Hand-held Sensor Research Articles

Design and Experimental Validation of a Fiber Bragg Grating-Enabled Force Sensor With an Ortho-Planar Spring-Based Flexure for Surgical Needle Insertion

This paper presents the development of a novel and high-precision Fiber Bragg Grating (FBG)-based sensor for the measurement of insertion forces during the needle puncture procedure. The proposed sensor mainly comprises two compact orthogonal planar springs connected in a parallel arrangement, one suspended FBG optical fiber and a hand-held sensor frame. Two orthogonal planar springs with curved limbs have been utilized to design the force-sensitive flexure with an excellent linear force-displacement relationship along the axial direction. The employed optical fiber has been arranged along the flexure’s central line, and it is tightly suspended under a pre-tension force with its two ends glued. This two-point pasting configuration can attain improved sensitivity and resolution and avoid the drawbacks of chirping and low repeatability. Finite element modeling (FEM)-enabled simulation has been implemented for design optimization to further advance the sensor sensitivity and performance investigation. The sensor’s axial sensitivity has been increased in the condition when the radial crosstalk decreased. The proposed sensor has been prototyped and calibrated to achieve a high resolution of 1.5mN within [0, 6N] and an excellent linearity with a small linearity error of 0.5%. This design can be conveniently customized to achieve adjustable measurement range and force sensitivity, which has been validated by the further simulation sensor version with a resolution of 4.9mN within [0, 20N]. In-vitro puncture experiments on silicone phantoms, artificial venipuncture simulation module and eggshell membrane, and ex-vivo puncture experiments on porcine liver and porcine aorta have been implemented to validate the effectiveness of the presented sensor design.

Laboratory and field measurement of magnetic susceptibility of Japanese agricultural soils for rapid soil assessment

Measuring volume magnetic susceptibility (κ) of soil allows rapid soil assessment. In this study, we measured the κ value of agricultural surface soils in Japan at national and farm scales in order to evaluate the scale-dependent relationship between soil κ and other soil properties and to examine the repeatability of the κ value measured in the laboratory and in the field. A handheld field sensor was used to measure two sample sets: (1) 164 samples collected throughout Japan (national scale), and (2) 246 samples collected from paddy fields in Takatsuki and 117 (39 sites × 3 times) samples collected from paddy fields in Mifune (farm scale). Laboratory measurements showed that the coefficients of variation of soil κ were 112% for the national scale samples, 13% for the Takatsuki samples, and 33–37% for the Mifune samples. The large variation at the national scale was due to several positive outliers, which were classified as Vitric or Silandic Andosols by the WRB classification. Repeated measurement of samples enabled estimation of the within-sample variation in κ, and the data was considered unreliable if the coefficient of variation exceeded 10%. The correlation analysis using the reliable data indicated that at the national scale, the κ value was most positively correlated with structural Fe (total Fe minus Fed) followed by Alo and the andic properties (Alo + 1/2Feo). In contrast, for the Mifune samples collected at the farm scale, the κ value was most positively correlated with sand content followed by K saturation percentage. Field measurements of soil κ at the 39 Mifune sites indicated that the coefficients of variation of κ at the same site were almost all below 10%. The within-site variations were due probably to the incomplete contact between the soil surface and the sensor. Our results suggest that measuring κ of agricultural surface soils in Japan can help to distinguish Vitric or Silandic Andosols from other soils, and to estimate the κ-related soil properties at the farm and national scales.

Common Questions When Using Soil Moisture Sensors for Citrus and Other Fruit Trees

This guide is for Extension personnel who may encounter questions from growers about the functioning and accuracy of soil moisture sensors (SMSs) for fruit tree production. The 4-page publication focuses on two types of handheld sensors currently used in Florida for irrigation management of citrus and other trees: the transmission line oscillator (TLO) and time-domain transmissometer (TDT). Written by Eric Herrera, Sandra M. Guzmán, and Eduart Murcia, and published by the UF/IFAS Department of Agricultural and Biological Engineering, February 2021.

