Pub Date : 2024-09-10DOI: 10.1109/LSENS.2024.3453558
V. Mallikarjuna Reddy M;P. S. Pandian;Karthick P A
Recent advancements and developments in the field of rehabi- litation lead to the invention of myoelectric control interfaces for patients with disabilities. However, decoding the motion intent from the surface electromyography (sEMG) signals of hamstrings and quadriceps is challenging due to its complex mechanics associated with weight bearing joints and stochastic, nonstationary, and multicomponent behavior of signals. In this letter, a novel approach is proposed for multiclass gait phase classification during level walking using temporal convolutional network (TCN) of sEMG signals. For this purpose, sEMG and inertial measurement unit (IMU) data were recorded concurrently from 20 healthy participants during level walking on treadmill at a speed of 2.5 km/h. sEMG were collected from the muscles, namely, rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and semitendinosus (SEM). The IMU measurements of knee flexion/extension data are utilized for labeling the four phases of gait cycle. The root mean square of sEMG epochs is used to design the TCN framework. The results show that the proposed framework has the ability to differentiate the four classes of gait with a maximum accuracy of 86.00% using the myoelectric activity from all the four muscles. The information from the muscle pairs SEM and VL, and RF and BF, yielded the correct detection rate of 83.00% and 84.00%, respectively. In addition, the accuracy is also improved by 6% with TCN when we compare accuracy obtained through convolutional neural network architecture. The findings suggest that the proposed approach is effective in decoding the motion intent of lower limb muscles, which may lead to the development of precise movement control of lower limb prosthesis.
{"title":"Multiclass Gait Phase Classification From the Temporal Convolutional Network of Wireless Surface Electromyography Measurements","authors":"V. Mallikarjuna Reddy M;P. S. Pandian;Karthick P A","doi":"10.1109/LSENS.2024.3453558","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3453558","url":null,"abstract":"Recent advancements and developments in the field of rehabi- litation lead to the invention of myoelectric control interfaces for patients with disabilities. However, decoding the motion intent from the surface electromyography (sEMG) signals of hamstrings and quadriceps is challenging due to its complex mechanics associated with weight bearing joints and stochastic, nonstationary, and multicomponent behavior of signals. In this letter, a novel approach is proposed for multiclass gait phase classification during level walking using temporal convolutional network (TCN) of sEMG signals. For this purpose, sEMG and inertial measurement unit (IMU) data were recorded concurrently from 20 healthy participants during level walking on treadmill at a speed of 2.5 km/h. sEMG were collected from the muscles, namely, rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and semitendinosus (SEM). The IMU measurements of knee flexion/extension data are utilized for labeling the four phases of gait cycle. The root mean square of sEMG epochs is used to design the TCN framework. The results show that the proposed framework has the ability to differentiate the four classes of gait with a maximum accuracy of 86.00% using the myoelectric activity from all the four muscles. The information from the muscle pairs SEM and VL, and RF and BF, yielded the correct detection rate of 83.00% and 84.00%, respectively. In addition, the accuracy is also improved by 6% with TCN when we compare accuracy obtained through convolutional neural network architecture. The findings suggest that the proposed approach is effective in decoding the motion intent of lower limb muscles, which may lead to the development of precise movement control of lower limb prosthesis.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274963","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}
Millimeter-wave (MMW) radar imaging technology has advanced significantly, providing high-resolution images crucial for various self-driving applications. This letter presents a novel approach for extracting road surfaces within a vehicle's lane using MMW radar imagery. First, the zonal connected area detection algorithm with sliding windows effectively detects feature points in the radar images. Second, the feature point classification algorithm, utilizing horizontal offset values, preliminarily identifies the feature points for the vehicle's lane boundary. Finally, the feature points are refined based on horizontal density, followed by boundary fitting to extract the road surface accurately. Experiments were conducted on three different scenarios and three distinct datasets to verify the effectiveness and generalization ability of the algorithm.
