In-field Testing Research Articles

Discriminating North American Swine Influenza Viruses with a Portable, One-Step, Triplex Real-Time RT-PCR Assay, and Portable Sequencing.

Swine harbors a genetically diverse population of swine influenza A viruses (IAV-S), with demonstrated potential to transmit to the human population, causing outbreaks and pandemics. Here, we describe the development of a one-step, triplex real-time reverse transcription-polymerase chain reaction (rRT-PCR) assay that detects and distinguishes the majority of the antigenically distinct influenza A virus hemagglutinin (HA) clades currently circulating in North American swine, including the IAV-S H1 1A.1 (α), 1A.2 (β), 1A.3 (γ), 1B.2.2 (δ1) and 1B.2.1 (δ2) clades, and the IAV-S H3 2010.1 clade. We performed an in-field test at an exhibition swine show using in-field viral concentration and RNA extraction methodologies and a portable real-time PCR instrument, and rapidly identified three distinct IAV-S clades circulating within the N.A. swine population. Portable sequencing is used to further confirm the results of the in-field test of the swine triplex assay. The IAV-S triplex rRT-PCR assay can be easily transported and used in-field to characterize circulating IAV-S clades in North America, allowing for surveillance and early detection of North American IAV-S with human outbreak and pandemic potential.

Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights.

Although low-cost air quality sensors facilitate the implementation of denser air quality monitoring networks, enabling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is often overlooked in laboratory testing. This gap was addressed by introducing an in-field calibration and validation of three PAQMON 1.0 mobile sensing low-cost platforms developed at the Mining and Metallurgy Institute in Bor, Republic of Serbia. A configuration tailored for monitoring PM2.5 and PM10 mass concentrations along with meteorological parameters was employed for outdoor measurement campaigns in Bor, spanning heating (HS) and non-heating (NHS) seasons. A statistically significant positive linear correlation between raw PM2.5 and PM10 measurements during both campaigns (R > 0.90, p ≤ 0.001) was observed. Measurements obtained from the uncalibrated NOVA SDS011 sensors integrated into the PAQMON 1.0 platforms exhibited a substantial and statistically significant correlation with the GRIMM EDM180 monitor (R > 0.60, p ≤ 0.001). The calibration models based on linear and Random Forest (RF) regression were compared. RF models provided more accurate descriptions of air quality, with average adjR2 values for air quality variables in the range of 0.70 to 0.80 and average NRMSE values between 0.35 and 0.77. RF-calibrated PAQMON 1.0 platforms displayed divergent levels of accuracy across different pollutant concentration ranges, achieving a data quality objective of 50% during both measurement campaigns. For PM2.5, uncertainty ( ) was below 50% for concentrations between 9.06 and 34.99μg/m3 in HS and 5.75 and 17.58μg/m3 in NHS, while for PM10, it stayed below 50% from 19.11 to 51.13μg/m3 in HS and 11.72 to 38.86μg/m3 in NHS.

System configuration, control development, and in-field validation of a hybrid electric wheel loader featuring electrically-boosted engine

This paper proposes an innovative next-generation wheel loader derived from a production series electric wheel loader by John Deere. The proposed wheel loader features a series hybrid electric powertrain with an energy storage system and an electrically-boosted turbocharged diesel engine. A detailed system design inclusive of powertrain configuration and control development including a novel heuristics-based vehicle power management is presented. A simulation model was created to evaluate the potential fuel savings of the proposed wheel loader, revealing a fuel saving potential of over 10% when compared to the baseline configuration. Subsequent in-field testing of an actual demo wheel loader verified its ability to achieve over 10% fuel savings, thereby confirming the simulation outcomes, demonstrating the promise of the proposed hybrid powertrain, and validating the efficacy of the control system developed in this research.

