Search NASA⌕ Search

SEARCH · Search NASA

Results for “Infrared Sensors”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

NWB Sensors Infrared Cloud Imager Data Products from SGP

NWB Sensors is a company which has developed a commercially available Infrared Cloud Imager (ICI). For more information, consult the company's webpage, https://www.nwbsensors.com/infrared-cloud-imager. To validate the radiometric accuracy of the ICI, NWB Sensors deployed it to the ARM SGP User Facility in 2023. The primary motivation of this deployment was to perform an intercomparison between the ICI and the Atmospheric Emitted Radiance Interferometer (AERI). The AERI spectral radiance data product can be integrated across the response function of the ICI and directly compared to the zenith radiance observed by the ICI. In addition, the ICI uses proprietary models of the downwelling clear-sky radiance in its cloud processing algorithms. They are based on surface meteorology and precipitable water vapor (PWV). These models were validated by comparing their predicted radiances to those derived from radiative transfer models of the ARM radiosondes. Finally, PWV observations derived from the ICI's onboard GNSS-based PWV retrieval system were compared against those from the microwave radiometer. This dataset contains the ICI radiance and cloud data products.

Atmosphere↗
DOE OSTI2024

Developing a robust sensor for infrared imaging bolometers

A new type of large area sensor for infrared imaging bolometers has been developed. It replaces the thin and fragile free-standing metal foils, which typically have been used, with a multi-layer coated sapphire (or diamond) substrate. Sapphire is transparent to mid-infrared wavelengths, is robust against transients, and can be thick enough to even be the vacuum window. The primary radiation absorber is still a thin deposited metal layer, but now it is partially insulated from the supporting sapphire substrate by a black (carbon-based) layer, which also acts as a blackbody remitter. Test results indicate 6× more noise equivalent power density (estimated NEPD = 23 W/m 2 at 5 ms camera exposure time, foil temperature decay time 60 ms) for a 2 μm gold-coated sapphire disk compared to estimated NEP = 4 W/m 2 at 1.8 ms exposure time, with foil decay time 420 ms, for a nominal 2.5 μm thick platinum-free-standing foil.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Sensor for infrared communication using plant nanobionics

A living plant can function as self-powered auto-samplers and preconcentrators of an analyte within ambient groundwater, detectors of the analyte contained therein. For example, a pair of near infrared (IR) fluorescent sensors embedded within the mesophyll of the plant leaf can be used as detectors of the nitroaromatic molecules, with the first IR channel engineered through CoPhMoRe to recognize analyte via an IR fluorescent emission and the second IR channel including a functionalized nanostructure that acts as an invariant reference signal.

Strano, Michael S.↗
DOE OSTI2025

Zero-Power Wireless Infrared Digitizing Sensors for Large Scale Energy-Smart Farm

This project, funded by ARPA-E and led by Northeastern University, developed zeropower infrared digitizing sensors to optimize irrigation and enhance crop yields. Traditional water stress detection methods are costly and require frequent maintenance, limiting their effectiveness. Our research identified shortwave infrared (SWIR) transmittance as the most reliable indicator of plant water stress and developed plasmonically enhanced micromechanical photoswitches (PMPs) that operate with minimal power. The sensors offer low-cost, large-scale deployment, auto-calibration across different crops, and a 10-year battery life, significantly reducing maintenance costs. The system achieved 4x greater accuracy than conventional soil moisture sensors while ensuring economic feasibility. By enabling precision irrigation, this technology conserves water, enhances crop productivity, and lowers operational costs, making it a scalable solution for sustainable agriculture and global food security.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Hot, cold, or just right? An infrared biometric sensor to improve occupant comfort and reduce overcooling in buildings via closed-loop control

To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Lidar-based remote infrared gas sensor for monitoring anthropogenic pollution: a proof of concept

We propose using wavelength-modulation laser absorption spectroscopy in combination with quadrature detection of scattered light for remote industrial pollution monitoring in the atmosphere with a compact lidar-based gas sensor, which can be mounted on board an unmanned aerial vehicle. The instrument can be used for detecting leaks in product pipe lines; monitoring toxic gases near landfill sites, waste incineration plants, and other hazardous man-made facilities; analysing the gas atmosphere in industrial buildings and structures; and monitoring engineering processes at a sensitivity level of tens of ppm m in gas concentration measurements at characteristic distances of tens of metres. (laser applications and other issues in quantum electronics)

