Pub Date : 2024-09-04DOI: 10.1109/LSENS.2024.3454708
Shengfei Xiao;Jun Wu;Peiyang Lin;Lei Qiao;Zhaoyang Qiu;Mingkun Su
In order to meet the increasing frequency demand for sensors and their related applications, cognitive radio (CR) technology has been integrated into wireless sensor networks, detecting available spectrum resources through collaborative spectrum sensing (CSS) among multiple sensors and avoiding harmful interference to the primary user. However, some malicious sensor nodes (MSNs) may also take advantage of collaborative opportunities to launch Byzantine attack, reducing the performance and efficiency of CSS. In order to suppress the negative impact of MSNs, this letter proposes a reputation-based self-differential sequential mechanism (R-SDSM) to defend against Byzantine attack. First, sensor nodes with high reputation value are prioritized to participate in CSS and complete the data fusion with more appropriate weight allocation. Furthermore, a self-differential sequential mechanism is proposed to reduce the reporting decisions required for the fusion center. Finally, numerical simulation results demonstrate that in contrast to other data fusion rules, the proposed R-SDSM provides higher detection accuracy and fewer reporting decisions.
{"title":"Reputation-Based Self-Differential Sequential Mechanism for Collaborative Spectrum Sensing Against Byzantine Attack in Cognitive Wireless Sensor Networks","authors":"Shengfei Xiao;Jun Wu;Peiyang Lin;Lei Qiao;Zhaoyang Qiu;Mingkun Su","doi":"10.1109/LSENS.2024.3454708","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3454708","url":null,"abstract":"In order to meet the increasing frequency demand for sensors and their related applications, cognitive radio (CR) technology has been integrated into wireless sensor networks, detecting available spectrum resources through collaborative spectrum sensing (CSS) among multiple sensors and avoiding harmful interference to the primary user. However, some malicious sensor nodes (MSNs) may also take advantage of collaborative opportunities to launch Byzantine attack, reducing the performance and efficiency of CSS. In order to suppress the negative impact of MSNs, this letter proposes a reputation-based self-differential sequential mechanism (R-SDSM) to defend against Byzantine attack. First, sensor nodes with high reputation value are prioritized to participate in CSS and complete the data fusion with more appropriate weight allocation. Furthermore, a self-differential sequential mechanism is proposed to reduce the reporting decisions required for the fusion center. Finally, numerical simulation results demonstrate that in contrast to other data fusion rules, the proposed R-SDSM provides higher detection accuracy and fewer reporting decisions.","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":"142275019","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 : 2024-09-02DOI: 10.1109/LSENS.2024.3453038
Ayman Morsy;Cédric Baijot;Gobinath Jegannathan;Thomas Lapauw;Thomas Van den Dries;Maarten Kuijk
This letter proposes a novel single photon avalanche diode (SPAD)-based pixel, designed for direct Time-of-Flight (ToF) imaging with in-pixel averaging, which provides a promising advancement in low-power and potentially high image resolution for outdoor applications. By utilizing a laser pulse and two orthogonal sinusoidal signals, the pixel averages out the detected ambient light while accumulating the laser pulse round-trip time. A prototype pixel array was fabricated using a 180 nm CMOS process, featuring a commercial SPAD cell. By characterizing one pixel and employing a 100 klux solar emulator as an ambient light source with a fixed 40 ambient-to-signal ratio over a 360 $^{circ }$ phase shift, equivalent to 6 m detection range, the maximum detected accuracy error was 3.3%, with a 5 cm precision.