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Navigator Notes

Nondestructive measurement of intramuscular fat content of fresh beef meat by a hand-held magnetic resonance sensor

ABSTRACT To realize nondestructive in-situ quantification of intramuscular fat in fresh beef meat, a lightweight (5.0-kg) hand-held sensor that consists of a planar radio-frequency coil and a unilateral (i.e., single-sided) magnetic circuit was constructed as a subunit of a time-domain proton magnetic resonance (MR) scanner system. The MR scanner based on proton relaxometry enables the nondestructive quantification of the fat content of meat by using the difference in the spin-spin relaxation times (T2) of water molecules in muscle and of fat molecules. The investigation depth of the sensor unit was 7 mm, which is sufficient to probe the meat beneath a subcutaneous fat layer less than a few millimeters thick. The scanner was successfully applied in a laboratory at a meat temperature of 30.5°C to quantify the fat content at 30 locations in fresh beef samples. The required measurement time was 22 s for each location. Reasonable agreement with a root-mean-square error as small as 5.4 wt% was obtained for fat quantification compared with the conventional destructive food analysis (Soxhlet extraction method). Thus, the portable MR scanner with a hand-held sensor unit is a promising nondestructive and noninvasive tool for the in-situ fat quantification of fresh beef (e.g., carcasses) with thin subcutaneous fat layers at meat processing factories.

Mobile low field magnetic resonance hardware development

Typically, NMR systems are bulky and expensive laboratory based equipment. For half a century after its scientific discovery taking NMR outside of a laboratory environment is still not a common practice due to the complexity of the underlining physical phenomena and its low sensitivity, to the myriad of technical challenges when integrating a complete system. The scarcity of compact and mobile NMR systems has prevented its proliferation into many other areas and applications.This paper describes the progress in the development of compact electronic spectrometers that we coupled with handheld sensors in order to provide complete mobile solutions. The key to this progress has been the modern advances in computing, electronics and permanent magnet technologies. Mobile NMR is now feasible as a valuable, non-invasive tool for industrial and medical applications. By leveraging the strengths of NMR, which is to probe at the molecular level and gain information about molecular structure, organisation, abundance and orientation, NMR is intrinsically suitable for non-destructive testing of a wide range of materials and their manufacturing processes.The development of complete NMR systems benefits from working across various disciplines and organisations. By embracing a collaborative approach we believe it will accelerate NMR technology to become more ubiquitous in the near future.

Non-Destructive Near-Infrared Sensor Method for Measuring Water Content of Intact Eggplant Leaves

The objective of this study was to evaluate the ability of a hand-held near-infrared (NIR) sensor and water absorption index to estimate the water content in intact eggplant leaves using a pot experiment. The NIR spectroscopy using a sin-gle wavelength (1940 nm) has already been used to estimate the water content of detached samples in a laboratory. However, it is not known whether this method can be used to estimate the water content of intact leaves of living plants. Therefore, we attempted to compare this method with destructive method by simulating drought stress and analyzing the relationship between leaf water content and soil matric potential. The water content of intact eggplant leaves was predict-ed with a calibration model using NIR reflectance. Both the measured and predicted water content of leaves decreased with decreasing soil matric potential. These results show that both destructive and NIR measurements can be used to estimate the water stress condition. Based on these findings, a hand-held NIR sensor is useful for estimating the water content of intact leaves of living plants.

Facile biosensor-based system for on-site quantification of total viable counts in food and environmental swabs

We describe a new biosensor platform for rapid and simple quantification of total aerobic viable counts of bacteria (TVC) in food and environmental swabs by oxygen respirometry. The system uses disposable swab vials with phosphorescent oxygen sensors integrated in the bottom part, a small block heater/incubator and a handheld sensor reader. In the testing, groups of 1–20 swabs samples were prepared using the standard method (ISO, 18593:2018) in sensor vials, which were then incubated at 30 °C and measured hourly in a contactless, non-invasive manner. The measurements reveal time profiles of dissolved O2 in each sample vial, from which Threshold Time of sensor signal was determined and then TVC values (CFU/cm2) were calculated using the calibration equation. The method covers the range of 0.65–7.87 Log (CFU/cm2) and produces results in 1–8 hrs. The test was validated with swab samples from surfaces contaminated with E. coli, with whole meat microbiota, and with real environmental swabs. The results showed no statistically significant difference with the reference method which takes 48–72 h. The swab testing platform is fast and accurate, simple (sample-and-measure), portable, low cost (<$5k), requires no serial dilutions and is suitable for on-site deployment and use.

