Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signal...Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals.In this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress noise.First,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal processing.Second,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the filter.This approach builds a bridge between deep learning and signal processing methodologies.Finally,we evaluate the performance of FM-FCN using remote photoplethysmography.Experimental results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)accuracy.It substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy requirements.The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction.The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN.展开更多
In this paper we present a large scale,passive positioning system that can be used for approximate localization in Global Positioning System(GPS)denied/spoofed environments.This system can be used for detecting GPS sp...In this paper we present a large scale,passive positioning system that can be used for approximate localization in Global Positioning System(GPS)denied/spoofed environments.This system can be used for detecting GPS spoofing as well as for initial position estimation for input to other GPS free positioning and navigation systems like Terrain Contour Matching(TERCOM).Our Location inference through Frequency Modulation(FM)Signal Integration and estimation(LoSI)system is based on broadcast FM radio signals and uses Received Signal Strength Indicator(RSSI)obtained using a Software Defined Radio(SDR).The RSSI thus obtained is used for indexing into an estimated model of expected FM spectrum for the entire United States.We show that with the hardware for data acquisition,a single point resolution of around 3 miles and associated algorithms,we are capable of positioning with errors as low as a single pixel(more precisely around 0.12 mile).The algorithm uses a largescale model estimation phase that computes the expected FM spectrum in small rectangular cells(realized using geohashes)across the Contiguous United States(CONUS).We define and use Dominant Channel Descriptor(DCD)features,which can be used for positioning using time varying models.Finally we use an algorithm based on Euclidean nearest neighbors in the DCD feature space for position estimation.The system first runs a DCD feature detector on the observed spectrum and then solves a subset query formulation to find Inference Candidates(IC).Finally,it uses a simple Euclidean nearest neighbor search on the ICs to localize the observation.We report results on 1500 points across Florida using data and model estimates from 2015 and 2017.We also provide a Bayesian decision theoretic justification for the nearest neighbor search.展开更多
基金supported by Ministry of Science and Technology of the People’s Republic of China(STI2030-Major Projects 2021ZD0201900)National Natural Science Foundation of China(grant mo.12090052)+2 种基金Natural Science Foundation of Liaoning Province(grant no.2023-MS-288)Fundamental Research Funds for the Central Universities(grant no.20720230017)Basic Public Welfare Research Program of Zhejiang Province(grant no.LGF20F030005).
文摘Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals.In this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress noise.First,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal processing.Second,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the filter.This approach builds a bridge between deep learning and signal processing methodologies.Finally,we evaluate the performance of FM-FCN using remote photoplethysmography.Experimental results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)accuracy.It substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy requirements.The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction.The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN.
文摘In this paper we present a large scale,passive positioning system that can be used for approximate localization in Global Positioning System(GPS)denied/spoofed environments.This system can be used for detecting GPS spoofing as well as for initial position estimation for input to other GPS free positioning and navigation systems like Terrain Contour Matching(TERCOM).Our Location inference through Frequency Modulation(FM)Signal Integration and estimation(LoSI)system is based on broadcast FM radio signals and uses Received Signal Strength Indicator(RSSI)obtained using a Software Defined Radio(SDR).The RSSI thus obtained is used for indexing into an estimated model of expected FM spectrum for the entire United States.We show that with the hardware for data acquisition,a single point resolution of around 3 miles and associated algorithms,we are capable of positioning with errors as low as a single pixel(more precisely around 0.12 mile).The algorithm uses a largescale model estimation phase that computes the expected FM spectrum in small rectangular cells(realized using geohashes)across the Contiguous United States(CONUS).We define and use Dominant Channel Descriptor(DCD)features,which can be used for positioning using time varying models.Finally we use an algorithm based on Euclidean nearest neighbors in the DCD feature space for position estimation.The system first runs a DCD feature detector on the observed spectrum and then solves a subset query formulation to find Inference Candidates(IC).Finally,it uses a simple Euclidean nearest neighbor search on the ICs to localize the observation.We report results on 1500 points across Florida using data and model estimates from 2015 and 2017.We also provide a Bayesian decision theoretic justification for the nearest neighbor search.
