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1
Deprez, Kenneth, Loek Colussi, Erdal Korkmaz, Sam Aerts, Derek Land, Stephan Littel, Leen Verloock, David Plets, Wout Joseph, and John Bolte. "Comparison of Low-Cost 5G Electromagnetic Field Sensors." Sensors 23, no. 6 (March 21, 2023): 3312. http://dx.doi.org/10.3390/s23063312.
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This paper compares different low-cost sensors that can measure (5G) RF-EMF exposure. The sensors are either commercially available (off-the-shelf Software Defined Radio (SDR) Adalm Pluto) or constructed by a research institution (i.e., imec-WAVES, Ghent University and Smart Sensor Systems research group (S³R), The Hague University of Applied Sciences). Both in-lab (GTEM cell) and in-situ measurements have been performed for this comparison. The in-lab measurements tested the linearity and sensitivity, which can then be used to calibrate the sensors. The in-situ testing confirmed that the low-cost hardware sensors and SDR can be used to assess the RF-EMF radiation. The variability between the sensors was 1.78 dB on average, with a maximum deviation of 5.26 dB. Values between 0.09 V/m and 2.44 V/m were obtained at a distance of about 50 m from the base station. These devices can be used to provide the general public and governments with temporal and spatial 5G electromagnetic field values.
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Song, Zhenfei, Wanfeng Zhang, Qi Wu, Huihui Mu, Xiaochi Liu, Linjie Zhang, and Jifeng Qu. "Field Distortion and Optimization of a Vapor Cell in Rydberg Atom-Based Radio-Frequency Electric Field Measurement." Sensors 18, no. 10 (September 22, 2018): 3205. http://dx.doi.org/10.3390/s18103205.
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Highly excited Rydberg atoms in a room-temperature vapor cell are promising for developing a radio-frequency (RF) electric field (E-field) sensor and relevant measurement standards with high accuracy and sensitivity. The all-optical sensing approach is based on electromagnetically-induced transparency and Autler-Townes splitting induced by the RF E-field. Systematic investigation of measurement uncertainty is of great importance for developing a national measurement standard. The presence of a dielectric vapor cell containing alkali atoms changes the magnitude, polarization, and spatial distribution of the incident RF field. In this paper, the field distortion of rubidium vapor cells is investigated, in terms of both field strength distortion and depolarization. Full-wave numerical simulation and analysis are employed to determine general optimization solutions for minimizing such distortion and validated by measuring the E-field vector distribution inside different vapor cells. This work can improve the accuracy of atom-based RF E-field measurements and contributes to the development of related RF quantum sensors.
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Thormählen, Lars, Dennis Seidler, Viktor Schell, Frans Munnik, Jeffrey McCord, and Dirk Meyners. "Sputter Deposited Magnetostrictive Layers for SAW Magnetic Field Sensors." Sensors 21, no. 24 (December 15, 2021): 8386. http://dx.doi.org/10.3390/s21248386.
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For the best possible limit of detection of any thin film-based magnetic field sensor, the functional magnetic film properties are an essential parameter. For sensors based on magnetostrictive layers, the chemical composition, morphology and intrinsic stresses of the layer have to be controlled during film deposition to further control magnetic influences such as crystallographic effects, pinning effects and stress anisotropies. For the application in magnetic surface acoustic wave sensors, the magnetostrictive layers are deposited on rotated piezoelectric single crystal substrates. The thermomechanical properties of quartz can lead to undesirable layer stresses and associated magnetic anisotropies if the temperature increases during deposition. With this in mind, we compare amorphous, magnetostrictive FeCoSiB films prepared by RF and DC magnetron sputter deposition. The chemical, structural and magnetic properties determined by elastic recoil detection, X-ray diffraction, and magneto-optical magnetometry and magnetic domain analysis are correlated with the resulting surface acoustic wave sensor properties such as phase noise level and limit of detection. To confirm the material properties, SAW sensors with magnetostrictive layers deposited with RF and DC deposition have been prepared and characterized, showing comparable detection limits below 200 pT/Hz1/2 at 10 Hz. The main benefit of the DC deposition is achieving higher deposition rates while maintaining similar low substrate temperatures.
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Kim, Sangkil, Manos Tentzeris, and Apostolos Georgiadis. "Hybrid Printed Energy Harvesting Technology for Self-Sustainable Autonomous Sensor Application." Sensors 19, no. 3 (February 11, 2019): 728. http://dx.doi.org/10.3390/s19030728.
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In this paper, the far-field energy harvesting system for self-sustainable wireless autonomous sensor application is presented. The proposed autonomous sensor system consists of a wireless power supplier (active antenna) and far-field energy harvesting technology-enabled autonomous battery-less sensors. The wireless power supplier converts solar power to electromagnetic power in order to transfer power to multiple autonomous sensors wirelessly. The autonomous sensors have far-field energy harvesters which convert transmitted RF power to voltage regulated DC power to power-on the sensor system. The hybrid printing technology was chosen to build the autonomous sensors and the wireless power suppliers. Two popular hybrid electronics technologies (direct nano-particle printing and indirect copper thin film printing techniques) are discussed in detail.
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Pekgor, Metin, Reza Arablouei, Mostafa Nikzad, and Syed Masood. "Displacement Estimation via 3D-Printed RFID Sensors for Structural Health Monitoring: Leveraging Machine Learning and Photoluminescence to Overcome Data Gaps." Sensors 24, no. 4 (February 15, 2024): 1233. http://dx.doi.org/10.3390/s24041233.
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Monitoring object displacement is critical for structural health monitoring (SHM). Radio frequency identification (RFID) sensors can be used for this purpose. Using more sensors enhances displacement estimation accuracy, especially when it is realized through the use of machine learning (ML) algorithms for predicting the direction of arrival of the associated signals. Our research shows that ML algorithms, in conjunction with adequate RFID passive sensor data, can precisely evaluate azimuth angles. However, increasing the number of sensors can lead to gaps in the data, which typical numerical methods such as interpolation and imputation may not fully resolve. To overcome this challenge, we propose enhancing the sensitivity of 3D-printed passive RFID sensor arrays using a novel photoluminescence-based RF signal enhancement technique. This can boost received RF signal levels by 2 dB to 8 dB, depending on the propagation mode (near-field or far-field). Hence, it effectively mitigates the issue of missing data without necessitating changes in transmit power levels or the number of sensors. This approach, which enables remote shaping of radiation patterns via light, can herald new prospects in the development of smart antennas for various applications apart from SHM, such as biomedicine and aerospace.
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Ariana, Aly Nur, and Zainal Abidin. "RANCANG BANGUN SISTEM IRIGASI PEMBIBITAN PENGKONDISIAN LAHAN PADI BERBASIS ATMEGA328 DAN MONITORING JARAK JAUH DENGAN RADIO FREKUENSI 433 MHZ." Jurnal Teknika 10, no. 1 (May 2, 2018): 999. http://dx.doi.org/10.30736/teknika.v10i1.207.
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System of open-close channel or water pipe irrigation is a widely used method. Required a system that can automate the system of irrigation field so that water contribution can be done evenly and enough. As the development of an automatic field irrigation system by utilizing electrode level sensor performance, to open and close the water distribution pipe automatically with the help of ATmega328 microcontroller as the central controller system that will be embedded code-code program that is needed and can be monitored from remotely wirelessly. In this research will discuss how to make an irrigation system based on atmega328 and remote monitoring with 433 mhz radio frequency and how efficient the tool is to influence the water level sensor and soil moisture sensor so that it can work well on the system. In principle the work system of this tool starts from the first Start and then ATMega328 pin initialization reads the soil moisture sensor and water level sensor with LCD display, and sends data to RF transmitter module to send to RF receiver. Making atmega328-based irrigation system and remote monitoring with 433mhz radio frequency is made by designing tools ranging from microcontrollers, ultrasonic sensors, soil moisture sensors that can create a tool to facilitate farmer in the fields.