Caracterización de variables físicas en La Escollera, una laguna costera artificial en Santa Marta, Caribe colombiano

Las lagunas costeras son foco de producción primaria y refugio de biodiversidad por su relativo confinamiento y baja profundidad. La Escollera es una laguna costera artificial situada en El Rodadero, Santa Marta (Caribe colombiano), excavada al inicio de la década de 1980 para albergar una marina y sus bordes sembrados con mangles. Para generar una línea base de variables físicas que contribuyan al entendimiento de la circulación del agua y de su influencia sobre los atributos del ecosistema, se obtienen de manera rutinaria datos de salinidad, temperatura, nivel del mar y penetración de la luz mediante registradores electrónicos y sensores de mano. Los datos de salinidad y temperatura entre enero de 2017 y febrero de 2018 siguieron el esquema estacional del mar abierto del área de Santa Marta alternando entre menor temperatura (mínima 24,6 °C) y mayor salinidad (máxima 38,9) en época seca y de afloramiento costero (diciembre a abril) y entre mayor temperatura (máxima 34,7 °C) y menor salinidad (&lt; 35) en época de lluvia (mayo a noviembre), con los más bajos valores de salinidad luego de aguaceros persistentes (mínimo 15,1). La columna de agua está generalmente bien mezclada y se estratifica después de aguaceros en época de lluvia (mezclándose rápidamente en horas o en días). Esto parece deberse principalmente al intercambio con el mar gracias a la marea, que oscila de +0,37 a –0,31 m (rango máximo de 0,68 m [datos de un año]), y sigue el patrón astronómico regular de mareas mixtas del Caribe. Las aguas de la laguna son regularmente verdes y de variada transparencia excepto durante fuertes lluvias, tras lo cual se tornan pardas. La extinción de luz en la vertical es en general alta y no muy variable (2-6,5 % cm-1), pero la luz, con frecuencia, alcanza el fondo, lo que permite la existencia de macroalgas bentónicas en algunas porciones de la laguna.

In-Situ Screening of Soybean Quality with a Novel Handheld Near-Infrared Sensor.

This study evaluates a novel handheld sensor technology coupled with pattern recognition to provide real-time screening of several soybean traits for breeders and farmers, namely protein and fat quality. We developed predictive regression models that can quantify soybean quality traits based on near-infrared (NIR) spectra acquired by a handheld instrument. This system has been utilized to measure crude protein, essential amino acids (lysine, threonine, methionine, tryptophan, and cysteine) composition, total fat, the profile of major fatty acids, and moisture content in soybeans (n = 107), and soy products including soy isolates, soy concentrates, and soy supplement drink powders (n = 15). Reference quantification of crude protein content used the Dumas combustion method (AOAC 992.23), and individual amino acids were determined using traditional protein hydrolysis (AOAC 982.30). Fat and moisture content were determined by Soxhlet (AOAC 945.16) and Karl Fischer methods, respectively, and fatty acid composition via gas chromatography-fatty acid methyl esterification. Predictive models were built and validated using ground soybean and soy products. Robust partial least square regression (PLSR) models predicted all measured quality parameters with high integrity of fit (RPre ≥ 0.92), low root mean square error of prediction (0.02–3.07%), and high predictive performance (RPD range 2.4–8.8, RER range 7.5–29.2). Our study demonstrated that a handheld NIR sensor can supplant expensive laboratory testing that can take weeks to produce results and provide soybean breeders and growers with a rapid, accurate, and non-destructive tool that can be used in the field for real-time analysis of soybeans to facilitate faster decision-making.

