scholarly journals RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1875 ◽  
Author(s):  
Noraini Azmi ◽  
Latifah Munirah Kamarudin ◽  
Ammar Zakaria ◽  
David Lorater Ndzi ◽  
Mohd Hafiz Fazalul Rahiman ◽  
...  
Keyword(s):  
Random Forest ◽  
Moisture Content ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Uhf Rfid ◽  
Input Feature ◽  
Moisture Content Level

Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors’ knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.

Monitoring rock displacement threshold with 1-bit sensing passive RFID tag

2021 ◽  
Author(s):  
Mathieu Le Breton ◽  
Nicolas Grunbaum ◽  
Laurent Baillet ◽  
Éric Larose
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Earth Science ◽  
Low Cost ◽  
New Technology ◽  
Uhf Rfid ◽  
Displacement Threshold ◽  
Frequency Identification ◽  
Passive Rfid

<p>Billions of passive Radiofrequency tags are produced by the Radio-Frequency Identification (RFID) industry every year to identify goods remotely. Enhanced RFID adds the capacity for localisation and sensing that can be used in earth science for long-term and spatially dense monitoring with low-cost tags. Localisation has been used already to monitor displacements of coarse sediment and landslides over several metres. Sensing capabilities have been developed in laboratories, but never implemented on real fields. This work presents the first RFID sensing application in earth science, using the simplest 1-bit sensor to detect millimetric motion of unstable rocks. The application required designing custom RFID tags adapted for outdoor usage at long range, adapting the data acquisition of an existing tag microcircuit, and designing a sensor that triggers when displacement exceeds a few millimetres, which threshold displacement can be adapted for each sensor. In complement, the system embeds displacement sensing to measure larger displacements> 5 mm, using the phase-based measurement already deployed on landslides. The presentation display results from laboratory tests and from an implementation in a real site in Eastern France. The advantages and drawbacks of the method are discussed, as well as the future potential RFID sensors that could monitor unstable terrains.</p><p>Author’s published work on the topic:</p><p>Le Breton, M., Baillet, L., Larose, E., Rey, E., Benech, P., Jongmans, D., Guyoton, F., 2017. Outdoor UHF RFID: Phase Stabilization for Real-World Applications. IEEE Journal of Radio Frequency Identification 1, 279–290.</p><p>Le Breton, M., Baillet, L., Larose, E., Rey, E., Benech, P., Jongmans, D., Guyoton, F., Jaboyedoff, M., 2019. Passive radio-frequency identification ranging, a dense and weather-robust technique for landslide displacement monitoring. Engineering Geology 250, 1–10.</p><p>Le Breton, M., 2019. Suivi temporel d’un glissement de terrain à l’aide d’étiquettes RFID passives, couplé à l’observation de pluviométrie et de bruit sismique ambiant (PhD Thesis). Université Grenoble Alpes, ISTerre, Grenoble, France.</p><p>Le Breton, M., Baillet, L., Larose, É., Rey, E., Jongmans, D., Guyoton, F., Benech, P., 2020. Passive RFID, a new technology for dense and long-term monitoring of unstable structures: review and prospective. (No. EGU2020-19726). Presented at the EGU2020, Copernicus Meetings. https://doi.org/10.5194/egusphere-egu2020-19726</p><p>Le Breton M., 2020, Suivi de terrains instables à l'aide d'un réseau dense de capteurs RFID: Émergence de nouvelles applications, presented at Journées Nationales de Géotechnique et de Géologie de l'ingénieur (JNGG), Jean Goguel Award public session, 2021.</p>

scholarly journals Flexible Radio-Frequency Identification (RFID) Tag Antenna for Sensor Applications

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4212 ◽  
Author(s):  
Mohammad Islam ◽  
Touhidul Alam ◽  
Iskandar Yahya ◽  
Mengu Cho
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Low Cost ◽  
System Level ◽  
Uhf Rfid ◽  
Rfid Tag ◽  
Sensor Applications ◽  
Frequency Identification ◽  
Flexible Radio ◽  
Read Range