Anti-crosstalk fiber-optic directional torsion sensor via intensity-modulated Lyot filter

In this paper, a fiber-optic directional torsion sensor based on the intensity modulated Lyot filter is completed to alleviate the crosstalk from ambient temperature and axial strain. The transmission is analyzed and compared with the varied twisted length. The experimental results show that the torsion response of 1.18 dB/° can be achieved by the 45° incident angle and 10-cm twisted length, with tiny wavelength shift (∼0.012 nm/°) and minimum range of π-phase jumping. Meanwhile, the crosstalk is well compressed within 0.006°/με and 0.23°/°C owing to ultralow axial-strain and temperature responses. In addition, an in-field test is performed electrically and the detection limit of 0.08° is witnessed experimentally.

High-power-density hybrid planar-type silicon thermoelectric generator with phononic nanostructures

Energy harvesting is essential for the internet-of-things networks where a tremendous number of sensors require power. Thermoelectric generators (TEGs), especially those based on silicon (Si), are a promising source of clean and sustainable energy for these sensors. Although large thermoelectric figure of merit has been reported for nanostructured Si material, however, nanostructuring has not been effectively used in device applications, and the reported performance of hybrid planar-type Si TEGs never exceeded normalized powers of 0.1μWcm−2K−2 due to the poor thermoelectric performance of Si and the suboptimal design of the devices. Here, we report a hybrid planar-type Si TEG with a normalized power of 1.3μWcm−2K−2 around room temperature. The increase in thermoelectric performance of Si by nanostructuring based on the phonon-glass electron-crystal concept and optimized three-dimensional heat-guiding structures resulted in a record-high power density. The improvement of power generation by a factor of 10 makes the once-a-day sensing applications realistic in a practical environment for the first time. In-field testing demonstrated that our Si TEG functions as a sufficient energy harvester. This demonstration paves the way for energy harvesting with a low-environmental load and cost-effective material with high throughput, a necessary condition for energy-autonomous sensor nodes for the trillion sensors universe.

Initial experimental testing of a hybrid solar-dish Brayton cycle for combined heat and power (ST-CHP)

The Solar Turbo Combined Heat and Power (ST-CHP) project developed a novel solar-gas hybrid prototype for combined heat and power generation in Pretoria, South Africa. A vacuum-membrane faceted parabolic dish with a large-pipe open-cavity receiver was coupled to a counterflow recuperator, a combustion chamber, a micro gas turbine with air bearings and a phase change thermal energy storage unit containing solar salts. This study aimed to validate the electrical output of the full-scale dish-Brayton prototype, addressing the literature gap on micro-scale dish-Brayton plants' in-field power generation. Solar hybridization of micro gas turbine technology can significantly reduce combustion fuel consumption. A performance analysis under relevant conditions revealed a late afternoon micro gas turbine output intermittently peaking at 0.4kWe and subsequently stabilizing to a steady state of 0.145kWe at 130krpm. A SimuPact numerical model and an analytical model supplemented the telemetry data to reduce interference in the experimental setup and fully characterize the ST-CHP prototype performance, estimating a steady-state turbine isentropic efficiency of 57%, a compressor efficiency of 71% and a collector efficiency of 17% due to the late afternoon steady-state point. Analytical case studies revealed that fuel savings of between 12% and 33% at the combustion chamber were achievable from the solarized preheating. A subsequent test of the micro gas turbine without solar hybridization or a recuperator resulted in 1.05kWe being generated. The study confirms the dish-Brayton prototype's viability for combined heat and power generation, producing an initial full-scale performance characterisation during in-field testing, and highlighting the impact of solar hybridization on turbine electrical output. Optical efficiency, insulation effectiveness, pressure losses, and turbine operating conditions were identified as critical areas requiring optimization to improve electrical output. The lessons learned, and the calibrated numerical model may be used to optimize the performance of the dish-Brayton plant in future work. The successful in-field full-scale power generation of the ST-CHP prototype adds to the available literature on dish-Brayton technology and brings the technology closer to a commercial product.