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Rushton, Lucas Martin, Laura Mae Ellis, Jake David Zipfel, Patrick Bevington, and Witold Chalupczak. "Performance of a Radio-Frequency Two-Photon Atomic Magnetometer in Different Magnetic Induction Measurement Geometries." Sensors 24, no. 20 (October 16, 2024): 6657. http://dx.doi.org/10.3390/s24206657.
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Measurements monitoring the inductive coupling between oscillating radio-frequency magnetic fields and objects of interest create versatile platforms for non-destructive testing. The benefits of ultra-low-frequency measurements, i.e., below 3 kHz, are sometimes outweighed by the fundamental and technical difficulties related to operating pick-up coils or other field sensors in this frequency range. Inductive measurements with the detection based on a two-photon interaction in rf atomic magnetometers address some of these issues as the sensor gains an uplift in its operational frequency. The developments reported here integrate the fundamental and applied aspects of the two-photon process in magnetic induction measurements. In this paper, all the spectral components of the two-photon process are identified, which result from the non-linear interactions between the rf fields and atoms. For the first time, a method for the retrieval of the two-photon phase information, which is critical for inductive measurements, is also demonstrated. Furthermore, a self-compensation configuration is introduced, whereby high-contrast measurements of defects can be obtained due to its insensitivity to the primary field, including using simplified instrumentation for this configuration by producing two rf fields with a single rf coil.
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Chou, Jung-Chuan, and Chien-Cheng Chen. "WEIGHTED DATA FUSION FOR FLEXIBLE pH SENSORS ARRAY." Biomedical Engineering: Applications, Basis and Communications 21, no. 06 (December 2009): 365–69. http://dx.doi.org/10.4015/s1016237209001465.
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Data fusion is a frequent statistic method that can be applied to sensor development field, such as multisensors and sensors array. In this study, the analytic data fusion methods consist of the arithmetic mean and weighted data fusion used to estimate the measured pH data of flexible pH sensors array. The main part of the flexible 2 × 4 pH sensors array was fabricated by screen printing, and the ruthenium dioxide ( RuO2 ) thin film on each sensor of the sensor array was deposited by radio frequency (RF)-sputtering method. In accordance with experiment results, the pH values estimated by weighted data fusion method are accurate than by arithmetic mean method. Furthermore, that the flexible sensors array is actually used to detect the pH value of different commercial drinks is also investigated.
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Zhang, Mingguang, Mengyun Li, Wei Xu, Fan Zhang, Daojin Yao, Xiaoming Wang, and Wentao Dong. "Soft Wireless Passive Chipless Sensors for Biological Applications: A Review." Biosensors 15, no. 1 (December 26, 2024): 6. https://doi.org/10.3390/bios15010006.
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Soft wireless passive sensors have been applied in biological, engineering, and other fields due to their advantages in powerless supply and remote data transmission. External information is obtained by soft wireless passive sensors via the external coils based on electromagnetic induction. The purpose of this review paper is to outline the biological applications of soft wireless passive chipless sensors and provide a classification of wireless passive sensors and an overall explanation of the main work. Three kinds of soft wireless sensors, soft wireless passive LC-resonant sensors, soft wireless radio frequency (RF) sensors, and soft wireless surface acoustic wave (SAW) sensors, are introduced with their working principles, equitant circuits, and biological applications. Soft wireless passive sensors with integrated LC-resonant units are applied to physical quantity measurements for denoting the mapping relationship between the frequency resonance and the monitored object. Utilizing the electromagnetic field principle, RF sensors enable wireless measurements and data exchange of physical parameters. SAW sensors with piezoelectric substrates are applied to physical parameter monitoring using guided waves in monitoring objects. Soft wireless passive sensors aim to monitor biological health without an external power supply or wired data communication, which would bring increased convenience to the lives of the people who use them.
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Tien, Chuen-Lin, Tzu-Chi Mao, and Chi-Yuan Li. "Lossy Mode Resonance Sensors Fabricated by RF Magnetron Sputtering GZO Thin Film and D-Shaped Fibers." Coatings 10, no. 1 (January 1, 2020): 29. http://dx.doi.org/10.3390/coatings10010029.
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We demonstrate a new refractive index (RI) and salinity sensor based on a lossy mode resonance (LMR) effect which combines fiber-optic side-polishing and radio-frequency (RF) sputtering techniques. The side-polished fiber can enhance optical fibers to generate an evanescent field in sensing applications. Gallium-doped zinc oxide (GZO) thin films produce a high attenuation lossy mode resonance effect that permits a highly sensitive refractive index and salinity fiber sensor. GZO thin film was prepared by an RF magnetron sputtering method. The thickness of the D-shaped fiber sensing device was 74.7 μm, and a GZO film thickness of 67 nm was deposited on the polished surface of the D-shaped fiber to fabricate LMR type liquid salinity sensors. The sensitivity of 3637.8 nm/RIU was achieved in the RI range of 1.333 to 1.392. To investigate the sensitivities of LMR salinity sensors, the NaCl solution salinities of 0%, 50%, 100%, 150%, 200%, and 250% were measured in this work. The experimental result shows that the sensitivity of the salinity sensor is 0.964 nm per salinity unit (SU).
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Jung. "An Integrated Photonic Electric-Field Sensor Utilizing a 1 × 2 YBB Mach-Zehnder Interferometric Modulator with a Titanium-Diffused Lithium Niobate Waveguide and a Dipole Patch Antenna." Crystals 9, no. 9 (September 2, 2019): 459. http://dx.doi.org/10.3390/cryst9090459.
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We studied photonic electric-field sensors using a 1 × 2 YBB-MZI modulator composed of two complementary outputs and a 3 dB directional coupler based on the electro-optic effect and titanium diffused lithium–niobate optical waveguides. The measured DC switching voltage and extinction ratio at the wavelength 1.3 μm were ~16.6 V and ~14.7 dB, respectively. The minimum detectable fields were ~1.12 V/m and ~3.3 V/m, corresponding to the ~22 dB and ~18 dB dynamic ranges of ~10 MHz and 50 MHz, respectively, for an rf power of 20 dBm. The sensor shows an almost linear response to the applied electric-field strength within the range of 0.29 V/m to 29.8 V/m.
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Natarajan, Vishwanath, Rajarajan Senguttuvan, Shreyas Sen, and Abhjit Chatterjee. "Built-in Test Enabled Diagnosis and Tuning of RF Transmitter Systems." VLSI Design 2008 (June 23, 2008): 1–10. http://dx.doi.org/10.1155/2008/418165.
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Built-In RF test is a challenging problem due to the need to measure the values of complex test specifications on-chip with the precision of external RF test equipment. BIT techniques are necessary for guiding system adaptation during field operation. Prior research has demonstrated that embedded RF sensors can generate significant information about RF circuit performance. In this paper, we propose a test methodology that enables efficient BIT and BIT-enabled tuning of RF systems. A test generation approach is developed that co-optimizes the applied test stimulus, the type of embedded sensors, and the system response capture mechanisms for maximal accuracy of the BIT procedure. This BIT technique is also used to perform diagnostic testing of the transmitter. The information gathered from diagnosis is used to tune the transmitter for improved performance. Simulation results demonstrate that BIT-assisted diagnosis and tuning can be performed with good accuracy using the proposed methodology.
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Rouzaud, A., and G. Pares. "Interposers: A Central Generic Technology for IoT." International Symposium on Microelectronics 2015, no. 1 (October 1, 2015): 000014–19. http://dx.doi.org/10.4071/isom-2015-tp13.