{"title":"An In-Pixel Ambient Suppression Method for Direct Time of Flight","authors":"Ayman Morsy;Cédric Baijot;Gobinath Jegannathan;Thomas Lapauw;Thomas Van den Dries;Maarten Kuijk","doi":"10.1109/LSENS.2024.3453038","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3453038","url":null,"abstract":"This letter proposes a novel single photon avalanche diode (SPAD)-based pixel, designed for direct Time-of-Flight (ToF) imaging with in-pixel averaging, which provides a promising advancement in low-power and potentially high image resolution for outdoor applications. By utilizing a laser pulse and two orthogonal sinusoidal signals, the pixel averages out the detected ambient light while accumulating the laser pulse round-trip time. A prototype pixel array was fabricated using a 180 nm CMOS process, featuring a commercial SPAD cell. By characterizing one pixel and employing a 100 klux solar emulator as an ambient light source with a fixed 40 ambient-to-signal ratio over a 360\u0000<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>\u0000 phase shift, equivalent to 6 m detection range, the maximum detected accuracy error was 3.3%, with a 5 cm precision.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397406","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 : 2024-08-30DOI: 10.1109/LSENS.2024.3452205
Talha Nadeem;Khurrram Ali;Muhammad Tahir
Sensors deployed in real-world conditions often produce measurements corrupted by outliers due to model uncertainties, changes in the surrounding environment, and/or data loss. As a result, managing these outliers becomes crucial for state estimation to avoid inaccurate estimations and a reduction in the reliability of results. To address this issue, we introduce a novel form of extended Kalman filter (EKF) based on the maximum a posteriori (MAP) principle for scenarios where outliers simultaneously occur in multiple dimensions. For detecting outliers during the filtering process, we introduce a novel variant of the normalized innovation ratio (NIR) test and embed it within the EKF framework. Our approach enhances the estimation accuracy and computational efficiency of state estimation process even when data from several sensors simultaneously contain outliers.
{"title":"NIR-EKF: Normalized Innovation Ratio-Based EKF for Robust State Estimation","authors":"Talha Nadeem;Khurrram Ali;Muhammad Tahir","doi":"10.1109/LSENS.2024.3452205","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3452205","url":null,"abstract":"Sensors deployed in real-world conditions often produce measurements corrupted by outliers due to model uncertainties, changes in the surrounding environment, and/or data loss. As a result, managing these outliers becomes crucial for state estimation to avoid inaccurate estimations and a reduction in the reliability of results. To address this issue, we introduce a novel form of extended Kalman filter (EKF) based on the maximum a posteriori (MAP) principle for scenarios where outliers simultaneously occur in multiple dimensions. For detecting outliers during the filtering process, we introduce a novel variant of the normalized innovation ratio (NIR) test and embed it within the EKF framework. Our approach enhances the estimation accuracy and computational efficiency of state estimation process even when data from several sensors simultaneously contain outliers.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174021","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 : 2024-08-29DOI: 10.1109/LSENS.2024.3451405
Ho-Jun Lee;Sae-Byeok Kyung;Sung-Won Kim;Eun-Yul Lee;Ju-Won Kim
This letter proposes a method to the estimation of tension force in tension members using the grated recurrent unit (GRU) algorithm. In this letter, a yoke-type elasto-magnetic (E/M) sensor was developed based on numerical ANSYS Maxwell simulations to enhance the applicability through the structural improvement of the existing solenoid-type magnetized E/M sensor. The induced voltage signal collected based on the yoke-type E/M sensor was applied to the GRU algorithm. As a result of applying the GRU model to the induced voltage signal data according to the change in tension force of the yoke-type E/M sensor, it was proven that high-accuracy tension force estimation is possible. These results suggest new possibilities for structural health monitoring technology through nondestructive testing. This study presents the applicability of artificial-intelligence-based techniques in nondestructive measurements of tension members for the health monitoring of structures.