Optically Based Hand-Held Sensor for Visualization and Quantification of Cryptosporidium Parvum

The ingestion of the protozoan parasite Cryptosporidium, resulting in the associated disease cryptosporidiosis, can cause major problems if introduced into a water supply for both animals and humans. This research focuses on the creation of a sensor to track and quantify the parasite with a digital camera system using Digital Inline Holographic Microscopy (DIHM) and software reconstruction. Since Cryptosporidium oocysts are not single celled and can be around 4–8 µm in diameter, they are easily detected with the DIHM at various depths within a 3.5 mL cuvette. As the sensor is improved by modifications, it could pick up behavior of movement and sizes of particles measured to positively identify the particle(s) being tracked as Cryptosporidium as others have achieved on Escherichia coli and other microorganisms. A significant advantage of this technique is the resulting sensor costs significantly less than traditional microscopes used in laboratory tests for detecting Cryptosporidium, or other methods like USEPA Method 1623 and can provide results within minutes. The sensor is a stellar alternative to current methods in detecting and quantifying Cryptosporidium.

Sulu Şartlar İçin Geliştirilen Ekmeklik Buğday Çeşitlerine Optik Sensor ile Azotlu Gübre Tavsiyesi

Bu çalışma, 2017-2019 yılları arasında Eskişehir Geçit Kuşağı Tarımsal Araştırma Enstitüsü arazisinde yürütülmüştür. Çalışmada, dört kışlık (Atay85, Hat 31, Yunus ve Nacibey) buğday çeşidinin sulu koşulları altında azot gübrelemesine verdikleri tepkiler, spektral yansımaya dayalı bitki örtüsü indeksleri ve bu endekslerden hesaplanan Mevsimsel Verim Tahminleri ile karşılaştırılmıştır. Bu amaçla GreenSeekerTM (NTech Industries, Inc., Ukiah, CA) el tipi sensör kullanılmıştır. Deneme, Tesadüf Bloklarında Faktöriyel deneme deseninde ve 6 farklı 0, 4, 8, 12, 16 ve 20 kg N/da azot oranları kullanılmıştır. Vejetasyon indeksleri (NDVI) Zadoks2.4, Zadoks3,0, Zadoks3,1 ve Zadoks3,2 büyüme dönemlerinde okunmuştur. Zadoks3,0 (sapa kalkma dönemi) en gerçekçi okuma dönemi olarak belirlenmiştir. Sistemin geleneksel çiftçi uygulamaları ile karşılaştırıldığında, 3 deneme ortalamasına göre, yeni sistemin, ilkbahar dönemi azot uygulamalarında 2,8 kg/da daha az N (ZD3,0) ile benzer verimler sağladığı ve ekonomik açıdan ümit verici olduğunu görülmüştür. Sensör uygulamasının çiftçi uygulamasından % 2 daha ekonomik olarak etkin olduğu belirlenmiştir. Ekonomik azot dozu sırasıyla Atay85, Hat 31, Yunus ve Nacibey 12,6 kg N/da, 14,1 kg N/da, 14,4 kg N/da ve 17,9 kg N/da azot dozu olarak belirlenmiştir.

The Urban Built Environment, Walking and Mental Health Outcomes Among Older Adults: A Pilot Study