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Also sometimes referred to as 2.5D integration, interposers are now a true and important component of the “3D world” based on die stacking technologies. Considered as an intermediate step in the field of high density die stacking (memory on logic, logic on logic, etc) this technology is on the other hand fully generic to manufacture heterogeneous systems which will constitute the basic of IoT (sensor with simple logic, RF capabilities and even energy harvesting). This paper presents first a short overview of the core technologies needed in this field. The versatility of these technical blocks, allowing to efficiently manufacture a large variety of interposer based devices, is then demonstrated through different subsystems related to IoT: sensors, passives, energy harvesters, RF communication. Some recent achievements in smart systems based on interposers mixing together passive components, microsystems and RF functions are then presented, and more particularly a system dedicated to short range high bandwidth communication for wireless HDMI and a system for implanted medical applications. In both cases, the gain in surface area is particularly important, making such solutions highly attractive.
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Bonavolontà, C., M. Valentino, V. Palmieri, and V. Rampazzo. "Magnetic field sensors applied to electropolishing of superconducting RF TESLA-type cavities." Physica C: Superconductivity 441, no. 1-2 (July 2006): 243–48. http://dx.doi.org/10.1016/j.physc.2006.03.058.
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Schmitz, Seán, Sherry Towers, Guillermo Villena, Alexandre Caseiro, Robert Wegener, Dieter Klemp, Ines Langer, Fred Meier, and Erika von Schneidemesser. "Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors." Atmospheric Measurement Techniques 14, no. 11 (November 17, 2021): 7221–41. http://dx.doi.org/10.5194/amt-14-7221-2021.
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Abstract. The last 2 decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, the air quality monitoring community, and for commercial and private use, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensor systems using reference instrumentation with user-friendly guidelines, open-access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO2) or ozone (O3). A full example of the application of this methodology to a case study in an urban environment using both multiple linear regression (MLR) and the random forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOSs). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensor systems, we suggest a number of best practices for future studies using low-cost sensor systems to ensure greater comparability of research.
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Xu, Haoran, Jianghua Ding, and Jian Dang. "Design and Characteristics of CMOS Inverter based on Multisim and Cadence." Journal of Physics: Conference Series 2108, no. 1 (November 1, 2021): 012034. http://dx.doi.org/10.1088/1742-6596/2108/1/012034.
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Abstract Known as complementary symmetrical metal oxide semiconductor (cos-mos), complementary metal oxide semiconductor is a metal oxide semiconductor field effect transistor (MOSFET) manufacturing process, which uses complementary and symmetrical pairs of p-type and n-type MOSFETs to realize logic functions. CMOS technology is used to build integrated circuit (IC) chips, including microprocessors, microcontrollers, memory chips (including CMOS BIOS) and other digital logic circuits. CMOS technology is also used in analog circuits, such as image sensors (CMOS sensors), data converters, RF circuits (RF CMOS), and highly integrated transceivers for various types of communications. Based on multisim 14.0 and cadence, the characteristics and performance of CMOS inverter are studied by simulation.
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Yang, Po-Hui, Ying-Sheng Chang, and Che-Tsung Chan. "ZnO and AZO Film Potentiometric pH Sensors Based on Flexible Printed Circuit Board." Chemosensors 10, no. 8 (July 26, 2022): 293. http://dx.doi.org/10.3390/chemosensors10080293.
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In this study, we deposited zinc oxide (ZnO) and aluminum-doped zinc oxide (AZO) on the electroless nickel immersion gold (ENIG) of a flexible printed circuit board (FPCB) as a potentiometric pH sensor. The sensing films of the pH sensor were fabricated by a radio frequency (RF) sputtering system and analyzed by field emission scanning electron microscope (FE-SEM) and X-ray photoelectron spectroscopy (XPS). In the pH 2 to 10 buffer solutions, it was observed that the characteristics of the pH sensor through the voltage–time (V-T) measurement system include average sensitivity and linearity, drift effect, and repeatability. According to the experimental results, the pH sensors in this study could exhibit good characteristics.
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Ashraf, Imran, Soojung Hur, and Yongwan Park. "MagIO: Magnetic Field Strength Based Indoor- Outdoor Detection with a Commercial Smartphone." Micromachines 9, no. 10 (October 20, 2018): 534. http://dx.doi.org/10.3390/mi9100534.
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A wide range of localization techniques has been proposed recently that leverage smartphone sensors. Context awareness serves as the backbone of these localization techniques, which helps them to shift the localization technologies to improve efficiency and energy utilization. Indoor-outdoor (IO) context sensing plays a vital role for such systems, which serve both indoor and outdoor localization. IO systems work with collaborative technologies including the Global Positioning System (GPS), cellular tower signals, Wi-Fi, Bluetooth and a variety of smartphone sensors. GPS- and Wi-Fi-based systems are power hungry, and their accuracy is severed by limiting factors like multipath, shadowing, etc. On the other hand, various built-in smartphone sensors can be deployed for environmental sensing. Although these sensors can play a crucial role, yet they are very less studied. This research aims at investigating the use of ambient magnetic field data alone from a smartphone for IO detection. The research first investigates the feasibility of utilizing magnetic field data alone for IO detection and then extracts different features suitable for IO detection to be used in machine learning-based classifiers to discriminate between indoor and outdoor environments. The experiments are performed at three different places including a subway station, a shopping mall and Yeungnam University (YU), Korea. The training data are collected from one spot of the campus, and testing is performed with data from various locations of the above-mentioned places. The experiment involves Samsung Galaxy S8, LG G6 and Samsung Galaxy Round smartphones. The results show that the magnetic data from smartphone magnetic sensor embody enough information and can discriminate the indoor environment from the outdoor environment. Naive Bayes (NB) outperforms with a classification accuracy of 83.26%, as against Support vector machines (SVM), random induction (RI), gradient boosting machines (GBM), random forest (RF), k-nearest neighbor (kNN) and decision trees (DT), whose accuracies are 67.21%, 73.38%, 73.40%, 78.59%, 69.53% and 68.60%, respectively. kNN, SVM and DT do not perform well when noisy data are used for classification. Additionally, other dynamic scenarios affect the attitude of magnetic data and degrade the performance of SVM, RI and GBM. NB and RF prove to be more noise tolerant and environment adaptable and perform very well in dynamic scenarios. Keeping in view the performance of these classifiers, an ensemble-based stacking scheme is presented, which utilizes DT and RI as the base learners and naive Bayes as the ensemble classifier. This approach is able to achieve an accuracy of 85.30% using the magnetic data of the smartphone magnetic sensor. Moreover, with an increase in training data, the accuracy of the stacking scheme can be elevated by 0.83%. The performance of the proposed approach is compared with GPS-, Wi-Fi- and light sensor-based IO detection.
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Kuriyama, Y., Y. Iwashita, Y. Fuwa, H. Tongu, H. Hayano, and R. L. Geng. "Development of a strip-shaped X-ray mapping system for 9-cell superconducting cavities." Journal of Instrumentation 19, no. 09 (September 1, 2024): P09037. http://dx.doi.org/10.1088/1748-0221/19/09/p09037.
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Abstract Electrons emitted via field emission during superconducting (SC) radio-frequency (RF) cavity tests at vertical test stands often collide with the iris region inside the cavity, generating X-rays at these locations. In 1.3 GHz 9-cell SC RF cavities designed for the International Linear Collider (ILC), stiffener rings located outside the iris region between cells can interfere with X-ray detection, complicating the precise identification of field emission sites. Hence, we developed a high-density strip X-ray mapping systems (sX-map) that can be inserted into the iris region of ILC-type 9-cell SC RF cavities. This sX-map facilitates efficient and accurate detection of X-rays generated near the irises, unaffected by the presence of stiffener rings. The sX-map consisted of 32 sensors per strip, with sensors spaced approximately 10 mm apart. It was deployed in every iris of the 9-cell cavity, using a total of 320 sensors. A multiplexer was employed to facilitate the readout of a large number of detectors using a minimal number of signal lines, connecting the strips inside within the vertical test cryostat. In a vertical test conducted at Jefferson Lab (JLab), we demonstrated the capability of sX-map to detect X-rays despite the presence of a stiffener ring. This paper presents the detailed design of the sX-map and the results from the vertical test at JLab.
