Pub Date : 2021-08-23DOI: 10.1109/SAS51076.2021.9530145
Bruno Tardif, D. Lo, R. Goubran
Exposure to gunshot sounds can cause hearing impairments. Measuring and analyzing these sounds can improve the design of hearing protectors and can help in enacting safety regulations. Furthermore, analyzing gunshot sounds can help identify the type of gun used. This is important for determining the appropriate public safety actions when a gunshot sound is detected in a public space. In this paper, we collected acoustic data from four different guns. To capture their sound including any non-symmetric sound propagation, 27 high dynamic range pressure microphones were placed around the guns forming a polar grid pattern. Audio signals were captured at 204.8 kHz sampling rate synchronously to preserve the fidelity of the impulse nature of the gunshots. In this study, an image-based analysis method was developed to take advantage of the recent advancement of image recognition techniques. Two spectral analysis methods: Short Time Fourier Transform (STFT) or Continuous Wavelet Transform (CWT), were then applied to get the spectrogram of the gunshot audio signal. Machine learning using the k-nearest neighbor and random subspaces was used to classify these spectrograms and identify which gun did the particular gunshot originated from. Under reverberant conditions, the STFT maintained a better identification accuracy than the CWT.
{"title":"Gunshot Sound Measurement and Analysis","authors":"Bruno Tardif, D. Lo, R. Goubran","doi":"10.1109/SAS51076.2021.9530145","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530145","url":null,"abstract":"Exposure to gunshot sounds can cause hearing impairments. Measuring and analyzing these sounds can improve the design of hearing protectors and can help in enacting safety regulations. Furthermore, analyzing gunshot sounds can help identify the type of gun used. This is important for determining the appropriate public safety actions when a gunshot sound is detected in a public space. In this paper, we collected acoustic data from four different guns. To capture their sound including any non-symmetric sound propagation, 27 high dynamic range pressure microphones were placed around the guns forming a polar grid pattern. Audio signals were captured at 204.8 kHz sampling rate synchronously to preserve the fidelity of the impulse nature of the gunshots. In this study, an image-based analysis method was developed to take advantage of the recent advancement of image recognition techniques. Two spectral analysis methods: Short Time Fourier Transform (STFT) or Continuous Wavelet Transform (CWT), were then applied to get the spectrogram of the gunshot audio signal. Machine learning using the k-nearest neighbor and random subspaces was used to classify these spectrograms and identify which gun did the particular gunshot originated from. Under reverberant conditions, the STFT maintained a better identification accuracy than the CWT.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122007627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/SAS51076.2021.9530090
Alexios Papaioannou, Panagiotis Verikios, C. Kouzinopoulos, D. Ioannidis, D. Tzovaras
Fire monitoring and detection systems can evaluate data from environmental or image sensors in order to predict occurrences of fire. It is a complex procedure that requires a significant amount of energy as input data is usually acquired from multiple sensors and the algorithms generally have an increased complexity. This paper introduces a low-power fire monitoring and detection system that utilizes data from two environmental sensors. As a predictive algorithm for fire occurrences, it uses a multilayer perceptron (MLP) with a combination of different optimizations, developing a model with low memory requirements and high -accuracy predictions. The accuracy of the proposed system was verified using a dataset created by the environmental sensors for fire incidents and its performance was compared to existing approaches. An evaluation of the proposed system's power consumption and memory requirements is also presented.
