检索结果-内蒙古大学图书馆

Deep Learning for Patient-Independent Epileptic Seizure Prediction Using Scalp EEG Signals

IEEE sensorS JOURNAL 2021年第7期21卷 9377-9388页

作者： Dissanayake, Theekshana Fernando, Tharindu Denman, Simon Sridharan, Sridha Fookes, Clinton Queensland Univ Technol SAIVT Res Grp Brisbane Qld 4000 Australia

Epilepsy is one of the most prevalent neurological diseases among humans and can lead to severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to mitigate injuries, and can be used to aid the treatment of patients with epilepsy. The purpose of a seizure prediction system is to successfully identify the pre-ictal brain stage, which occurs before a seizure event. Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset, and have been identified as a real-world solution to the seizure prediction problem. However, little attention has been given for designing such models to adapt to the high inter-subject variability in EEG data. We propose two patient-independent deep learning architectures with different learning strategies that can learn a global function utilizing data from multiple subjects. Proposed models achieve state-of-the-art performance for seizure prediction on the CHB-MIT-EEG dataset, demonstrating 88.81% and 91.54% accuracy respectively. In conclusion, the Siamese model trained on the proposed learning strategy is able to learn patterns related to patient variations in data while predicting seizures. Our models show superior performance for patient-independent seizure prediction, and the same architecture can be used as a patient-specific classifier after model adaptation. We are the first study that employs model interpretation to understand classifier behavior for the task for seizure prediction, and we also show that the MFCC feature map utilized by our models contains predictive biomarkers related to interictal and pre-ictal brain states.

关键词： Brain modeling Predictive models data models Electroencephalography Biological system modeling Analytical models Adaptation models sensor data processing machine learning neural networks biomedical signal processing model interpretation seizure prediction electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Know the Flow: Non-Contact Magnetic Flow Rate Sensing for Water Meters

引用

IEEE sensorS JOURNAL 2021年第1期21卷 802-811页

作者： Ponce, Eric A. Leeb, Steven B. Lindahl, Peter A. MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Exponent Inc Natick MA 01760 USA

Positive displacement water meters are found throughout the world's water distribution networks to totalize water consumption. Containing internal permanent magnets, the water meters are underutilized sensors. Flow data from these meter locations can be used to non-intrusively identify the operation of individual water loads from an aggregate data stream at the meter. This article models these meters as nonideal magnetic encoders and presents a methodology for inexpensively retrofitting these meters with digital flow rate measurement capabilities, effectively bringing them into the growing body of smart metering devices and non-intrusive load monitoring without any contact with the water passing through them. We present a model of the magnetic field produced by these meters, discuss associated magnetic sensing hardware, and describe compensation, flow rate estimation, and post-processing algorithms for flow rate extraction. Verification is provided with experimental results.

关键词： Meters Magnetic sensors Couplers Magnetic resonance imaging Magnetic recording Magnetic flux Fluid flow measurement magnetic sensors sensor data processing sensor applications

来源：评论

学校读者我要写书评

暂无评论

Human Activity Recognition From Multi-modal Wearable sensor data Using Deep Multi-stage LSTM Architecture Based on Temporal Feature Aggregation 63

Human Activity Recognition From Multi-modal Wearable Sensor ...

引用

63rd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Mahmud, Tanvir Akash, Shaimur Salehin Fattah, Shaikh Anowarul Zhu, Wei-Ping Ahmad, M. Omair Bangladesh Univ Engn & Technol Dept EEE Dhaka Bangladesh Concordia Univ Dept ECE Montreal PQ Canada

ISBN: (纸本)9781728180588

Activity recognition from wearable sensors is a promising field of research with a wide variety of applications to track human activity from distant positions. In this paper, a multi-stage long short term memory (LSTM) based deep neural network is proposed to integrate multimodal features from numerous sensors for activity recognition. In the first stage, for separately extracting effective temporal features from each sensor, an individual stack of LSTM layers are introduced on each sensor data. Afterward, extracted features from numerous sensors are aggregated maintaining their temporal dependency. Finally, for joint optimization of the aggregated multimodal features, a global feature optimizer network is proposed consisting of multiple LSTM layers followed by series of densely connected layers that extracts the global features through the fusion of multimodal features. Extensive experimentations on a publicly available dataset provide very satisfactory performance with an average F1 score of 83.9%.