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Costanzo, Alessandra, Elisa Augello, Giulia Battistini, Francesca Benassi, Diego Masotti, and Giacomo Paolini. "Microwave Devices for Wearable Sensors and IoT." Sensors 23, no. 9 (April 28, 2023): 4356. http://dx.doi.org/10.3390/s23094356.
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The Internet of Things (IoT) paradigm is currently highly demanded in multiple scenarios and in particular plays an important role in solving medical-related challenges. RF and microwave technologies, coupled with wireless energy transfer, are interesting candidates because of their inherent contactless spectrometric capabilities and for the wireless transmission of sensing data. This article reviews some recent achievements in the field of wearable sensors, highlighting the benefits that these solutions introduce in operative contexts, such as indoor localization and microwave sensing. Wireless power transfer is an essential requirement to be fulfilled to allow these sensors to be not only wearable but also compact and lightweight while avoiding bulky batteries. Flexible materials and 3D printing polymers, as well as daily garments, are widely exploited within the presented solutions, allowing comfort and wearability without renouncing the robustness and reliability of the built-in wearable sensor.
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Katbay, Zahra, Dimitrios Sounas, and Mohammed Ismail. "Scatterers in the Rx Near Field for RF Energy Harvesting Efficiency Enhancement." Energies 15, no. 6 (March 14, 2022): 2109. http://dx.doi.org/10.3390/en15062109.
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In this paper, we investigate the enhancement of RF–RF energy harvesting efficiency (erf–rf) in multipath environments in the context of wireless power transfer (WPT). For this, we used a retrodirective transmitting (Tx) antenna array resonating at 2.4 GHz and a receiving (Rx) antenna surrounded by scatterers placed in the Rx near field. The Rx resides in the Tx far field. We showed that in a medium made of a random distribution of scatterers, a time-reversed wave field interacts with the random medium to regenerate not only the propagating waves but also the evanescent waves required to refocus the energy at the receiver location. The system was enclosed inside a 3 m3 cubical reverberating room to create a strong multipath environment. The study was done for homogeneous (free space) and heterogeneous (multipath environment) media. Different WPT scenarios were considered for different applications: 4 × 1, 4 × 2 and 4 × 4 multi input-multi output (MIMO) systems. The simulation results show that using near-field scattering generates signal focusing at the source location, which increases the RF–RF energy harvesting efficiency, especially in a multipath environment. The average received power in the frequency band 2.4–2.5 GHz was greatly enhanced in the presence of the scatterers. The investigated WPT approach showed encouraging results for charging/powering-up sensors, IoT and smart devices in indoor environments.
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Lee, In-Young, and Donggu Im. "Low Power RF Interface of the Near-field Communications Tag IC for Sensors." JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE 23, no. 2 (April 30, 2023): 112–17. http://dx.doi.org/10.5573/jsts.2023.23.2.112.
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Hu, Bo Zhou, Meng Chun Pan, Peng Jiang, Wu Gang Tian, Jia Fei Hu, and Jing Hua Hu. "The Research on Magnetic Target Detection Technology Based on Wireless Sensor Network." Applied Mechanics and Materials 644-650 (September 2014): 1213–17. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1213.
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In the conditions of magnetic dipole model, this paper proposed forward a centroid localization algorithm on magnetic anomaly target based on wireless sensor network node which distribution are random and the improved the weighted centroid localization algorithm based on magnetic induction intensity. According to the fluctuation of magnetic field intensity which detected by magnetic sensors, that can detect the existence of magnetic anomaly target and its location. Established an experimental system of the wireless sensor network for magnetic anomaly detection whose core designs including the HMC1043 three-axis magnetic resistance sensor and the CC2530 Zigbee RF chip. The experimental results show that the algorithm can accurately positioning the magnetic anomaly target within the network.
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Zappa, Luca, Matthias Forkel, Angelika Xaver, and Wouter Dorigo. "Deriving Field Scale Soil Moisture from Satellite Observations and Ground Measurements in a Hilly Agricultural Region." Remote Sensing 11, no. 22 (November 6, 2019): 2596. http://dx.doi.org/10.3390/rs11222596.
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Agricultural and hydrological applications could greatly benefit from soil moisture (SM) information at sub-field resolution and (sub-) daily revisit time. However, current operational satellite missions provide soil moisture information at either lower spatial or temporal resolution. Here, we downscale coarse resolution (25–36 km) satellite SM products with quasi-daily resolution to the field scale (30 m) using the random forest (RF) machine learning algorithm. RF models are trained with remotely sensed SM and ancillary variables on soil texture, topography, and vegetation cover against SM measured in the field. The approach is developed and tested in an agricultural catchment equipped with a high-density network of low-cost SM sensors. Our results show a strong consistency between the downscaled and observed SM spatio-temporal patterns. We found that topography has higher predictive power for downscaling than soil texture, due to the hilly landscape of the study area. Furthermore, including a proxy of vegetation cover results in considerable improvements of the performance. Increasing the training set size leads to significant gain in the model skill and expanding the training set is likely to further enhance the accuracy. When only limited in-situ measurements are available as training data, increasing the number of sensor locations should be favored over expanding the duration of the measurements for improved downscaling performance. In this regard, we show the potential of low-cost sensors as a practical and cost-effective solution for gathering the necessary observations. Overall, our findings highlight the suitability of using ground measurements in conjunction with machine learning to derive high spatially resolved SM maps from coarse-scale satellite products.
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Vazquez, Fabian, Alejandro Villareal, Alfredo Rodriguez, Rodrigo Martin, Sergio Solis-Najera, and Oscar Rene Marrufo Melendez. "Electric Field Sensing with a Modified SRR for Wireless Telecommunications Dosimetry." Electronics 10, no. 3 (January 26, 2021): 295. http://dx.doi.org/10.3390/electronics10030295.
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Split ring resonators (SRRs) have been used extensively in metamaterials, showing a strong localization and enhancement of fields, which significantly improves the sensitivity and resolution of the electromagnetic field sensors. We propose the development of an electric field sensor for 2.4 GHz industrial, scientific, and medical (ISM) band applications, by modifying the renowned SRR to contain three concentric pairs of rings. The reduced size makes the sensor affordable for experiments by inserting it in phantoms in order to measure the specific absorption rate (SAR). Power was transmitted from a patch antenna to SRR, with a varying set of distances 1λ, 2λ, 3λ, or 5λ. Experimental measurements of power were conducted with and without a cylindrical distilled-water phantom with agar (4.54%) and NaCl (0.95%). We then computed the electric and magnetic fields and the SAR using these experimental readings of power for different distances. Our sensor was able to measure power values from 20 nW to 0.3 µW with no phantom, and 1 nW to 10 nW with a phantom, in accordance with the values reported for radiofrequency (RF) dosimetry. The sensitivity as a function of the distance determined for the specific case of a phantom was 0.3 µW/cm.
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Hu, Hao, Yun Ren, Hongkui Zhou, Weidong Lou, Pengfei Hao, Baogang Lin, Guangzhi Zhang, Qing Gu, and Shuijin Hua. "Oilseed Rape Yield Prediction from UAVs Using Vegetation Index and Machine Learning: A Case Study in East China." Agriculture 14, no. 8 (August 8, 2024): 1317. http://dx.doi.org/10.3390/agriculture14081317.
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Yield prediction is an important agriculture management for crop policy making. In recent years, unmanned aerial vehicles (UAVs) and spectral sensor technology have been widely used in crop production. This study aims to evaluate the ability of UAVs equipped with spectral sensors to predict oilseed rape yield. In an experiment, RGB and hyperspectral images were captured using a UAV at the seedling (S1), budding (S2), flowering (S3), and pod (S4) stages in oilseed rape plants. Canopy reflectance and spectral indices of oilseed rape were extracted and calculated from the hyperspectral images. After correlation analysis and principal component analysis (PCA), input spectral indices were screened to build yield prediction models using random forest regression (RF), multiple linear regression (MLR), and support vector machine regression (SVM). The results showed that UAVs equipped with spectral sensors have great potential in predicting crop yield at a large scale. Machine learning approaches such as RF can improve the accuracy of yield models in comparison with traditional methods (e.g., MLR). The RF-based training model had the highest determination coefficient (R2) (0.925) and lowest relative root mean square error (RRMSE) (5.91%). In testing, the MLR-based model had the highest R2 (0.732) and lowest RRMSE (11.26%). Moreover, we found that S2 was the best stage for predicting oilseed rape yield compared with the other growth stages. This study demonstrates a relatively accurate prediction for crop yield and provides valuable insight for field crop management.