{"title":"Estimation of Tension Force in Tension Members Using GRU Algorithm Based on Yoke-Type Elasto-Magnetic Sensor Data","authors":"Ho-Jun Lee;Sae-Byeok Kyung;Sung-Won Kim;Eun-Yul Lee;Ju-Won Kim","doi":"10.1109/LSENS.2024.3451405","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3451405","url":null,"abstract":"This letter proposes a method to the estimation of tension force in tension members using the grated recurrent unit (GRU) algorithm. In this letter, a yoke-type elasto-magnetic (E/M) sensor was developed based on numerical ANSYS Maxwell simulations to enhance the applicability through the structural improvement of the existing solenoid-type magnetized E/M sensor. The induced voltage signal collected based on the yoke-type E/M sensor was applied to the GRU algorithm. As a result of applying the GRU model to the induced voltage signal data according to the change in tension force of the yoke-type E/M sensor, it was proven that high-accuracy tension force estimation is possible. These results suggest new possibilities for structural health monitoring technology through nondestructive testing. This study presents the applicability of artificial-intelligence-based techniques in nondestructive measurements of tension members for the health monitoring of structures.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174016","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 : 2024-08-29DOI: 10.1109/LSENS.2024.3451470
Qinglu Zheng;Youyuan Wang;Zhanxi Zhang
The normal operation of electrical equipment is related to the stability of the power system. The dry-type transformer, as an important part of the distribution network, directly guarantees that users can use high-quality electricity. At present, most of the fault diagnosis of dry-type transformers is limited to the detection and maintenance of power outages, and there are few studies on nondestructive testing of power outages. In this letter, the operation state of the dry-type transformer is judged by the small-sample acoustic array signal, and the highly correlated intrinsic mode components are extracted by empirical mode decomposition (EMD); the highly correlated intrinsic mode components are further denoised by combining the adaptive wavelet basis transform. Then, the Hilbert transform is used to fuse the multichannel signals to form the original eigentensor. The principal component analysis is used to reduce the dimensionality of the original eigentensor to reduce the feature information surplus. The improved residual network is used to classify different features of dry-type transformers. It is verified that the proposed method has a high accuracy of 97.8% under the premise of small-sample datasets, which is better than that of the same type of detection method and has good robustness.
{"title":"Fault Diagnosis Algorithm for Dry-Type Transformer Based on Deep Learning of Small-Sample Acoustic Array Signals","authors":"Qinglu Zheng;Youyuan Wang;Zhanxi Zhang","doi":"10.1109/LSENS.2024.3451470","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3451470","url":null,"abstract":"The normal operation of electrical equipment is related to the stability of the power system. The dry-type transformer, as an important part of the distribution network, directly guarantees that users can use high-quality electricity. At present, most of the fault diagnosis of dry-type transformers is limited to the detection and maintenance of power outages, and there are few studies on nondestructive testing of power outages. In this letter, the operation state of the dry-type transformer is judged by the small-sample acoustic array signal, and the highly correlated intrinsic mode components are extracted by empirical mode decomposition (EMD); the highly correlated intrinsic mode components are further denoised by combining the adaptive wavelet basis transform. Then, the Hilbert transform is used to fuse the multichannel signals to form the original eigentensor. The principal component analysis is used to reduce the dimensionality of the original eigentensor to reduce the feature information surplus. The improved residual network is used to classify different features of dry-type transformers. It is verified that the proposed method has a high accuracy of 97.8% under the premise of small-sample datasets, which is better than that of the same type of detection method and has good robustness.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165003","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}
This letter addresses the challenge of efficient direction of arrival (DOA) estimation in 3-D sparse arrays, crucial for applications, such as radar and wireless communication systems. We introduce a novel methodology that combines the Nystrom approximation with the unitary root-multiple signal classification (MUSIC) algorithm to precisely estimate DOAs while significantly reducing computational complexity. Our approach strategically selects a subset of sensors using the Nystrom approximation, resulting in a notable decrease in simulation time compared to conventional methods, such as Root-MUSIC and MR-ESPRIT. Extensive simulations validate the efficacy of our method, demonstrating a reduction of up to 39% in simulation time with a sensor subset size of 20. This technique, which enhances efficiency, has the potential to transform DOA estimation in 3-D sparse arrays, making it suitable for real-world applications that demand rapid and accurate signal processing.