The benefits of walking in older age include improved cognitive health (e.g., mental alertness, improved memory functioning) and a reduced risk of stress, depression and dementia. However, research capturing the benefits of walking among older people in real-time as they navigate their world is currently very limited. This study explores cognitive health and well-being outcomes in older people as they walk in their local neighborhood environment. Residents from an independent living facility for older people (mean age 65, n = 11) walked from their home in two dichotomous settings, selected on the basis of significantly different infrastructure, varying levels of noise, traffic and percentage of green space. Employing a repeated-measures, cross over design, participants were randomly allocated to one of two groups, and walked on different days in an urban busy “gray” district (a busy, built up commercial street) vs. an urban quiet “green” district (a quiet residential area with front gardens and street trees). Our study captured real-time air quality and noise data using hand-held Airbeam sensors and physiologic health data using a smart watch to capture heart rate variability (a biomarker of stress). Cognitive health outcome measures were a pre- and post-walk short cognitive reaction time (SRT) test and memory recall of the route walked (captured via a drawn mental map). Emotional well-being outcomes were a pre- and post-walk mood scale capturing perceived stress, happiness and arousal levels. Findings showed significant positive health benefits from walking in the urban green district on emotional well-being (happiness levels) and stress physiology (p < 0.05), accompanied by faster cognitive reaction times post-walk, albeit not statistically significant in this small sample. Cognitive recall of the route varied between urban gray and urban green conditions, as participants were more likely to rely on natural features to define their routes when present. The environmental and physiologic data sets were converged to show a significant effect of ambient noise and urban conditions on stress activation as measured by heart rate variability. Findings are discussed in relation to the complexity of combining real-time environmental and physiologic data and the implications for follow-on studies. Overall, our study demonstrates the viability of using older people as citizen scientists in the capture of environmental and physiologic stress data and establishes a new protocol for exploring relationships between the built environment and cognitive health in older people.

HEAD: smootH Estimation of wAlking Direction with a handheld device embedding inertial, GNSS, and magnetometer sensors

Pedestrian navigation with handheld sensors is still particularly complex. Pedestrian Dead Reckoning method is generally used, but the estimation of the walking direction remains problematic because the device's pointing direction does not always correspond to the walking direction. To overcome this difficulty, it is possible to use gait modeling based approaches. But, these methods suffer from sporadic erroneous estimates and their accumulation over time. The HEAD (smootH Estimation of wAlking Direction) filter uses WAISS and MAGYQ angular estimates as observations to correct the walking direction and to obtain more robust and smooth results. TDCP updates are applied to constrain the walking direction estimation error while pseudo-ranges directly update the position. HEAD is tested by 5 subjects over 21 indoor/outdoor acquisitions (between 720 m and 1.3 km). A 54% improvement is achieved thanks to the fusion in texting mode. The median obtained angular error is 5.5 degrees in texting mode and 12 degrees in pocket mode.

Multi-variable selection strategy based on near-infrared spectra for the rapid description of dianhong black tea quality.

The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors.

A Sustainability Assessment of the Greenseeker N Management Tool: A Lysimetric Experiment on Barley

A preliminary study was conducted to analyze the sustainability of barley production through: (i) investigating sensor-based nitrogen (N) application on barley performance, compared with conventional N management (CT); (ii) assessing the potential of the Normalized Difference Vegetation Index (NDVI) at different growth stages for within-season predictions of crop parameters; and (iii) evaluating sensor-based fertilization benefits in the form of greenhouse gasses mitigation. Barley was grown under CT, sensor-based management (RF) and with no N fertilization (Control). NDVI measurements and RF fertilization were performed using a GreenSeeker™ 505 hand-held optical sensor. Gas emissions were measured using a static chamber method with a portable gas analyzer. Results showed that barley yield was not statistically different under RF and CF, while they both differed significantly from Control. Highly significant positive correlations were observed between NDVI and production parameters at harvesting from the middle of stem elongation to the medium milk stage across treatments. Our findings suggest that RF is able to decrease CO2 emission in comparison with CF. The relationship between N fertilization and CH4 emission showed high variability. These preliminary results provide an indication of the benefits achieved using a simple proximal sensing methodology to support N fertilization.

Engineering methods and technologies of remote sensing in an economic entity for the transition to a highly productive agricultural sector

The aim of this article is to illustrate an integration of methods, abilities and the usage of remote sensing in agricultural part of economy. In this project it was clearly stated that, the use of remote sensing in agricultural sector is based on the interaction of electromagnetic radiation with soil or vegetation and refers to the non-contact measurement of radiation reflected or emitted from agricultural fields. Different types of platform for measurements were listed in this research work such as: satellites, aircraft, tractors and handheld sensors.

