IEEE Sensors Letters最新文献_第2页

Joint Impact of Missing Samples and Finite ADC Resolution on Parameter Estimation From Sensor Signals 缺失样本和有限ADC分辨率对传感器信号参数估计的共同影响

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-11 DOI: 10.1109/LSENS.2026.3733269

Michkath Omanda Bouraima;Maciej Zawodniok

引用次数: 0

STIR: A Bio-Inspired Stimulus-Triggered Internal Recovery Filter for Real-Time IMU-Based Preimpact Fall Detection STIR：一种生物激励触发的内部恢复滤波器，用于基于imu的实时预冲击跌落检测

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-21 DOI: 10.1109/LSENS.2026.3735933

Liang Qi;Di Ge;Enci Xie;Chenyu Tang

{"title":"STIR: A Bio-Inspired Stimulus-Triggered Internal Recovery Filter for Real-Time IMU-Based Preimpact Fall Detection","authors":"Liang Qi;Di Ge;Enci Xie;Chenyu Tang","doi":"10.1109/LSENS.2026.3735933","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3735933","url":null,"abstract":"Preimpact fall (PIF) detection aims to recognize fall-related motions within the short interval before body impact, requiring both reliable early recognition and efficient on-device inference. However, existing deep learning methods often incur high latency due to complex temporal feature extraction. This letter presents stimulus-triggered internal recovery filter (STIR), a bio-inspired stimulus-triggered internal recovery filter for lightweight inertial measurement unit (IMU)-based PIF detection. STIR generates temporal response features through acceleration stimuli and internal state recovery, which are then fed into a lightweight MLP classifier for recognition. On the SisFall dataset, the proposed STIR-based model achieves 97.72% test accuracy with only 26.3 k floating-point operations (FLOPs). Compared with the lightweight one-dimensional convolutional neural network (1DCNN) baseline, STIR reduces FLOPs by 97.0% and inference latency by 91.3%. On the STM32U575RIT6 microcontroller unit (MCU), it achieves an average inference time of 2.6 ms with an estimated energy consumption of 26.32 <italic>μJ, while requiring only 2.56 KB of peak random-access memory (RAM) and 49.29 KB of flash storage. These results suggest that STIR has potential for real-time IMU-based PIF detection on resource-constrained wearable devices.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"5505904-5505904"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131091","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}

引用次数: 0

A Delta-Based SAR ROIC for Low-Voltage Wide-Input-Range Gas Sensor Readout 基于delta的低电压宽输入范围气体传感器读出SAR ROIC

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-07 DOI: 10.1109/LSENS.2026.3731262

Soon-Kyu Kwon;Jun-Nyeong Kim;Hyeon-June Kim

{"title":"A Delta-Based SAR ROIC for Low-Voltage Wide-Input-Range Gas Sensor Readout","authors":"Soon-Kyu Kwon;Jun-Nyeong Kim;Hyeon-June Kim","doi":"10.1109/LSENS.2026.3731262","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3731262","url":null,"abstract":"This letter presents a delta-conversion successive approximation register (SAR)-based readout integrated circuit (ROIC) for low-power gas sensing with large sensor output swings. Instead of digitizing the absolute sensor output, the proposed architecture converts only the intersample difference using back-switching-based residue generation. It compresses the effective conversion range by generating the intersample residue inside the capacitor digital-to-analog converter, enabling low-voltage operation while directly sampling the full sensor output. The SAR conversion is further limited to a small least significant bit range, reducing comparison cycles and switching activity. A 10 bit prototype was fabricated in a 0.18 <italic>μm CMOS process and operates at 1.2 V. The proposed ROIC consumes 89.3 <italic>μW, achieving a 57.5% reduction compared to a conventional full-range SAR analog-to-digital converter, while reducing comparator power from 70% to 2.8%. The measured results show a maximum reconstruction error of 1.2 mV, relative error below 0.09%. These results demonstrate accurate signal reconstruction with improved energy efficiency for low-power gas monitoring systems.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"5000904-5000907"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131256","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}

引用次数: 0

Real-Time Cross-Session EEG-Based Biometric System Using Siamese EEGAuthNet and Triplet Manifold Alignment 基于Siamese EEGAuthNet和三重流形对齐的实时跨会话脑电图生物识别系统

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-08-12 DOI: 10.1109/LSENS.2026.3723007

