Pub Date : 2025-02-26DOI: 10.1109/JSEN.2025.3542781
Nannan Zhao;Yishan Su;Chengzhi Li;Xianghan Wang
Underwater wireless sensor networks (UWSNs) are increasingly being applied in various fields, and an efficient routing protocol is critical for ensuring reliable data transmission in UWSNs. Due to the dynamic topology and limited energy conditions of UWSNs, the design of routing protocols usually comprehensively consider multiple metrics such as node mobility and energy efficiency to achieve reliable routing decisions. However, these parameters have different characteristics and often involve trade-offs. For example, energy efficiency prioritizes nodes with higher remaining energy for data forwarding to extend network lifespan but may result in selecting nodes with poor channel conditions, reducing reliability. Conversely, prioritizing link quality often favors well-connected nodes, which may have lower energy due to frequent use. Traditional additive composite functions struggle to balance these trade-offs dynamically, potentially leading to suboptimal routing decisions. Moreover, the rapid changes of acoustic channels in underwater environments can result in time-varying link quality. To address these issues, this article proposes a fuzzy control-based channel-aware reliable routing protocol for underwater sensor networks (FCCR). The proposed protocol can coordinate and handle multiple metrics to optimize the selection of the next-hop node, thereby improving data forwarding efficiency and reliability. Additionally, in UWSNs, node movement or failure may cause the communication link between nodes to break, resulting in routing void. The protocol also incorporates a void recovery mechanism, which effectively addresses the routing void problem. The field experiment and simulation results demonstrated that the proposed protocol performs well in terms of packet delivery ratio (PDR), average end-to-end delay (EED), and energy efficiency.
{"title":"Fuzzy Control-Based Channel-Aware Reliable Routing Protocol for Underwater Sensor Networks","authors":"Nannan Zhao;Yishan Su;Chengzhi Li;Xianghan Wang","doi":"10.1109/JSEN.2025.3542781","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3542781","url":null,"abstract":"Underwater wireless sensor networks (UWSNs) are increasingly being applied in various fields, and an efficient routing protocol is critical for ensuring reliable data transmission in UWSNs. Due to the dynamic topology and limited energy conditions of UWSNs, the design of routing protocols usually comprehensively consider multiple metrics such as node mobility and energy efficiency to achieve reliable routing decisions. However, these parameters have different characteristics and often involve trade-offs. For example, energy efficiency prioritizes nodes with higher remaining energy for data forwarding to extend network lifespan but may result in selecting nodes with poor channel conditions, reducing reliability. Conversely, prioritizing link quality often favors well-connected nodes, which may have lower energy due to frequent use. Traditional additive composite functions struggle to balance these trade-offs dynamically, potentially leading to suboptimal routing decisions. Moreover, the rapid changes of acoustic channels in underwater environments can result in time-varying link quality. To address these issues, this article proposes a fuzzy control-based channel-aware reliable routing protocol for underwater sensor networks (FCCR). The proposed protocol can coordinate and handle multiple metrics to optimize the selection of the next-hop node, thereby improving data forwarding efficiency and reliability. Additionally, in UWSNs, node movement or failure may cause the communication link between nodes to break, resulting in routing void. The protocol also incorporates a void recovery mechanism, which effectively addresses the routing void problem. The field experiment and simulation results demonstrated that the proposed protocol performs well in terms of packet delivery ratio (PDR), average end-to-end delay (EED), and energy efficiency.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"12222-12235"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1109/JSEN.2025.3542848
Chengyu Zhang;Jinxiu Guo;Baojun Chen;Siyang Zuo
Gastrointestinal (GI) cancers have a significant impact on human health due to their high mortality rate. In endoscopic submucosal dissection (ESD), surgical instruments require both sufficient flexibility and operational freedom, as well as a precise and sensitive force sensing function to avoid the risks associated with insufficient or excessive contact force between the tip and the tissue. These requirements pose significant challenges to the development of flexible surgical instruments with force sensing capabilities. In this work, we propose a 6-DOF flexible instrument based on tendon-actuated stacked-buckle joints. This instrument can achieve large hemispherical and cylindrical workspaces with a radius of 27 mm, and the measured tip positioning accuracy of the continuum is better than 0.36 mm. In addition, a fiber Bragg grating (FBG)-based three-axis force sensing unit is integrated into the distal end of the electric-knife instrument. The calibration experiment shows that the flexible instrument operates under three-axis force with high resolutions of 0.169, 0.172, and 0.398 mN for Fx, Fy, and Fz, respectively. The dynamic performance and temperature decoupling algorithm indicate that the proposed FBG sensing mechanism can accurately detect three-axis force. The ex vivo swine stomach ESD experiment has demonstrated the clinical application potential and practicality of the instrument.