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Osadchuk, Alexander, Vladimir Osadchuk, and Iaroslav Osadchuk. "RESEARCH ON A MAGNETIC FIELD SENSOR WITH A FREQUENCY OUTPUT SIGNAL BASED ON A TUNNEL-RESONANCE DIODE." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 10, no. 4 (December 20, 2020): 51–56. http://dx.doi.org/10.35784/iapgos.2357.
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Based on the consideration of physical processes in a tunnel-resonant diode under the action of a magnetic field, the construction of an autogenerating magnetic field sensor with a frequency output signal is proposed. The use of devices with negative differential resistance makes it possible to significantly simplify the design of magnetic field sensors in the entire RF frequency range. Depending on the operating modes of the sensor, an output signal can be obtained in the form of harmonic oscillations, as well as in the form of pulse oscillations of a special form.
The study of the characteristics of the magnetic field sensor is based on the complete equivalent circuit of the tunnel-resonant diode. The equivalent circuit takes into account both the capacitive and inductive properties of the tunneling resonant diode. The inductive component exists under any operating conditions, as a result of the fact that the current flowing through the device is always lagging behind the voltage that caused it, which corresponds to the inductive response of a tunnel-resonant diode.
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Ni, Jingfeng, Shengya Yang, and Yujiao Liu. "Data Cleaning Model of Mine Wind Speed Sensor Based on LOF-GMM and SGAIN." Applied Sciences 15, no. 4 (February 10, 2025): 1801. https://doi.org/10.3390/app15041801.
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To improve the quality of mine ventilation wind speed sensor data, a data cleaning model for mine ventilation wind speed sensors based on LOF-GMM and SGAIN is proposed. First, the LOF-GMM algorithm was used to identify wind speed sensor data, cluster the data, and determine the threshold of the local outlier factor, enabling automatic identification of abnormal data and recognition of ventilation fault state information. Abnormal data were then removed to create blank missing points. Finally, wind speed data from the normal operating state of the ventilation system were used to train the SGAIN model to obtain its optimal parameters. The trained SGAIN model was then used to fill in the blank points. The results show that the proposed method can effectively detect abnormal wind speed sensor data and identify ventilation system fault information. In terms of imputation performance, this model outperformed other data imputation models such as GAIN, RF, and DAE. Although the imputation speed was slightly lower than that of the RF and DAE models, considering the high accuracy requirements of mine wind speed data, SGAIN is more suitable for use in the field of mine ventilation.
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Lazaro, Antonio, Ramon Villarino, Marc Lazaro, Nicolau Canellas, Beatriz Prieto-Simon, and David Girbau. "Recent Advances in Batteryless NFC Sensors for Chemical Sensing and Biosensing." Biosensors 13, no. 8 (July 31, 2023): 775. http://dx.doi.org/10.3390/bios13080775.
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This article reviews the recent advances in the field of batteryless near-field communication (NFC) sensors for chemical sensing and biosensing. The commercial availability of low-cost commercial NFC integrated circuits (ICs) and their massive integration in smartphones, used as readers and cloud interfaces, have aroused great interest in new batteryless NFC sensors. The fact that coil antennas are not importantly affected by the body compared with other wireless sensors based on far-field communications makes this technology suitable for future wearable point-of-care testing (PoCT) devices. This review first compares energy harvesting based on NFC to other energy-harvesting technologies. Next, some practical recommendations for designing and tuning NFC-based tags are described. Power transfer is key because in most cases, the energy harvested has to be stable for several seconds and not contaminated by undesired signals. For this reason, the effect of the dimensions of the coils and the conductivity on the wireless power transfer is thoroughly discussed. In the last part of the review, the state of the art in NFC-based chemical and biosensors is presented. NFC-based tags (or sensor tags) are mainly based on commercial or custom NFC ICs, which are used to harvest the energy from the RF field generated by the smartphone to power the electronics. Low-consumption colorimeters and potentiostats can be integrated into these NFC tags, opening the door to the integration of chemical sensors and biosensors, which can be harvested and read from a smartphone. The smartphone is also used to upload the acquired information to the cloud to facilitate the internet of medical things (IoMT) paradigm. Finally, several chipless sensors recently proposed in the literature as a low-cost alternative for chemical applications are discussed.
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K S, Abbirame, Haripriya A S, Aravind V, and Jamuna G. "An electronic gadget to control wheelchair motion using tongue gesture." International Journal of Pharmacy and Biomedical Engineering 4, no. 1 (April 25, 2017): 1–3. http://dx.doi.org/10.14445/23942576/ijpbe-v4i1p101.
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Tongue drive system (TDS) is a tongue operated unobtrusive assistive technology,which can potentially provide people with severe disabilities with effective access and environment control.It translate user’s intentions into control commands by detecting and classifying their voluntary tongue motion utilizing a small permanent magnet,secured on the tongue and an array of magnetic sensors mounted on the mouth the magnetic sensor are nothing but hall effect sensors. a hall effect sensor is a transducerthat varies its output voltage in response to changes in magnetic field.in its simplest form the sensor operates as an analogue transducer, directly returning a voltage. With a known magnetic field, its distance from the hall plate can be determined. Wheel chair is made up of high torque geared DC motors, the motors direction can be changed through the set of instructions given from the hall effect sensor and the action of these instructions is already loaded into the microcontroller using embedded C programming. Complete system proposed in this paper has been designed around PIC microcontroller and a RF module. The design has been tested and result achieved confirms the design approach illustrated.
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Marsic, Vlad, Soroush Faramehr, Joe Fleming, Peter Ball, Shumao Ou, and Petar Igic. "Buried RF Sensors for Smart Road Infrastructure: Empirical Communication Range Testing, Propagation by Line of Sight, Diffraction and Reflection Model and Technology Comparison for 868 MHz–2.4 GHz." Sensors 23, no. 3 (February 2, 2023): 1669. http://dx.doi.org/10.3390/s23031669.
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Updating the road infrastructure requires the potential mass adoption of the road studs currently used in car detection, speed monitoring, and path marking. Road studs commonly include RF transceivers connecting the buried sensors to an offsite base station for centralized data management. Since traffic monitoring experiments through buried sensors are resource expensive and difficult, the literature detailing it is insufficient and inaccessible due to various strategic reasons. Moreover, as the main RF frequencies adopted for stud communication are either 868/915 MHz or 2.4 GHz, the radio coverage differs, and it is not readily predictable due to the low-power communication in the near proximity of the ground. This work delivers a reference study on low-power RF communication ranging for the two above frequencies up to 60 m. The experimental setup employs successive measurements and repositioning of a base station at three different heights of 0.5, 1 and 1.5 m, and is accompanied by an extensive theoretical analysis of propagation, including line of sight, diffraction, and wall reflection. Enhancing the tutorial value of this work, a correlation analysis using Pearson’s coefficient and root mean square error is performed between the field test and simulation results.
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Akhtar, Pervez, T. J. Ali, and R. Mahmud. "An Investigation into the Effects of Substrate Temperature on Magnetic Properties of RF Sputtered NiFe Films." Key Engineering Materials 442 (June 2010): 109–15. http://dx.doi.org/10.4028/www.scientific.net/kem.442.109.