{"title":"A low-power embedded system for fire monitoring and detection using a multilayer perceptron","authors":"Alexios Papaioannou, Panagiotis Verikios, C. Kouzinopoulos, D. Ioannidis, D. Tzovaras","doi":"10.1109/SAS51076.2021.9530090","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530090","url":null,"abstract":"Fire monitoring and detection systems can evaluate data from environmental or image sensors in order to predict occurrences of fire. It is a complex procedure that requires a significant amount of energy as input data is usually acquired from multiple sensors and the algorithms generally have an increased complexity. This paper introduces a low-power fire monitoring and detection system that utilizes data from two environmental sensors. As a predictive algorithm for fire occurrences, it uses a multilayer perceptron (MLP) with a combination of different optimizations, developing a model with low memory requirements and high -accuracy predictions. The accuracy of the proposed system was verified using a dataset created by the environmental sensors for fire incidents and its performance was compared to existing approaches. An evaluation of the proposed system's power consumption and memory requirements is also presented.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116457508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/SAS51076.2021.9530085
M. Mozafari, R. Goubran, J. Green
Stress is a psychological condition that affects daily life, and chronic stress can result in cardiovascular disease and reduced productivity. Mental stress can be induced when difficult and time-limited tasks are assigned. Several groups have studied the relationship between physiologic signals and a subject's stress level. Through machine learning and signal processing, stress level can be automatically inferred from raw physiologic signals. As each person can have a specific physiologic reaction pattern to stress, it becomes problematic for a classifier to work well on a new subject. In this study, transfer learning is used to solve the problem of inter-subject variability. Methods are developed here to classify five levels of stress based on physiologic signals comprising photoplethysmogram (PPG), galvanic skin response (GSR), abdominal respiration, and thoracic respiration. Domain adaptation methods based on information-theoretical learning and transfer component analysis (TCA) are shown to reduce inter-subject variability of both GSR and respiratory signals. A fusion model was also designed to combine classification scores from each signal to reduce the effect of low-quality recording. The proposed method is shown to increase accuracy from 68.79% to 76.70% and Intraclass Correlation Coefficient (ICC) from 83.82% to 96.55%.
{"title":"A Fusion Model for Cross-Subject Stress Level Detection Based on Transfer Learning","authors":"M. Mozafari, R. Goubran, J. Green","doi":"10.1109/SAS51076.2021.9530085","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530085","url":null,"abstract":"Stress is a psychological condition that affects daily life, and chronic stress can result in cardiovascular disease and reduced productivity. Mental stress can be induced when difficult and time-limited tasks are assigned. Several groups have studied the relationship between physiologic signals and a subject's stress level. Through machine learning and signal processing, stress level can be automatically inferred from raw physiologic signals. As each person can have a specific physiologic reaction pattern to stress, it becomes problematic for a classifier to work well on a new subject. In this study, transfer learning is used to solve the problem of inter-subject variability. Methods are developed here to classify five levels of stress based on physiologic signals comprising photoplethysmogram (PPG), galvanic skin response (GSR), abdominal respiration, and thoracic respiration. Domain adaptation methods based on information-theoretical learning and transfer component analysis (TCA) are shown to reduce inter-subject variability of both GSR and respiratory signals. A fusion model was also designed to combine classification scores from each signal to reduce the effect of low-quality recording. The proposed method is shown to increase accuracy from 68.79% to 76.70% and Intraclass Correlation Coefficient (ICC) from 83.82% to 96.55%.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128015361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/SAS51076.2021.9530165
S. Mileiko, Oktay Cetinkaya, A. Yakovlev, Domenico Balsamo
Ultrasonic technologies have established themselves as optimal solutions for water flow rate measurement thanks to the high reliability and efficiency they offer. However, the existing applications often require ultrasonic sensors to be embedded in pipes, i.e., intrusive, which significantly increases initial deployment and maintenance costs. Considering the volume of employment, one to each house, the water meters have to be designed in a way that the consumers can deploy and maintain them without any skilled labourer. Hence, this paper proposes a delta time-of-flight $(Delta ToF)$ -based non-intrusive sensor system for plug-and-play ultrasonic water flow rate metering. After introducing the measurement theory and our proposed design, we experimentally evaluated the performance of three different ΔToF calculation methods in terms of memory and computation requirements through a dedicated testbed consisting of a closed-loop multi-pipe layout. The results helped us to determine the optimal $Delta ToF$ method for the employed platform, which is then used to select the best sensor and housing setting that is operable even under the worst deployment conditions (pipe material, diameter). Compared to its intrusive counterparts, the proposed system achieved a sufficient level of precision $(pm 5.7%)$ and hence provided the possibility of replacing the conventional methods of intrusive water metering at the household level.