Chetan Tanaji Rakshe;Anchal Chaudhary;Jac Fredo Agastinose Ronickom

{"title":"Real-Time Cross-Session EEG-Based Biometric System Using Siamese EEGAuthNet and Triplet Manifold Alignment","authors":"Chetan Tanaji Rakshe;Anchal Chaudhary;Jac Fredo Agastinose Ronickom","doi":"10.1109/LSENS.2026.3723007","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3723007","url":null,"abstract":"Electroencephalography (EEG)-based biometric authentication provides a highly secure, inherently nonreplicable system for robust and reliable person identification. Its practical adoption is limited by the lack of real-time frameworks and the need to retrain the entire model when incorporating new users. In this study, we present a sensor-centric biometric system utilizing a deep learning architecture integrated with a session-invariant approach. We considered EEG signals from 30 subjects recorded using a multiparadigm condition. The EEG signals were preprocessed and fed to a novel Siamese EEGAuthNet architecture integrated with a cross-session triplet mining strategy. The real-time testing phase was implemented as a distributed processing pipeline using ZeroMQ-based asynchronous message bus. Furthermore, we applied an exponential moving smoothing algorithm to stabilize the biometric manifold against transient noise. The experimental results on a multisubject population demonstrate a state-of-the-art zero-shot equal error rate of 12.42% <inline-formula><tex-math>$pm$</tex-math></inline-formula> 7.29% for unseen subjects. We achieved high-throughput inference (0.4 mega samples per second) with minimal latency (39.52 ms). Our results establish a robust pathway for deployable, session-invariant neural identity in real-world environments.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"7004904-7004907"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131209","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}

引用次数: 0

Impedimetric Sensor for Oil Concentration Detecting in the Cold Rolling Steel Process : A Laboratory Proof of Concept 用于冷轧钢过程中油浓度检测的阻抗传感器：概念的实验室验证

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-08-26 DOI: 10.1109/LSENS.2026.3727850

Marcelo Curtes Martins;Pedro Bertemes-Filho;Marcos Antonio Rebello

{"title":"Impedimetric Sensor for Oil Concentration Detecting in the Cold Rolling Steel Process : A Laboratory Proof of Concept","authors":"Marcelo Curtes Martins;Pedro Bertemes-Filho;Marcos Antonio Rebello","doi":"10.1109/LSENS.2026.3727850","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3727850","url":null,"abstract":"This letter investigates the effects of oil concentration and temperature on the electrical impedance of oil–water emulsions used in the cold rolling of flat carbon steels, using electrochemical impedance spectroscopy (EIS) with stainless steel electrodes over a frequency range of 1–200 kHz, for oil concentrations between 0.5% and 4.0% (v/v) and temperatures between 23 °C and 60 °C. The impedance modulus decreased with increasing oil concentration, attributed to polar additives and surfactants that increase the emulsion's ionic conductivity, and decreased with increasing temperature due to enhanced ionic mobility. A power-law model relating impedance modulus to oil concentration achieved R2 = 0.98 (RMSE = 0.14% and MAE = 0.10%), and a combined model incorporating temperature achieved comparable accuracy (R2 = 0.98). Repeatability across the repeated measurement series for each condition was high, with coefficients of variation below 4% (below 2% for most concentrations). A frequency-resolved sensitivity analysis confirmed that averaging over the full 1–200 kHz range does not compromise sensitivity, with R2 exceeding 0.99 at every frequency. Obtained under controlled laboratory conditions, these results demonstrate the feasibility of EIS as a basis for real-time, automated oil concentration monitoring in cold rolling emulsions, supporting future validation under industrial conditions. Leave-one-series-out cross-validation gave out-of-sample errors of RMSE = 0.140% and MAE = 0.102% (v/v). The attribution of the impedance decrease to polar additives is an interpretation not yet confirmed by chemical characterization. These results constitute a laboratory proof of concept obtained with freshly prepared emulsions; applicability to real industrial emulsions, including the effects of contaminants, aging and process variability, remains to be demonstrated.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"4503904-4503904"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11667268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131252","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}

引用次数: 0

GA-EMDNet: Graph Attention-Guided Eye Movement Detection Network From EMG Sensor Signals 图注意引导眼动检测网络从肌电传感器信号

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-08-31 DOI: 10.1109/LSENS.2026.3729915

