High-resolution Sensor Research Articles

Silicon on isolator ribbon field-effect nanotransistors for high-sensitivity low-power biosensor

Silicon nanowire field-effect transistors are discussed as biological sensors due to their excellent sensitivity due to the large surface-to-volume ratio and high selectivity with respect to a large number of analytes. A miniature sensor based on a long-channel fin field-effect transistor as a surface charge detector is being investigated. The three-gate configuration offers undeniable advantages over planar devices, since the edges are about a hundred nanometers wide and are characterized by increased conductivity, which leads to higher sensitivity. The characteristics of the transistor are optimized using 3D modeling performed by the computer-aided design software package TCAD, depending on the topological parameters of the transistor and the level of control voltages. Based on the obtained simulation results, a chip was manufactured on a SOI substrate based on self-aligning CMOS-compatible technological processes from top to bottom. It is established that thin structures with a reduced level of doping and low supply power have promising electrical characteristics for an effective approach to scaling a high-resolution pH sensor, which is of particular interest to integrated pH bioanalytics based on CMOS technology.

Tunable magnetic and magnetotransport properties in locally epitaxial La0.67Sr0.33MnO3 thin films on polycrystalline SrTiO3, by control of grain size

La0.67Sr0.33MnO3 (LSMO) thin films have been grown by pulsed laser deposition on SrTiO3 using combinatorial substrate epitaxy (CSE) approach, i.e. polycrystalline substrates with micrometer-size grains. The crystallographic domains size of those polycrystalline substrates can be controlled between 2 and 45 µm depending on the annealing temperature during synthesis. Each grain of the substrate acts as a single crystalline growth template promoting local epitaxy with a reproduction of the substrate grain structure in the thin film. Therefore, a fine-tuning of the substrate grain metrics and high crystalline quality of locally epitaxial LSMO film, allows to combine the advantages of polycrystalline, i.e. the presence of low field magnetoresistance (LFMR) and the possibility to use very thin films, with a pronounced magnetic shape anisotropy. For this, the magnetic and transport properties of the films are showing a strong influence with varying grain metrics of the substrate. High Curie temperatures, important values of the LFMR and anisotropy for optimized substrate grain metrics with the relative orientation of the magnetic field to the film plane underline the high quality of the films and the advantage of the CSE approach. The obtained LSMO thin films may have an interest for high-resolution low field magnetic sensors application.

Quantitative remote sensing of forest ecosystem services in sub-Saharan Africa's urban landscapes: a review.

A dearth of information on urban ecosystem services in the past decades has led to little consolidation of such information for informed planning, decision-making and policy development in sub-Saharan African cities. However, the increasing recognition of the value of urban ecological processes and services as well as their contribution to climate change adaptation and mitigation has recently become an area of great research interest. Specifically, the emerging geospatial analytical approaches like remote sensing have led to an increase in the number of studies that seek to quantify and map urban ecosystem services at varying scales. Hence, this study sought to review the current remote sensing trends, challenges and prospects in quantifying urban ecosystem services in sub-Saharan Africa cities. Literature shows that consistent modelling and understanding of urban ecosystem services using remotely sensed approaches began in the 1990s, with an average of five publications per year after around 2010. This is mainly attributed to the approach's ability to provide fast, accurate and repeated spatial information necessary for optimal and timely quantification and mapping of urban ecosystem services. Although commercially available high spatial resolution sensors (e.g. the Worldview series, Quickbird and RapidEye) with higher spatial and spectral properties have been valuable in providing highly accurate and reliable data for quantification of urban ecosystem services, their adoption has been limited by high image acquisition cost and small spatial coverage that limits regional assessment. Thus, the newly launched sensors that provide freely and readily available data (i.e. Landsat 8 and 9 OLI, Sentinel-2) are increasingly becoming popular. These sensors provide data with improved spatial and spectral properties, hence valuable for past, current and future urban ecosystem service assessment, especially in developing countries. Therefore, the study provides guidance for future studies to continuously assess urban ecosystem services in order to achieve the objectives of Kyoto Protocol and Reducing Emissions from Deforestation and forest Degradation (REDD +) of promoting climate-resilient and sustainable cities, especially in developing world.