{"title":"Unitary Root-MUSIC Method With Nystrom Approximation for 3-D Sparse Array DOA Estimation in Sensor Networks","authors":"Veerendra D;Miguel Villagomez-Galindo;Ana Beatriz Martínez Valencia;Niranjan KR;Arora Jasmineet Kaur;Upendra Kumar Potnuru;Jasgurpreet Singh Chohan;Bade Venkata Suresh;Sudhanshu Maurya","doi":"10.1109/LSENS.2024.3451723","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3451723","url":null,"abstract":"This letter addresses the challenge of efficient direction of arrival (DOA) estimation in 3-D sparse arrays, crucial for applications, such as radar and wireless communication systems. We introduce a novel methodology that combines the Nystrom approximation with the unitary root-multiple signal classification (MUSIC) algorithm to precisely estimate DOAs while significantly reducing computational complexity. Our approach strategically selects a subset of sensors using the Nystrom approximation, resulting in a notable decrease in simulation time compared to conventional methods, such as Root-MUSIC and MR-ESPRIT. Extensive simulations validate the efficacy of our method, demonstrating a reduction of up to 39% in simulation time with a sensor subset size of 20. This technique, which enhances efficiency, has the potential to transform DOA estimation in 3-D sparse arrays, making it suitable for real-world applications that demand rapid and accurate signal processing.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275020","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 : 2024-08-29DOI: 10.1109/LSENS.2024.3451960
Abisek Dahal;Soumen Moulik
Human activity recognition (HAR) plays an important role in various domains, such as healthcare, elderly care, sports, gait analysis, and security surveillance. Despite its significance in various fields, attaining a high accuracy remains a formidable challenge. This letter proposes a multimodel stacking and ensemble framework for HAR. The proposed model uses a horizontal stacking approach integrating three different model, namely, ridge regression, LightGBM, and gradient boosting machine (GBM) combined to form a blended model. GBM is also serves as the meta-learner in this configuration. By leveraging this stacking framework, our model significantly enhances the accuracy of HAR. The proposed model achieves remarkable performance in publicly available datasets with accuracy rates of 98% on the HCI-HAR dataset, 99.10% on the WISDM dataset, and 99.20% on the mHealth dataset thereby surpassing existing benchmarks in machine learning. Therefore, the proposed model uses an ensemble stacking model to represent a promising avenue for enhancing HAR and has potential applications in various fields.
人类活动识别（HAR）在医疗保健、老年人护理、体育运动、步态分析和安全监控等多个领域发挥着重要作用。尽管它在各个领域都具有重要意义，但要达到高精度仍然是一项艰巨的挑战。本文提出了一种用于 HAR 的多模型堆叠和集合框架。所提出的模型采用水平堆叠方法，将脊回归、LightGBM 和梯度提升机（GBM）这三种不同的模型结合起来，形成一个混合模型。在这种配置中，GBM 也是元学习器。通过利用这种堆叠框架，我们的模型大大提高了 HAR 的准确性。所提出的模型在公开数据集上取得了卓越的性能，在 HCI-HAR 数据集上的准确率为 98%，在 WISDM 数据集上的准确率为 99.10%，在 mHealth 数据集上的准确率为 99.20%，从而超越了机器学习领域的现有基准。因此，所提出的模型使用了集合堆叠模型，是提高 HAR 的一个有前途的途径，在各个领域都有潜在的应用前景。
{"title":"The Multimodel Stacking and Ensemble Framework for Human Activity Recognition","authors":"Abisek Dahal;Soumen Moulik","doi":"10.1109/LSENS.2024.3451960","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3451960","url":null,"abstract":"Human activity recognition (HAR) plays an important role in various domains, such as healthcare, elderly care, sports, gait analysis, and security surveillance. Despite its significance in various fields, attaining a high accuracy remains a formidable challenge. This letter proposes a multimodel stacking and ensemble framework for HAR. The proposed model uses a horizontal stacking approach integrating three different model, namely, ridge regression, LightGBM, and gradient boosting machine (GBM) combined to form a blended model. GBM is also serves as the meta-learner in this configuration. By leveraging this stacking framework, our model significantly enhances the accuracy of HAR. The proposed model achieves remarkable performance in publicly available datasets with accuracy rates of 98% on the HCI-HAR dataset, 99.10% on the WISDM dataset, and 99.20% on the mHealth dataset thereby surpassing existing benchmarks in machine learning. Therefore, the proposed model uses an ensemble stacking model to represent a promising avenue for enhancing HAR and has potential applications in various fields.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165033","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}
Quantification of multiple drugs is of great importance and urgently needed in therapeutic drug monitoring (TDM) and personalized therapy. Especially, based on cyclic voltammograms (CVs) obtained by electrochemical sensors, the use of artificial neural networks (ANNs) has been widely attempted in the accurate quantification of drug concentrations, enabling the development of point-of-care and potentially system-level wearable devices. However, most of the work only considers the accuracy of how the predicted value is close to the actual value, which does not consider whether the predicted drug concentration is underestimated. In practical drug quantification, potential toxicity due to overexposure with underestimated quantification can lead to endangering the patient's body. Therefore, avoiding underestimating the concentration of drugs based on existing quantification models is required and necessary to optimize the conventional loss function at the output stage of ANN. In this letter, a novel loss function based on mean squared error (MSE), WeightedMSE, is proposed for avoiding underestimated quantification. It can be changed flexibly by adjusting parameters in order to adapt the acceptable overestimation range corresponding to the different types of drugs. A simulated dataset and a real dataset of etoposide and methotrexate are used as drug models, demonstrating that the proposed method can avoid underestimation in predicted values by over 98% in quantifying the concentration of multiple drugs and showing significant effectiveness for the development of point-of-care and wearable monitoring systems.