42 ENGINEERING↗
DOE OSTI2025

Hot, Cold, or Just Right? Using a Wide-View Infrared Biometric Sensor to Improve Occupant Comfort and Reduce Overcooling in Buildings via Closed-loop Control (CRADA Final Report)

This research adds to the understanding of building energy management by fundamentally challenging the long-standing paradigm of controlling an environmental parameter—air temperature—and assuming occupant comfort will follow. The project developed and demonstrated a novel approach that directly senses physiological indicators of human comfort to enable closedloop, human-centric control of heating, ventilation, and air conditioning (HVAC) systems. This work shifts the focus from open-loop environmental control to a closed-loop system that prioritizes the occupants' actual thermal state. The primary benefit to the public is the potential for a simultaneous improvement in the quality of indoor environments, where people spend most of their time, and a significant reduction in national energy consumption. Overcooling, or the overuse of cooling energy in a way that discomforts occupants, wastes approximately 0.5 Quads of primary energy annually in U.S. commercial buildings and creates unproductive indoor environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A data-driven approach to sampling matrix selection for compressive sensing

Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term `frequency.' In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images.

compressive sensing, hyperspectral imaging, Walsh-↗
DOE OSTI2020

More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing

Compressive sensing (CS) is a method of sampling which permits some classes of signals to be reconstructed with high accuracy even when they were sampled at sub-Nyquist rates. In this paper we explore a phenomenon in which bandwise CS sampling of a hyperspectral data cube followed by reconstruction can actually result in amplification of chemical signals contained in the cube. Perhaps most surprisingly, chemical signal amplification generally seems to increase as the level of sampling decreases. In some examples, the chemical signal is significantly stronger in a data cube reconstructed from 10% CS sampling than it is in the raw, 100% sampled data cube. We explore this phenomenon in two real-world datasets including the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Each of these datasets contains the release of a chemical simulant, such as glacial acetic acid, triethyl phospate, and sulfur hexafluoride, and in all cases we use the adaptive coherence estimator (ACE) to detect a target signal in the hyperspectral data cube. We end the paper by suggesting some theoretical justifications for why chemical signals would be amplified in CS sampled and reconstructed hyperspectral data cubes and discuss some practical implications.

Hyperspectral imagery, Compressive Sensing, single↗
DOE OSTI2020

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

One of the fundamental assumptions of compressive sensing (CS) is that a signal can be reconstructed from a small number of samples by solving an optimization problem with the appropriate regularization term. Two standard regularization terms are the L1 norm and the total variation (TV) norm. We present a comparison of CS reconstruction results based on these two approaches in the context of chemical detection, and we demonstrate that optimization based on the L1 norm outperforms optimization based on the TV norm. Our comparison is driven by CS sampling, reconstruction, and chemical detection in two real-world datasets: the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Both datasets contain the release of a chemical simulant such as glacial acetic acid, triethyl phosphate, and sulfur hexafluoride. For chemical detection we use the adaptive coherence estimator (ACE) and bulk coherence, and we propose algorithmic ACE thresholds to define the presence or absence of a chemical of interest in both un-compressed data cubes and reconstructed data cubes. The un-compressed data cubes provide an approximate ground truth. We demonstrate that optimization based on either the L1 norm or TV norm results in successful chemical detection at a compression rate of 90%, but we show that L1 optimization is preferable. We present quantitative comparisons of chemical detection on reconstructions from the two methods, with an emphasis on the number of pixels with an ACE value above the threshold.

Hyperspectral imagery, L1-norm, total variation, A↗
DOE OSTI2021

Hamilton: Flexible, Open Source $10 Wireless Sensor System for Energy Efficient Building Operation