关键词： Activity recognition LSTM temporal features neural network sensor data processing

来源：评论

学校读者我要写书评

暂无评论

Wavelet-Based Convolutional Neural Network for Denoising Partial Discharge Signals Extracted via Acoustic Emission sensors

IEEE SENSORS LETTERS

引用

IEEE sensorS LETTERS 2024年第7期8卷 1页

作者： Kumar, Chandan Ganguly, Biswarup Dey, Debangshu Chatterjee, Saibal Natl Inst Technol Mizoram Dept Elect Engn Aizawl India Natl Inst Technol Silchar Dept Elect Engn Silchar Assam India Jadavpur Univ Dept Elect Engn Kolkata India

This letter aims to propose a wavelet-based convolutional neural network architecture to eliminate noise from partial discharge (PD) signals produced in an electrical apparatus. The PD signals are recorded via three acoustic emission (AE) sensors placed on the surfaces of an emulated cubical tank developed in the laboratory. For this proposed research, an encoder-decoder framework has been modeled to produce noise-free signals. The advantages of the proposed denoising method are twofold. First, various mother wavelets are employed as convolutional filters in the convolutional block of the encoder and the decoder. Second, discrete-wavelet-transform-based downsampling and upsampling operations have been incorporated to reconstruct the PD signals more effectively than the classical pooling operation. Experiments have been performed taking both the measured and simulated PD signals with various signal-to-noise-ratio levels. Moreover, ablation analysis has been carried out by varying various wavelets during sampling operations. The proposed denoising scheme can also be implemented for condition monitoring in other signal modalities.

关键词： sensors Noise reduction Convolutional neural networks Noise Wavelet transforms Partial discharges Convolution sensor applications acoustic emission (AE) sensor feature extraction partial discharge (PD) signals sensor data processing

来源：评论

学校读者我要写书评

暂无评论

Image presentation and effective classification of odor intensity levels using multi-channel electronic nose technology combined with GASF and CNN

引用

sensorS AND ACTUATORS B-CHEMICAL 2023年 395卷

作者： Xiong, Lijian He, Meng Hu, Can Hou, Yuxin Han, Shaoyun Tang, Xiuying China Agr Univ Coll Engn Beijing 100083 Peoples R China Tarim Univ Coll Mech & Elect Engn Alar 843300 Peoples R China Tarim Univ Modern Agr Engn Key Lab Univ Educ Dept Xinjiang Uy Alar 843300 Peoples R China

This paper proposes a novel data processing scheme for electronic noses that combines the gramian angular field (GAF) and convolutional neural network (CNN) to achieve high performance in classifying five levels of odor intensity. Specifically, a multi-channel e-nose was developed to detect various gases, including hydrogen sulfide, ammonia, sulfur dioxide, trimethylamine, and alkane gases, among others, in complex odor components. The sensor array was optimized through Spearman correlation analysis of the sensor signals and artificial olfactory odor intensity levels. Moreover, the one-dimensional temporal sensor data was converted into two-dimensional color images using the GAF (GASF/GADF) algorithm. This approach enables a more detailed presentation of deep features while retaining the time-domain dependence of the signals. To enhance the performance of classification, a multi-scale feature fusion network (MFFNet) was designed. Notably, GASF-converted images are more effective in characterizing sensor data for different odor intensity levels than GADF-converted images. Compared to classical CNN classification models such as AlexNet, GoogLeNet, and ResNet18, MFFNet achieved the highest accuracy and macro average F1-score on the test set, which were 93.75% and 93.34%, respectively. The results demonstrate the efficient classification of odor intensity levels by combining multi-channel e-nose technology, GASF, and CNN.