Microwave Photonic Interrogation of a High-Speed and High-Resolution Multipoint Refractive Index Sensor

Microwave photonic interrogation of a high-speed and high-resolution multipoint refractive index (RI) sensor based on an Au-coated tilted fiber grating (Au-TFBG) array is proposed and experimentally demonstrated. Due to the surface plasmon resonance (SPR), the optical spectrum of an Au-TFBG has a dip in the spectral envelope. When an Au-TFBG is immersed in a solution and the RI of the solution changes, the location of the dip will shift, reflecting the change of the RI. For multipoint sensing, an Au-TFBG array with the array elements located at different locations is employed. However, due to the overlap of the spectra of the Au-TFBGs in the array, the dips cannot be precisely located by optical spectrum measurement. A solution is to convert the spectra to the time domain based on spectral shaping and wavelength-to-time (SS-WTT) mapping with the mapped temporal waveforms separated by different time delays. By using a digital signal processor (DSP), the sensing information can be extracted at a high speed and high resolution. The proposed approach is evaluated experimentally. Experimental results show that the sensor has high RI accuracies of 3.1×10^−5, 2.5×10^−5 and 2.6×10^−5 refractive index unit (RIU) and a fast sensing speed of 11.75 kHz.

Unsupervised Optical Classification of the Seabed Color in Shallow Oligotrophic Waters from Sentinel-2 Images: A Case Study in the Voh-Koné-Pouembout Lagoon (New Caledonia)

Monitoring chlorophyll-a concentration or turbidity is crucial for understanding and managing oligo- to mesotrophic coastal waters quality. However, mapping bio-optical components from space in such shallow settings remains challenging because of the strong interference of the complex bathymetry and various seabed colors. Correcting the total satellite reflectance signal from the seabed reflectance in ocean color with high resolution sensors is promising. This article shows how unsupervised clustering approaches can be applied to Sentinel-2 images to classify seabed colors in shallow waters of a tropical oligotrophic lagoon in New Caledonia. Data processing included Lyzenga correction for estimating the water column reflectance, optical spectra standardization for attenuating water absorption effects and clustering using the unsupervised k-means method. This methodological approach was applied on the 497, 560, 664 and 704 nm optical bands of the selected Sentinel-2 image. When applied on non-standardized data, our unsupervised classification retrieved three seafloor clusters, whereas five seafloor clusters could be retrieved using standardized data. For each of these two trials, the computed membership values explained more than 75% of the inertia in each Sentinel-2 wavelength band used for the clustering. However, the accuracy of the method was slightly improved when applied on standardized data. Confusion index mapping of the unsupervised clustering retrieved from these data emphasized the relevance and robustness of our methodological approach. Such an approach for seabed colors classification in optically complex shallow settings will be particularly helpful to improve remote sensing of biogeochemical indicators such as chlorophyll-a concentration and turbidity in fragile coastal environments.

Subfield maize yield prediction improves when in-season crop water deficit is included in remote sensing imagery-based models

In-season prediction of crop yield is a topic of research studied by several scientists using different methods. Seasonal forecasts provide critical insights to different stakeholders who use the information for strategic and tactical decisions. In this study, we propose a novel scalable method to forecast in season subfield crop yield through a machine learning model based on remotely sensed imagery and data from a process-based crop model on a cumulative crop drought index (CDI) designed to capture the impact of in-season crop water deficit on crops. To evaluate the performance of our proposed model, we used 352 growers' fields of different sizes across the states of Michigan, Indiana, Iowa, and Illinois, with 2520 respective yield maps generated by combine harvesters equipped with precise high-resolution yield monitor sensor, over multiple years (from 2006 up to 2019). We obtained high resolution digital elevation model, climate, and soil data to execute the SALUS model, a process-based crop model, to calculate the CDI for each field used in the study. We used Landsat Analysis Ready Dataset (ARD) products generated by USGS as image source to calculate the green chlorophyll vegetation index (GCVI). We found that the inclusion of the CDI in remote sensing-based random forest models substantially improved in-season subfield corn yield prediction. The addition of the CDI in the yield prediction model showed that the greatest improvements in predictions were observed in the driest year (2012) in our case study. The proposed approach also showed that the subfield spatial variations of corn yield are better captured with the inclusion of CDI for most fields. The earliest prediction in the growing season with GCVI and CDI together outperformed the latest prediction with GCVI alone, highlighting the potential of CDI for predicting spatial variability of maize yield around grain filling period, which is on average close to two months before typical crop harvest in the US Midwest.