{"title":"A Non-Intrusive Ultrasonic Sensor System for Water Flow Rate Measurement","authors":"S. Mileiko, Oktay Cetinkaya, A. Yakovlev, Domenico Balsamo","doi":"10.1109/SAS51076.2021.9530165","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530165","url":null,"abstract":"Ultrasonic technologies have established themselves as optimal solutions for water flow rate measurement thanks to the high reliability and efficiency they offer. However, the existing applications often require ultrasonic sensors to be embedded in pipes, i.e., intrusive, which significantly increases initial deployment and maintenance costs. Considering the volume of employment, one to each house, the water meters have to be designed in a way that the consumers can deploy and maintain them without any skilled labourer. Hence, this paper proposes a delta time-of-flight $(Delta ToF)$ -based non-intrusive sensor system for plug-and-play ultrasonic water flow rate metering. After introducing the measurement theory and our proposed design, we experimentally evaluated the performance of three different ΔToF calculation methods in terms of memory and computation requirements through a dedicated testbed consisting of a closed-loop multi-pipe layout. The results helped us to determine the optimal $Delta ToF$ method for the employed platform, which is then used to select the best sensor and housing setting that is operable even under the worst deployment conditions (pipe material, diameter). Compared to its intrusive counterparts, the proposed system achieved a sufficient level of precision $(pm 5.7%)$ and hence provided the possibility of replacing the conventional methods of intrusive water metering at the household level.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128105638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/SAS51076.2021.9530046
Simon Nutsch, M. Sauer
In this paper a method to test the latency, accuracy and power as well as energy demand of carbon dioxide sensors with the target on Heating, Ventilation and Air Conditioning (HVAC) applications is presented. In 24 trials the CO2 concentration in a measurement chamber was increased from ambient air to 1860 parts per million (ppm) in four steps. The CO2 concentration in the chamber was measured by the Testo 480 Indoor Air Quality (IAQ) analyzer and nine different non-dispersive infrared (NDIR) CO2 sensors. Furthermore, the design and components of the measurement chamber and the system to read the sensor values and measure the power and energy demand of the sensors are described. Although the measured data do not allow a statement about the actual sensor accuracy due to the small sample size and the accuracy of the used reference analyzer it is possible to declare if a sensor suitable for the application in demand control ventilation systems. To determine the sensor latency a method to measure the time a sensor needs to settle in a specific bound is shown.
{"title":"Method to determine the suitability of non-dispersive infrared carbon dioxide sensor models in Heating, Ventilation and Air Conditioning systems","authors":"Simon Nutsch, M. Sauer","doi":"10.1109/SAS51076.2021.9530046","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530046","url":null,"abstract":"In this paper a method to test the latency, accuracy and power as well as energy demand of carbon dioxide sensors with the target on Heating, Ventilation and Air Conditioning (HVAC) applications is presented. In 24 trials the CO2 concentration in a measurement chamber was increased from ambient air to 1860 parts per million (ppm) in four steps. The CO2 concentration in the chamber was measured by the Testo 480 Indoor Air Quality (IAQ) analyzer and nine different non-dispersive infrared (NDIR) CO2 sensors. Furthermore, the design and components of the measurement chamber and the system to read the sensor values and measure the power and energy demand of the sensors are described. Although the measured data do not allow a statement about the actual sensor accuracy due to the small sample size and the accuracy of the used reference analyzer it is possible to declare if a sensor suitable for the application in demand control ventilation systems. To determine the sensor latency a method to measure the time a sensor needs to settle in a specific bound is shown.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126455723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/SAS51076.2021.9530095
Gray D. Thurston, J. Schmalzel, B. Barrowes
Electromagnetic induction has been utilized in the past by the United States Army Corps of Engineers as a method of detecting unexploded ordinance. Recently an EMI instrument was built that extended the traditional EMI frequency range from 100 kHz to 15 MHz to aid in the detection of nonmetallic ordinance, landmines, and improvised explosive devices. Extending that research, the iFROST mapper was built to use the same HFEMI technique to characterize arctic soil and subsurface permafrost deposits. This paper details the original iFROST mapper software and hardware systems as well as a new HFEMI device that improves on the original iFROST mapper design.
{"title":"Next Generation Geophysical Assessment System","authors":"Gray D. Thurston, J. Schmalzel, B. Barrowes","doi":"10.1109/SAS51076.2021.9530095","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530095","url":null,"abstract":"Electromagnetic induction has been utilized in the past by the United States Army Corps of Engineers as a method of detecting unexploded ordinance. Recently an EMI instrument was built that extended the traditional EMI frequency range from 100 kHz to 15 MHz to aid in the detection of nonmetallic ordinance, landmines, and improvised explosive devices. Extending that research, the iFROST mapper was built to use the same HFEMI technique to characterize arctic soil and subsurface permafrost deposits. This paper details the original iFROST mapper software and hardware systems as well as a new HFEMI device that improves on the original iFROST mapper design.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129176608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/SAS51076.2021.9530168
Shourya Mukherjee, Tapabrata Sen, C. Anoop, S. Sen
In this paper, a novel, simplified semi-analytical approach for modelling and performance study of Eddy-Current (EC) probes has been proposed for detecting thin defects in nonmagnetic, metallic objects. The proposed technique is based on the volume integral form of Biot-Savart law. The expression of the net magnetic field, at a suitable point in the probe, is evaluated. Thus, the methodology is suitable for integrated magnetic sensor-based EC-probes. The semi-analytical method is applied to two different probe configurations, and their responses are derived. The performance of the proposed technique is comparable to simulation results from commercial finite-element-based software and experimental results obtained using hardware prototypes of the EC probes. Thus, this paper provides a simple mathematical approach for analyzing EC-based defect detection problems.