Reduced On-chip Storage of Seeds for Built-in Test Generation

Logic built-in self-test ( LBIST ) approaches use an on-chip logic block for test generation and thus enable in-field testing. Recent reports of silent data corruption underline the importance of in-field testing. In a class of storage-based LBIST approaches, compressed tests are stored on-chip and decompressed by an on-chip decompression logic. The on-chip storage requirements may become a bottleneck when the number of compressed tests is large. In this case, using each compressed test for applying several different tests allows the storage requirements to be reduced. However, producing different tests from each compressed test has a hardware overhead. This article suggests a new on-chip storage scheme for compressed tests that eliminates the additional hardware overhead. Under the new storage scheme, a set of N B -bit compressed tests targeting a set of faults F 0 is translated into a sequence S of N ⋅ B bits. Every B consecutive bits of S are considered as a compressed test. The sequence S thus yields close to N ⋅ B compressed tests, magnifying the test data stored in S almost B times. Taking advantage of the extra tests, the article describes a software procedure that is applied offline to reduce S without losing fault coverage of F 0 . Experimental results for benchmark circuits demonstrate significant reductions in the storage requirements of S and significant increases in the fault coverage of a second set of faults, F 1 .

Distributed IMU Sensors for In-Field Dynamic Measurements on an Alpine Ski.

Modern ski design is an inherently time-consuming process that involves an iterative feedback loop comprised of design, manufacturing and in-field qualitative evaluations. Additionally consumers can only rely on qualitative evaluation for selecting the ideal ski, and due to the variation in skier styles and ability levels, consumers can find it to be an inconsistent and expensive experience. We propose supplementing the design and evaluation process with data from in-field prototype testing, using a modular sensor array that can be ported to nearly any ski. This paper discusses a new distributed Inertial Measurement Unit (IMU) suite, including details regarding the design and operation, sensor validation experiments, and outdoor in-field testing results. Data are collected from a set of spatially distributed IMUs located on the upper surface of the ski. We demonstrate that this system and associated post-processing algorithms provide accurate data at a high rate (>700 Hz), enabling the measurement of both structural and rigid ski characteristics, and are robust to repetitive testing in outdoor winter conditions.

An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics

The ambition of this study was to justify the possibility of wheat trait prediction using NDVI from a newly developed Plant-O-Meter sensor. Acquired data from Plant-O-Meter was matched with GreenSeeker's, which was designated as a reference. The experiment was carried out in the field during the 2022 growing season at the long-term experimental field. The experimental design included five different winter wheat genotypes and 20 different NPK fertilizer treatments. The GreenSeeker sensor always gave out NDVI values that were higher than those of the Plant-O-Meter by, on average, 0.029 (6.36%). The Plant-O-Meter sensor recorded similar NDVI values (94% of the variation is explained, p<0.01). The Plant-O-Meter's NDVIs had a higher CV for different wheat varieties and different sensing dates. For almost all varieties, GreenSeeker exceeded Plant-O-Meter in predicting yields for the early (March 21st) and late (June 6th) growing seasons. NDVIGreenSeeker data improved yield modeling performance by an average of 5.1% when compared to NDVIPlant-O-Meter; in terms of plant height prediction, NDVIGreenSeeker was 3% more accurate than NDVIPlant-O-Meter and no changes in spike length prediction were found. A compact, economical and user-friendly solution, the Plant-O-Meter, is straightforward to use in wheat breeding programs as well as mercantile wheat production.

In-Field Testing of Functionally-Possible Transition Faults With High Activation Frequencies

In-Field Testing of Functionally-Possible Transition Faults With High Activation Frequencies

Background Receiver IQ Imbalance Correction for In-Field Testing

Background Receiver IQ Imbalance Correction for In-Field Testing

PFAS Electroanalysis in Low-Oxygen River Water Using Electrogenerated Dioxygen.