在治疗药物监测（TDM）和个性化治疗中，多种药物的定量分析具有重要意义和迫切需求。特别是，基于电化学传感器获得的循环伏安图（CVs），人工神经网络（ANNs）已被广泛应用于药物浓度的精确定量，从而促进了护理点和潜在的系统级可穿戴设备的开发。然而，大多数工作只考虑了预测值与实际值接近的准确性，而没有考虑预测的药物浓度是否被低估。在实际的药物定量分析中，由于定量估计不足而造成的过度暴露所带来的潜在毒性可能会危及患者的身体。因此，在现有定量模型的基础上避免低估药物浓度，需要在 ANN 的输出阶段优化传统的损失函数。本文提出了一种基于均方误差（MSE）的新型损失函数--WeightedMSE，以避免低估定量。它可以通过调整参数灵活改变，以适应与不同类型药物相对应的可接受的高估范围。使用依托泊苷和甲氨蝶呤的模拟数据集和真实数据集作为药物模型，证明所提出的方法在量化多种药物浓度时可避免预测值低估 98% 以上，对开发护理点和可穿戴监测系统具有显著效果。
{"title":"Optimized Quantification of Multiple Drug Concentrations by WeightedMSE With Machine Learning on Electrochemical Sensor","authors":"Tatsunori Matsumoto;Lin Du;Francesca Rodino;Yann Thoma;Chinthaka Premachandra;Sandro Carrara","doi":"10.1109/LSENS.2024.3452009","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3452009","url":null,"abstract":"Quantification of multiple drugs is of great importance and urgently needed in therapeutic drug monitoring (TDM) and personalized therapy. Especially, based on cyclic voltammograms (CVs) obtained by electrochemical sensors, the use of artificial neural networks (ANNs) has been widely attempted in the accurate quantification of drug concentrations, enabling the development of point-of-care and potentially system-level wearable devices. However, most of the work only considers the accuracy of how the predicted value is close to the actual value, which does not consider whether the predicted drug concentration is underestimated. In practical drug quantification, potential toxicity due to overexposure with underestimated quantification can lead to endangering the patient's body. Therefore, avoiding underestimating the concentration of drugs based on existing quantification models is required and necessary to optimize the conventional loss function at the output stage of ANN. In this letter, a novel loss function based on mean squared error (MSE), WeightedMSE, is proposed for avoiding underestimated quantification. It can be changed flexibly by adjusting parameters in order to adapt the acceptable overestimation range corresponding to the different types of drugs. A simulated dataset and a real dataset of etoposide and methotrexate are used as drug models, demonstrating that the proposed method can avoid underestimation in predicted values by over 98% in quantifying the concentration of multiple drugs and showing significant effectiveness for the development of point-of-care and wearable monitoring systems.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235828","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}
Malaria continues to pose a significant global health challenge, with substantial impediments arising from the need for more reliable, effective, and economically viable diagnostic tools, particularly for early detection. This research introduces a novel microfluidic device designed for malaria-diagnostics through the detection of hemozoin (Hz), a prevalent biomarker for the disease. Our methodology involves the collection of a minimal blood sample, which is subsequently processed through a microfluidic system. This system exploits the paramagnetic properties of Hz to isolate infected blood cells using magnetophoretic separation. The detection process employs a relative capacitive measurement technique capable of quantifying Hz concentrations ranging from 417 $fM$ to 17 $pM$ , facilitating and enhancing malaria diagnosis. Simulations results confirm the efficacy of our device in providing a rapid, cost-effective, and readily producible diagnostic solution. This research demonstrates the potential of integrating advanced microfluidic technology and sensitive detection systems into a compact, portable unit, offering significant improvements over existing malaria diagnostic tools.