{"title":"Vehicle Road Lane Extraction Using Millimeter-Wave Radar Imagery for Self-Driving Applications","authors":"Weixue Liu;Yuexia Wang;Jiajia Shi;Quan Shi;Zhihuo Xu","doi":"10.1109/LSENS.2024.3456120","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3456120","url":null,"abstract":"Millimeter-wave (MMW) radar imaging technology has advanced significantly, providing high-resolution images crucial for various self-driving applications. This letter presents a novel approach for extracting road surfaces within a vehicle's lane using MMW radar imagery. First, the zonal connected area detection algorithm with sliding windows effectively detects feature points in the radar images. Second, the feature point classification algorithm, utilizing horizontal offset values, preliminarily identifies the feature points for the vehicle's lane boundary. Finally, the feature points are refined based on horizontal density, followed by boundary fitting to extract the road surface accurately. Experiments were conducted on three different scenarios and three distinct datasets to verify the effectiveness and generalization ability of the algorithm.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235827","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}
The Internet of Things (IoT) relies on accurate distance estimation between devices, crucial for localization in various applications. While received signal strength indicator (RSSI)-based ranging lacks precision and time-of-flight narrow band systems perform poorly, phase-based ranging emerges as the preferred choice for Bluetooth Low Energy (BLE). Infineon's BLE prototype and its performance with a novel processing pipeline based on the minimum variance distortionless response (MVDR) algorithm are presented in this letter. Our pipeline comprises subalgorithms for preprocessing, scene identification, feature selection, feature engineering, and postprocessing. Preprocessing includes zero distance calibration, low-pass filtering, and time history averaging. Scene identification adapts parameters to environmental conditions. MVDR algorithms enable high-resolution feature transformation to project the residual phase correction term to the range domain. Postprocessing includes a tracker and data-dependent adaptation. Postprocessing in conjunction with feature selection tracks the line of sight path, minimizing distance jitter. Our proposed pipeline achieves a $text{90}{%}$ peak error of $leq$$text{1.6} ,text{m}$ without data-dependent adaptation and $leq$$text{1.2} ,text{m}$ with data-dependent adaptation and tracking, outperforming existing methods in the literature. This work demonstrates the potential of Infineon's BLE channel sounding for accurate range estimation in IoT applications.
物联网（IoT）依赖于设备之间精确的距离估计，这对各种应用中的定位至关重要。基于接收信号强度指示器（RSSI）的测距缺乏精确性，飞行时间窄带系统性能不佳，而基于相位的测距则成为蓝牙低功耗（BLE）的首选。本信介绍了英飞凌的 BLE 原型及其基于最小方差无失真响应 (MVDR) 算法的新型处理流水线的性能。我们的流程包括预处理、场景识别、特征选择、特征工程和后处理等子算法。预处理包括零距离校准、低通滤波和时间历程平均。场景识别可根据环境条件调整参数。MVDR 算法实现了高分辨率特征转换，将残差相位校正项投射到范围域。后处理包括跟踪器和数据适应。后处理与特征选择相结合，可跟踪视线路径，最大限度地减少距离抖动。我们提出的管道实现了$text{90}{%}$峰值误差为$leq$text{1.6｝不依赖于数据的自适应误差为 $leq$text{1.2}$ ，而依赖于数据的自适应误差为 $leq$text{1.2}$ 。,text{m}$与数据相关的自适应和跟踪，优于文献中的现有方法。这项工作展示了英飞凌 BLE 信道探测在物联网应用中进行精确范围估计的潜力。
{"title":"Enhancing Bluetooth Channel Sounding Performance in Complex Indoor Environments","authors":"Avik Santra;Igor Kravets;Nazarii Kotliar;Ashutosh Pandey","doi":"10.1109/LSENS.2024.3456002","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3456002","url":null,"abstract":"The Internet of Things (IoT) relies on accurate distance estimation between devices, crucial for localization in various applications. While received signal strength indicator (RSSI)-based ranging lacks precision and time-of-flight narrow band systems perform poorly, phase-based ranging emerges as the preferred choice for Bluetooth Low Energy (BLE). Infineon's BLE prototype and its performance with a novel processing pipeline based on the minimum variance distortionless response (MVDR) algorithm are presented in this letter. Our pipeline comprises subalgorithms for preprocessing, scene identification, feature selection, feature engineering, and postprocessing. Preprocessing includes zero distance calibration, low-pass filtering, and time history averaging. Scene identification adapts parameters to environmental conditions. MVDR algorithms enable high-resolution feature transformation to project the residual phase correction term to the range domain. Postprocessing includes a tracker and data-dependent adaptation. Postprocessing in conjunction with feature selection tracks the line of sight path, minimizing distance jitter. Our proposed pipeline achieves