关键词： Electronic nose GAF(GASF/GADF) Odor intensity levels CNN sensor data processing

来源：评论

学校读者我要写书评

暂无评论

An Efficient data processing Scheme for Wireless sensor Network Monitoring Using a Machine Learning Model 11

An Efficient Data Processing Scheme for Wireless Sensor Netw...

引用

11th International Conference on Mobile Computing and Ubiquitous Network (ICMU)

作者： Shen, Zhishu Tagami, Atsushi Higashino, Teruo KDDI Res Inc Fujimino Japan Osaka Univ Grad Sch Informat Sci & Technol Osaka Japan

ISBN: (纸本)9784907626341

Wireless sensor networks (WSNs) are playing an increasingly important role in monitoring massive sensors to precisely detect anomalous phenomena, including anomalous events and sensor data faults. Prior studies preferred to dig the event anomaly (e. g., hotspots in a room), while sensor data faults were simply regarded as noise. Considering that different anomalies arise for different reasons, some substantial hidden problems such as internal sensor failures may be ignored. In this study, we propose an efficient data processing scheme using machine learning model with the objective of achieving satisfactory anomaly detection performance during WSN monitoring. Our proposal analyzes the difficulty of detecting different types of fault data and the influence of each type on event detection results. The machine learning model is adopted to analyze the sensor data correlation, to achieve satisfactory performance for both event detection and fault detection by analyzing the correlated sensor data. At each monitoring time during the data monitoring process, the trivial sensor data faults that might affect the event detection results are filtered out before executing event detection. Meanwhile, at much longer monitoring time intervals, random fault detection is performed to find potentially hidden failures of sensors. Numerical experiments conducted in a real WSN environment show that neural network model outperforms other machine learning models in anomaly detection, and the results by adopting neural network model verify the feasibility of our proposed scheme which attains acceptable performance in detecting both types of anomalies.

关键词： Wireless sensor network event detection fault detection sensor data processing machine learning

来源：评论

学校读者我要写书评

暂无评论

Hue-Preserved Quantile-Based Global Contrast Enhancement of Breast Thermograms for Abnormality Detection

IEEE SENSORS LETTERS

引用

IEEE sensorS LETTERS 2023年第9期7卷 1页

作者： Dey, Ankita Rajan, Sreeraman Carleton Univ Syst & Comp Engn Ottawa ON K1S5B6 Canada

With breast cancer cases rapidly increasing worldwide, thermography serves as a potential imaging modality for early abnormality detection. Breast thermograms are low-resolution pseudocolored images that need contrast enhancement for better differentiability between normal and abnormal breasts. Without hue-preservation, conventional contrast enhancement techniques distort the color spaces and, hence, cannot be applied for contrast enhancement of color images, such as breast thermograms. This letter proposes a novel hue-preserving quantile-based global contrast enhancement (QGCE) of breast thermograms for abnormality detection. A comparative analysis of different types of contrast enhancement techniques with and without hue-preservation is shown. An exhaustive comparison of abnormality detection performance in the presence of different types of noises is also presented. The proposed technique achieves noise resilient detection accuracy of 91%. All evaluations are done using 85 breast thermograms from publicly available datasets.