{"title":"A Novel 6-DOF Flexible Instrument With High-Resolution Three-Axis Force Sensing Unit for Endoscopic Submucosal Dissection","authors":"Chengyu Zhang;Jinxiu Guo;Baojun Chen;Siyang Zuo","doi":"10.1109/JSEN.2025.3542848","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3542848","url":null,"abstract":"Gastrointestinal (GI) cancers have a significant impact on human health due to their high mortality rate. In endoscopic submucosal dissection (ESD), surgical instruments require both sufficient flexibility and operational freedom, as well as a precise and sensitive force sensing function to avoid the risks associated with insufficient or excessive contact force between the tip and the tissue. These requirements pose significant challenges to the development of flexible surgical instruments with force sensing capabilities. In this work, we propose a 6-DOF flexible instrument based on tendon-actuated stacked-buckle joints. This instrument can achieve large hemispherical and cylindrical workspaces with a radius of 27 mm, and the measured tip positioning accuracy of the continuum is better than 0.36 mm. In addition, a fiber Bragg grating (FBG)-based three-axis force sensing unit is integrated into the distal end of the electric-knife instrument. The calibration experiment shows that the flexible instrument operates under three-axis force with high resolutions of 0.169, 0.172, and 0.398 mN for Fx, Fy, and Fz, respectively. The dynamic performance and temperature decoupling algorithm indicate that the proposed FBG sensing mechanism can accurately detect three-axis force. The ex vivo swine stomach ESD experiment has demonstrated the clinical application potential and practicality of the instrument.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11111-11122"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1109/TPS.2025.3532778
Song Jiang;Chen Zhu;Yonggang Wang;Qian Qu;Zhonghang Wu
The atmospheric pressure low-temperature plasma jet (APPJ) can generate a wide variety of excited and active particles, with broad application prospects. Due to the strong spatiotemporal distribution characteristics of particles, the physical and chemical properties of plasma jets can be adjusted by changing the type and ratio of working gas, which is crucial for improving jet treatment efficiency and achieving specific treatment effects. This article innovatively analyzes the variations in the spatial distribution, intensity distribution, and activation region distribution of short-lived active substances that are crucial in the application of plasma jets under different gas backgrounds. Under the context of multiple variables, the physical characteristics of the plasma jets are comprehensively analyzed. The results show that the addition of N2 and O2 will weaken the discharge current and power, especially oxygen. After doping with 1% nitrogen, the jet length remains basically unchanged, but after doping with 1% oxygen, the jet length sharply decreases. Most active substances are concentrated at the nozzle of the jet pipe. The activation region of $mathrm {OH}(mathrm {A}^{2}Sigma ^{+}to mathrm {X}^{2}{Pi })$ is significantly lower than that of $mathrm {N}_{2}(mathrm {C}^{3}Pi _{mathrm {u}}to mathrm {B}^{3}Pi _{mathrm {g}})$ . As the N2 content increases from 0% to 1%, the activation region of $mathrm {OH}(mathrm {A}^{2}Sigma ^{+}to mathrm {X}^{2}Pi)$ decays from 17 to 12 mm and the activation region of $mathrm {N}_{2}(mathrm {C}^{3}{Pi }_{mathrm {u}}to mathrm {B}^{3}Pi _{mathrm {g}})$ remains unchanged, but the intensity of $mathrm {N}_{2}(mathrm {C}^{3}Pi _{mathrm {u}}to mathrm {B}^{3}Pi _{mathrm {g}})$ spectral line rises rapidly. As the O2 content increases from 0% to 0.1%, the activation region of $mathrm {OH}(mathrm {A}^{2}{Sigma }^{+}to mathrm {X}^{2}{Pi)}$ has decayed below 10 mm. The intensity of $mathrm {N}_{2}(mathrm {C}^{3}{Pi }_{mathrm {u}}to mathrm {B}^{3}Pi _{mathrm {g}})$ spectral extremely reduced by 48%. The increase in N2 content will lead to an increase in vibration and rotation temperature, while O2 is the opposite. From the axial spatial distribution, the vibrational temperature does not change much, but the rotational temperature decreases with increasing distance from the electrodes and eventually reaches equilibrium with the room temperature of 295 K.
