IEEE Open Journal of Engineering in Medicine and Biology最新文献_第3页

Transcranial Temporal Interference Stimulation: A Brief Review of Architectures, Circuits, and Application Challenges 经颅颞叶干扰刺激：结构、电路和应用挑战的简要回顾。

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-04-30 DOI: 10.1109/OJEMB.2026.3689259

Arnau Diez-Clos;Simona Losacco;Alejandro D. Fernández Schrunder;Stéphane Emery;Friedhelm C. Hummel;Mahsa Shoaran;Komail Badami

引用次数: 0

Early Motor Pattern Analysis Using a Single RGB-D Camera: A Case Study of Twins at Divergent Risk for Motor Impairment 使用单个RGB-D相机进行早期运动模式分析：运动障碍风险不同的双胞胎的案例研究

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-06-17 DOI: 10.1109/OJEMB.2026.3704501

D. Balta;I. G. Porco;H. Hoang;A. Cereatti;P. S. Lum;U. Della Croce;M. M. Schladen

{"title":"Early Motor Pattern Analysis Using a Single RGB-D Camera: A Case Study of Twins at Divergent Risk for Motor Impairment","authors":"D. Balta;I. G. Porco;H. Hoang;A. Cereatti;P. S. Lum;U. Della Croce;M. M. Schladen","doi":"10.1109/OJEMB.2026.3704501","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3704501","url":null,"abstract":"Early identification of motor impairment in infants at developmental risk can benefit from objective assessment methods. In this feasibility study, we evaluated quantitative kinematic metrics extracted from home-based recordings as indicators of developmental differences between twins with divergent perinatal outcomes. Two 4-month-old twins, one typically developing and one at-risk of motor impairment, were recorded using a single RGB-D camera at home. Videos of up to three minutes per twin were captured at seven timepoints, about 30 days apart. Seven upper body points of interest were tracked using DeepLabCut and their 3D coordinates calculated with an improved version of a previously published method adapted for home use. Thirteen metrics were estimated at each timepoint. Most metrics were consistent with the trends reported in the literature, distinguishing divergent perinatal outcomes. These findings demonstrated that the proposed method is usable for home-based data collection, compensating for motion artifacts and reducing manual labeling time.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"250-258"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11569336","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480524","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

Time-Aware Multimodal Sensor Fusion for Blood Glucose Forecasting in Non-Diabetic Adults 时间感知多模态传感器融合用于非糖尿病成人血糖预测

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-06-19 DOI: 10.1109/OJEMB.2026.3705652

Aashritha Machiraju;Ebrahim Farahmand;Shovito Barua Soumma;Asiful Arefeen;Reza Rahimi Azhghan;Carol Johnston;Hassan Ghasemzadeh

{"title":"Time-Aware Multimodal Sensor Fusion for Blood Glucose Forecasting in Non-Diabetic Adults","authors":"Aashritha Machiraju;Ebrahim Farahmand;Shovito Barua Soumma;Asiful Arefeen;Reza Rahimi Azhghan;Carol Johnston;Hassan Ghasemzadeh","doi":"10.1109/OJEMB.2026.3705652","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3705652","url":null,"abstract":"Glucose dysregulation in non-diabetic individuals can precede metabolic disease onset by years, yet most glucose prediction models target diabetic populations with structured insulin records. This article presents Patch Time-Aware Cross-Attention (Patch-TACA), a multimodal transformer-based framework for long-term blood glucose forecasting in healthy individuals. The approach combines patch-based self-supervised pretraining with a time-aware cross-attention mechanism to integrate continuous glucose monitoring (CGM) data with physiological and behavioral measures, such as accelerometry, heart rate, electrodermal activity, and meal macronutrients, without requiring explicit resampling of irregularly sampled signals. Patch-TACA is assessed using data from twelve healthy participants across three laboratory sessions. It achieves an RMSE of 14.26 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 3.48 mg/dL at the 90-minute prediction horizon, outperforming GlySim and Gluformer baselines. Moreover, employing a self-supervised method for pre-training reduced RMSE by 7.9% compared to training with random weight initialization, and hyperglycemia prediction accuracy reached 93.9%. These results demonstrate that multimodal sensor fusion with self-supervised learning enables accurate long-horizon glucose forecasting in non-diabetic populations, supporting proactive metabolic health monitoring before clinical disease onset.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"241-249"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11571010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442950","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

