RECOGNITION OF GAS-AIR MIXTURES BY A SINGLE GAS SENSOR BASED ON TIN DIOXIDE NANOWHISKERS

Нитевидные нанокристаллы диоксида олова были выращены методом физического осаждения из паровой фазы и перенесены на контактную систему методом замороженной капли. Полученные сенсоры обладают газочувствительностью, воздействие паров газов-восстановителей приводит к увеличению их проводимости. Показано, что существует долговременный дрейф проводимости сенсора при воздействии пробы. Исследована концентрационная зависимость чувствительности сенсора к парам этанола, ацетона и пропанол-2 при температуре 300 °С. На основе анализа концентрационных зависимостей проводимости рассчитаны энергии десорбции частиц, а также положения донорных уровней, индуцированных при адсорбции газов-восстановителей, относительно акцепторного уровня кислорода. Показано, что рассчитанные параметры имеют существенно меньший по сравнению с проводимостью временной дрейф. Использование указанных параметров позволяет распознавать газовоздушные смеси, т.е. однозначно отнести одну из трех исследованных проб к её классу. Tin dioxide nanowhiskers were grown by physical vapor deposition and transferred to the contact system by the frozen drop method. The sensors demonstrate gas-sensitivity. Exposing sensors to the atmosphere contained vapors of reducing-gases leads to an increasing of their conductivity. A long-term drift of the sensor conductivity during reducing-gas exposition was shown. A sensitivity response vs concentration for ethanol, acetone, and propanol-2 vapors at temperature 300 °C was investigated. Desorption energies of the particles and the positions of the donor levels induced by adsorption of reducing gases particles were calculated by analysis of the conductivity vs concentration dependence. The calculated parameters had a significantly smaller time drift in comparison with the conductivity. Using of these parameters makes possible to recognize gas-air mixtures: classify the each of three studied samples to one of classes.

Processing of Seismic-While-Drilling Data from the DrillCAM System Acquired with Wireless Geophones, Top-Drive, and Downhole Vibrations Sensors

We present processing details of seismic-while-drilling data recently acquired on one of the onshore wells by a prototype DrillCAM system with wireless geophones, top-drive, and downhole vibration sensors. The general flow follows an established practice and consists of correlation with a drillbit pilot signal, vertical stacking, and pilot deconvolution. This work's novelty is the usage of the memory-based near-bit sensor with a significant time drift reaching 30-40 minutes at the end of each drilling run. A data-driven automatic time alignment procedure is developed to accurately eliminate time drift error by utilizing the top-drive acceleration sensor as a reference. After the alignment, the processing flow can utilize the top-drive or the near-bit pilots similarly. We show each processing step's effect on the final data quality and discuss some implementation details.

Data Agreement Analysis And Correction of Comparative Geomagnetic Vector Observations

Geomagnetism, similar to other areas of geophysics, is an observation-based science. Data agreement between comparative geomagnetic vector observations is one of the most important evaluation criteria for high-quality geomagnetic data. The main influencing factors affecting the agreement between comparative observational data are the attitude angle, scale factor, long-term time drift, and temperature. In this paper, we propose a method based on a genetic algorithm and linear regression to correct for these effects and use the distribution pattern of points in Bland–Altman plots with a 95% confidence interval length to qualitatively and quantitatively evaluate the agreement between the comparative observational data. In Bland–Altman plots with better agreement, that is, with the corrected data, more than 95% of the points are distributed within the 95% confidence interval and there is no obvious pattern in the distribution of the points. Meanwhile, the length of 95% confidence interval decreased significantly after the correction. The method presented here has positive effects on the vector instrumentation detection, enhancing the robustness of comparative observatory observations and reducing errors in judgments of the size and arrival time of large magnetic disturbances or rapid magnetic variations.

Effect of PMMA Removal Methods on Opto-Mechanical Behaviors of Optical Fiber Resonant Sensor With Graphene Diaphragm

Regarding the dependence of the treatment of removing polymethyl methacrylate (PMMA) from graphene upon the prestress in the film, two typical PMMA removal methods including acetone-vaporing and high-temperature annealing were investigated based on the opto-mechanical behaviors of the developed optical fiber Fabry-Perot (F-P) resonant sensor with a 125-µm diameter and ∼10-layer-thickness graphene diaphragm. The measured resonant responses showed that the F-P sensor via annealing process exhibited the resonant frequency of 481 kHz and quality factor of 1 034 at ∼2 Pa and room temperature, which are respectively 2.5 times and 33 times larger than the acetone-treated sensor. Moreover, the former achieved a high sensitivity of 110.4 kHz/kPa in the tested range of 2 Pa–2.5 kPa, apparently superior to the sensitivity of 16.2 kHz/kPa obtained in the latter. However, the time drift of resonant frequency also mostly tended to occur in the annealed sensor, thereby shedding light on the opto-mechanical characteristics of graphene-based F-P resonant sensors, along with an optimized optical excitation and detection scheme.