关键词： Breast Image color analysis Color Histograms Testing Tumors Gray-scale sensor applications sensor data processing bilateral symmetry breast cancer contrast enhancement (CE) gamut hue-preservation (HP) thermography white-light dilution

来源：评论

学校读者我要写书评

暂无评论

ADA-SR: Activity detection and analysis using security robots for reliable workplace safety

引用

WORK-A JOURNAL OF PREVENTION ASSESSMENT & REHABILITATION 2021年第3期68卷 935-943页

作者： Zhang, Guangnan Jing, Wang Tao, Hai Rahman, Md Arafatur Salih, Sinan Q. AL-Saffar, Ahmed Zhang, Renrui Baoji Univ Arts & Sci Sch Comp Sci Baoji Peoples R China Univ Malaysia Pahang Fac Comp IBM CoE Pahang Malaysia Univ Malaysia Pahang Earth Resources & Sustainabil Ctr Pahang Malaysia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Univ Teknol MARA Inst Big Data Analyt & Artificial Intelligence IB Shah Alam Malaysia

BACKGROUND: Human-Robot Interaction (HRI) has become a prominent solution to improve the robustness of real-time service provisioning through assisted functions for day-to-day activities. The application of the robotic system in security services helps to improve the precision of event detection and environmental monitoring with ease. OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process. RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset. CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.

关键词： Monitoring convolution neural networks event detection HRI sensor data processing

来源：评论

学校读者我要写书评

暂无评论

System for Monitoring and Control of Vehicle's Carbon Emissions Using Embedded Hardwares and Cloud Applications 18th

System for Monitoring and Control of Vehicle's Carbon Emissi...

引用

18th International Conference on Service-Oriented Computing (ICSOC)

作者： Tsokov, Tsvetan Kostadinov, Hristo Bulgarian Acad Sci Inst Math & Informat Sofia Bulgaria

ISBN: (纸本)9783030763527;9783030763510

Today, the electronic devices such as sensors and actuators, forming the Internet of Things (IoT) are presented naturally in the people's day to day life. Billions of devices are sensing and acting upon the physical world and exchange information. All major industries like transportation, manufacturing, healthcare, agriculture, etc. adopt IoT solutions. Not only people and industry are affected in a positive way by IoT, but also the nature and environment. The IoT is recognized as a key lever in the urge to save the climate. It has a major potential in reducing air carbon emissions and pollution. Taking into account the promising sectors of IoT application, this paper proposes a solution for monitoring and control of carbon emissions from vehicles. It consists of hardware device that ingests data related to vehicles' carbon emissions and cloud based services for data storage, analysis and representation. It controls the carbon emissions via notifications and vehicle's power restrictions.

关键词： Clustering Internet of things Reduction of carbon emissions sensor data processing

来源：评论

学校读者我要写书评

暂无评论

Image Fusion with Sparse Representation: A Novel Local Contrast-Based Preprocessing Strategy

IEEE SENSORS LETTERS

引用

IEEE sensorS LETTERS 2022年第5期6卷 1页

作者： Chen, Yuhang Liu, Aiping Liu, Yu Qian, Ruobing Xie, Qingguo Chen, Xun Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Peoples R China Hefei Univ Technol Dept Biomed Engn Hefei 230009 Peoples R China Univ Sci & Technol China Affiliated Hosp USTC 1 Dept Neurosurg Div Life Sci & Med Hefei 230001 Peoples R China

In the field of image fusion, great progress has been achieved in sparse representation (SR)-based methods with the advent of considerable efficacious algorithms being developed. The improvement of SR-based image fusion methods mainly focuses on pursuing better signal representation as well as effective measurement to sparse coefficients. However, the preprocessing strategy, which plays an important role in SR-based fusion methods, is rarely investigated. The traditional preprocessing strategies such as the zero-mean normalized strategy result in the loss of low-frequency information and insufficient detail extraction. To address the above issues, we propose a local contrast (LC)-based preprocessing strategy, since local contrast can be harnessed to capture geometrical visual patterns as well as preserve comprehensive information. Experimental results on multifocus and multimodal image fusion tasks demonstrate that the proposed preprocessing strategy outperforms the traditional ones in terms of both visual quality and objective evaluation.

关键词： Image fusion sensors Dictionaries Filtering Image reconstruction data mining Visualization sensor signal processing detail extraction image fusion local contrast (LC) sensor data processing sparse representation (SR)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：