{"title":"The Spatial Resolution Effect of N2/O2 on Atmospheric Pressure Ar Plasma Jet","authors":"Song Jiang;Chen Zhu;Yonggang Wang;Qian Qu;Zhonghang Wu","doi":"10.1109/TPS.2025.3532778","DOIUrl":"https://doi.org/10.1109/TPS.2025.3532778","url":null,"abstract":"The atmospheric pressure low-temperature plasma jet (APPJ) can generate a wide variety of excited and active particles, with broad application prospects. Due to the strong spatiotemporal distribution characteristics of particles, the physical and chemical properties of plasma jets can be adjusted by changing the type and ratio of working gas, which is crucial for improving jet treatment efficiency and achieving specific treatment effects. This article innovatively analyzes the variations in the spatial distribution, intensity distribution, and activation region distribution of short-lived active substances that are crucial in the application of plasma jets under different gas backgrounds. Under the context of multiple variables, the physical characteristics of the plasma jets are comprehensively analyzed. The results show that the addition of N2 and O2 will weaken the discharge current and power, especially oxygen. After doping with 1% nitrogen, the jet length remains basically unchanged, but after doping with 1% oxygen, the jet length sharply decreases. Most active substances are concentrated at the nozzle of the jet pipe. The activation region of <inline-formula> <tex-math>$mathrm {OH}(mathrm {A}^{2}Sigma ^{+}to mathrm {X}^{2}{Pi })$ </tex-math></inline-formula> is significantly lower than that of <inline-formula> <tex-math>$mathrm {N}_{2}(mathrm {C}^{3}Pi _{mathrm {u}}to mathrm {B}^{3}Pi _{mathrm {g}})$ </tex-math></inline-formula>. As the N2 content increases from 0% to 1%, the activation region of <inline-formula> <tex-math>$mathrm {OH}(mathrm {A}^{2}Sigma ^{+}to mathrm {X}^{2}Pi)$ </tex-math></inline-formula> decays from 17 to 12 mm and the activation region of <inline-formula> <tex-math>$mathrm {N}_{2}(mathrm {C}^{3}{Pi }_{mathrm {u}}to mathrm {B}^{3}Pi _{mathrm {g}})$ </tex-math></inline-formula> remains unchanged, but the intensity of <inline-formula> <tex-math>$mathrm {N}_{2}(mathrm {C}^{3}Pi _{mathrm {u}}to mathrm {B}^{3}Pi _{mathrm {g}})$ </tex-math></inline-formula> spectral line rises rapidly. As the O2 content increases from 0% to 0.1%, the activation region of <inline-formula> <tex-math>$mathrm {OH}(mathrm {A}^{2}{Sigma }^{+}to mathrm {X}^{2}{Pi)}$ </tex-math></inline-formula> has decayed below 10 mm. The intensity of <inline-formula> <tex-math>$mathrm {N}_{2}(mathrm {C}^{3}{Pi }_{mathrm {u}}to mathrm {B}^{3}Pi _{mathrm {g}})$ </tex-math></inline-formula> spectral extremely reduced by 48%. The increase in N2 content will lead to an increase in vibration and rotation temperature, while O2 is the opposite. From the axial spatial distribution, the vibrational temperature does not change much, but the rotational temperature decreases with increasing distance from the electrodes and eventually reaches equilibrium with the room temperature of 295 K.","PeriodicalId":450,"journal":{"name":"IEEE Transactions on Plasma Science","volume":"53 3","pages":"389-397"},"PeriodicalIF":1.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1109/JSEN.2025.3542972
Chi Xu;Peifeng Zhang;Haibin Yu
Employing cognitive radio to facilitate edge computing is a promising solution to address the spectrum scarcity problem during massive task offloading. This article studies an edge computing cognitive radio network (EC-CRN), where multiple cognitive end devices (CEDs) offload tasks to multiple cognitive base stations (CBSs) for parallel edge computing over the licensed spectrum underlying primary users (PUs). To jointly optimize resource allocation and task scheduling, we formulate a long-term average system cost minimization (ASCM) problem subject to the constraints of end-edge task division, the maximum transmission power of CEDs, the peak interference power to PUs, the computing frequency of CBSs and the long-term energy consumption of CEDs. Due to the long-term objective and long-term constraint coupled slot-by-slot, we employ the Lyapunov optimization theory to derive the upper bound of the Lyapunov drift for the virtual energy consumption backlog and transform the original problem into a one-slot Lyapunov drift-plus-penalty minimization problem. Furthermore, we model the transformed problem by the Markov decision process (MDP) and propose the Lyapunov-guided