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At least 631 records · Page 35

Resurgence of Electron Quantum Tunneling Sensors

Quantum tunneling sensors are typically ultra-sensitive devices that have been specifically designed to convert a stimulus into an electronic signal using the wondrous principles of quantum mechanical tunneling. In the early 1990s, William Kaiser developed one of the first micromachined quantum tunneling sensors as part of his work with the NASA Jet Propulsion Laboratory. Since then, there have been scattered attempts at utilizing this phenomenon for the development of a variety of physical and chemical sensors. Although these devices demonstrate unique characteristics, such as high sensitivity, the principle of quantum tunneling often acts as a double-edged sword and is responsible for certain drawbacks of this sensor family. In this review, we briefly explain the underlying working principles of quantum tunneling and how they are used to design miniaturized quantum tunneling sensors. We then proceed to describe an overview of the various attempts at developing such sensors. Next, we discuss their current necessity and recent resurgence. Finally, we describe various advantages and shortcomings of these sensors and end this review with an insight into the potential of this technology and prospects.

Banerjee, Aishwaryadev↗

Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures

The rapid expansion of distributed and edge computing platforms—spanning autonomous vehicles, IoT sensors, and healthcare monitors—has heightened concerns about data privacy. Differential Privacy (DP) offers a rigorous mathematical framework to protect sensitive information while retaining analytical utility. This tutorial introduces the foundations of DP for both numerical and categorical datasets and extends the discussion to correlation-aware techniques tailored for structured and high-dimensional data. Hands-on demonstrations will begin with the PETINA (Privacy prEservaTIoN Algorithms) package for numerical data and continue with MIC-DP (Maximum Information Correlated Differential Privacy) for tabular data. Designed for researchers and practitioners in secure systems, embedded architectures, and AI accelerators, the tutorial emphasizes practical and scalable methods for integrating DP into real-world system designs.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗

Recycling isotachophoresis - A novel approach to preparative protein fractionation

The concept of automated recycling isotachophoresis (RITP) as a purification methodology is discussed, in addition to a description of the apparatus. In the present automated RITP, the computer system follows the achievement of steady state using arrays of universal and specific sensors, monitors the position of the front edge of the zone structure, activates the counterflow if the leading boundary passes a specified position along the separation axis, or changes the applied current, accordingly. The system demonstrates high resolution, in addition to higher processing rates than are possible in zone electrophoresis or isoelectric focusing.

Sloan, Jeffrey E.↗

Learned navigation in unknown terrains: A retraction method

The problem of learned navigation of a circular robot R, of radius delta (is greater than or equal to 0), through a terrain whose model is not a-priori known is considered. Two-dimensional finite-sized terrains populated by an unknown (but, finite) number of simple polygonal obstacles are also considered. The number and locations of the vertices of each obstacle are unknown to R. R is equipped with a sensor system that detects all vertices and edges that are visible from its present location. In this context two problems are covered. In the visit problem, the robot is required to visit a sequence of destination points, and in the terrain model acquisition problem, the robot is required to acquire the complete model of the terrain. An algorithmic framework is presented for solving these two problems using a retraction of the freespace onto the Voronoi diagram of the terrain. Algorithms are then presented to solve the visit problem and the terrain model acquisition problem.

Rao, Nageswara S. V.↗

Experimental Feedback Control of Flow Induced Cavity Tones

An experimental study of the application of discrete-time, linear quadratic control design methods to the cavity tone problem is described. State space models of the dynamics from a synthetic jet actuator at the leading edge of the cavity to two pressure sensors in the cavity were computed from experimental data. Variations in model order, control order, control bandwidth, and properties of a Kalman state estimator were studied. Feedback control reduced the levels of multiple cavity tones at Mach 0.275, 0.35, and 0.45. Closed loop performance was often limited by excitation of sidebands of cavity tones, and creation of new tones in the spectrum. State space models were useful for explaining some of these limitations, but were not able to account for non-linear dynamics, such as interactions between tones at different frequencies.