{"title":"A Semi-Analytical Method for Modelling of EC Probes for Detection of Thin Defects in Metals","authors":"Shourya Mukherjee, Tapabrata Sen, C. Anoop, S. Sen","doi":"10.1109/SAS51076.2021.9530168","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530168","url":null,"abstract":"In this paper, a novel, simplified semi-analytical approach for modelling and performance study of Eddy-Current (EC) probes has been proposed for detecting thin defects in nonmagnetic, metallic objects. The proposed technique is based on the volume integral form of Biot-Savart law. The expression of the net magnetic field, at a suitable point in the probe, is evaluated. Thus, the methodology is suitable for integrated magnetic sensor-based EC-probes. The semi-analytical method is applied to two different probe configurations, and their responses are derived. The performance of the proposed technique is comparable to simulation results from commercial finite-element-based software and experimental results obtained using hardware prototypes of the EC probes. Thus, this paper provides a simple mathematical approach for analyzing EC-based defect detection problems.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131272720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/SAS51076.2021.9530142
I. Savukov, Young Jin Kim
We introduce a new ultra-sensitive adiabatic magnetometer that has a broad bandwidth and can operate in the presence of magnetic fields and gradients. It follows conceptually typical implementations of atomic magnetometers based on alkali-metal vapor cells and lasers for optical pumping and optical Faraday effect detection, while its unique feature is a measurement of an oscillating magnetic field along the probe beam direction at frequencies lower than the resonant frequency, proportional to a static magnetic field along the pump beam direction. The bandwidth of the adiabatic magnetometer scales as the strength of the field along the pump beam. From our theoretical studies it is expected that the adiabatic magnetometer can reach 1 fT sensitivity with a bandwidth of 10 kHz, which any type of atomic magnetometers cannot achieve. Among anticipated various applications of this adiabatic magnetometer are biomagnetic sensing, nuclear magnetic resonance detection, and alkali-metal density measurements. We experimentally conducted alkali-metal density measurements, as an example of applications.
{"title":"Broadband Ultra-Sensitive Adiabatic Magnetometer","authors":"I. Savukov, Young Jin Kim","doi":"10.1109/SAS51076.2021.9530142","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530142","url":null,"abstract":"We introduce a new ultra-sensitive adiabatic magnetometer that has a broad bandwidth and can operate in the presence of magnetic fields and gradients. It follows conceptually typical implementations of atomic magnetometers based on alkali-metal vapor cells and lasers for optical pumping and optical Faraday effect detection, while its unique feature is a measurement of an oscillating magnetic field along the probe beam direction at frequencies lower than the resonant frequency, proportional to a static magnetic field along the pump beam direction. The bandwidth of the adiabatic magnetometer scales as the strength of the field along the pump beam. From our theoretical studies it is expected that the adiabatic magnetometer can reach 1 fT sensitivity with a bandwidth of 10 kHz, which any type of atomic magnetometers cannot achieve. Among anticipated various applications of this adiabatic magnetometer are biomagnetic sensing, nuclear magnetic resonance detection, and alkali-metal density measurements. We experimentally conducted alkali-metal density measurements, as an example of applications.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122385528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/SAS51076.2021.9530141
Cheng-Ru Li, Chih-Chung Yang, H. Tsai, Chun-Han Chou, Kuo-Cheng Huang, Yu-Hsuan Lin
Urine test paper is often used to roughly judge the course of certain diseases, such as diabetes. Excessive glucose in the urine will react with the chemicals in the test strip, and then show an appropriate color to provide human observation and judgment. Generally speaking, the color of the test strip can only tell the approximate glucose concentration in urine through visual observation. Accurate quantification must rely on professional electrochemical analysis equipment. In this study, an imaging system with color correction was developed to quantify the color of urine glucose test paper. Through the composition of high color rendering lighting, darkroom barrel and color algorithm, the measurement results successfully achieved color accuracy with a color deviation of less than 3. A color distribution display method that is more suitable for human observation has been successfully established. The method of swatch contribution analysis has also been developed to numerically quantify the glucose concentration in urine. The results show that the developed system can greatly improve the resolution of urine glucose test strip. This research provides a low-cost, high-quality detection concept that can be used for home medical diagnosis and related chronic disease applications.