resource allocation and task scheduling (LRATS) algorithm based on the deep reinforcement learning algorithm with proximal policy optimization (PPO), where the policy network is updated by the policy gradient ascent with adaptive trajectory expectation sampling, and the value network is updated by minimizing the mean squared error of temporal difference (TD). By comparing with benchmark algorithms based on greedy, particle swarm optimization (PSO), deep deterministic policy gradient (DDPG), twin delayed deep deterministic (TD3), and soft actor-critic (SAC) and making ablation experiments, we validate that the proposed algorithm can stably converge with a larger reward and effectively reduce the system cost.
{"title":"Lyapunov-Guided Resource Allocation and Task Scheduling for Edge Computing Cognitive Radio Networks via Deep Reinforcement Learning","authors":"Chi Xu;Peifeng Zhang;Haibin Yu","doi":"10.1109/JSEN.2025.3542972","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3542972","url":null,"abstract":"Employing cognitive radio to facilitate edge computing is a promising solution to address the spectrum scarcity problem during massive task offloading. This article studies an edge computing cognitive radio network (EC-CRN), where multiple cognitive end devices (CEDs) offload tasks to multiple cognitive base stations (CBSs) for parallel edge computing over the licensed spectrum underlying primary users (PUs). To jointly optimize resource allocation and task scheduling, we formulate a long-term average system cost minimization (ASCM) problem subject to the constraints of end-edge task division, the maximum transmission power of CEDs, the peak interference power to PUs, the computing frequency of CBSs and the long-term energy consumption of CEDs. Due to the long-term objective and long-term constraint coupled slot-by-slot, we employ the Lyapunov optimization theory to derive the upper bound of the Lyapunov drift for the virtual energy consumption backlog and transform the original problem into a one-slot Lyapunov drift-plus-penalty minimization problem. Furthermore, we model the transformed problem by the Markov decision process (MDP) and propose the Lyapunov-guided resource allocation and task scheduling (LRATS) algorithm based on the deep reinforcement learning algorithm with proximal policy optimization (PPO), where the policy network is updated by the policy gradient ascent with adaptive trajectory expectation sampling, and the value network is updated by minimizing the mean squared error of temporal difference (TD). By comparing with benchmark algorithms based on greedy, particle swarm optimization (PSO), deep deterministic policy gradient (DDPG), twin delayed deep deterministic (TD3), and soft actor-critic (SAC) and making ablation experiments, we validate that the proposed algorithm can stably converge with a larger reward and effectively reduce the system cost.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"12253-12264"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10906327","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, to protect source-location-privacy (SLP) in underwater acoustic sensor networks (UASNs), some schemes through the collaboration of multi-autonomous underwater vehicle (AUV) have been proposed. However, the long end-to-end delay in these schemes leads to untimely data delivery. To address this issue and enhance SLP protection, a low-delay SLP protection scheme with multi-AUV (LDSLP-MA) collaboration for UASNs is proposed in this article. In the LDSLP-MA scheme, a multipath technique including multipath routing as well as multi-AUV collaboration is employed to enhance SLP protection. Additionally, through strategically assigning dwelling and target areas for AUVs, the delay taken by multi-AUV scheduling is minimized while the diversity of data transmission paths and SLP protection is enhanced. Specifically, the optimal target area is selected through gray relational analysis. Simulation results demonstrate that the LDSLP-MA scheme achieves an extended safety period, decreased energy consumption, and reduced delay compared to other schemes. Notably, in comparison to multi-AUV collaboration-based SLP protection schemes like the push-based probabilistic method for SLP protection (PP-SLPP) and stratification-based SLP (SSLP), LDSLP-MA increases the safety period by over 100%, reduces delay by over 82%, and lowers average node energy consumption by over 65%.