Cabell, Randolph H.↗

Smart sensors using artificial intelligence for on-detector electronics and ASICs

Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noise. This has led to a explosion of more and more data being generated in next-generation experiments. Therefore, the need for near-sensor, at the data source, processing with more powerful algorithms is becoming increasingly important to more efficiently capture the right experimental data, reduce downstream system complexity, and enable faster and lower-power feedback loops. In this paper, we discuss the motivations and potential applications for on-detector AI. Furthermore, the unique requirements of particle physics can uniquely drive the development of novel AI hardware and design tools. We describe existing modern work for particle physics in this area. Finally, we outline a number of areas of opportunity where we can advance machine learning techniques, codesign workflows, and future microelectronics technologies which will accelerate design, performance, and implementations for next generation experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Generic Sensor Modeling Using Pulse Method

Recent development of high spatial resolution satellites such as IKONOS, Quickbird and Orbview enable observation of the Earth's surface with sub-meter resolution. Compared to the 30 meter resolution of Landsat 5 TM, the amount of information in the output image was dramatically increased. In this era of high spatial resolution, the estimation of spatial quality of images is gaining attention. Historically, the Modulation Transfer Function (MTF) concept has been used to estimate an imaging system's spatial quality. Sometimes classified by target shapes, various methods were developed in laboratory environment utilizing sinusoidal inputs, periodic bar patterns and narrow slits. On-orbit sensor MTF estimation was performed on 30-meter GSD Landsat4 Thematic Mapper (TM) data from the bridge pulse target as a pulse input . Because of a high resolution sensor s small Ground Sampling Distance (GSD), reasonably sized man-made edge, pulse, and impulse targets can be deployed on a uniform grassy area with accurate control of ground targets using tarps and convex mirrors. All the previous work cited calculated MTF without testing the MTF estimator's performance. In previous report, a numerical generic sensor model had been developed to simulate and improve the performance of on-orbit MTF estimating techniques. Results from the previous sensor modeling report that have been incorporated into standard MTF estimation work include Fermi edge detection and the newly developed 4th order modified Savitzky-Golay (MSG) interpolation technique. Noise sensitivity had been studied by performing simulations on known noise sources and a sensor model. Extensive investigation was done to characterize multi-resolution ground noise. Finally, angle simulation was tested by using synthetic pulse targets with angles from 2 to 15 degrees, several brightness levels, and different noise levels from both ground targets and imaging system. As a continuing research activity using the developed sensor model, this report was dedicated to MTF estimation via pulse input method characterization using the Fermi edge detection and 4th order MSG interpolation method. The relationship between pulse width and MTF value at Nyquist was studied including error detection and correction schemes. Pulse target angle sensitivity was studied by using synthetic targets angled from 2 to 12 degrees. In this report, from the ground and system noise simulation, a minimum SNR value was suggested for a stable MTF value at Nyquist for the pulse method. Target width error detection and adjustment technique based on a smooth transition of MTF profile is presented, which is specifically applicable only to the pulse method with 3 pixel wide targets.

Helder, Dennis L.↗

Frontside-micromachined planar piezoresistive vibration sensor: Evaluating performance in the low frequency test range

Using a surface piezoresistor diffusion method and front-side only micromachining process, a planar piezoresistive vibration sensor was successfully developed with a simple structure, lower processing cost and fewer packaging difficulties. The vibration sensor had a large sector proof mass attached to a narrow flexure. Optimization of the boron diffusion piezoresistor placed on the edge of the narrow flexure greatly improved the sensitivity. Planar vibration sensors were fabricated and measured in order to analyze the effects of the sensor dimensions on performance, including the values of flexure width and the included angle of the sector. Sensitivities of fabricated planar sensors of 0.09–0.46 mV/V/g were measured up to a test frequency of 60 Hz. The sensor functioned at low voltages (<3 V) and currents (<1 mA) with a high sensitivity and low drift. At low background noise levels, the sensor had performance comparable to a commercial device.

36 MATERIALS SCIENCE↗

Asset Monitoring using Smart Sensing and Advanced Analytics for the Distribution Network

As the electric grid transitions into a highly distributed and intelligent grid, electric utilities are becoming increasingly concerned with monitoring the health and performance of their distribution assets. As Internet of Things technologies and data analytics become inexpensive and ubiquitous, low-cost asset monitoring becomes more feasible. Moreover, using edge analytics, computations can be off-loaded to these intelligent sensors to report only actionable information to the cloud. This needs to be done in a highly distributed manner using non-intrusive sensors and device physics that seamlessly integrates with legacy infrastructure. Here, this paper reviews existing sensor based solutions, identifies t heir issues, proposes a novel solution by exploring the required attributes and proposes an approach for low-cost distribution transformer health monitoring.