a \u0000<inline-formula><tex-math>$text{90}{%}$</tex-math></inline-formula>\u0000 peak error of \u0000<inline-formula><tex-math>$leq$</tex-math></inline-formula>\u0000<inline-formula><tex-math>$text{1.6} ,text{m}$</tex-math></inline-formula>\u0000 without data-dependent adaptation and \u0000<inline-formula><tex-math>$leq$</tex-math></inline-formula>\u0000<inline-formula><tex-math>$text{1.2} ,text{m}$</tex-math></inline-formula>\u0000 with data-dependent adaptation and tracking, outperforming existing methods in the literature. This work demonstrates the potential of Infineon's BLE channel sounding for accurate range estimation in IoT applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320443","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 : 2024-09-09DOI: 10.1109/LSENS.2024.3456289
Anshu Panbude;Pandiyarasan Veluswamy
In this letter, we propose a self-powered thermoelectric generator (TEG) to map out the thermal energy to electricity conversion. The wearable flexible thermoelectric generator (FTEG) could generate electric potential from the human skin and environment. The FTEG comes into consideration as an auxiliary supply/passive sensor for power generation to self-charge mode. In this letter, we study the reliability of the FTEG to resist chemicals, water, and moisture. For long-term reliability of the wearable FTEGs, the electrical, mechanical, and thermal performances are significant. The 8-leg FTEG in outdoor conditions at merely 2 °C temperature gradient between human skin and the environment generates an output potential of 0.63 mV to display its sensitivity to temperature variations. The simple fabrication of the TEG performance is stable under water to demonstrate the weathering protection and can withstand 1300 bending cycles. In addition, the interfacial microstructures are investigated to understand the effects of mechanical stress on the thermoelectric leg and bonding material. The mechanical strength to bend and withstand the electrical parameters without significant changes makes it an outstanding candidate for wearable applications.
{"title":"Self-Powered Standalone Performance of Thermoelectric Generator for Body Heat Harvesting","authors":"Anshu Panbude;Pandiyarasan Veluswamy","doi":"10.1109/LSENS.2024.3456289","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3456289","url":null,"abstract":"In this letter, we propose a self-powered thermoelectric generator (TEG) to map out the thermal energy to electricity conversion. The wearable flexible thermoelectric generator (FTEG) could generate electric potential from the human skin and environment. The FTEG comes into consideration as an auxiliary supply/passive sensor for power generation to self-charge mode. In this letter, we study the reliability of the FTEG to resist chemicals, water, and moisture. For long-term reliability of the wearable FTEGs, the electrical, mechanical, and thermal performances are significant. The 8-leg FTEG in outdoor conditions at merely 2 °C temperature gradient between human skin and the environment generates an output potential of 0.63 mV to display its sensitivity to temperature variations. The simple fabrication of the TEG performance is stable under water to demonstrate the weathering protection and can withstand 1300 bending cycles. In addition, the interfacial microstructures are investigated to understand the effects of mechanical stress on the thermoelectric leg and bonding material. The mechanical strength to bend and withstand the electrical parameters without significant changes makes it an outstanding candidate for wearable applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450922","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 : 2024-09-09DOI: 10.1109/LSENS.2024.3456292
Mahdi Saleh
This letter evaluates the performance of a nonsupervised clustering method for identifying abrupt changepoints in streaming sensor data. The proposed method utilizes the Jenks natural breaks (JNB) algorithm, applied in near real time using sliding temporal windows to analyze sections of sensor data and identify instances of significant phase changes. It is suitable for sensing applications that rely on detecting instantaneous changes in the sensed data for fast decisions, such as fire alarms, fault detection, and activity recognition. The method was applied to a custom dataset from 12 electrodes transitioning among different materials. Performance was evaluated based on detection accuracy and delay comparisons. Results demonstrate that applying JNB in a sliding window with a step size of half its length achieves the highest detection accuracy and the lowest error delay compared to nonoverlapping windows.