{"title":"A Low-Delay Source-Location-Privacy Protection Scheme With Multi-AUV Collaboration for Underwater Acoustic Sensor Networks","authors":"Xiaojing Tian;Xiujuan Du;Xiuxiu Liu;Lijuan Wang;Lei Zhao","doi":"10.1109/JSEN.2025.3542783","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3542783","url":null,"abstract":"In recent years, to protect source-location-privacy (SLP) in underwater acoustic sensor networks (UASNs), some schemes through the collaboration of multi-autonomous underwater vehicle (AUV) have been proposed. However, the long end-to-end delay in these schemes leads to untimely data delivery. To address this issue and enhance SLP protection, a low-delay SLP protection scheme with multi-AUV (LDSLP-MA) collaboration for UASNs is proposed in this article. In the LDSLP-MA scheme, a multipath technique including multipath routing as well as multi-AUV collaboration is employed to enhance SLP protection. Additionally, through strategically assigning dwelling and target areas for AUVs, the delay taken by multi-AUV scheduling is minimized while the diversity of data transmission paths and SLP protection is enhanced. Specifically, the optimal target area is selected through gray relational analysis. Simulation results demonstrate that the LDSLP-MA scheme achieves an extended safety period, decreased energy consumption, and reduced delay compared to other schemes. Notably, in comparison to multi-AUV collaboration-based SLP protection schemes like the push-based probabilistic method for SLP protection (PP-SLPP) and stratification-based SLP (SSLP), LDSLP-MA increases the safety period by over 100%, reduces delay by over 82%, and lowers average node energy consumption by over 65%.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"12236-12252"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1109/JSEN.2025.3543178
Yao Song;Shijie Guo;Bowen Liang
This study presents a novel, noninvasive system that quantifies thoracoabdominal respiratory movements and correlates them with back pressure fluctuations, advancing respiratory monitoring. A key contribution is the development of a method to measure the 3-D movements of the chest and abdomen using a 3-D motion capture system, enabling high-precision analysis of respiratory dynamics. In addition, another significant contribution of this work is the development of a novel correlation model that integrates the thoracoabdominal motion data with back pressure fluctuations recorded by a tactile sensor array. This integration represents a critical departure from previous research, which often examines these parameters in isolation. By linking back pressure fluctuations to respiratory displacements, our method facilitates noncontact, unobtrusive respiratory monitoring, offering a practical and patient friendly alternative to traditional contact-based techniques such as respiratory inductance plethysmography (RIP). Furthermore, this study identifies optimal regions for pressure monitoring, which enhances the system’s precision and usability for long-term monitoring in clinical and home environments. This step addresses a critical gap in the current literature, where practical implementation strategies are often overlooked. The results reveal a clear correlation between pressure fluctuations and respiratory movements, forming the basis for a model that can predict respiratory states. This system provides significant potential for practical application, offering a promising solution for reliable, unobtrusive respiratory monitoring in both clinical and home settings.