Asset monitoring↗

Effects of Fin Leading Edge Sweep on Shock-Shock Interaction at Mach 6

The effects of fin leading edge sweep on peak heating rates due to shock-shock interaction have been experimentally examined in the Langley 20-Inch Mach 6 Tunnel. The shock interaction was produced by the intersection of a planar incident shock (16.8 deg shock angle relative to the freestream, generated by a 9 deg wedge) with the bow shock formed around a O.5-inch diameter cylindrical leading edge fin. Heating distributions along the leading edge stagnation line have been obtained using densely spaced thin film resistive-type sensors. Schlieren images were obtained to illustrate the very complex shock-shock interactions. The fin leading edge sweep angle was varied from 15-degrees swept back to 45-degrees swept forward for a freestream unit Reynolds number of 2 x 10(exp 6)/ft. Two models were utilized during the study, one with 0.025-inch spacing between gage centers, and the other 0.015-inch spacing. Gage spatial resolution on the order of 0.015-in appeared to accurately capture the narrow spike in heating. Peak heating due to shock interaction was maximized when the fin was swept forward 15 deg and 25 deg, both promoting augmentations about 7 times the baseline value. The schlieren images for these cases revealed Type 4 and Type 3 interactions, respectively.

Berry, Scott A.↗

Detection and Location of Transverse Matrix Cracks in Cross-Ply Gr/Ep Composites Using Acoustic Emission

Transverse matrix cracking in cross-ply gr/ep laminates was studied with advanced acoustic emission (AE) techniques. The primary goal of this research was to measure the load required to initiate the first transverse matrix crack in cross-ply laminates of different thicknesses. Other methods had been previously used for these measurements including penetrant enhanced radiography, optical microscopy, and audible acoustic microphone measurements. The former methods required that the mechanical test be paused for measurements at load intervals. This slowed the test procedure and did not provide the required resolution in load. With acoustic microphones, acoustic signals from cracks could not be clearly differentiated from other noise sources such as grip damage, specimen slippage, or test machine noise. A second goal for this work was to use the high resolution source location accuracy of the advanced acoustic emission techniques to determine whether the crack initiation site was at the specimen edge or in the interior of the specimen.In this research, advanced AE techniques using broad band sensors, high capture rate digital waveform acquisition, and plate wave propagation based analysis were applied to cross-ply composite coupons with different numbers of 0 and 90 degree plies. Noise signals, believed to be caused by grip damage or specimen slipping, were eliminated based on their plate wave characteristics. Such signals were always located outside the sensor gage length in the gripped region of the specimen. Cracks were confirmed post-test by microscopic analysis of a polished specimen edge, backscatter ultrasonic scans, and in limited cases, by penetrant enhanced radiography. For specimens with three or more 90 degree plies together, there was an exact 1-1 correlation between AE crack signals and observed cracks. The ultrasonic scans and some destructive sectioning analysis showed that the cracks extended across the full width of the specimen. Furthermore, the locations of the cracks from the AE data were in excellent agreement with the locations measured with the microscope. The high resolution source location capability of this technique, combined with an array of sensors, was able to determine that the cracks initiated at the specimen edges, rather than in the interior. For specimens with only one or two 90 degree plies, the crack-like signals were significantly smaller in amplitude and there was not a 1-1 correlation to observed cracks. This was similar to previous results. In this case, however, ultrasonic and destructive sectioning analysis revealed that the cracks did not extend across the specimen. They initiated at the edge, but did not propagate any appreciable distance into the specimen. This explains the much smaller AE signal amplitudes and the difficulty in correlating these signals to actual cracks in this, as well as in the previous study.