Counterfactual Modeling With Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation 基于微调llm的反事实建模用于健康干预设计和传感器数据增强

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-03-28 DOI: 10.1109/OJEMB.2026.3697994

Shovito Barua Soumma;Asiful Arefeen;Stephanie M. Carpenter;Melanie Hingle;Hassan Ghasemzadeh

{"title":"Counterfactual Modeling With Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation","authors":"Shovito Barua Soumma;Asiful Arefeen;Stephanie M. Carpenter;Melanie Hingle;Hassan Ghasemzadeh","doi":"10.1109/OJEMB.2026.3697994","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3697994","url":null,"abstract":"Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction. Therefore, CFs can be used as (i) interventions for abnormality prevention and (ii) augmented data for training robust models. we conduct a comprehensive evaluation of CF generation using large language models (LLMs), including GPT-4 (zero-shot and few-shot) and two open-source models—BioMistral-7B and LLaMA-3.1-8B—in both pretrained and fine-tuned configurations. Using the multimodal AI-READI clinical dataset, we assess CFs across three dimensions: intervention quality, feature diversity, and augmentation effectiveness. Fine-tuned LLMs, particularly LLaMA-3.1-8B, produce CFs with high plausibility (up to 99%), strong validity (up to 0.99), and realistic, behaviorally modifiable feature adjustments. When used for data augmentation under controlled label-scarcity settings, LLM-generated CFs substantially restore classifier performance, yielding an average 20% F1 recovery across three scarcity scenarios. Compared with optimization-based baselines such as DiCE, CFNOW, and NICE, LLMs offer a flexible, model-agnostic approach that generates moreclinically plausible, behaviorally actionable and semantically coherent counterfactuals. Overall, this work demonstrates the promise of LLM-driven counterfactuals for both interpretable intervention design and data-efficient model training in sensor-based digital health.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"232-240"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11537391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443132","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

Quantitative Analysis of the Impact of Region of Interest Information on Deep Learning Algorithms for Thyroid Ultrasound Imaging 感兴趣区域信息对甲状腺超声成像深度学习算法影响的定量分析

IF 2.9

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-02-23 DOI: 10.1109/OJEMB.2026.3667415

Hyunju Lee;Jin Young Kwak;Eunjung Lee

{"title":"Quantitative Analysis of the Impact of Region of Interest Information on Deep Learning Algorithms for Thyroid Ultrasound Imaging","authors":"Hyunju Lee;Jin Young Kwak;Eunjung Lee","doi":"10.1109/OJEMB.2026.3667415","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3667415","url":null,"abstract":"<italic>Goal: To quantitatively assess the impact of incorporating radiologist-defined Region of Interest (ROI) information in training deep learning models for thyroid ultrasound image classification and lesion localization. Methods: We compared a conventional convolutional neural network (CNN) trained without ROI information, interpreted through Grad-CAM for attention visualization, to Faster R-CNN and YOLOv2 models trained with radiologist-validated ROI masks. We also introduced an adapted mosaic-based composite input, derived from mosaic augmentation but implemented as fixed 1 × 2 and 2 × 2 layouts, to improve class balance and spatial diversity in training. Results: Models trained with ROI guidance achieved higher performance in both localization and classification compared to those trained without ROI. The average classification accuracy increased from about 80% in the baseline CNN to around 85% in ROI-guided models that shows an improvement of approximately 5 percentage points. The mean intersection over union between detected and radiologist-defined ROIs increased from approximately 33% to over 70%. The adapted mosaic input further stabilized performance across epochs and improved sensitivity while maintaining comparable specificity. Conclusions: Incorporating radiologist-defined ROI information and structured mosaic inputs significantly improves both diagnostic accuracy and localization precision. These results demonstrate that integrating ROI-guided learning with context-preserving composite inputs provides a reproducible framework for developing reliable AI systems in thyroid ultrasonography.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"172-179"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828942","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

Seizure Prediction Using Channel-Optimized Intracranial EEG With Variable Interictal and Preictal Durations 利用可变间隔期和间隔期的通道优化颅内脑电图预测癫痫发作

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-06-08 DOI: 10.1109/OJEMB.2026.3701297