Aarav Raina;Dipit Madan;Suraj Prakash Sahoo;Sumit Kumar Jindal;Pradeep Kumar Das

{"title":"GA-EMDNet: Graph Attention-Guided Eye Movement Detection Network From EMG Sensor Signals","authors":"Aarav Raina;Dipit Madan;Suraj Prakash Sahoo;Sumit Kumar Jindal;Pradeep Kumar Das","doi":"10.1109/LSENS.2026.3729915","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3729915","url":null,"abstract":"Investigating and categorizing muscle-generated bioelectrical activity specifically electromyography (EMG) recordings associated with extraocular muscles (EOM) is fundamental for building advanced assistive systems. The dynamic and time-varying nature of these physiological waveforms demands analytical techniques capable of capturing temporal dependencies for accurate interpretation and classification. In this study, we propose a compact graph-based representation for EMG of EOM signal classification that transforms 1-D temporal waveforms into structured relational graphs. Specifically, each signal instance is segmented into temporal windows that are modeled as graph nodes, with edges encoding both local temporal continuity and feature-level similarity, enabling the learning of nonlocal dependencies. A graph attention network is employed to adaptively weight internode relationships and extract discriminative representations without reliance on frequency-domain decomposition or signal reconstruction, replacing complex domain-specific feature engineering with lightweight statistical descriptors computed directly on raw temporal windows. The proposed approach achieves a peak classification accuracy of 99.17% and mean classification accuracy of 98.33% (95% CI: [96.79%, 99.55%]) across a fivefold cross-validation with 80–20 split and 98.17% (95% CI: [96.99%, 99.35%]) across a tenfold cross-validation with 90–10 split, demonstrating competitive performance against strong classical baselines. The results highlight the importance of appropriate graph construction in leveraging attention-based graph models for time-dependent biomedical signal analysis.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"7004704-7004704"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131230","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}

引用次数: 0

Data-Driven Impedimetric Sensor for Senescent Fruits: A Case Study on Papaya Ripeness Classification 衰老水果数据驱动阻抗传感器——以木瓜成熟度分类为例

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-01 DOI: 10.1109/LSENS.2026.3729560

Yashasvi Saxena;Dibakar Roy;Chiranjib Ghosh;Avishek Adhikary

{"title":"Data-Driven Impedimetric Sensor for Senescent Fruits: A Case Study on Papaya Ripeness Classification","authors":"Yashasvi Saxena;Dibakar Roy;Chiranjib Ghosh;Avishek Adhikary","doi":"10.1109/LSENS.2026.3729560","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3729560","url":null,"abstract":"This letter presents a novel method to detect the change in papaya ripe stages starting from an unripe harvested state. First, papaya bioimpedance spectroscopy (BIS) data through its ripening time-line are collected for up to ten days over 18 samples and then the data are labelled into four ripe classes based on ethylene emission pattern and various other physical factors. This original training dataset is class-balanced using a typical synthetic minority oversampling technique algorithm resulting in total 1232 training data. From a hold-out samples of four papayas, 69 test data are also obtained. The work investigates three different dimension reduction techniques linear discriminant analysis, principal component analysis (PCA), and t-stochastic neighbor embedding (t-SNE), to reduce the order and find the axes most sensitive to papaya ripening. Although both PCA and t-SNE shows comparable accuracy, t-SNE requires less number of components. With first five t-SNE components of phase spectrum 95.2% accuracy (fivefold cross-validated and averaged over 40 iteration) is achieved with a typical ensemble subspace <inline-formula> <tex-math>$k$</tex-math> </inline-formula>-nearest neighbor (KNN) model. This letter includes detailed study results, Receiver Operating Characteristic (RoC) plot and stage wise precision, recall, and F1 score while step-by-step developing the proof-of-concept of machine learning-BIS based sensing mechanism for the ripe class identification of papaya.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"6011304-6011307"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131269","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}

引用次数: 0

Cuffless Blood Pressure Estimation From Wearable Photoplethysmography and Windkessel Model-Regularized Learning Approach 基于可穿戴光电容积脉搏波和Windkessel模型的无袖带血压估计-正则化学习方法

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-08-14 DOI: 10.1109/LSENS.2026.3724173

Adeeba Khan;Swarubini P J;Ryunosuke Kirita;Tomohiko Igasaki;Nagarajan Ganapathy

{"title":"Cuffless Blood Pressure Estimation From Wearable Photoplethysmography and Windkessel Model-Regularized Learning Approach","authors":"Adeeba Khan;Swarubini P J;Ryunosuke Kirita;Tomohiko Igasaki;Nagarajan Ganapathy","doi":"10.1109/LSENS.2026.3724173","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3724173","url":null,"abstract":"Wearable cuffless blood pressure (BP) monitoring using photoplethysmography (PPG) sensors has gained attention for noninvasive hemodynamic assessment. However, most deep learning methods are purely data-driven and lack physiological consistency. This study presents a physiology-informed learning framework for PPG-based BP estimation by integrating cardiovascular dynamics derived from the Windkessel model into a bounded loss function. The proposed framework incorporates physiological constraints by enforcing exponentially decaying arterial pressure using a PPG-derived diastolic time constant. PPG signals from ten healthy subjects were collected using a wearable finger-based sensor during rest, affective stimulation, and Stroop test conditions to introduce variability. The system complies with Association for the Advancement of Medical Instrumentation standards, achieving a mean absolute error of 2.90 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 1.20 mmHg for DBP and 4.10 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 1.41 mmHg for SBP. These results, from a small, homogeneous cohort, support feasibility of physiology-informed learning and motivate validation on larger, diverse populations toward clinical translation.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"6011804-6011804"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133459","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}

