromolecules, their assemblies, single cells and even microorganisms. Reaching these goals will require a synergistic combination of multiple experimental techniques with molecular calculations and predictive simulations, and the design and development of new techniques and capabilities that bridge current knowledge and technology gaps. Neutron scattering provides unique information about the biomacromolecular structure and function and can play a major role in achieving these goals. A workshop was held to engage the scientific community in identifying pressing challenges in biochemistry, structural biology, enzymology and structure-guided drug design not solved with the current neutron scattering technologies or utilizing other structural biology techniques such as X-ray crystallography, NMR, and cryo-EM. The workshop brought together structural biology, biochemistry and computational experts, as well as early career researchers and students, creating a forum for discussing scientific advancement and collaboration. The workshop included a one-day satellite training workshop where graduate students and postdoctoral researchers were educated in the application of neutron crystallography and small-angle scattering in structural biology. Furthermore, the Instrument Scientific Advisory Board (ISAB) for the development of a macromolecular neutron diffractometer at ORNL’s Second Target Station was introduced at the workshop. The major outcome was that neutrons can provide atomic-level understanding of biomacromolecular structure, function and dynamics which is of paramount importance for addressing the identified challenges. Neutron crystallography, in particular, can resolve long-standing biochemical issues regarding enzyme function by delineating the underlying chemistry and can have a major impact on the design of small-molecule therapeutics, especially in combination with molecular computation (quantum chemistry and molecular dynamics simulations) and the emerging artificial intelligence (AI)-assisted drug design technologies. The unique properties of neutrons, including their high sensitivity to hydrogen and their non-destructive nature, make them ideal probes of biological matter. There is a palpable need in the scientific community to expand and enhance the impact of neutron sciences on biology. Neutron crystallography is the only structural biology method capable of determining positions of all hydrogen atoms in proteins, nucleic acids and their complexes at near-physiological temperatures and of unstable species at cryogenic temperatures. Moreover, neutron analysis is non-ionizing, non-destructive and does not perturb the structure or redox chemistry of active site metal centers and clusters in proteins, which can be invaluable for studying radiation-sensitive metalloprotein complexes. Further, neutron energies used in scattering applications are similar to atomic motions, permitting neutron spectroscopies to characterize the dynamics of biomacromolecules on the picosecond to microsecond timescales. The different sensitivities of neutrons to protium (H) and deuterium (D) isotopes of hydrogen allow enhanced visibility of specific parts of biological complexes through isotopic labeling. The impact of neutrons will be most powerful when neutron scattering is combined with complementary experimental techniques that use photons and electrons, and with high-performance computing. The interconnection and mutuality of the experimental and theoretical capabilities will drive discoveries in biological and health sciences to generate more complete picture of complex biological systems. The major limitation in the field of biological neutron crystallography has been signal-to-noise, demanding large samples that are difficult to produce for the majority of biomacromolecules and limiting the applicability of this technique in biological sciences. A neutron crystallography instrument at the Second Target Station will revolutionize biological science with neutrons by engaging a large scientific community of structural biologists, enabling successful neutron diffraction experiments from radically smaller biomacromolecular crystals, resolving unanswered biochemical questions, and meaningfully contributing to rational drug design. The meeting highlighted 10 grand challenges that will be addressed with this advanced capability over the next decade and beyond, and the recommendations required to help address them are given below.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

Insect-Inspired Flight Control for Unmanned Aerial Vehicles

Flight-control and navigation systems inspired by the structure and function of the visual system and brain of insects have been proposed for a class of developmental miniature robotic aircraft called "biomorphic flyers" described earlier in "Development of Biomorphic Flyers" (NPO-30554), NASA Tech Briefs, Vol. 28, No. 11 (November 2004), page 54. These form a subset of biomorphic explorers, which, as reported in several articles in past issues of NASA Tech Briefs ["Biomorphic Explorers" (NPO-20142), Vol. 22, No. 9 (September 1998), page 71; "Bio-Inspired Engineering of Exploration Systems" (NPO-21142), Vol. 27, No. 5 (May 2003), page 54; and "Cooperative Lander-Surface/Aerial Microflyer Missions for Mars Exploration" (NPO-30286), Vol. 28, No. 5 (May 2004), page 36], are proposed small robots, equipped with microsensors and communication systems, that would incorporate crucial functions of mobility, adaptability, and even cooperative behavior. These functions are inherent to biological organisms but are challenging frontiers for technical systems. Biomorphic flyers could be used on Earth or remote planets to explore otherwise difficult or impossible to reach sites. An example of an exploratory task of search/surveillance functions currently being tested is to obtain high-resolution aerial imagery, using a variety of miniaturized electronic cameras. The control functions to be implemented by the systems in development include holding altitude, avoiding hazards, following terrain, navigation by reference to recognizable terrain features, stabilization of flight, and smooth landing. Flying insects perform these and other functions remarkably well, even though insect brains contains fewer than 10(exp -4) as many neurons as does the human brain. Although most insects have immobile, fixed-focus eyes and lack stereoscopy (and hence cannot perceive depth directly), they utilize a number of ingenious strategies for perceiving, and navigating in, three dimensions. Despite their lack of stereoscopy, insects infer distances to potential obstacles and other objects from image motion cues that result from their own motions in the environment. The concept of motion of texture in images as a source of motion cues is denoted generally as the concept of optic or optical flow. Computationally, a strategy based on optical flow is simpler than is stereoscopy for avoiding hazards and following terrain. Hence, this strategy offers the potential to design vision-based control computing subsystems that would be more compact, would weigh less, and would demand less power than would subsystems of equivalent capability based on a conventional stereoscopic approach.

Thakoor, Sarita↗