{"title":"Noninvasive Respiratory Monitoring Through Correlation of Thoracoabdominal Movements and Pressure Fluctuations","authors":"Yao Song;Shijie Guo;Bowen Liang","doi":"10.1109/JSEN.2025.3543178","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543178","url":null,"abstract":"This study presents a novel, noninvasive system that quantifies thoracoabdominal respiratory movements and correlates them with back pressure fluctuations, advancing respiratory monitoring. A key contribution is the development of a method to measure the 3-D movements of the chest and abdomen using a 3-D motion capture system, enabling high-precision analysis of respiratory dynamics. In addition, another significant contribution of this work is the development of a novel correlation model that integrates the thoracoabdominal motion data with back pressure fluctuations recorded by a tactile sensor array. This integration represents a critical departure from previous research, which often examines these parameters in isolation. By linking back pressure fluctuations to respiratory displacements, our method facilitates noncontact, unobtrusive respiratory monitoring, offering a practical and patient friendly alternative to traditional contact-based techniques such as respiratory inductance plethysmography (RIP). Furthermore, this study identifies optimal regions for pressure monitoring, which enhances the system’s precision and usability for long-term monitoring in clinical and home environments. This step addresses a critical gap in the current literature, where practical implementation strategies are often overlooked. The results reveal a clear correlation between pressure fluctuations and respiratory movements, forming the basis for a model that can predict respiratory states. This system provides significant potential for practical application, offering a promising solution for reliable, unobtrusive respiratory monitoring in both clinical and home settings.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11226-11235"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1109/JSEN.2025.3543182
Venkata Narayana Vaddi;Prakash Kodali
Epileptic seizures represent a critical concern in mental health, arising from abnormal synchronization and rapid neuronal activity in the brain. The consequences of such seizures, including loss of consciousness and cognitive impairment, have significant psychological, social, and cognitive implications. Electroencephalography (EEG) and sensor data play a pivotal role in epilepsy detection, leveraging machine learning techniques to analyze extensive datasets. This article presents a novel approach that combines long short-term memory (LSTM) networks and wavelet transform (WT) techniques to enhance seizure detection accuracy. The proposed multimodule approach, featuring residual neural networks and convolutional neural networks (CNNs), coupled with k-fold validation, achieves an accuracy of 98.5%, sensitivity of 99.0%, specificity of 98.0% and an AUC-receiver operating characteristic (ROC) value of 0.95 for classifying interictal epileptiform discharges. This underscores the model’s efficacy in addressing the complexities of EEG signal analysis.
{"title":"Enhanced Epilepsy Sensitivity and Detection Rate With Improved Specificity by Integration of Modified LSTM Networks","authors":"Venkata Narayana Vaddi;Prakash Kodali","doi":"10.1109/JSEN.2025.3543182","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543182","url":null,"abstract":"Epileptic seizures represent a critical concern in mental health, arising from abnormal synchronization and rapid neuronal activity in the brain. The consequences of such seizures, including loss of consciousness and cognitive impairment, have significant psychological, social, and cognitive implications. Electroencephalography (EEG) and sensor data play a pivotal role in epilepsy detection, leveraging machine learning techniques to analyze extensive datasets. This article presents a novel approach that combines long short-term memory (LSTM) networks and wavelet transform (WT) techniques to enhance seizure detection accuracy. The proposed multimodule approach, featuring residual neural networks and convolutional neural networks (CNNs), coupled with k-fold validation, achieves an accuracy of 98.5%, sensitivity of 99.0%, specificity of 98.0% and an AUC-receiver operating characteristic (ROC) value of 0.95 for classifying interictal epileptiform discharges. This underscores the model’s efficacy in addressing the complexities of EEG signal analysis.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11963-11970"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Terahertz (THz) biosensors as an advanced approach for nondestructive, label-free, and long-term monitoring of cellular states, hold tremendous potential in early disease screening and cellular drug sensitivity testing. This article presents a THz metasurface composed of two semicircular split-ring resonators based on quasi-bound states in the continuum (Q-BIC) mechanism. The localized field enhancement of the Q-BIC structure can significantly boost strong light-matter interactions, enabling highly sensitive detection of biological analytes. The sensitivity of the presented biosensor reaches as high as 517 GHz/RIU. Via resonance frequency shifts, we achieve ultrasensitive distinguishment between different concentrations of cancer cells (HepG2) and normal cells (293T). The results demonstrate that, without the need for additional factors, the frequency shift of the Q-BIC resonance can successfully identify different types of biological cells of varying cell concentrations. The experimental sensitivity reaches up to 456.64 kHz/(cell mL$^{-{1}}$ ), with a detection limit of $5 times ; 10^{{3}}$ cells/mL, confirming the feasibility of the designed structure. The QBIC-based THz biosensor exhibits significant potential in distinguishing different biological cells and detecting low-concentration cells, paving a new path for ultrasensitive biomedical detection.