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The paper describes results of investigation on sputtered NiFe Films to determine the sputter deposition condition that could produce magnetic field sensors with the desired magnetic properties. The magnetic materials used in such devices should have a low coercive force, a low anisotropy field and a low magnetization dispersion, α50. .The results of systematic investigation of radio-frequency (RF) sputtered, RF biased, 82-18 % NiFe films showed (i) Improved Kobelev methods were applied to magneto-optic measurement techniques, suppress the magnetization ripples when subjected under the application of dc field. (ii) Anisotropy field results indicated an inverse trend with increasing substrate temperature (iii) the experimental measurements on magnetization dispersion relatively constant up to 200 °C with α50 approximately 1.2°, it then increases more sharply to about 3.5° at 400 °C substrate temperature. The work also provide understanding of the effects on the magnetic properties of sputtered magnetic films that is very limited as current literature is almost entirely limited to evaporated magnetic films.
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SUN, NIAN X., and GOPALAN SRINIVASAN. "VOLTAGE CONTROL OF MAGNETISM IN MULTIFERROIC HETEROSTRUCTURES AND DEVICES." SPIN 02, no. 03 (September 2012): 1240004. http://dx.doi.org/10.1142/s2010324712400048.
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Multiferroic materials and devices have attracted intensified recent interests due to the demonstrated strong magnetoelectric (ME) coupling in new multiferroic materials and devices with unique functionalities and superior performance characteristics. Strong ME coupling has been demonstrated in a variety of multiferroic heterostructures, including bulk magnetic on ferro/piezoelectric multiferroic heterostructures, magnetic film on ferro/piezoelectric slab multiferroic heterostructures, thin film multiferroic heterostructures, etc. Different multiferroic devices have been demonstrated, which include magnetic sensors, energy harvesters, and voltage tunable multiferroic RF/microwave devices which are compact, lightweight, and power efficient. In this progress report, we cover the most recent progress on multiferroic heterostructures and devices with a focus on voltage tunable multiferroic heterostructures and devices with strong converse ME coupling. Recent progress on magnetic-field tunable RF/microwave devices are also covered, including novel non-reciprocal tunable bandpass filters with ultra wideband isolation, compact, low loss and high power handling phase shifters, etc. These novel tunable multiferroic heterostructures and devices and tunable magnetic devices provide great opportunities for next generation reconfigurable RF/microwave communication systems and radars, Spintronics, magnetic field sensing, etc.
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Mo, Lingfei, Yaojie Zhu, and Lujie Zeng. "A Multi-Label Based Physical Activity Recognition via Cascade Classifier." Sensors 23, no. 5 (February 26, 2023): 2593. http://dx.doi.org/10.3390/s23052593.
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Physical activity recognition is a field that infers human activities used in machine learning techniques through wearable devices and embedded inertial sensors of smartphones. It has gained much research significance and promising prospects in the fields of medical rehabilitation and fitness management. Generally, datasets with different wearable sensors and activity labels are used to train machine learning models, and most research has achieved satisfactory performance for these datasets. However, most of the methods are incapable of recognizing the complex physical activity of free living. To address the issue, we propose a cascade classifier structure for sensor-based physical activity recognition from a multi-dimensional perspective, with two types of labels that work together to represent an exact type of activity. This approach employed the cascade classifier structure based on a multi-label system (Cascade Classifier on Multi-label, CCM). The labels reflecting the activity intensity would be classified first. Then, the data flow is divided into the corresponding activity type classifier according to the output of the pre-layer prediction. The dataset of 110 participants has been collected for the experiment on PA recognition. Compared with the typical machine learning algorithms of Random Forest (RF), Sequential Minimal Optimization (SMO) and K Nearest Neighbors (KNN), the proposed method greatly improves the overall recognition accuracy of ten physical activities. The results show that the RF-CCM classifier has achieved 93.94% higher accuracy than the 87.93% obtained from the non-CCM system, which could obtain better generalization performance. The comparison results reveal that the novel CCM system proposed is more effective and stable in physical activity recognition than the conventional classification methods.
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Loubet, Gaël, Alexandru Takacs, Ethan Gardner, Andrea De Luca, Florin Udrea, and Daniela Dragomirescu. "LoRaWAN Battery-Free Wireless Sensors Network Designed for Structural Health Monitoring in the Construction Domain." Sensors 19, no. 7 (March 28, 2019): 1510. http://dx.doi.org/10.3390/s19071510.
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This paper addresses the practical implementation of a wireless sensors network designed to actualize cyber-physical systems that are dedicated to structural health monitoring applications in the construction domain. This network consists of a mesh grid composed of LoRaWAN battery-free wireless sensing nodes that collect physical data and communicating nodes that interface the sensing nodes with the digital world through the Internet. Two prototypes of sensing nodes were manufactured and are powered wirelessly by using a far-field wireless power transmission technique and only one dedicated RF energy source operating in the ISM 868 MHz frequency band. These sensing nodes can simultaneously perform temperature and relative humidity measurements and can transmit the measured data wirelessly over long-range distances by using the LoRa technology and the LoRaWAN protocol. Experimental results for a simplified network confirm that the periodicity of the measurements and data transmission of the sensing nodes can be controlled by the dedicated RF source (embedded in or just controlled by the associated communicating node), by tuning the radiated power density of the RF waves, and without any modification of the software or the hardware implemented in the sensing nodes.
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Abubakar, Dauda, Naser Mahmoud Ahmed, and Shahrom Mahmud. "The Study on the Effect of Wet and Dry Oxidation of Nickel Thin Film on Sensitivity of EGFET Based pH Sensor." Solid State Phenomena 290 (April 2019): 199–207. http://dx.doi.org/10.4028/www.scientific.net/ssp.290.199.
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The study is based on the use of NiO as an extended gate of field effect transistor (EGFET) using ITO/glass as a substrate for the sensitivity of pH sensor membrane. The NiO thin film was synthesis by wet and dry thermal oxidation method of Nickel metal thin film deposited by RF sputtering. The sensitivity of the NiO membrane was measured and comparatively analysed against the two different oxidation methods. Structural and morphological properties were investigated for both thin films. The sensitivities measurements of the two membranes were made as pH sensors. The results confirmed that NiO membrane grown by dry oxidation had much better sensitivity (87 μA/pH and 54 mV/pH) compared to wet oxidation membrane sample (52 μA/pH and 48 mV/pH).
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Vazquez, Rigoberto, Elizaveta Motovilova, and Simone Angela Winkler. "Stretchable Sensor Materials Applicable to Radiofrequency Coil Design in Magnetic Resonance Imaging: A Review." Sensors 24, no. 11 (May 24, 2024): 3390. http://dx.doi.org/10.3390/s24113390.
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Abstract: Wearable sensors are rapidly gaining influence in the diagnostics, monitoring, and treatment of disease, thereby improving patient outcomes. In this review, we aim to explore how these advances can be applied to magnetic resonance imaging (MRI). We begin by (i) introducing limitations in current flexible/stretchable RF coils and then move to the broader field of flexible sensor technology to identify translatable technologies. To this goal, we discuss (ii) emerging materials currently used for sensor substrates, (iii) stretchable conductive materials, (iv) pairing and matching of conductors with substrates, and (v) implementation of lumped elements such as capacitors. Applicable (vi) fabrication methods are presented, and the review concludes with a brief commentary on (vii) the implementation of the discussed sensor technologies in MRI coil applications. The main takeaway of our research is that a large body of work has led to exciting new sensor innovations allowing for stretchable wearables, but further exploration of materials and manufacturing techniques remains necessary, especially when applied to MRI diagnostics.
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Schucknecht, Anne, Bumsuk Seo, Alexander Krämer, Sarah Asam, Clement Atzberger, and Ralf Kiese. "Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets." Biogeosciences 19, no. 10 (June 1, 2022): 2699–727. http://dx.doi.org/10.5194/bg-19-2699-2022.