Sensors for improving building performance are rapidly populating the market, driven in part by the drive to reduce greenhouse gas emissions resulting from energy production as well as improve the interior environment for healthy and more productive spaces. UC Berkeley has led wireless sensor development over the past 25 years (e.g., Telos mote), with the Hamilton (named after Alexander Hamilton on the US $10 bill) as the most recent. The Hamilton sensor was designed as a low-cost high-performance sensor that is modular and interoperable. The objective of the Hamilton project was to create, evaluate and establish the technological foundations for secure and easy to deploy building energy efficiency applications utilizing pervasive, low-cost wireless sensors integrated with traditional Building Management Systems (BMS), consumer-sector building components, and powerful data analytics. The project included iterative hardware design, incorporating a high-performance database (BTrDb, http://btrdb.io/), creating and iterating the development of secure data middleware (BOSSwave, WAVE/WAVEMQ), working with and pushing the development of an open-source tiny operating system RiotOS, and implementing and improving protocols such as Thread/OpenThread and TCP/IP. The hardware benefited from careful design to drive down the cost; the design included a System-on-a-Chip (SoC), chip antenna, single crystal and five passive components. Careful design of the operating system created a low-power design to enable a long life with small batteries. The hardware included several sensors: temperature, radiant temperature, relative humidity, magnetometer, accelerometer, and light, with an optional occupancy (Passive InfraRed) sensor. The project was the basis of several applications, both internal to the research team and other researchers and professionals at other institutions. Several applications used the sensor hardware as the basis for other complex devices. Other applications used the sensors to improve building performance through interoperating with the building Heating Ventilation and Air-Conditioning (HVAC) system, such as using occupancy and/or distributed temperature sensing to reduce HVAC zone energy while still providing thermal comfort and to reduce peak loads in small commercial buildings. We demonstrated cloud-based energy analytics, implemented a schedule and a Model Predictive Controller in a small commercial building to optimize HVAC energy, occupancy and electricity price. Initial integration of these technological innovations was performed through the creation of execution containers containing the WAVE agent and various driver, proxy, or building system function logic. The research added to the understanding of efficient sensor hardware, secure middleware, time-series data management (high performance database), efficient communication protocols, and interoperating with applications and building systems. The project showed the technical effectiveness and economic feasibility of creating a low-cost, modular, and easy-to-deploy sensor. Through conversations with multiple end users, the research team discovered that many customers wanted data management and services in addition to the sensors. HamiltonIOT developed packages of sensors, border router, and data services to provide a seamless “plug-and-play” sensor deployment. Some customers were willing to pay for higher quality sensors (such as light); some customers wanted a robust enclosure (waterproof).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Estimation of wintertime cloud radiative effects in the Western Arctic, a function of cloud-moisture-coupling and sea ice conditions

Cloud radiative effects are estimated using up- and downward ground-based observations of broadband thermal infrared sensors in cloudy conditions which are provided by the ARM data repository. The ARM Surface Radiation measurement is analyzed. To study the cloud radiative effects, the observed thermal infrared measurements under cloudy conditions are analyzed together with microphysical cloud properties like liquid and ice water path obtained from the CloudNet algorithms applied to the remote-sensing instrumentation suite at the ARM site in Utqiagvik. Cloud observations have been sorted depending on the coupling of the cloud with upwind water vapor transport events that can interact with sea ice upwind. Statistics of surface net thermal fluxes and cloud water/ice properties show a distinguished distribution for coupled and decoupled clouds. It was found that coupled clouds contain more liquid water and are more efficient reduce the cooling of surface LW radiation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Methods and apparatus for improved thermal monitoring by correlating infrared emissivity to surface topography

Embodiments of the present disclosure provide an improved method and apparatus for thermal monitoring for a metal additive manufacturing system. An emissivity value of a top surface of an object to be manufactured is determined based, at least in part, on an arithmetic product of a predetermined roughness value and a predetermined slope-related value. The predetermined roughness value and slope-related values are predetermined based, at least in part, on measurements of a previously manufactured object. The system sinters a metal to form the top surface of the object to be manufactured. An infrared sensor detects radiation from at least a portion of the top surface of the object to be manufactured. A temperature is generated based, at least in part, on the detected infrared radiation and the emissivity value and the generated temperature is applied to a temperature utilization component of the system.

Taylor, Samantha↗
DOE OSTI2022

Evaluation of the Reliability of Passive Infrared (PIR) Occupancy Sensors for Residential Indoor Lighting