{"title":"Label-Free Detection of Biological Cells Using Quasi-Bound States in the Continuum With Terahertz Metasurface","authors":"Liran Shen;Heng Liu;Xue Ke;Yuqi Cao;Yi Zhang;Liangfei Tian;Jiani Chen;Pingjie Huang;Guangxin Zhang","doi":"10.1109/JSEN.2025.3542832","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3542832","url":null,"abstract":"Terahertz (THz) biosensors as an advanced approach for nondestructive, label-free, and long-term monitoring of cellular states, hold tremendous potential in early disease screening and cellular drug sensitivity testing. This article presents a THz metasurface composed of two semicircular split-ring resonators based on quasi-bound states in the continuum (Q-BIC) mechanism. The localized field enhancement of the Q-BIC structure can significantly boost strong light-matter interactions, enabling highly sensitive detection of biological analytes. The sensitivity of the presented biosensor reaches as high as 517 GHz/RIU. Via resonance frequency shifts, we achieve ultrasensitive distinguishment between different concentrations of cancer cells (HepG2) and normal cells (293T). The results demonstrate that, without the need for additional factors, the frequency shift of the Q-BIC resonance can successfully identify different types of biological cells of varying cell concentrations. The experimental sensitivity reaches up to 456.64 kHz/(cell mL<inline-formula> <tex-math>$^{-{1}}$ </tex-math></inline-formula>), with a detection limit of <inline-formula> <tex-math>$5 times ; 10^{{3}}$ </tex-math></inline-formula> cells/mL, confirming the feasibility of the designed structure. The QBIC-based THz biosensor exhibits significant potential in distinguishing different biological cells and detecting low-concentration cells, paving a new path for ultrasensitive biomedical detection.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10984-10991"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1109/JSEN.2025.3544018
Gang Chen;Tianyi Shang;Wenrui Song;Weihan Shao;Hu Sun;Xinlin Qing
Structural health monitoring (SHM) integrates advanced sensor networks and machine learning (ML) technologies, aiming to automatically extract and identify damage features from sensor data of engineering structures, thus enabling real-time assessment of structural integrity and early diagnosis of potential damage. However, these damage features often include redundant or irrelevant features, which pose challenges for effective feature extraction and damage diagnosis. To solve these problems, a feature selection (FS) algorithm based on multilayer cooperative particle swarm optimizer (MCPSO) is proposed. In MCPSO, the three learning strategies of midpoint sample, random sample, and comprehensive sample are skillfully mixed into the particle swarm optimizer (PSO), and the hierarchical structure is used to update the population. The damage feature subset is optimized by simulating the search process of multilayer particle swarm, and the feature set most sensitive to structural damage is identified to improve the accuracy and reliability of damage detection. Taking the multidamage state monitoring of bolted structure as a verification case, the ultrasonic-guided waves (UGWs) signals of bolted structure in different states are collected by lead zirconate titanate sensors. The experimental results show that compared with the ML algorithm, MCPSO can select a stable and effective feature subset from the noise data and realize the identification and quantification of various damage states, such as health, crack, loosening, and loosening-crack composite damage, which provides a universal method for the technical development and engineering practice in the field of SHM.
{"title":"Multilayer Cooperative Particle Swarm Optimizer for Feature Selection in Structural Health Monitoring","authors":"Gang Chen;Tianyi Shang;Wenrui Song;Weihan Shao;Hu Sun;Xinlin Qing","doi":"10.1109/JSEN.2025.3544018","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3544018","url":null,"abstract":"Structural health monitoring (SHM) integrates advanced sensor networks and machine learning (ML) technologies, aiming to automatically extract and identify damage features from sensor data of engineering structures, thus enabling real-time assessment of structural integrity and early diagnosis of potential damage. However, these damage features often include redundant or irrelevant features, which pose challenges for effective feature extraction and damage diagnosis. To solve these problems, a feature selection (FS) algorithm based on multilayer cooperative particle swarm optimizer (MCPSO) is proposed. In MCPSO, the three learning strategies of midpoint sample, random sample, and comprehensive