In this paper, an inkjet-printed flexible Radio-Frequency Identification (RFID) tag antenna is proposed for an ultra-high frequency (UHF) sensor application. The proposed tag antenna facilitates a system-level solution for low-cost and faster mass production of RFID passive tag antenna. The tag antenna consists of a modified meander line radiator with a semi-circular shaped feed network. The structure is printed on photo paper using silver nanoparticle conductive ink. The generic design outline, as well as tag antenna performances for several practical application aspects are investigated. The simulated and measured results verify the coverage of universal UHF RFID band with an omnidirectional radiation pattern and a long-read range of 15 ft. In addition, the read range for different bending angles and lifetimes of the tag antenna are also demonstrated.

scholarly journals Small Size and Low Cost UHF RFID Tag Antenna Mountable on Metallic Objects

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Sergio López-Soriano ◽  
Josep Parrón
Keyword(s):  
Radio Frequency ◽  
Transmission Line ◽  
Radio Frequency Identification ◽  
Low Cost ◽  
Experimental Results ◽  
Uhf Rfid ◽  
Rfid Tag ◽  
Frequency Identification ◽  
European Regulations ◽  
Metallic Object

Reducing tag size while maintaining good performance is one of the major challenges in radio-frequency identification applications (RFID), in particular when labeling metallic objects. In this contribution, a small size and low cost tag antenna for identifying metal objects in the European UHF band (865–868 MHz) is presented. The antenna consists of a transmission line mounted on an inexpensive thin dielectric which is proximity-coupled to a short-ended patch mounted on FR4 substrate. The overall dimensions of the tag are 33.5 × 30 × 3.1 mm. Experimental results show that, for an EIRP of 3.2 W (European regulations), such a small and cheap tag attains read ranges of about 5 m when attached to a metallic object.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

Preliminary Screening of COVID-19 Infection Employing Machine Learning Techniques From Simple Blood Profile

2021 ◽  
Vol 6 (3) ◽  
pp. 35-47
Author(s):  
Anirudh Reddy Cingireddy ◽  
Robin Ghosh ◽  
Supratik Kar ◽  
Venkata Melapu ◽  
Sravanthi Joginipeli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Naive Bayes ◽  
Albert Einstein ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Blood Profile ◽  
Molecular Tests ◽  
Large Populations

Frequent testing of the entire population would help to identify individuals with active COVID-19 and allow us to identify concealed carriers. Molecular tests, antigen tests, and antibody tests are being widely used to confirm COVID-19 in the population. Molecular tests such as the real-time reverse transcription-polymerase chain reaction (rRT-PCR) test will take a minimum of 3 hours to a maximum of 4 days for the results. The authors suggest using machine learning and data mining tools to filter large populations at a preliminary level to overcome this issue. The ML tools could reduce the testing population size by 20 to 30%. In this study, they have used a subset of features from full blood profile which are drawn from patients at Israelita Albert Einstein hospital located in Brazil. They used classification models, namely KNN, logistic regression, XGBooting, naive Bayes, decision tree, random forest, support vector machine, and multilayer perceptron with k-fold cross-validation, to validate the models. Naïve bayes, KNN, and random forest stand out as the most predictive ones with 88% accuracy each.

Explore the Use of Handwriting Information and Machine Learning Techniques in Evaluating Mental Workload

2020 ◽  
pp. 1423-1439
Author(s):  
Zhiming Wu ◽  
Tao Lin ◽  
Ningjiu Tang
Keyword(s):  
Machine Learning ◽  
Interaction Design ◽  
Mental Stress ◽  
Mental Workload ◽  
Low Cost ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Psychological Evidence

Mental workload is considered one of the most important factors in interaction design and how to detect a user's mental workload during tasks is still an open research question. Psychological evidence has already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental stress, but this phenomenon has not been explored adequately. The intention of this paper is to explore the possibility of evaluating mental workload with handwriting information by machine learning techniques. Machine learning techniques such as decision trees, support vector machine (SVM), and artificial neural network were used to predict mental workload levels in the authors' research. Results showed that it was possible to make prediction of mental workload levels automatically based on handwriting patterns with relatively high accuracy, especially on patterns of children. In addition, the proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost.