Xiaoshuang Wang;Meng Li;Tommi Kärkkäinen;Hailong Liu;Jianwen Lin;Fengyu Cong

{"title":"Seizure Prediction Using Channel-Optimized Intracranial EEG With Variable Interictal and Preictal Durations","authors":"Xiaoshuang Wang;Meng Li;Tommi Kärkkäinen;Hailong Liu;Jianwen Lin;Fengyu Cong","doi":"10.1109/OJEMB.2026.3701297","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3701297","url":null,"abstract":"<italic>Objective: Predicting epileptic seizures using electroencephalogram (EEG) signals can provide a critical window for intervention to suppress impending seizures. However, the impact of varying interictal and preictal durations on seizure prediction remains unclear and warrants further exploration. Considering this, this study focuses on seizure prediction using intracranial EEG (iEEG) signals with variable interictal and preictal durations. <italic>Method: In this work, we discuss six pairwise combinations in total, involving two interictal durations (24 h and 48 h) and three preictal durations (30 min, 60 min and 90 min), during seizure prediction analysis. Furthermore, we also compare three classification strategies: the all-channel strategy (Strategy 1), the single-channel-based majority voting strategy (Strategy 2) and the channel-optimization-based majority voting strategy (Strategy 3), across each pairwise combination. <italic>Results: The long-term SWEC-ETHZ iEEG dataset is utilized to evaluate our method. The results reveal that our method achieves up to 87.7% sensitivity, 94.6% specificity, 92.8% accuracy, 0.912 Area Under the Curve (AUC) and 0.858 F1-score at the segment-based level, along with 95.9% sensitivity and 0.05/h false prediction rate (FPR) at the event-based level. Moreover, the overall performance of Strategy 3 surpasses that of Strategies 1 and 2 in each pairwise combination, indicating that channel optimization is beneficial for improving seizure prediction performance. <italic>Conclusion: This work shows high effectiveness in predicting seizures using iEEG signals, with the ability to forecast upcoming epileptic events up to 90 min in advance across both 24-h and 48-h interictal durations.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"250-260"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11553376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480530","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

Focused Microwave Clinical System for Targeted Delivery of Hyperthermia Treatment in Locally Advanced Breast Cancer 聚焦微波临床系统用于局部晚期乳腺癌热疗的靶向递送

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-07-22 DOI: 10.1109/OJEMB.2026.3715979

Rahul Choudhary;Aishwarya Gunasekaran;Jagan Ganapathy;Gautam Madhavan Nair;Kavitha Arunachalam

引用次数: 0

Physiologically Interpretable Photoplethysmography Features Associated With Peripheral Artery Disease 与外周动脉疾病相关的生理可解释的光容积脉搏波特征

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-09-08 DOI: 10.1109/OJEMB.2026.3732080

Ava J. Fascetti;Mustafa H. Naguib;Elsie G. Ross;Chris Longhurst;Pam R. Taub;Shamim Nemati;Edward J. Wang;Mattheus Ramsis

{"title":"Physiologically Interpretable Photoplethysmography Features Associated With Peripheral Artery Disease","authors":"Ava J. Fascetti;Mustafa H. Naguib;Elsie G. Ross;Chris Longhurst;Pam R. Taub;Shamim Nemati;Edward J. Wang;Mattheus Ramsis","doi":"10.1109/OJEMB.2026.3732080","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3732080","url":null,"abstract":"<italic>Objective: Peripheral artery disease (PAD) is a common and frequently underdiagnosed cardiovascular condition. Photoplethysmography (PPG) is a widely available optical technique with strong potential to support peripheral vascular assessment, yet its feature-level relationship to PAD severity has not been systematically characterized at scale. This study aims to characterize physiologically informed, feature-level relationships between PPG pulse morphology and PAD severity using a large clinical dataset benchmarked against ankle-brachial index (ABI). <italic>Results: Using a large, multi-site clinical dataset consisting of 5,282 toe PPG waveforms from 2,599 unique patients, we systematically evaluated a broad set of PPG features. Features capturing waveform timing and shape exhibited consistent associations with disease severity, including pulse broadening and delayed waveform progression. In contrast, raw signal amplitude did not reliably associate with ABI. Feature associations were consistent across complementary feature classes, and exclusions did not introduce demographic bias. <italic>Conclusions: These findings show that PAD severity is reflected in coordinated changes in PPG pulse timing and shape, providing physiological context for waveform features. This work provides a feature-level reference framework for physiologically interpretable use of PPG in peripheral vascular assessment.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"308-315"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11683901","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133708","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