{"title":"Quantifying the glucose concentration in urine test strip with a color-calibrated imaging system","authors":"Cheng-Ru Li, Chih-Chung Yang, H. Tsai, Chun-Han Chou, Kuo-Cheng Huang, Yu-Hsuan Lin","doi":"10.1109/SAS51076.2021.9530141","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530141","url":null,"abstract":"Urine test paper is often used to roughly judge the course of certain diseases, such as diabetes. Excessive glucose in the urine will react with the chemicals in the test strip, and then show an appropriate color to provide human observation and judgment. Generally speaking, the color of the test strip can only tell the approximate glucose concentration in urine through visual observation. Accurate quantification must rely on professional electrochemical analysis equipment. In this study, an imaging system with color correction was developed to quantify the color of urine glucose test paper. Through the composition of high color rendering lighting, darkroom barrel and color algorithm, the measurement results successfully achieved color accuracy with a color deviation of less than 3. A color distribution display method that is more suitable for human observation has been successfully established. The method of swatch contribution analysis has also been developed to numerically quantify the glucose concentration in urine. The results show that the developed system can greatly improve the resolution of urine glucose test strip. This research provides a low-cost, high-quality detection concept that can be used for home medical diagnosis and related chronic disease applications.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115867497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.1109/SAS51076.2021.9530063
M. Bruzzi, I. Cappelli, A. Fort, A. Pozzebon, M. Tani, V. Vignoli
This paper aims at demonstrating the feasibility of a LoRaWAN-based sensor node for temperature monitoring, autonomously powered by a polycrystalline silicon photovoltaic module with possible applications within the Internet of Things (loT) domain in the horticulture field. The commercial solar cell was characterized under two light sources: a conventional white 4000 K Light Emitting Diode (LED) and a red and far red (R:FR) lamp peaked at 655 nm and 730 nm. The sensor node is equipped with a RFM95x LoRa transceiver which proved to be a valid technology in those application scenarios where robustness and low power consumption are required. The energy harvesting features are performed by a nano-power boost charger buck converter which deals with the power extraction from the photovoltaic module, the LiPo battery charging/discharging management and the supply of the sensor node. Field tests demonstrate that under R:fr light source, the energy self-sufficiency of the system is achieved: a positive balance between the battery charge and discharge is measured, sufficient both for the node working operation and for the battery charging.
{"title":"Polycrystalline silicon photovoltaic harvesting for indoor IoT systems under red- far red artificial light","authors":"M. Bruzzi, I. Cappelli, A. Fort, A. Pozzebon, M. Tani, V. Vignoli","doi":"10.1109/SAS51076.2021.9530063","DOIUrl":"https://doi.org/10.1109/SAS51076.2021.9530063","url":null,"abstract":"This paper aims at demonstrating the feasibility of a LoRaWAN-based sensor node for temperature monitoring, autonomously powered by a polycrystalline silicon photovoltaic module with possible applications within the Internet of Things (loT) domain in the horticulture field. The commercial solar cell was characterized under two light sources: a conventional white 4000 K Light Emitting Diode (LED) and a red and far red (R:FR) lamp peaked at 655 nm and 730 nm. The sensor node is equipped with a RFM95x LoRa transceiver which proved to be a valid technology in those application scenarios where robustness and low power consumption are required. The energy harvesting features are performed by a nano-power boost charger buck converter which deals with the power extraction from the photovoltaic module, the LiPo battery charging/discharging management and the supply of the sensor node. Field tests demonstrate that under R:fr light source, the energy self-sufficiency of the system is achieved: a positive balance between the battery charge and discharge is measured, sufficient both for the node working operation and for the battery charging.","PeriodicalId":224327,"journal":{"name":"2021 IEEE Sensors Applications Symposium (SAS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115141914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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