Per- and polyfluoroalkyl substances (PFAS), nicknamed "forever chemicals" due to the strength of their carbon-fluorine bonds, are a class of potent micropollutants that cause deleterious health effects in mammals. The current state-of-the-art detection method requires the collection and transport of water samples to a centralized facility where chromatography and mass spectrometry are performed for the separation, identification, and quantification of PFAS. However, for efficient remediation efforts to be properly informed, a more rapid in-field testing method is required. We previously demonstrated the development and use of dioxygen as the mediator molecule. The use of dioxygen is predicated on the assumption that there will be consistent ambient dioxygen levels in natural waters. This is not always the case in hypoxic groundwater and at high altitudes. To overcome this challenge and further advance the strategies that will enable in-field electroanalysis of PFAS, we demonstrate, as a proof of concept, that dioxygen can be generated in solution through the hydrolysis of water. The electrogenerated dioxygen can then be used as a mediator molecule for the indirect detection of PFOS via molecularly imprinted polymer (MIP)-based electroanalysis. We demonstrate that calibration curves can be constructed with high precision and sensitivity (LOD < 1 ppt or 1 ng/L). Our results provide a foundation for enabling in-field hypoxic PFAS electroanalysis.

Self-Test Library Generation for In-Field Test of Path Delay Faults

New semiconductor technologies for advanced applications are more prone to defects and imperfections related, among several different causes, to the manufacturing process, aging and cross-talks. These phenomena negatively affect the circuit’s timing and can be effectively modeled by means of the path delay fault (PDF) model. While path delay testing is currently supported by commercial Automatic Test Pattern Generation tools for scan designs, functional testing covering PDFs is not widely adopted, mainly because of the high cost for test generation. On the other side, functional test is already widely adopted for in-field test of stuck-at faults, which is often performed resorting to the execution of suitable test programs (Self Test Libraries, or STLs). This approach is attractive, since it can be performed at-speed with limited time constraints and high flexibility, making it a suitable in-field test solutions. Previous work assessed the feasibility and validity of functional approaches based on test programs targeting PDFs. In this work, we present the first systematic method for the development of very high fault coverage test programs for PDFs, which largely outperform test programs written for other fault models. Moreover, the proposed method allows the identification of functionally untestable faults. The effectiveness of the proposed approach was proven on an open-source RISC-V processor core, where 100% coverage of the functionally testable longest paths was achieved, compared with an initial coverage of 0.52% achieved with test programs targeting stuck-at faults. Results demonstrate that shorter paths are also effectively covered.

P071 Simple Vestibular-Occular Motor Assessment as a Predictor of Driving Performance Vulnerability following extended Wakefulness

Abstract Introduction Driver fatigue is a significant contributor to road crashes, but identifying individuals at driving risk is challenging. We examined the potential of simple baseline vestibular ocular motor system (VOMS) assessment via virtual reality goggles to predict subsequent vulnerability to driving simulator impairment following extended wakefulness. Methods 49 individuals (Mean±SD Age 32.6±12.9, 45% Males) underwent 9hr baseline sleep opportunity followed by approximately ~29hrs extended wakefulness with five 60min driving assessments. Cluster analysis classified drivers into vulnerable (n=17) or resistant (n=32) groups based on their worst steering deviation and number of crashes from driving tests. Baseline VOMS were performed ~10mins prior to the first three drives (1, 7 and 13hrs of wakefulness). XGBoost machine learning model was trained using baseline VOMs features to predict vulnerable vs resistant groups from driving tests 4 and 5 (19 and 25hrs of wakefulness) Model performance was evaluated using 5-fold cross-validation approach using ROC analysis. Results XGBoost machine learning ranked all 70 VOMS metrics on their importance in predicting vulnerable vs resistant groups. Top 10 VOMs metrics assessed during baseline non-sleep deprived tests demonstrated a strong ability to predict the driver's performance following extended wakefulness, differentiating between the vulnerable vs resistant groups (AUC 0.73 [95%CI 0.61-0.83, p&amp;lt;0.001]). Conclusion VOMs tests conducted at baseline holds promise for predicting future driving impairment. This approach has the potential to be highly valuable in determining an individual's fitness to drive. Future validation in independent samples, sleep disordered population and in-field on-road testing are needed to confirm these promising findings.