Cardiac stress T1-mapping response and extracellular volume stability of MOLLI-based T1-mapping methods

Stress and rest T1-mapping may assess for myocardial ischemia and extracellular volume (ECV). However, the stress T1 response is method-dependent, and underestimation may lead to misdiagnosis. Further, ECV quantification may be affected by time, as well as the number and dosage of gadolinium (Gd) contrast administered. We compared two commonly available T1-mapping approaches in their stress T1 response and ECV measurement stability. Healthy subjects (n = 10, 50% female, 35 ± 8 years) underwent regadenoson stress CMR (1.5 T) on two separate days. Prototype ShMOLLI 5(1)1(1)1 sequence was used to acquire consecutive mid-ventricular T1-maps at rest, stress and post-Gd contrast to track the T1 time evolution. For comparison, standard MOLLI sequences were used: MOLLI 5(3)3 Low (256 matrix) & High (192 matrix) Heart Rate (HR) to acquire rest and stress T1-maps, and MOLLI 4(1)3(1)2 Low & High HR for post-contrast T1-maps. Stress and rest myocardial blood flow (MBF) maps were acquired after IV Gd contrast (0.05 mmol/kg each). Stress T1 reactivity (delta T1) was defined as the relative percentage increase in native T1 between rest and stress. Myocardial T1 values for delta T1 (dT1) and ECV were calculated. Residuals from the identified time dependencies were used to assess intra-method variability. ShMOLLI achieved a greater stress T1 response compared to MOLLI Low and High HR (peak dT1 = 6.4 ± 1.7% vs. 4.8 ± 1.3% vs. 3.8 ± 1.0%, respectively; both p < 0.0001). ShMOLLI dT1 correlated strongly with stress MBF (r = 0.77, p < 0.001), compared to MOLLI Low HR (r = 0.65, p < 0.01) and MOLLI High HR (r = 0.43, p = 0.07). ShMOLLI ECV was more stable to gadolinium dose with less time drift (0.006–0.04% per minute) than MOLLI variants. Overall, ShMOLLI demonstrated less intra-individual variability than MOLLI variants for stress T1 and ECV quantification. Power calculations indicate up to a fourfold (stress T1) and 7.5-fold (ECV) advantage in sample-size reduction using ShMOLLI. Our results indicate that ShMOLLI correlates strongly with increased MBF during regadenoson stress and achieves a significantly higher stress T1 response, greater effect size, and greater ECV measurement stability compared with the MOLLI variants tested.

Characterization and Programming Algorithm of Phase Change Memory Cells for Analog In-Memory Computing

In this paper, a thorough characterization of phase-change memory (PCM) cells was carried out, aimed at evaluating and optimizing their performance as enabling devices for analog in-memory computing (AIMC) applications. Exploiting the features of programming pulses, we discuss strategies to reduce undesired phenomena that afflict PCM cells and are particularly harmful in analog computations, such as low-frequency noise, time drift, and cell-to-cell variability of the conductance. The test vehicle is an embedded PCM (ePCM) provided by STMicroelectronics and designed in 90-nm smart power BCD technology with a Ge-rich Ge-Sb-Te (GST) alloy for automotive applications. On the basis of the results of the characterization of a large number of cells, we propose an iterative algorithm to allow multi-level cell conductance programming, and its performances for AIMC applications are discussed. Results for a group of 512 cells programmed with four different conductance levels are presented, showing an initial conductance spread under 6%, relative current noise less than 9% in most cases, and a relative conductance drift of 15% in the worst case after 14 h from the application of the programming sequence.

ACOUSTIC EMISSION SIGNALS LOCATION IN DURALUMIN AND CARBON FIBER SAMPLES BY OPTICAL FIBER AND PIEZOELECTRIC TRANSDUCER SENSORS ANTENNA

The issues are considered of acoustic emission (AE) signals location generated by various simulators (Su–Nielsen, electronic simulator, metal balls of various diameter from 0.5 to 1.5 mm, dropped from different heights). Signals generated by built-in electronic simulator with a frequency of 1 Hz location is analyzed performed by an antenna consisting of two Fabry–Perot fiber-optic sensors (FOS) and two piezoelectric transducers, mounted on samples made of duralumin and T800 carbon fiber. A carbon fiber reinforced plastic sample with the defined above antenna mounted was tested with a VEDS-10 vibration stand. AE signals were registered under the vibration frequency of 20…50 Hz in the acceleration range of 0.3…0.8g. The influence of the time drift recorded by the piezoelectric transducers on the measurement results is determined. It was found that the time drift was not recorded by fiber-optic FOS sensors, but their sensitivity is more than 14 times less than that of piezoelectric transducers.

Detecting and Tracking the Positions of Wild Ungulates Using Sound Recordings

Monitoring wild ungulates such as deer is a highly challenging issue faced by wildlife managers. Wild ungulates are increasing in number worldwide, causing damage to ecosystems. For effective management, the precise estimation of their population size and habitat is essential. Conventional methods used to estimate the population density of wild ungulates, such as the light census survey, are time-consuming with low accuracy and difficult to implement in harsh environments like muddy wetlands. On the other hand, unmanned aerial vehicles are difficult to use in areas with dense tree cover. Although the passive acoustic monitoring of animal sounds is commonly used to evaluate their diversity, the potential for detecting animal positions from their sound has not been sufficiently investigated. This study introduces a new technique for detecting and tracking deer position in the wild using sound recordings. The technique relies on the time lag among three recorders to estimate the position. A sound recording system was also developed to overcome the time drift problem in the internal clock of recorders, by receiving time information from GPS satellites. Determining deer position enables the elimination of repetitive calls from the same deer, thus providing a promising tool to track deer movement. The validation results revealed that the proposed technique can provide reasonable accuracy for the experimental and natural environment. The identification of deer calls in Oze National Park over a period of two hours emphasizes the great potential of the proposed technique to detect repetitive deer calls, and track deer movement. Hence, the technique is the first step toward designing an automated system for estimating the population of deer or other vocal animals using sound recordings.