引用次数: 0

Comparative Assessment of PPG Signal Quality Across Fingers for Wearable Health Monitoring and Implications for Sensor Design Optimization 可穿戴健康监测中手指间PPG信号质量的比较评估及其对传感器设计优化的影响

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-08-24 DOI: 10.1109/LSENS.2026.3726883

Karlen Aleksanyan;Xueyicheng Xu;Zheng Liu;Haitao Lin;Gokul Nathan;Alexander V. Mamishev

{"title":"Comparative Assessment of PPG Signal Quality Across Fingers for Wearable Health Monitoring and Implications for Sensor Design Optimization","authors":"Karlen Aleksanyan;Xueyicheng Xu;Zheng Liu;Haitao Lin;Gokul Nathan;Alexander V. Mamishev","doi":"10.1109/LSENS.2026.3726883","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3726883","url":null,"abstract":"In clinical settings, every minute of delay in treating a cardiopulmonary emergency can reduce a patient's chance of survival by up to 10%. Photoplethysmography (PPG) is a low-cost, noninvasive method widely adopted in both clinical, and consumer settings to monitor cardiopulmonary signals. Therefore, clinical PPG pulse oximeter equipment is used on 88.8% of inpatient days in U.S. hospitals. However, poor PPG signal quality due to motion artifacts and improper sensor placement results in frequent cardio-respiratory monitor alarms, with 80% of these deemed insignificant, causing alarm fatigue that delays intervention in real cardiopulmonary emergencies. This study examines the effect of finger choice on PPG signal quality, utilizing the signal-to-noise ratio (SNR) as the primary signal quality index (SQI). PPG data were collected from all five fingers of 24 racially diverse participants using a MAX30102-based sensing platform. The statistical results based on repeated measures ANOVA, followed by post-hoc testing, showed that the thumb consistently exhibited the highest SNR, indicating superior signal quality compared to other fingers, and potentially enhancing the reliability of cardio-respiratory monitor alarms. The index finger also showed a statistically significant advantage in SNR over the middle finger. These findings potentially inform future wearable sensor design to incorporate thumb-specific fixtures, adaptive sensor positioning, and thumb-oriented mechanical and optical enhancements to optimize monitoring accuracy and reliability.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"6011204-6011204"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133881","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}

引用次数: 0

Odor Source Localization System Inspired by Odor Information Processing of an Insect 基于昆虫气味信息处理的气味源定位系统

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-21 DOI: 10.1109/LSENS.2026.3735894

Ryoko Sekiwa;Hirono Ohashi;Tatsuya Ibuki;Shunsuke Shigaki

{"title":"Odor Source Localization System Inspired by Odor Information Processing of an Insect","authors":"Ryoko Sekiwa;Hirono Ohashi;Tatsuya Ibuki;Shunsuke Shigaki","doi":"10.1109/LSENS.2026.3735894","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3735894","url":null,"abstract":"In this study, we aimed to artificially reproduce insect- specific odor information processing and implement it in robots. We focused on the odor source localization behavior of male silkmoths and measured electroantennographic (EAG) responses in freely walking insects using an automatic tracking device. The relationship between the trajectories of the silkmoths and EAG amplitudes suggests that the silkmoth does not exhibit large EAG amplitudes near the odor source, but rather in the early stages of its search, away from the odor source. Moreover, the relationship between the EAG amplitudes and translational velocity indicates that the silkmoths move significantly faster when the EAG responses exceed 1 mV, which occurs more frequently during the early stages of the search. Overall, these findings suggest that the silkmoth adopts a search strategy in which it rapidly reduces its distance from the source during the early phase of exploration and subsequently decreases its locomotor velocity in the vicinity of the source. Based on these findings, we modeled the velocity-modulation mechanism in response to the odor concentration and implemented it in a robot. Finally, we experimentally evaluated the search performance of this robot. The results revealed that this mechanism, which incorporates insect odor information processing, effectively improved search efficiency.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"5506004-5506004"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131271","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}

引用次数: 0