Solid-state lighting (SSL) technologies have penetrated the general illumination market in recent years, largely replacing conventional technologies such as incandescent and fluorescent lighting. Most of the initial excitement about light-emitting diode (LED) sources for SSL devices focused on their energy savings potential resulting from vast improvements in source efficiency and luminous efficacy compared with conventional illumination products. More recently, the focus has shifted toward other aspects of lighting application efficiency, namely intensity effectiveness and spectral efficiency, because of the ease of controlling both the light intensity of LEDs with drive voltage and the color properties of the LED-based illuminators. To capitalize on the energy savings of increased intensity effectiveness and spectral efficiency, a lighting control system (LCS) is often used. While the current penetration of LCSs is relatively modest, it is anticipated that lighting controls (i.e., connected lighting, controls and LED and conventional lighting) could have an installed penetration as higher as 46% by the year 2035, saving an additional 1.3 quads of energy. Despite the large energy savings that can be gained from using LCSs, standard test methods for evaluating sensors employed in LCSs and reliability data of the LCSs and their components are generally lacking. The National Electrical Manufacturers Association (NEMA) developed the only standard, NEMA WD 7-2011 (R2016), to test occupancy and motion sensor performance (herein referred to as “the NEMA protocol”). In a previous U.S. Department of Energy (DOE) effort, some concerns of the NEMA protocol were identified (e.g., strict height and weight limits on test subjects, large amounts of manual effort, sometimes inconsistent repeatability). During the current work, a consistent detection length test (DLT) and a Robotic Sensor Evaluation System (RoboSES) were developed to alleviate some of these concerns. RoboSES acts as a human surrogate by using thermal pads on a mannequin and a remote-controlled mobile base to test a sensor’s field of view (FOV). This report builds on the earlier DOE efforts to understand sensor technologies used in LCSs for general illumination. Specifically, this report describes the optimization of RoboSES, characterizes and establishes test methods to assess the reliability of multiple passive infrared (PIR) sensors, and reports the findings of robustness and reliability testing on two commercial PIR sensors intended for residential applications. The information presented in this report is gained from up to 4,000 hours (hrs) of accelerated stress tests (ASTs).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Phase-Exchange-Driven Wake-Up and Fatigue in Ferroelectric Hafnium Zirconium Oxide Films

Ferroelectric hafnium zirconium oxide holds great promise for a broad spectrum of complementary metal–oxide–semiconductor (CMOS) compatible and scaled microelectronic applications, including memory, low-voltage transistors, and infrared sensors, among others. An outstanding challenge hindering the implementation of this material is polarization instability during field cycling. Herein, the nanoscale phenomena contributing to both polarization fatigue and wake-up are reported. Using synchrotron X-ray diffraction, the conversion of non-polar tetragonal and polar orthorhombic phases to a non-polar monoclinic phase while field cycling devices comprising noble metal contacts is observed. This phase exchange accompanies a diminishing ferroelectric remanent polarization and provides device-scale crystallographic evidence of phase exchange leading to ferroelectric fatigue in these structures. A reduction in the full width at half-maximum of the superimposed tetragonal (101) and orthorhombic (111) diffraction reflections is observed to accompany wake-up in structures comprising tantalum nitride and tungsten electrodes. Combined with polarization and relative permittivity measurements, the observed peak narrowing and a shift in position to lower angles is attributed, in part, to a phase exchange of the non-polar tetragonal to the polar orthorhombic phase during wake-up. These results provide insight into the role of electrodes in the performance of hafnium oxide-based ferroelectrics and mechanisms driving wake-up and fatigue, and demonstrate a non-destructive means to characterize the phase changes accompanying polarization instabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Preliminary Experiences with the Rexon UL-320-FDR: An Automated Thermoluminescent Dosimeter Reader with Removable Contact Heating Planchets and an Infrared Temperature Feedback System

The Rexon UL-320 FDR is a novel resistive-heating thermoluminescent dosimeter reader with a unique temperature measurement system and an automated dosimeter processing mechanism. The removable contact heating planchets have black-body adhesives on the back for capturing temperature information with infrared sensors. A heating cycle feedback loop ensures accurate, precise, and reproducible heating sequences. Heating rates between 0.8 and 40°C s -1 for up to 1,000 s are possible. Photomultiplier tube sensitivity and drift, dark current counts, and planchet glow were measured experimentally. Additionally, 25 LiF:Mg,Ti dosimeters were calibrated to demonstrate reader performance. Sensitivity was optimized at 1,200 V, which produced the highest reference light count to dark current count ratio while extending photomultiplier tube life. Dark current counts measured with typical time-temperature profiles for LiF:Mg,Ti were below 10 counts per channel but increased by up to 2.5% for more extreme heating cycles. Reader sensitivity drifts of up to 10% were observed during extended automated operations with typical time-temperature profiles. Total counts resulting from planchet glow decreased with faster heating rates. Calibrations performed with LiF:Mg,Ti dosimeters yielded results comparable to more established reader designs. Spikes were observed in ~3% of the glow curves from planchet dust and oil burning off at elevated temperatures. The use of N 2 gas and sensitivity drift corrections are recommended to improve dosimetry performance for the UL-320 FDR reader.

61 RADIATION PROTECTION AND DOSIMETRY↗