{"title":"Evaluation of Jenks Natural Breaks Clustering Algorithm for Changepoint Identification in Streaming Sensor Data","authors":"Mahdi Saleh","doi":"10.1109/LSENS.2024.3456292","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3456292","url":null,"abstract":"This letter evaluates the performance of a nonsupervised clustering method for identifying abrupt changepoints in streaming sensor data. The proposed method utilizes the Jenks natural breaks (JNB) algorithm, applied in near real time using sliding temporal windows to analyze sections of sensor data and identify instances of significant phase changes. It is suitable for sensing applications that rely on detecting instantaneous changes in the sensed data for fast decisions, such as fire alarms, fault detection, and activity recognition. The method was applied to a custom dataset from 12 electrodes transitioning among different materials. Performance was evaluated based on detection accuracy and delay comparisons. Results demonstrate that applying JNB in a sliding window with a step size of half its length achieves the highest detection accuracy and the lowest error delay compared to nonoverlapping windows.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313083","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 : 2024-09-06DOI: 10.1109/LSENS.2024.3455429
Koundinya Varma;Brahad Kokad;Anis Fatema;Aftab M. Hussain
Analysis of foot pressure, also known as plantar pressure analysis, plays a pivotal role in biomedical assessments related to posture and gait analysis. Extensive research has been conducted on leveraging this technique for clinical purposes, leading to the development of flexible pressure sensors. In this letter, we present the use of in-shoe flexible pressure sensor array for determining the nature of the walking surface. The sensor system is fabricated using eight low-cost and robust, in-shoe pressure sensors that leverage the piezoresistivity of velostat. The sensor array was characterized for four different surface types. Random Forest (RF) algorithm was used to classify the surfaces with 86% accuracy. Based on this analysis, we propose a novel method for analyzing various surfaces based on their attributes such as firmness, rigidity, and penetrability. Such a device can be used for ascertaining surface characteristics after construction, or playing surfaces in a stadium.
{"title":"Surface Characterization by Plantar Pressure Analysis Using Low-Cost in-Shoe Sensor Array","authors":"Koundinya Varma;Brahad Kokad;Anis Fatema;Aftab M. Hussain","doi":"10.1109/LSENS.2024.3455429","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3455429","url":null,"abstract":"Analysis of foot pressure, also known as plantar pressure analysis, plays a pivotal role in biomedical assessments related to posture and gait analysis. Extensive research has been conducted on leveraging this technique for clinical purposes, leading to the development of flexible pressure sensors. In this letter, we present the use of in-shoe flexible pressure sensor array for determining the nature of the walking surface. The sensor system is fabricated using eight low-cost and robust, in-shoe pressure sensors that leverage the piezoresistivity of velostat. The sensor array was characterized for four different surface types. Random Forest (RF) algorithm was used to classify the surfaces with 86% accuracy. Based on this analysis, we propose a novel method for analyzing various surfaces based on their attributes such as firmness, rigidity, and penetrability. Such a device can be used for ascertaining surface characteristics after construction, or playing surfaces in a stadium.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235693","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 : 2024-09-04DOI: 10.1109/LSENS.2024.3454764
Abhijeet Gorey;Rajat Das;Chirabrata Bhaumik;Tapas Chakravarty;Arpan Pal
Inline measurement of paint viscosity in the flowing conditions is extremely important for the paint manufacturing industry. This study proposes a noninvasive, cost-effective, inline method to measure paint's viscosity using frequency domain photoacoustic (PA) sensing. Through a PA signal, three different frequency and time domain features, namely, spectral amplitude ratio, acoustic attenuation, and acoustic wave velocity, are extracted. Due to the lower accuracy (<90%) of the aforementioned features, a novel statistical feature, i.e., the harmonic mean is derived from the existing features to enhance the accuracy of the measurement. To mitigate the experimental challenges, the viscosity model is trained from the PA data under static condition and tested for the paint under flowing condition. In the flowing conditions, the accuracy in the measurement is found to be less than 93%. Hence, a correction factor is introduced, which considers the Doppler shift in the PA wave velocity due to the paint flow. With this correction factor, the accuracy of the viscosity measurement is found to be greater than 95%. The developed viscosity model is validated through the fourfold cross-validation and the results are confirmed for their repeatability and tested with different paint samples.