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Abstract. Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UASs) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (linear model; random forests, RFs; gradient-boosting machines, GBMs), and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors but was not available in our study. Therefore, we tested the added value of this structural information with in situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in southern Germany to obtain in situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized, and all model setups were run with a 6-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor–predictor set combinations with average (avg; cross-validated, cv) Rcv2 of 0.48, RMSEcv,avg of 53.0 g m2, and rRMSEcv,avg (relative) of 15.9 % for DM and with Rcv,avg2 of 0.40, RMSEcv,avg of 0.48 wt %, and rRMSEcv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms, and predictor sets notably improved the model performance. The best model performance for the estimation of DM (Rcv2=0.67, RMSEcv=41.9 g m2, rRMSEcv=12.6 %) was achieved with an RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ sensor data (Rcv2=0.47, RMSEcv=0.45 wt %, rRMSEcv=14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating the ML algorithm improved the model performance substantially, which shows the importance of this step.
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Rong, Guoguang, Yuqiao Zheng, and Mohamad Sawan. "Energy Solutions for Wearable Sensors: A Review." Sensors 21, no. 11 (May 31, 2021): 3806. http://dx.doi.org/10.3390/s21113806.
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Wearable sensors have gained popularity over the years since they offer constant and real-time physiological information about the human body. Wearable sensors have been applied in a variety of ways in clinical settings to monitor health conditions. These technologies require energy sources to carry out their projected functionalities. In this paper, we review the main energy sources used to power wearable sensors. These energy sources include batteries, solar cells, biofuel cells, supercapacitors, thermoelectric generators, piezoelectric and triboelectric generators, and radio frequency (RF) energy harvesters. Additionally, we discuss wireless power transfer and some hybrids of the above technologies. The advantages and drawbacks of each technology are considered along with the system components and attributes that make these devices function effectively. The objective of this review is to inform researchers about the latest developments in this field and present future research opportunities.
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Pranga, Joanna, Irene Borra-Serrano, Jonas Aper, Tom De Swaef, An Ghesquiere, Paul Quataert, Isabel Roldán-Ruiz, Ivan A. Janssens, Greet Ruysschaert, and Peter Lootens. "Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning." Remote Sensing 13, no. 17 (September 1, 2021): 3459. http://dx.doi.org/10.3390/rs13173459.
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High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was to examine the potential of unmanned aerial vehicle (UAV)-based structural and spectral features and their combination in herbage yield predictions across diploid and tetraploid varieties and breeding populations of perennial ryegrass (Lolium perenne L.). Canopy structural (i.e., canopy height) and spectral (i.e., vegetation indices) information were derived from data gathered with two sensors: a consumer-grade RGB and a 10-band multispectral (MS) camera system, which were compared in the analysis. A total of 468 field plots comprising 115 diploid and 112 tetraploid varieties and populations were considered in this study. A modelling framework established to predict dry matter yield (DMY), was used to test three machine learning algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machines (SVM). The results of the nested cross-validation revealed: (a) the fusion of structural and spectral features achieved better DMY estimates as compared to models fitted with structural or spectral data only, irrespective of the sensor, ploidy level or machine learning algorithm applied; (b) models built with MS-based predictor variables, despite their lower spatial resolution, slightly outperformed the RGB-based models, as lower mean relative root mean square error (rRMSE) values were delivered; and (c) on average, the RF technique reported the best model performances among tested algorithms, regardless of the dataset used. The approach introduced in this study can provide accurate yield estimates (up to an RMSE = 308 kg ha−1) and useful information for breeders and practical farm-scale applications.
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Akhtar, Pervez, Tariq Javid Ali, and Arshad Aziz. "Some Aspects of Deposition Parameters of RF Sputtered Ferromagnetic Film Germane to the Study of Magnetoresistive Sensing Devices." Advanced Materials Research 264-265 (June 2011): 160–65. http://dx.doi.org/10.4028/www.scientific.net/amr.264-265.160.
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The paper describes results of investigation on sputtered NiFe Films to determine the sputter deposition condition that could produce magnetic field sensors with the desired magnetic and magnetoresistive properties. The magnetic thin films materials used in such devices should have a low coercive force, a low anisotropy field and low magnetization dispersion, α50 with high magnetoresistance ratio. From the results presented, it is shown that that the most useful films for 82%Ni-Fe composition are produced at 200 °C-250 °C moderate substrate temperatures. It is also possible to specify the substrate bias potential and sputter gas pressure sputter deposition conditions under which the 82%Ni-Fe thin films may be employed for the production of magnetoresistine sensors with near optimum magnetic and magnetoresistive properties. The work also provide understanding of the effects on the magnetic properties of sputtered magnetic films that is very limited as current literature is almost entirely limited to evaporated magnetic films.
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Delgado-Rajo, Francisco, Alexis Melian-Segura, Victor Guerra, Rafael Perez-Jimenez, and David Sanchez-Rodriguez. "Hybrid RF/VLC Network Architecture for the Internet of Things." Sensors 20, no. 2 (January 15, 2020): 478. http://dx.doi.org/10.3390/s20020478.
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In recent years, there has been a remarkable advance in monitoring technologies in many environments, be they urban or rural. These technologies, included in the Internet of Things (IoT) domain, allow remote control and acquisition of data from sensors for their subsequence analysis. All these systems are based on the interaction between sensors and actuators. To achieve this goal, it is necessary to provide a very high level of connectivity between the devices, especially as far as wireless systems are concerned. In this sense, there is a great variety of standards in the market of communication networks oriented to this end. One of the biggest challenges today is to allow inter-operability between these different technologies in order to homogenize this field. In addition to this, it is intended to introduce new communication techniques that can provide certain additional advantages to those already existing. The main idea is the creation of a cellular network where radiofrequency and optical technologies coexist, and whose link with the rest of the world is through long-range and low-consumption wireless technologies. The center of each cell, that is the lighting system, can be powered using solar panels, as can the existing systems in the market. The objective is that these panels are capable of providing the necessary energy to the rest of the necessary systems.
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Chen, Chia-Hsun, Shu-Bai Liu, and Sheng-Po Chang. "Fabrication and Characterization of In0.9Ga0.1O EGFET pH Sensors." Coatings 11, no. 8 (August 3, 2021): 929. http://dx.doi.org/10.3390/coatings11080929.
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In this study, the In0.9Ga0.1O sensing membrane were deposited by using the RF magnetron sputtering at room temperature and combined with commercial MOSFETs as the extended gate field effect transistor (EGFET) pH sensors. The sensing performance of the In0.9Ga0.1O EGFET pH sensors were measured and analyzed in the pH value of range between 2 to 12. In the saturation region, the pH current sensitivity calculated from the linear relationship between the IDS and pH value was approximately 56.64 μA/pH corresponding to the linearity of 97.8%. In the linear region, the pH voltage sensitivity exhibited high sensitivity and linearity of 43.7 mV/pH and 96.3%, respectively. The In0.9Ga0.1O EGFET pH sensors were successfully fabricated and exhibited great linearity. The analyzed results indicated that the In0.9Ga0.1O was a robust material as a promising sensing membrane and effectively used for pH sensing detection application.
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Wang, Yifeng, Ping Wang, Zihan Li, Zhengxing Chen, and Qing He. "Forecasting Urban Rail Transit Vehicle Interior Noise and Its Applications in Railway Alignment Design." Journal of Advanced Transportation 2020 (June 23, 2020): 1–13. http://dx.doi.org/10.1155/2020/5896739.
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In this study, a data-driven interior noise prediction model is developed for vehicles on an urban rail transit system based on random forest (RF) and a vehicle/track coupling dynamic model (VTCDM). The proposed prediction model can evaluate and optimize the sustainability of railway alignment from the perspective of interior noise. First, a data collection framework via embedded sensors of onboard smartphones was developed. Then, for establishing the mapping relationship between the dynamic responses of the car body and interior noise, the collected dataset was fed to the RF. Parameter, error distribution, and feature importance analyses were conducted for evaluating and optimizing the performance of the RF. With the optimized parameters, the probability of prediction errors being within 5 dB was 86.9%. Next, the VTCDM was established using an existing industry multibody simulation tool and verified through a comparison between the simulated and field dynamic responses. Finally, a case study that extends the application of this interior noise prediction model to railway alignment design is presented.