ReHeartNet: Reconstruct Electrocardiogram From Photoplethysmography by Using Dense Connected Deep Learning Model ReHeartNet：利用密集连接深度学习模型从光电容积脉搏图重构心电图

IF 2.9

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-03-03 DOI: 10.1109/OJEMB.2026.3670010

Shuenn-Yuh Lee;Kai-Ze Lei;Ju-Yi Chen;Chun-Rong Huang

{"title":"ReHeartNet: Reconstruct Electrocardiogram From Photoplethysmography by Using Dense Connected Deep Learning Model","authors":"Shuenn-Yuh Lee;Kai-Ze Lei;Ju-Yi Chen;Chun-Rong Huang","doi":"10.1109/OJEMB.2026.3670010","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3670010","url":null,"abstract":"<italic>Goal: To enable comfortable and non-invasive heart rhythm monitoring, this work aims to reconstruct electrocardiogram (ECG) signals from photoplethysmogram (PPG) signals, eliminating the need for multiple electrode attachments, which are often inconvenient and may cause skin irritation. <italic>Method: To achieve high-fidelity ECG reconstruction from PPG inputs, we propose ReHeartNet, a novel neural network that formulates the task as a regression problem. To capture the multi-scale temporal and frequency relationships between PPG and ECG signals, the model employs densely connected bidirectional long short-term memory (DC-BiLSTM) blocks. To enhance reconstruction accuracy, hierarchical features from different BiLSTM layers are fused within the network architecture. <italic>Results: To validate the proposed method, experiments were conducted on four datasets: MIMIC-III, BIDMC, TBME-RR, and CBIC-Heart. ReHeartNet consistently outperforms baselines based on generative adversarial networks (GAN), recurrent neural networks (RNN), and transformers. <italic>Conclusions: To support reliable cardiac monitoring in various populations, ReHeartNet demonstrates strong generalization and robustness in ECG reconstruction for healthy individuals and patients with circulatory diseases and arrhythmias, using only wearable PPG signals.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"106-112"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11419800","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557310","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

Leveraging Vision Transformers for High-Precision Classification of Cancer Cell Cultures: A Comparative Study on MDA-MB-231 and PC3 Datasets 利用视觉转换器对癌细胞培养物进行高精度分类：MDA-MB-231和PC3数据集的比较研究

IF 2.9

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-02-23 DOI: 10.1109/OJEMB.2026.3666980

Noreen Fayyaz Khan;Lu Liu;Lucas Bierscheid;John C. Wilkinson;Changhui Yan

{"title":"Leveraging Vision Transformers for High-Precision Classification of Cancer Cell Cultures: A Comparative Study on MDA-MB-231 and PC3 Datasets","authors":"Noreen Fayyaz Khan;Lu Liu;Lucas Bierscheid;John C. Wilkinson;Changhui Yan","doi":"10.1109/OJEMB.2026.3666980","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3666980","url":null,"abstract":"<italic>Goal: Accurate classification of cancer cell cultures is critical for understanding tumor behavior, drug responses, and disease progression. Traditional manual evaluation methods are often subjective and prone to errors, necessitating automated approaches based on deep learning. <italic>Methods: We design a pipeline using Otsu thresholding, morphological filtering, and watershed segmentation, coupled with class-balanced augmentation. A baseline CNN, two attention-augmented variants: CNN-SE (squeeze-and-excitation) and CNN-CBAM (channel-spatial attention) and a ViT are benchmarked. All models are tuned through a 5-fold cross-validation grid search and evaluated on MDA-MB-231 (triple negative breast cancer) and PC3 (prostate cancer) cell images. The study employed both quantitative and qualitative approaches to comprehensively assess the effectiveness and reliability of the proposed model. <italic>Results: Attention modules substantially strengthen CNN performance but ViT achieves the best overall accuracy and generalization, particularly reflecting an advantage in modeling long-range dependencies. <italic>Conclusions: Channel and spatial attention narrows the gap between CNNs and transformers, while transformers provide the highest end-to-end performance. These results support attention-augmented CNNs and ViTs as robust complementary tools for automated analysis of cancer cell cultures.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"70-78"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11406888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557420","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