sample are skillfully mixed into the particle swarm optimizer (PSO), and the hierarchical structure is used to update the population. The damage feature subset is optimized by simulating the search process of multilayer particle swarm, and the feature set most sensitive to structural damage is identified to improve the accuracy and reliability of damage detection. Taking the multidamage state monitoring of bolted structure as a verification case, the ultrasonic-guided waves (UGWs) signals of bolted structure in different states are collected by lead zirconate titanate sensors. The experimental results show that compared with the ML algorithm, MCPSO can select a stable and effective feature subset from the noise data and realize the identification and quantification of various damage states, such as health, crack, loosening, and loosening-crack composite damage, which provides a universal method for the technical development and engineering practice in the field of SHM.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"12525-12537"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work presents the application of zinc oxide (ZnO) and zinc ferrite (ZnFe2O4) for electrochemical pH sensing. ZnO and ZnFe2O4 are synthesized by auto-combustion synthesis method. Field emission scanning electron microroscopic (FESEM) images revealed that ZnO particles have pyramid- and spherical-shaped morphology with micrometer dimensions, while ZnFe2O4 particles have spherical shape at the nanoscale. The surface-modified screen-printed electrodes with ZnO and ZnFe2O4 particles are initially characterized by the ferri/ferrocyanide redox couple. Significant improvement in sensitivity (bare carbon: $6.3~pm ~0.4~mu $ A/mM, ZnO: $8.5~pm ~0.3~mu $ A/mM, ZnFe2O4: $8.9~pm ~0.5~mu $ A/mM) and rate constant (bare carbon: $10~pm ~1~{text {ms}}^{-{1}}$ , ZnO: $46~pm ~4~{text {ms}}^{-{1}}$ , ZnFe2O4: $42~pm ~3~{text {ms}}^{-{1}}$ ) is observed with the surface-modified sensors. Chronopotentiometric pH response of the sensors showed hysteresis behavior with pH loop. No interference effects are observed, and the pH sensitivity of the bare carbon sensor ($23.9~pm ~1.4$ mV/pH) is increased by the introduction of ZnO ($38.1~pm ~1.3$ mV/pH) and ZnFe2O4 ($37.2~pm ~1.1$ mV/pH) particles. Stability of the pH response is discussed, and ways for its improvement are proposed.
{"title":"Comparative Study of ZnO and ZnFe₂O₄ Microparticle and Nanoparticle-Based Screen-Printed Electrodes in pH Sensing","authors":"Mallikarjun Madagalam;Filippo Franceschini;Catarina Fernandes;Michele Rosito;Elisa Padovano;Sandro Carrara;Alberto Tagliaferro;Mattia Bartoli;Irene Taurino","doi":"10.1109/JSEN.2025.3543243","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543243","url":null,"abstract":"This work presents the application of zinc oxide (ZnO) and zinc ferrite (ZnFe2O4) for electrochemical pH sensing. ZnO and ZnFe2O4 are synthesized by auto-combustion synthesis method. Field emission scanning electron microroscopic (FESEM) images revealed that ZnO particles have pyramid- and spherical-shaped morphology with micrometer dimensions, while ZnFe2O4 particles have spherical shape at the nanoscale. The surface-modified screen-printed electrodes with ZnO and ZnFe2O4 particles are initially characterized by the ferri/ferrocyanide redox couple. Significant improvement in sensitivity (bare carbon: <inline-formula> <tex-math>$6.3~pm ~0.4~mu $ </tex-math></inline-formula>A/mM, ZnO: <inline-formula> <tex-math>$8.5~pm ~0.3~mu $ </tex-math></inline-formula>A/mM, ZnFe2O4: <inline-formula> <tex-math>$8.9~pm ~0.5~mu $ </tex-math></inline-formula>A/mM) and rate constant (bare carbon: <inline-formula> <tex-math>$10~pm ~1~{text {ms}}^{-{1}}$ </tex-math></inline-formula>, ZnO: <inline-formula> <tex-math>$46~pm ~4~{text {ms}}^{-{1}}$ </tex-math></inline-formula>, ZnFe2O4: <inline-formula> <tex-math>$42~pm ~3~{text {ms}}^{-{1}}$ </tex-math></inline-formula>) is observed with the surface-modified sensors. Chronopotentiometric pH response of the sensors showed hysteresis behavior with pH loop. No interference effects are observed, and the pH sensitivity of the bare carbon sensor (<inline-formula> <tex-math>$23.9~pm ~1.4$ </tex-math></inline-formula> mV/pH) is increased by the introduction of ZnO (<inline-formula> <tex-math>$38.1~pm ~1.3$ </tex-math></inline-formula> mV/pH) and ZnFe2O4 (<inline-formula> <tex-math>$37.2~pm ~1.1$ </tex-math></inline-formula> mV/pH) particles. Stability of the pH response is discussed, and ways for its improvement are proposed.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10602-10612"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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