Mapping South America’s Drylands through Remote Sensing—A Review of the Methodological Trends and Current Challenges

The scientific grasp of the distribution and dynamics of land use and land cover (LULC) changes in South America is still limited. This is especially true for the continent’s hyperarid, arid, semiarid, and dry subhumid zones, collectively known as drylands, which are under-represented ecosystems that are highly threatened by climate change and human activity. Maps of LULC in drylands are, thus, essential in order to investigate their vulnerability to both natural and anthropogenic impacts. This paper comprehensively reviewed existing mapping initiatives of South America’s drylands to discuss the main knowledge gaps, as well as central methodological trends and challenges, for advancing our understanding of LULC dynamics in these fragile ecosystems. Our review centered on five essential aspects of remote-sensing-based LULC mapping: scale, datasets, classification techniques, number of classes (legends), and validation protocols. The results indicated that the Landsat sensor dataset was the most frequently used, followed by AVHRR and MODIS, and no studies used recently available high-resolution satellite sensors. Machine learning algorithms emerged as a broadly employed methodology for land cover classification in South America. Still, such advancement in classification methods did not yet reflect in the upsurge of detailed mapping of dryland vegetation types and functional groups. Among the 23 mapping initiatives, the number of LULC classes in their respective legends varied from 6 to 39, with 1 to 14 classes representing drylands. Validation protocols included fieldwork and automatic processes with sampling strategies ranging from solely random to stratified approaches. Finally, we discussed the opportunities and challenges for advancing research on desertification, climate change, fire mapping, and the resilience of dryland populations. By and large, multi-level studies for dryland vegetation mapping are still lacking.

Comparative Analysis of Several Typical Landsat 8 OLI Cloud Detection Methods

The Landsat 8 Operational Land Imager (OLI) is a high-resolution satellite sensor that is carried on the next-generation Landsat which was launched by the National Aeronautics and Space Administration (NASA) in 2013. Compared to the previous land observation satellites in this series, Landsat 8 OLI has been further optimized in regard to its band setting and data acquisition frequency. The application scope of the data and the ability of information extraction have been further expanded and enhanced. However, the existence of clouds reduces the efficiency and the quality of satellite data. Therefore, in order to identify cloud pixels with a high level of accuracy and efficiency, a variety of cloud detection methods have been developed for Landsat 8 OLI sensors. Distinct treatment methods have been adopted for identifying different surface backgrounds and various types of cloud characteristics in order to improve the accuracy of cloud detection. The present study compares and analyzes the detection results from five typical cloud detection algorithms for different cloud types over different ground types. In addition, it discusses the various cloud detection algorithms used for different types of adaptive backgrounds and clouds in order to shed light upon the comprehensive application of different methods. Subsequently, these findings may provide a reference for the development of an algorithm with a higher precision of cloud detection.

Controlled laser-induced fabrication of multichannel solid-state nanopore sensors for studying single-molecule protein dynamics

Nanopores have emerged in recent years as a sensitive and informative sensor for probing biomolecules at a single-molecule level. Nanopore sensing is achieved by recording the ion flux through a small pore submerged in an aqueous electrolyte by applying a voltage bias across it. The essential parameters of an analyte are then deduced through resistive pulses in the ionic current as it is electrokinetically captured and driven through the pore. Solid-state nanopores have recently gained traction as high-resolution sensors for protein characterization because unlike their lipid-embedded biological pore analogs, the solid-state membrane is stable at higher voltages and the pore size can be tailored to be compatible with the studied molecule of choice.

Automated georeferencing of Diwata-2 multispectral imagery using feature matching

The Diwata-2 microsatellite is a 56 kg optical microsatellite that features different optical sensors such as the medium resolution Spaceborne Multispectral Sensor (SMI) and a high-resolution sensor called High Precision Telescope (HPT). This research aims to develop an automated georeferencing workflow for coregistered multiband HPT and SMI imagery with a GSD of 5 meters and 127 meters respectively. Georeferencing is done using a mixture of FAST and SIFT feature matching algorithms where HPT imagery used SIFT descriptors and detectors and SMI used FAST detectors and SIFT descriptors. The research used freely available Landsat- 8 imagery which was processed into cloud-free mosaics as reference data. Accuracy assessment is automated using AROSICS software which performs geometric correction and calculates the RMSE of local shifts based on image tie points which are automatically generated using the Diwata image as secondary image and Landsat-8 as reference image. Output of the feature matching algorithms has achieved high accuracy with an average RMSE value of 2.55 meters for HPT imagery and 65.96 meters for SMI imagery. Both RMSE values are close to half the GSD of both sensors which indicate sub-pixel precision. Future research for this method includes additional method for keypoint filtering to further increase the accuracy of the feature matching.