在线测量流动状态下的涂料粘度对涂料制造业极为重要。本研究提出了一种利用频域光声（PA）传感技术测量涂料粘度的无创、经济、在线方法。通过 PA 信号，可提取三种不同的频域和时域特征，即频谱振幅比、声衰减和声波速度。由于上述特征的准确度较低（<90%），因此从现有特征中提取了一种新的统计特征，即谐波平均值，以提高测量的准确度。为减轻实验挑战，我们根据静态 PA 数据训练粘度模型，并对流动条件下的涂料进行测试。在流动条件下，测量精度低于 93%。因此，引入了一个校正因子，该因子考虑了涂料流动导致的 PA 波速多普勒偏移。使用该校正系数后，粘度测量的准确度大于 95%。开发的粘度模型通过四重交叉验证进行了验证，结果的可重复性得到了确认，并用不同的油漆样品进行了测试。
{"title":"Photoacoustic Sensing System for Noninvasive and Real-Time Measurement of Paint's Viscosity in Flowing Conditions","authors":"Abhijeet Gorey;Rajat Das;Chirabrata Bhaumik;Tapas Chakravarty;Arpan Pal","doi":"10.1109/LSENS.2024.3454764","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3454764","url":null,"abstract":"Inline measurement of paint viscosity in the flowing conditions is extremely important for the paint manufacturing industry. This study proposes a noninvasive, cost-effective, inline method to measure paint's viscosity using frequency domain photoacoustic (PA) sensing. Through a PA signal, three different frequency and time domain features, namely, spectral amplitude ratio, acoustic attenuation, and acoustic wave velocity, are extracted. Due to the lower accuracy (<90%) of the aforementioned features, a novel statistical feature, i.e., the harmonic mean is derived from the existing features to enhance the accuracy of the measurement. To mitigate the experimental challenges, the viscosity model is trained from the PA data under static condition and tested for the paint under flowing condition. In the flowing conditions, the accuracy in the measurement is found to be less than 93%. Hence, a correction factor is introduced, which considers the Doppler shift in the PA wave velocity due to the paint flow. With this correction factor, the accuracy of the viscosity measurement is found to be greater than 95%. The developed viscosity model is validated through the fourfold cross-validation and the results are confirmed for their repeatability and tested with different paint samples.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328402","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}
The pH value in bodily fluids is a crucial diagnostic marker. Conventional glass-rod pH sensors display reliability in aqueous solutions, but the pH-sensitive glass membrane makes them prone to inaccuracies in viscous solutions due to elevated junction potentials and bulky design hinders miniaturization. To overcome this issue, this work introduces a new pH sensor design and fabrication that enables miniaturization and reliability in aqueous and viscous solutions and facilitates insertion into a needle for in vivo monitoring. Utilizing a printing technique for the application of iridium oxide (IrOx) and silver/silver chloride coating on a single flexible polyimide substrate offers cost-effectiveness and production scalability. The sensor then is tailored with a sharp blade to a narrow strip that fits into a 20-gauge needle. The electrochemical measurements demonstrate that electrodes produced through this method demonstrate an accuracy of up to 0.1 pH within a narrow pH range (7.35–7.45) in buffer solutions and real human serum tests.