Prosser, W. H.↗

5G Enabled Energy Innovation: Advanced Wireless Networks for Science

Digital wireless communication has become a foundational technology for the nation. The U.S. Department of Energy’s Office of Science (DOE-SC) is the Nation’s largest supporter of basic research in the physical sciences discovering new materials, designing advanced microelectronics, and understanding the physics of radio frequency signaling. The expanding national rollout of a new fifth-generation (5G) mobile network, coupled with the torrent of scientific data generated by next-generation devices such as battery-powered Internet of Things (IoT) sensors, has created an urgent need to enhance cutting-edge wireless technology. Breakthroughs in the deployment, integration, security, and operational range of wireless networking can provide new scientific capabilities for the next decade - from autonomous mobile instruments for scientific user facilities to intelligent sensors networks distributed over thousands of kilometers to study environmental processes. To realize this promise, however, we must continue to drive innovations in computing, artificial intelligence (AI), advanced materials, high-speed networking, and microelectronics. In March 2020, the DOE-SC convened a workshop to identify the potential opportunities and explore the scientific challenges of advanced wireless technologies.

42 ENGINEERING↗

Pilot-Scale Validation of Distributed Optical Fiber Sensors for Underground Pipeline Monitoring

Distributed fiber optic sensing is a cutting-edge technology that has found extensive applications in the monitoring of Ensuring the safety, integrity, and operational efficiency of underground product pipelines is vital for maintaining the nation’s critical infrastructure. Monitoring parameters such as hoop strain, pressure, and acoustic vibrations is key to detecting potential leaks, intrusions, or structural issues. Distributed optical fiber sensor (DOFS) systems provide a compelling solution for continuous, real-time monitoring over long distances. This paper details the development and pilot-scale implementation of DOFS systems for underground pipeline monitoring, evolving from a proof-of-concept stage. Multiple custom-designed DOFS interrogator units—such as optical frequency-domain reflectometry (OFDR), Brillouin optical time-domain analysis (BOTDA), and multimodal interferometer-based fiber acoustic sensors—were employed to measure key parameters like hoop strain, pressure, and acoustic vibrations. The underground product pipeline's outer diameter is 30 inches, the wall thickness is 1.28 inches, and the 3-foot depth. The fiber deployment strategies, and sensing data acquisition methods for these systems are discussed. The results demonstrate the effectiveness of DOFS in detecting hoop strain, temperature changes, and acoustic vibrations, showcasing their potential for real-time monitoring and enhancing pipeline safety.

distributed fiber sensing↗

Incubating advances in integrated photonics with emerging sensing and computational capabilities

As photonic technologies grow in multidimensional aspects, integrated photonics holds a unique position and continuously presents enormous possibilities for research communities. Applications include data centers, environmental monitoring, medical diagnosis, and highly compact communication components, with further possibilities continuously growing. Herein, we review state-of-the-art integrated photonic on-chip sensors that operate in the visible to mid-infrared wavelength region on various material platforms. Among the different materials, architectures, and technologies leading the way for on-chip sensors, we discuss the optical sensing principles that are commonly applied to biochemical and gas sensing. Our focus is on passive optical waveguides, including dispersion-engineered metamaterial-based structures, which are essential for enhancing the interaction between light and analytes in chip-scale sensors. We harness a diverse array of cutting-edge sensing technologies, heralding a revolutionary on-chip sensing paradigm. Our arsenal includes refractive-index-based sensing, plasmonics, and spectroscopy, which forge an unparalleled foundation for innovation and precision. Furthermore, we include a brief discussion of recent trends and computational concepts, incorporating Artificial Intelligence & Machine Learning (AI/ML) and deep learning approaches over the past few years to improve the qualitative and quantitative analysis of sensor measurements.