O072 Simple Vestibular-Occular Motor Assessment as a Predictor of Alertness State and Driving Impairment during Extended Wakefulness

Abstract Introduction Driver fatigue contributes to 2-16% of road crashes, highlighting the need for improved detection of at-risk drivers. We used a novel and brief test of vestibular ocular motor system (VOMS) assessed via virtual reality goggles to predict alertness state and driving simulator performance during 29hr extended wakefulness. Methods 49 individuals (Mean±SD Age, 32.6±12.9, 45% Males) undergone 9hr baseline sleep opportunity followed by ~29hrs extended wakefulness with five 60min driving assessments. Cluster analysis, combining steering deviation and number of crashes were used to split participants into groups of either poor vs good driving performance. VOMS assessment was conducted using virtual reality goggles approximately 10mins before and after each drive. Predictive importance of VOMs metrics were ranked using XGBoost machine learning model, which was then utilized to distinguish between poor vs good driving groups. Model performance was evaluated using a 5-fold cross-validation approach using ROC analysis. Results XGBoost machine learning ranked all 70 VOMS metrics on their importance in predicting driving performance group for each drive. Top 10 metrics from pre-drive VOMS test predicted both daytime driving (tests 1-3, AUC 0.8 [95%CI 0.64-0.93], p&amp;lt;0.001) and night-time driving (tests 4-5, AUC 0.78 [95%CI 0.6-0.95, p&amp;lt;0.001]). Post-driving VOMS assessments also predicted daytime (AUC 0.74 [95%CI 0.53-0.9, p&amp;lt;0.001] and night-time driving (AUC 0.76 [95%CI 0.52-0.94, p&amp;lt;0.001]). Conclusion VOMS assessment show promise as a short and effective assessment of sleepiness to predict driving failure. Future validation in independent samples, sleep disordered population and in-field on-road testing are needed to confirm these promising findings.

Variability and the Correlation of Kinematic and Temporal Parameters in Different Modalities of the Reverse Punch Measured by Sensors

The influence of joint motion on punch efficiency before impact is still understudied. The same applies to the relationship between the kinematic and temporal parameters of a reverse punch (RP) that determines a score. Therefore, the aim of this study was to investigate if the exclusion or inclusion of body segments affects the acceleration, velocity, rotation angle, and timeline of execution, and to examine the correlation between these quantities. Seven elite male competitors—senior European and World Championship medalists—participated in the in-field testing. Quantities were acquired in the developmental phase of RP through three modalities of execution. Synchronized real-time data were obtained using combined multimodal sensors and camera fusion. The main findings of the study have highlighted the significant differences in the temporal and kinematic variables of RP that arise from the modality of execution. Large and medium correlation coefficients were obtained between the examined variables of body and hand. In conclusion, the results show that measured parameters are affected by segmental body activation. Moreover, their interdependence influences punch execution. The presented interdisciplinary approach provides insightful feedback for: (i) development of reliable and easy-to-use technical solutions in combat sports monitoring; and (ii) improvements in karate training.

Storage-Based Logic Built-In Self-Test With Cyclic Tests

Logic built-in self-test (LBIST) eliminates the need for external test data, and thus facilitates in-field testing. Instead of pseudo-random tests that are typically produced by on-chip test generation logic, storage-based LBIST uses deterministic test data entries, which are stored on-chip, for applying tests that are closer to deterministic tests. This article describes a storage-based LBIST approach that uses a unique type of scan-based tests referred to as cyclic tests. Cyclic tests have cyclic scan enable and scan in sequences, with a higher proportion of functional capture cycles compared with conventional scan-based tests. This results in more clock cycles where fault effects can be captured in the flip-flops. The improved fault detection capabilities this provides helps balance the number of applied tests and the storage requirements. Experimental results for benchmark circuits demonstrate the effectiveness of cyclic tests.