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Bances, Enrique, Antonio Fiestas, and Hartmut Witte. "Novel wireless measurement system of pressure dedicated to in vivo studies." Current Directions in Biomedical Engineering 2, no. 1 (September 1, 2016): 123–27. http://dx.doi.org/10.1515/cdbme-2016-0030.
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AbstractThis paper reports on the development of a wireless system to monitor the thoracic cavity pressure in mammals. This project seeks to open a new field using the radio frequency (RF) technology in the studies of the interaction between breathing and locomotion in mammals. Furthermore, this study embodies the first step to develop a method of telemetry and remote monitoring based on implantable devices. In addition, it can be modified with other sensors to measure different physiological parameters.
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Narmilan, Amarasingam, Felipe Gonzalez, Arachchige Surantha Ashan Salgadoe, and Kevin Powell. "Detection of White Leaf Disease in Sugarcane Using Machine Learning Techniques over UAV Multispectral Images." Drones 6, no. 9 (September 1, 2022): 230. http://dx.doi.org/10.3390/drones6090230.
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Sugarcane white leaf phytoplasma (white leaf disease) in sugarcane crops is caused by a phytoplasma transmitted by leafhopper vectors. White leaf disease (WLD) occurs predominantly in some Asian countries and is a devastating global threat to sugarcane industries, especially Sri Lanka. Therefore, a feasible and an effective approach to precisely monitoring WLD infection is important, especially at the early pre-visual stage. This work presents the first approach on the preliminary detection of sugarcane WLD by using high-resolution multispectral sensors mounted on small unmanned aerial vehicles (UAVs) and supervised machine learning classifiers. The detection pipeline discussed in this paper was validated in a sugarcane field located in Gal-Oya Plantation, Hingurana, Sri Lanka. The pixelwise segmented samples were classified as ground, shadow, healthy plant, early symptom, and severe symptom. Four ML algorithms, namely XGBoost (XGB), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN), were implemented along with different python libraries, vegetation indices (VIs), and five spectral bands to detect the WLD in the sugarcane field. The accuracy rate of 94% was attained in the XGB, RF, and KNN to detect WLD in the field. The top three vegetation indices (VIs) for separating healthy and infected sugarcane crops are modified soil-adjusted vegetation index (MSAVI), normalized difference vegetation index (NDVI), and excess green (ExG) in XGB, RF, and DT, while the best spectral band is red in XGB and RF and green in DT. The results revealed that this technology provides a dependable, more direct, cost-effective, and quick method for detecting WLD.
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Jiemsakul, T., O. Trithaveesak, Win Bunjongpru, C. Hruanun, Amporn Poyai, and J. Nukeaw. "Application of Double Gate Ion Sensitive Field Effect Transistor for Detection of Fluid Flow Rate in Micro-Channel." Advanced Materials Research 93-94 (January 2010): 109–12. http://dx.doi.org/10.4028/www.scientific.net/amr.93-94.109.
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In this work a micro flow sensor, using a double gate Ion Sensitive Field Effect Transistor (ISFET) and two metal electrodes, for fluid flow rate measurement in micro-channels were fabricated and demonstrated. By the channel fabrication the molds were patterned reversely on a silicon wafer using Deep Reactive Ion Etcher (DRIE). The double gate ISFET and two metal electrodes were placed on the mold in the distance 15 millimeters. The channels were formed using polydimethylsiloxane (PDMS) from the mold with the width 1000 and 2000 micrometer and the depth of 250 micrometer. After removing PDMS from the mold the channel was bonded with glass substrate by RF plasma technique. By the verification of flow sensors working range water and one mole of sodium nitrate solution were alternated in flow channel. The fluid flow rate were compared with the flow rate from weighing. It found from the comparison that the high deviation was found at low flow rate. Furthermore, the deviation depends also on the dimension of the flow channel.
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Dhanda, P., S. Nandy, SPS Kushwaha, S. Ghosh, YVN Krishna Murthy, and VK Dadhwal. "Optimizing spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms." Progress in Physical Geography: Earth and Environment 41, no. 3 (April 5, 2017): 247–67. http://dx.doi.org/10.1177/0309133317693443.
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Forests sequester large quantity of carbon in their woody biomass and hence accurate estimation of forest biomass is extremely crucial. The present study aims at combining information from spaceborne LiDAR (ICESat/GLAS) and high resolution optical data to estimate forest biomass. Estimation of aboveground biomass (AGB) at ICESat/GLAS footprint level was done by integrating data from multiple sensors using two regression algorithms, viz. random forest (RF) and support vector machine (SVM). The study used forest height and canopy return ratio (rCanopy) for determination of effective size of ICESat/GLAS footprints for field data collection. The forest height was predicted with root mean square error (RMSE) of 1.35 m. The study showed that six most important parameters derived from LiDAR, and passive optical data were able to explain 78.7% (adjusted) variation in the observed AGB with an RMSE of 13.9 Mg ha–1. It was also observed that 15 most important parameters were able to explain 83% (adjusted) variation in the observed AGB. It was found that SVM regression algorithm explained 88.7% of variation in AGB with an RMSE of 13.6 Mg ha–1 on the combined datasets while RF regression algorithm explained 83.5% of variation in AGB with an RMSE of 20.57 Mg ha–1. The study demonstrated that RF regression algorithm performs equally well on datasets irrespective of the correlation of underlying variables with the predicted variable whereas SVM regression was found to perform well on those datasets which had a subset of underlying variables that are correlated with the predicted variable. The study highlighted that sensor integration approach is more accurate than single sensor approach in predicting the AGB.
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Tabuchi, Hibiki, Yuichiro Matsuzaki, Noboru Furuya, Yuta Nakano, Hideyuki Watanabe, Norio Tokuda, Norikazu Mizuochi, and Junko Ishi-Hayase. "Temperature sensing with RF-dressed states of nitrogen-vacancy centers in diamond." Journal of Applied Physics 133, no. 2 (January 14, 2023): 024401. http://dx.doi.org/10.1063/5.0129706.
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Using the electronic spin of nitrogen-vacancy (NV) centers in diamond is a promising approach to realizing high-precision temperature sensors; furthermore, pulsed optically detected magnetic resonance (pulsed-ODMR) is one way to measure the temperature using these NV centers. However, pulsed-ODMR techniques such as D-Ramsey, thermal echo, or thermal Carr–Purcell–Meiboom–Gill sequences require careful calibration and strict time synchronization to control the microwave (MW) pulses, which complicates their applicability. Continuous-wave ODMR (CW-ODMR) is a more advantageous way to measure temperature with NV centers because it can be implemented simply by continuous application of a green laser and MW radiation. However, CW-ODMR has lower sensitivity than pulsed-ODMR. Therefore, it is important to improve the temperature sensitivity of CW-ODMR techniques. Herein, we thus propose and demonstrate a method for measuring temperature using CW-ODMR with a quantum spin state dressed by a radio-frequency (RF) field under a transverse magnetic field. The use of an RF field is expected to suppress the inhomogeneous broadening resulting from strain and/or electric-field variations. The experimental results confirm that the linewidth is decreased in the proposed scheme when compared to the conventional scheme. In addition, we measured the temperature sensitivity to be about [Formula: see text], and this is approximately eight times better than that of the conventional scheme.
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Artusio-Glimpse, Alexandra, Matthew T. Simons, Nikunjkumar Prajapati, and Christopher L. Holloway. "Modern RF Measurements With Hot Atoms: A Technology Review of Rydberg Atom-Based Radio Frequency Field Sensors." IEEE Microwave Magazine 23, no. 5 (May 2022): 44–56. http://dx.doi.org/10.1109/mmm.2022.3148705.
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