A Wireless Hay Bale Status Sensor Suite Using PCB Sensor Technology

2017 ◽  
Vol 2017 (DPC) ◽  
pp. 1-31
Author(s):  
Jeff D. Craven ◽  
Andrew W. Muscha ◽  
R. Chase Harrison ◽  
Markus A. R. Kreitzer ◽  
Robert N. Dean ◽  
...  
Keyword(s):  
Moisture Content ◽  
Spontaneous Combustion ◽  
Thermophilic Bacteria ◽  
Low Cost ◽  
The United States ◽  
Circuit Board ◽  
Sensor Technology ◽  
Moisture Content Level ◽  
Suitable Environment ◽  
Sensor Suite

The spontaneous combustion of curing hay bales poses serious safety and financial issues to farmers and ranchers across the United States and abroad. The primary cause of this spontaneous combustion is the baling of hay before it has adequately dried and reached a sufficiently low moisture content level. This inadequate drying is primarily due to the farmer allowing the hay to dry in the field after cutting for a given period of time. But unfortunately, this does not always ensure that the hay has sufficiently dried before baling. Spontaneous combustion of hay bales is due to a proliferation of thermophilic bacteria that thrive in a moist and hot environment. If the moisture content of hay is greater than 20%, it provides a suitable environment for mesophilic bacteria, which can heat the hay to as a high as 140°F. Although this is not problematic in and of itself, a 140°F hay bale is a suitable environment for the proliferation of thermophilic bacteria, which can further heat the hay to 170oF. At this temperature, the hay can spontaneous combust, destroying the hay and risking the loss of buildings, equipment, livestock and agricultural workers. To combat this problem, a low-cost, low-power, wireless hay bale status sensor suite has been developed so that the farmer can easily and safely monitor the conditions inside a curing hay bale, to give the farmer time to take action before the bale spontaneously combusts. The battery operated sensor suite has two sensors in contact with the hay inside the bale, a printed circuit board (PCB) moisture content sensor and a discrete temperature sensor. The extremely low-cost of the PCB moisture content sensor is what enables the practicality of the sensor suite. WiFi is used to transmit moisture content and temperature data to the farmer's smartphone when it comes within range. The sensor suite is placed inside the bale at the time of baling. After the bale has fully cured, in four to six weeks, the reusable sensor suite can be removed and used in a new bale.

scholarly journals Performance of NO, NO<sub>2</sub> low cost sensors and three calibration approaches within a real world application

2018 ◽  
Vol 11 (6) ◽  
pp. 3717-3735 ◽  
Author(s):  
Alessandro Bigi ◽  
Michael Mueller ◽  
Stuart K. Grange ◽  
Grazia Ghermandi ◽  
Christoph Hueglin
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Support Vector Regression ◽  
Low Cost ◽  
Multivariate Linear Regression ◽  
Pollution Level ◽  
Support Vector ◽  
Non Linear ◽  
Linear Algorithms ◽  
Urban Sites

Abstract. Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices are being delivered to the public. Notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using three different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4-month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, noise and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE  <  5 ppb, R2 between 0.74 and 0.95 and MAE between 2 and 4 ppb. The combined use of both NO and NO2 sensor output in the estimate of each pollutant showed some contribution by NO sensor to NO2 estimate and vice-versa. All algorithms exhibited a drift ranging between 5 and 10 ppb for Random Forest and 15 ppb for Multivariate Linear Regression at the end of the deployment. The lowest concentration correctly estimated, with a 25 % relative expanded uncertainty, resulted in ca. 15–20 ppb and was provided by the non-linear algorithms. As an assessment for the suitability of the tested sensors for a targeted application, the probability of resolving hourly concentration difference in cities was investigated. It was found that NO concentration differences of 5–10 ppb (8–10 for NO2) can reliably be detected (90 % confidence), depending on the air pollution level. The findings of this study, although derived from a specific sensor type and sensor model, are based on a flexible methodology and have extensive potential for exploring the performance of other low cost sensors, that are different in their target pollutant and sensing technology.

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