Performance Comparison between Mini-LED Backlit LCD and OLED Display for 15.6-Inch Notebook Computers

We evaluated and compared the performance of a 15.6-inch mini-LED backlit LCD (mLCD) with a commercial OLED (organic light-emitting diode) panel. Both displays exhibited outstanding properties, but the mLCD had advantages in peak brightness, gamma curve, luminance uniformity at low gray scales, color volume, power consumption, and ambient contrast ratio. In comparison with previous measurement methods, our color measurement adopted the reference method recently recommended by the International Committee for Display Metrology (ICDM). The LMK imaging photometer with a high-resolution CMOS sensor was employed to characterize the halo effect and reconstruct the real luminance profiles based on the non-ideal luminance profile of the OLED display. We also proposed a novel strategy to measure the point spread function of the backlight module.

An interferometric inertial sensor for low-frequency seismic isolation

In this paper, a high-resolution Horizontal Interferometric Inertial Sensor (HINS) is developed for active seismic isolation of gravitational wave detection instrument. Its resonance frequency can be tuned down to as low as 0.08 Hz thanks to the use of a Lehman pendulum and the inverted pendulum like mechanisms. Combined with a novel homodyne quadrature interferometer, a high resolution of 2 × 10−13 m∕Hz (or 7.9 × 10−12 (m/s2)∕Hz) at 1 Hz can be obtained. A dynamic analysis is performed to investigate the response of the HINS to different types of excitations. A noise budgeting of the HINS is also conducted, which indicates that the resolution of HINS is limited by the thermomechancial noise from 0.01 to 1 Hz and by the thermal electrical noise of the photodetectors above 1 Hz.

Observations of Dynamic Turbulence in the Lower Stratosphere over Inner Mongolia Using a High-resolution Balloon Sensor Constant Temperature Anemometer

Observations of Dynamic Turbulence in the Lower Stratosphere over Inner Mongolia Using a High-resolution Balloon Sensor Constant Temperature Anemometer

A high-resolution refractive index sensor of partially cleaved PCF based on surface plasmon resonance

A high-resolution refractive index sensor of partially cleaved PCF based on surface plasmon resonance

High-resolution pressure transducer design and associated circuitry to build a network-ready smart sensor for distributed measurement in oil and gas production wells

Miniaturized single-mode thickness-shear pressure transducer combined with high-temperature SOI, silicon on insulator, integrated circuit technology is proposed as network-ready high-pressure high-resolution smart sensor for distributed data acquisition in oil and gas production wells. The transducer miniaturization is investigated with a full 3D computer model previously developed by the authors to assess the impact of intrinsic losses and various geometrical features on transducer performance. Over the last decades there has been a trend toward size reduction of high-resolution pressure transducer. The implemented model provides insight into the evolution of high-resolution pressure transducers from Hewlett-Packard™ to Quartzdyne™ and beyond. Distributed measurement in production oil wells in extreme harsh environment, such as found in the pre-salt layer, is an unsolved problem. The industry move toward electrified wells offers an opportunity for application of smart sensor technology and power line communications to achieve distributed high-resolution data acquisition.

UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: a meta-review

UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: a meta-review

VOLUMETRIC MODELING OF Pinus taeda L. FROM ORBITAL IMAGES

This work aimed to investigate the potential of image-derived indices derived from Sentinel-2/MSI images in the volumetric modeling of a stand of Pinus taeda L. located in Bom Retiro, State of Santa Catarina. For this purpose, field data derived from a forest inventory were used, by the fixed area method and simple random sampling with an allocation of 18 circular plots of 400 m². The remotely located data comprised an orbital image from the Sentinel-2/MSI sensor. From this image, 14 average vegetation indices per plot were calculated. These indices were correlated with the volume by plot (m³ 0.04 ha-1) derived from the inventory. The indices with the best correlation for volume by plot (m³ 0.04 ha-1) were the Generalized Difference Vegetation Index (GDVI) and Adjusted Soil Vegetation Index (SAVI) with 0.39 and 0.36, respectively. The best regression model completed using these VIs estimated the volume by plot with R² controls of 0.9402 and Syx of 1.44%. The use of spectral indices generated from Sentinel-2/MSI sensor data enabled the volumetric estimate of the Pinus taeda L. stand, not revealing differences between the volume accumulated by forest inventory and by orbital images. However, it is worth pointing out that new tests be carried out on other forest species and with medium to high spatial resolution sensors.