WATER AREA OBSERVATION EXPERIMENT USING OPTICAL SENSOR FOR EXTRACTING INUNDATION BOUNDARY AT NIGHT

Abstract. During the occurrence of floods, the government generally tries to determine the extent of the continuously changing inundation (water) area as a disaster response for planning evacuations and designing drainage systems. One of the most common methods to identify this area during the daytime involves the interpretation of aerial optical images and videos that are obtained using an airplane, helicopter, or satellite. However, these aerial approach methods have not been established convincingly for their use during the night time. Therefore, to identify the water area at night, two hand-held optical sensors were used in this study, namely: an ultrasensitive camera (Sony SL camera α7S), and a thermal infrared camera (Optris thermography PI640). The night-time aerial performance was evaluated using a helicopter, along with a supplementary night-time examination on the ground. Herein, we targeted river water areas during normal conditions. As a result, it was determined that the water area could be identified clearly with an ultrasensitive camera that has an ISO sensitivity between 51200 and 102400. The thermal infrared camera was able to identify the water area to have a higher temperature than that of the surrounding area. However, in some cases, the water area boundaries were not clear. Thus, as the thermal infrared camera was susceptible to varying conditions, supplemental use of an ultrasensitive camera was determined to be effective for the identification of inundation (water) areas at night. The significance of this study is established by the fact that the studied sensors can be easily transported in a helicopter or mounted on a UAV for night-time applications.

Optically Based Bacteria Hand-Held Sensor: From Fundamentals to Proof of Concept

A prototype of a hand-held particle sensor based on digital inline holographic microscopy was developed to work as an early warning device to detect bacteria Escherichia coli (E. coli) in water within minutes. The aim of the device is to record holographic images of water samples and perform rapid analyses (less than 20 min) of these samples to give timely information about the levels of particulates, with the goal of detecting and quantifying pathogens such as E. coli in water samples. Using the hand-held sensor, laboratory trials were performed to compare the effectiveness of the sensor with traditional methods of detecting E. coli in water. To address the linearity of the sensor signal, a dilution series covering nine decades of bacterial suspensions were analyzed and a linear regression coefficient of determination (R2) of 86.4% was revealed. Preliminary studies have shown that the sensor can detect average values of E. coli levels from 7 colony forming units (CFU)/mL to 106 CFU/mL in deionized water spiked with the bacteria. The device can be essential for accurate and precise bacteria counts in drinking water systems inline or at home. The sensor’s major advantages are its portability, low cost, ease of operation, low power consumption rate, and no consumption of chemical reagents is needed to visualize and count E. coli.

Rapid and robust on-scene detection of cocaine in street samples using a handheld near-infrared spectrometer and machine learning algorithms.

On‐scene drug detection is an increasingly significant challenge due to the fast‐changing drug market as well as the risk of exposure to potent drug substances. Conventional colorimetric cocaine tests involve handling of the unknown material and are prone to false‐positive reactions on common pharmaceuticals used as cutting agents. This study demonstrates the novel application of 740–1070 nm small‐wavelength‐range near‐infrared (NIR) spectroscopy to confidently detect cocaine in case samples. Multistage machine learning algorithms are used to exploit the limited spectral features and predict not only the presence of cocaine but also the concentration and sample composition. A model based on more than 10,000 spectra from case samples yielded 97% true‐positive and 98% true‐negative results. The practical applicability is shown in more than 100 case samples not included in the model design. One of the most exciting aspects of this on‐scene approach is that the model can almost instantly adapt to changes in the illicit‐drug market by updating metadata with results from subsequent confirmatory laboratory analyses. These results demonstrate that advanced machine learning strategies applied on limited‐range NIR spectra from economic handheld sensors can be a valuable procedure for rapid on‐site detection of illicit substances by investigating officers. In addition to forensics, this interesting approach could be beneficial for screening and classification applications in the pharmaceutical, food‐safety, and environmental domains.