{"title":"A Miniature pH Sensor in a Subcutaneous Injection Needle for Biofluid Sensing","authors":"Khengdauliu Chawang;Sen Bing;Jon Stellar;J.-C. Chiao","doi":"10.1109/LSENS.2024.3454486","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3454486","url":null,"abstract":"The pH value in bodily fluids is a crucial diagnostic marker. Conventional glass-rod pH sensors display reliability in aqueous solutions, but the pH-sensitive glass membrane makes them prone to inaccuracies in viscous solutions due to elevated junction potentials and bulky design hinders miniaturization. To overcome this issue, this work introduces a new pH sensor design and fabrication that enables miniaturization and reliability in aqueous and viscous solutions and facilitates insertion into a needle for in vivo monitoring. Utilizing a printing technique for the application of iridium oxide (IrOx) and silver/silver chloride coating on a single flexible polyimide substrate offers cost-effectiveness and production scalability. The sensor then is tailored with a sharp blade to a narrow strip that fits into a 20-gauge needle. The electrochemical measurements demonstrate that electrodes produced through this method demonstrate an accuracy of up to 0.1 pH within a narrow pH range (7.35–7.45) in buffer solutions and real human serum tests.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Terahertz (THz) light field imaging inherently allows capturing the 3-D geometry of a target but at the cost of an increased data volume. Compressive sensing techniques are instrumental in minimizing data acquisition requirements. However, they often rely on computationally expensive sparse reconstruction approaches with high memory footprint. This research introduces an advanced coarse-to-fine (CTF) sparse 3-D reconstruction strategy aimed at enhancing the precision of reconstructed images while significantly reducing computational load and memory footprint. By employing a sequence of sensing matrices of increasing resolution, our approach avoids falling into an ill-conditioned inversion and strikes a balance between reconstruction quality and computational efficiency. We demonstrate the effectiveness of this CTF strategy through its integration with several established algorithms, including basis pursuit (BP), fast iterative shrinkage-threshold algorithm (FISTA), and others. The results showcase the potential of the CTF approach to improve 3-D image reconstruction accuracy and processing speed in THz light field imaging.
{"title":"Coarse-to-Fine Sparse 3-D Reconstruction in THz Light Field Imaging","authors":"Abdulraouf Kutaish;Miguel Heredia Conde;Ullrich Pfeiffer","doi":"10.1109/LSENS.2024.3454567","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3454567","url":null,"abstract":"Terahertz (THz) light field imaging inherently allows capturing the 3-D geometry of a target but at the cost of an increased data volume. Compressive sensing techniques are instrumental in minimizing data acquisition requirements. However, they often rely on computationally expensive sparse reconstruction approaches with high memory footprint. This research introduces an advanced coarse-to-fine (CTF) sparse 3-D reconstruction strategy aimed at enhancing the precision of reconstructed images while significantly reducing computational load and memory footprint. By employing a sequence of sensing matrices of increasing resolution, our approach avoids falling into an ill-conditioned inversion and strikes a balance between reconstruction quality and computational efficiency. We demonstrate the effectiveness of this CTF strategy through its integration with several established algorithms, including basis pursuit (BP), fast iterative shrinkage-threshold algorithm (FISTA), and others. The results showcase the potential of the CTF approach to improve 3-D image reconstruction accuracy and processing speed in THz light field imaging.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313082","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 : 2024-09-04DOI: 10.1109/LSENS.2024.3454718
Haoyu Liang;Jun Wu;Tianle Liu;Hao Wang;Weiwei Cao
In order to meet the frequency requirements of unmanned aerial vehicles (UAVs), sensors assist UAVs in cooperative spectrum sensing (CSS) to identify available spectrum resources and opportunistically access the channel being underutilized by the primary user (PU). However, in such a UAV-assisted cognitive wireless sensor network (CWSN), the cooperative mode among multiple UAVs with built-in sensors may incur high overhead costs, resulting in the spectrum sensing performance degradation. Therefore, we introduce a differential sequential 1, which incorporates a differential mechanism and leverages the sequential idea based on the classical voting rule to enhance the CSS performance and efficiency. In view of this, we formulate three scenarios to characterize the PU activity and introduce a multislot cooperative mode within a single UAV with built-in sensor to realize cooperative gain. Finally, simulation results demonstrate that the superiority of our proposal with respect to the detection performance and sample size is evident.
为了满足无人飞行器（UAV）的频率要求，传感器协助无人飞行器进行合作频谱感知（CSS），以识别可用频谱资源，并伺机访问主用户（PU）未充分利用的信道。然而，在这种无人机辅助认知无线传感器网络（CWSN）中，多个内置传感器的无人机之间的合作模式可能会产生高昂的开销成本，导致频谱感知性能下降。因此，我们引入了一种差分序列 1，它结合了差分机制，并利用基于经典投票规则的序列思想来提高 CSS 性能和效率。有鉴于此，我们制定了三种场景来描述 PU 活动，并在单个内置传感器的无人机内引入多频段合作模式，以实现合作增益。最后，仿真结果表明，我们的建议在检测性能和样本量方面具有明显的优势。
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