A Data-Driven Waveform Adaptation Method for Mm-Wave Gait Classification at the Edge

The ever-increasing need for real-time 3D perception for autonomy has resulted in development of a new generation of high-resolution perception sensors paired with powerful edge processors and advanced perception algorithms. However, processing of the large data volumes generated by such high-resolution sensors still imposes a great challenge for resource-limited edge devices and systems. In this work, we visit this problem in the context of pedestrian gait analysis using high-resolution mm-Wave sensors with application in autonomous driving. We propose reducing the total computational load of a resource-limited perception system by dynamically controlling the mm-Wave sensor's resolution on the fly. We provide results on three dynamic sensor adaptation approaches including an end-to-end Deep Q-Learning approach that can overcome the resource and performance limitations inherent to conventional static methods.

Recognition of Polar Lows in Sentinel-1 SAR Images With Deep Learning

In this article, we explore the possibility of detecting polar lows in C-band synthetic aperture radar (SAR) images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images divided into two classes, representing the presence and absence of a maritime mesocyclone, respectively. The dataset is constructed using the ECMWF reanalysis version 5 (ERA5) dataset as baseline and it consists of 2004 annotated images. To our knowledge, this is the first dataset of its kind to be publicly released. The dataset is used to train a deep learning model to classify the labeled images. Evaluated on an independent test set, the model yields an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> score of 0.95, indicating that polar lows can be consistently detected from SAR images. Interpretability techniques applied to the deep learning model reveal that atmospheric fronts and cyclonic eyes are key features in the classification. Moreover, experimental results show that the model is accurate even if: 1) such features are significantly cropped due to the limited swath width of the SAR; 2) the features are partly covered by sea ice; and 3) land is covering significant parts of the images. By evaluating the model performance on multiple input image resolutions (pixel sizes of 500 m, 1 km, and 2 km), it is found that higher resolution yield the best performance. This emphasizes the potential of using high-resolution sensors like SAR for detecting polar lows, as compared to conventionally used sensors such as scatterometers.

Federated Onboard-Ground Station Computing With Weakly Supervised Cascading Pyramid Attention Network for Satellite Image Analysis

With advances in NanoSat (CubeSat) and high-resolution sensors, the amount of raw data to be analyzed by human supervisors has been explosively increasing for satellite image analysis. To reduce the raw data, the satellite onboard AI processing with low-power COTS (Commercial, Off-The-Shelf) HW has emerged from a real satellite mission. It filters the useless data (e.g. cloudy images) that is worthless to supervisors, achieving efficient satellite-ground station communication. In the application for complex object recognition, however, additional explanation is required for the reliability of the AI prediction due to its low performance. Although various eXplainable AI (XAI) methods for providing human-interpretable explanation have been studied, the pyramid architecture in a deep network leads to the background bias problem which visual explanation only focuses on the background context around the object. Missing the small objects in a tiny region leads to poor explainability although the AI model corrects the object class. To resolve the problems, we propose a novel federated onboard-ground station (FOGS) computing with Cascading Pyramid Attention Network (CPANet) for reliable onboard XAI in object recognition. We present an XAI architecture with a cascading attention mechanism for mitigating the background bias for the onboard processing. By exploiting the localization ability in pyramid feature blocks, we can extract high-quality visual explanation covering the both semantic and small contexts of an object. For enhancing visual explainability of complex satellite images, we also describe a novel computing federation with the ground station and supervisors. In the ground station, active learning-based sample selection and attention refinement scheme with a simple feedback method are conducted to achieve the robustness of explanation and efficient supervisor’s annotation cost, simultaneously. Experiments on various datasets show that the proposed system improves accuracy in object recognition and accurate visual explanation detecting small contexts of objects even in a peripheral region. Then, our attention refinement mechanism demonstrates that the inconsistent explanation can be efficiently resolved only with very simple selection-based feedback.