{"title":"A Novel Microfluidic System for Capacitive Detection Via Magnetophoretic Separation of Malaria-Infected Red Blood Cells","authors":"Amirmahdi Tavakolidakhrabadi;Théo Domange;Clémentine Naım;Francesca Rodino;Ali Meimandi;Cédric Bessire;Sandro Carrara","doi":"10.1109/LSENS.2024.3451238","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3451238","url":null,"abstract":"Malaria continues to pose a significant global health challenge, with substantial impediments arising from the need for more reliable, effective, and economically viable diagnostic tools, particularly for early detection. This research introduces a novel microfluidic device designed for malaria-diagnostics through the detection of hemozoin (Hz), a prevalent biomarker for the disease. Our methodology involves the collection of a minimal blood sample, which is subsequently processed through a microfluidic system. This system exploits the paramagnetic properties of Hz to isolate infected blood cells using magnetophoretic separation. The detection process employs a relative capacitive measurement technique capable of quantifying Hz concentrations ranging from 417 \u0000<inline-formula><tex-math>$fM$</tex-math></inline-formula>\u0000 to 17 \u0000<inline-formula><tex-math>$pM$</tex-math></inline-formula>\u0000, facilitating and enhancing malaria diagnosis. Simulations results confirm the efficacy of our device in providing a rapid, cost-effective, and readily producible diagnostic solution. This research demonstrates the potential of integrating advanced microfluidic technology and sensitive detection systems into a compact, portable unit, offering significant improvements over existing malaria diagnostic tools.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169703","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 : 2024-08-27DOI: 10.1109/LSENS.2024.3450498
Chi Tran Nhu;Loc Do Quang;Chun-Ping Jen;Trinh Chu Duc;Tung Bui Thanh
In this letter, a protein enrichment microfluidic platform with an integrated bioelectrochemical sensing system has been proposed and demonstrated for the first time, enabling protein preconcentration and detection. The proposed chip was composed of an electrochemical biosensor integrated into a preconcentrator with a dual-gate structure. The bioelectrochemical sensor had three electrodes, including working, counter, and reference electrodes. The working and counter electrodes were made of gold, while the reference electrode was made of Ag/AgCl. The preconcentrator was designed with three microchannels, with a main channel electrically connected to two subchannels through Nafion ion-selective membranes. The chip was fabricated using photolithography and soft lithography techniques. Ag and AgCl layers were deposited on the gold electrode to form the reference electrode. The Nafion membrane was created using the microflow patterning technique. Then, the gold electrode surface was modified to attach anti-albumin antibodies (anti-bovine serum albumin—anti-BSA) and form the biosensor. Bovine serum albumin–fluorescein isothiocyanate conjugate was specifically bound to anti-BSA through the protein preconcentration process at the biosensor area. The experimental results show that bovine serum albumin (BSA) proteins were concentrated successfully after applying potentials to the ends of the microchannels. The protein concentration increased 25 times after 80 s. The change in the electrochemical impedance spectroscopy (EIS) signal demonstrates the specific binding between BSA and anti-BSA on the electrode surface. In addition, the results also show the significant effectiveness of the protein preconcentration process for improving the binding ability and electrical signal amplification of the bioelectrochemical sensor. With the obtained results, a lab-on-a-chip system can be developed to quantify protein concentration and diagnose some cancer diseases.
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