(Invited) Translational Applications of Nanostructured Biosensors: Diagnostics at the Point of Care

Development of diagnostic devices with clinically relevant sensitivity and rapidity is highly desirable for decreasing the delay between diagnosis and treatment. Diagnostic inefficiency permeates multiple medical fields including infectious diseases and antimicrobial resistance (both recognized by WHO among paramount threats and research priorities). Molecular detection is also central to cancer, where therapies are often out of step with disease complexity and progression. The respective challenges may be addressed through the application of nanomaterial and high-throughput devices that offer unique advantages. In Mahshid Lab, we develop novel paradigms in point of care diagnosis via synergistically combining innovative nanostructured sensors with fluidic sample delivery systems and biomolecular assay capabilities (Nano/Bio diagnostic devices).From an engineering perspective, the lab seeks to use the remarkable intrinsic properties of novel nanomaterials, to render them capable of sensing the specific biomolecules. Such miniaturized sensors could be integrated with automated lab-chip devices and deployed to diagnose molecular changes in biological systems and in disease such as cancer (by targeting new cancer biomarkers) or to detect infectious agents in biological samples, e.g. in blood, saliva and urine. From a health industry perspective, we target the advancement of the automated and portable tools for in-field testing, remote locations and hospitals in close collaboration with clinicians to validate the devices with clinical samples. In particular and in the context of infectious disease, Mahshid lab has developed SALIVERA analogous to a qPCR, and NFluidEX analogous to a glucometer, that enabled rapid portable automated monitoring of SARS-CoV-2 infection in patient saliva and antibodies in patient blood, respectively. In the context of cancer, Mahshid lab has developed MoSERS, anon-chip approach for molecular profiling of extracellular vesicles (a new cancer biomarker)on-chip approach for molecular profiling of extracellular vesicles (a new cancer biomarker)in plasma and cerebrospinal fluid of glioblastoma patients. The proposed hybrid devices are capable of working with small sample volumes and precise dosing of reagents, enabling the transition to a portable diagnostic tool.

NVIDIA MATHS: Mechanism to Access Test-Data Over High-Speed Links

MATHS (Mechanism to Access Test-Data over High-Speed Link) provides a high-throughput PCIe based system to structurally test system-on-chips (SOCs) at wafer and system-level. The system removes the need for expensive test equipment by eliminating the input/output pin (IO) requirements and memory per IO needs. It simplifies the ATE architecture and design to enable smaller form factors and reduce capital costs of ownership. MATHS enables eliminating the assembly test-insertion by directly testing the SOCs on system level platforms, further reducing the costs. Since the mechanism is based on PCIe standards, it is highly portable across all platforms including ATE, system-level test, board, and in-field testing.

Oriented-aptamer encoded magnetic nanosensor with laser-induced fluorescence for ultrasensitive test of okadaic acid

Okadaic acid (OA) biotoxin acts a well-established inhibitor of protein phosphatase even a tumor promoter of human being, arouse great attention in safety monitoring. However, the powerful and convenient nanosensing technologies for addressing the demands such as rapidity, high sensitivity, and stability in the in-field test of OA shellfish toxin is still scarce. Herein, a high-performance magnetic biometric nanosensor (MBNS) integrating oriented aptamers and ultrasensitive laser-induced fluorescence (LIF) was firstly proposed for the in-field detection of trace OA in seafoods. High-density aptamers hybridized with FAM-labeled cDNA were tethered to the surface of AuNPs on magnetic MIL-101@Fe3O4, and then finely regulated by mercaptohexyl alcohol (MCH) to be orderly assembled, as was successfully utilized to engineer an active biological nanosensor for highly specific recognition of OA. Aptamers anchored on magnetic Fe3O4@MOF@AuNPs activate a biometric microreactor of OA, in which the superior LIF properties, conformation regulation of aptamer, and the specific recognition using aptamer genes were adopted. The magnetic nanosensor with an excellent specificity and super sensitivity for OA analysis was achieved within 20 min. Moreover, the content of captured OA could facilely be recorded by measuring the fluorescence intensity, and the limit of detection (LOD) and limit of quantitation of OA (LOQ) reached 0.015 and 0.050 ng/mL respectively, which was far better than most aptamer-based biometric sensing methods. The feasibility for accurate test of trace OA toxin in the fortified shellfish samples was validated with the recovery yields of 88.2–107.5% and RSD of 0.5–7.6%, respectively. The result demonstrated that the oriented-aptamer encoded MNS had significant practical values in rapid and ultrasensitive detection of OA biotoxin and the related safety applications.