Jain, Sourabh (ORCID:0000000279923275)↗

UASs in the VOG/Edge/FOG Sensor Web Environment

This paper will describe the evolution of information collection, derivation and delivery mechanisms in sensor webs utilizing Uninhabited Aerial Systems (UAS).We will discuss the movement to "Fog Computing", also known as Edge Computing. Fog Computing facilitates the distribution of common operations and networking between edge devices and cloud computing facilities, optimizing the production of actionable intelligence. Initially, UASs utilized onboard data collection as standard, with minimal data downloaded directly. Information products were derived in conventional computational environments, generally desk top computers, and information products made available to the Science Community in weeks or months. With the increased availability, and increasingly lower costs, of beyond line of sight (BLOS) satellite based communication, transmission rates and data volumes increased, and processing migrated to Cloud based services. Contemporary UASs are moving some of that information product derivation to on vehicle services, and are creating a distributed Cloud/Fog environment. The Author will describe the technological advances that have made this possible, including low power multi-core Central Processing Units (CPU), and, more recently, the availability of high end Graphical Processing Units (GPU) that consume only a few watts. Intelligent system software, leveraging these hardware advances, finally allows for information product generation on-board, rather than simple data collection. Additionally, intelligent flight control systems now support mutual vehicle to vehicle collaboration, allowing UASs to create ad-hoc sensor webs on demand, as required. Also discussed will be the lessons learned by the Authors' development of data systems for NASA's large High Altitude Long Endurance (HALE) UASs like Predator and Global Hawk, and how those lessons are being applied to other UAS development This paper will focus on applications, rather a deep dive into the technology, and will highlight improving data management through these new technologies.

UAS↗

Novel Data Driven Noise Emulation Framework using Deep Neural Network for Generating Synthetic PMU Measurements

Sensors play a critical role in supporting day-to-day grid operations and they are essential to operator’s decision-making process. Furthermore, sensors and sensor behaviors need to be emulated with grid simulations to perform modeling studies and to design cutting edge power systems applications. Ensuring the accurate behavior of these applications requires accurate emulation of sensors and pertinent signals. However, most grid simulators and modeling tools assume either zero error scenarios or simplistic noise models that may not always correlate to real-world sensors. To address the above issue, this work presents an initial study on the noise characteristics of phasor measurement units (PMUs), along with models for recreating their unique noise signatures. The proposed methods (both analytical and machine-learning-based) provide a substantial increase in a sensor’s model fidelity, a feature that can be leveraged by an end-user application to yield more accurate system representations. The proposed methods were then applied to micro PMU data from the EPFL microgrid campus to extract sensor noise profiles. This data was used to train a deep learning model, which was tested to emulate the noise characteristics present in actual signals. Based on the observed results and the employed data-driven methodology, the proposed methods may be adapted to replicate the behavior of other grid sensors and power new applications capable of detecting sensor degradation and eventual device failures in near real-time.

PMU, noise emulation, synthetic measurements, deep↗

Desert-Urban System Integrated Atmospheric Monsoon (DUSTIEAIM) in the Southwestern United States Science Plan

The Desert-Urban System Integrated Atmospheric Monsoon (DUSTIEAIM) campaign is a groundbreaking, high-impact scientific mission that will transform how we understand and respond to energy and water challenges in one of America’s fastest-growing and most heat-stressed urban regions: Phoenix, Arizona. Starting in April 2026, this 18-month field campaign harnesses the full power of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility and an interdisciplinary science team including national laboratories, universities, and agencies with a broad range of subject-matter expertise. With cutting-edge instruments, active and passive ground-based sensors, radars, and integrated modeling, DUSTIEAIM will deliver the most comprehensive environmental data set ever collected for a desert-urban-agricultural interface.

54 ENVIRONMENTAL SCIENCES↗

Comparison of environmental conditions in the Bering Sea and Davis Strait and the effects on microwave signature returns; March and April, 1979

Aircraft data collected in the Bering Sea in March, 1979 using a 6.6 GH sub z (C Band) microwave radiometer and a 13.9 GH sub z (Ku Band) scatterometer, reinforce the difficulties in interpreting first year ice types found near the ice edge in a marginal ice zone. An ice interpretation scheme using data taken with a 13.3 GH sub z (Ku Band) scatterometer and a 19.4 GH sub z (K Band) radiometer in Davis Strait also shows ambiguity in the first year ice signal and indicates that ice interpretation becomes more difficult near the ice edge and under warmer conditions. This report also compares X Band SAR data taken in Davis Strait with similar imagery collected in the Bering Sea. Ice core samples from the Bering test area offer a basis for speculation on changes in ice morphology which affect the signature return at the ice edge, and help explain the difficulty of the sensors in discerning the two different ice types found on the photography and in the core samples.

Mcnutt, S. L.↗