In this paper,we propose a BPR-CNN(Biometric Pattern Recognition-Convolution Neural Network)classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction...In this paper,we propose a BPR-CNN(Biometric Pattern Recognition-Convolution Neural Network)classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction by EF(Electric Field)sensors.Currently,an EF sensor or EPS(Electric Potential Sensor)system is attracting attention as a next-generationmotion sensing technology due to low computation and price,high sensitivity and recognition speed compared to other sensor systems.However,it remains as a challenging problem to accurately detect and locate the authentic motion signal frame automatically in real-time when sensing body-motions such as hand motion,due to the variance of the electric-charge state by heterogeneous surroundings and operational conditions.This hinders the further utilization of the EF sensing;thus,it is critical to design the robust and credible methodology for detecting and extracting signals derived from the motion movement in order to make use and apply the EF sensor technology to electric consumer products such as mobile devices.In this study,we propose a motion detection algorithm using a dynamic offset-threshold method to overcome uncertainty in the initial electrostatic charge state of the sensor affected by a user and the surrounding environment of the subject.This method is designed to detect hand motions and extract its genuine motion signal frame successfully with high accuracy.After setting motion frames,we normalize the signals and then apply them to our proposed BPR-CNN motion classifier to recognize their motion types.Conducted experiment and analysis show that our proposed dynamic threshold method combined with a BPR-CNN classifier can detect the hand motions and extract the actual frames effectively with 97.1%accuracy,99.25%detection rate,98.4%motion frame matching rate and 97.7%detection&extraction success rate.展开更多
Large-bandwidth,high-sensitivity,and large dynamic range electric field sensors are gradually replacing their traditional counterparts.The lithium-niobate-on-insulator(LNOI)material has emerged as an ideal platform fo...Large-bandwidth,high-sensitivity,and large dynamic range electric field sensors are gradually replacing their traditional counterparts.The lithium-niobate-on-insulator(LNOI)material has emerged as an ideal platform for developing such devices,owing to its low optical loss,high electro-optical modulation efficiency,and significant bandwidth potential.In this paper,we propose and demonstrate an electric field sensor based on LNOI.The sensor consists of an asymmetric Mach–Zehnder interferometer(MZI)and a tapered dipole antenna array.The measured fiber-to-fiber loss is less than−6.7 dB,while the MZI structure exhibits an extinction ratio of greater than 20 dB.Moreover,64-QAM signals at 2 GHz were measured,showing an error vector magnitude(EVM)of less than 8%.展开更多
This paper presents a high performance electric field micro sensor with combined differential structure.The sensor consists of two backward laid micro-machined chips,each packaged by polymer and metal.The novel combin...This paper presents a high performance electric field micro sensor with combined differential structure.The sensor consists of two backward laid micro-machined chips,each packaged by polymer and metal.The novel combined differential structure effectively reduces various environmental affections,such as thermal drift,humidity drift and electrostatic charge accumulation.The sensor is tested in near-ground place as well as balloon-borne sounding.In different weather conditions,the measurement results showed good agreement with those of the commercial electric field mill.展开更多
基金This work was supported by the NRF of Korea grant funded by the Korea government(MIST)(No.2019 R1F1A1062829).
文摘In this paper,we propose a BPR-CNN(Biometric Pattern Recognition-Convolution Neural Network)classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction by EF(Electric Field)sensors.Currently,an EF sensor or EPS(Electric Potential Sensor)system is attracting attention as a next-generationmotion sensing technology due to low computation and price,high sensitivity and recognition speed compared to other sensor systems.However,it remains as a challenging problem to accurately detect and locate the authentic motion signal frame automatically in real-time when sensing body-motions such as hand motion,due to the variance of the electric-charge state by heterogeneous surroundings and operational conditions.This hinders the further utilization of the EF sensing;thus,it is critical to design the robust and credible methodology for detecting and extracting signals derived from the motion movement in order to make use and apply the EF sensor technology to electric consumer products such as mobile devices.In this study,we propose a motion detection algorithm using a dynamic offset-threshold method to overcome uncertainty in the initial electrostatic charge state of the sensor affected by a user and the surrounding environment of the subject.This method is designed to detect hand motions and extract its genuine motion signal frame successfully with high accuracy.After setting motion frames,we normalize the signals and then apply them to our proposed BPR-CNN motion classifier to recognize their motion types.Conducted experiment and analysis show that our proposed dynamic threshold method combined with a BPR-CNN classifier can detect the hand motions and extract the actual frames effectively with 97.1%accuracy,99.25%detection rate,98.4%motion frame matching rate and 97.7%detection&extraction success rate.
基金supported by the National Key Research and Development Program of China(No.2021YFB2800104)the National Natural Science Foundation of China(Nos.62175079 and 62205119).
文摘Large-bandwidth,high-sensitivity,and large dynamic range electric field sensors are gradually replacing their traditional counterparts.The lithium-niobate-on-insulator(LNOI)material has emerged as an ideal platform for developing such devices,owing to its low optical loss,high electro-optical modulation efficiency,and significant bandwidth potential.In this paper,we propose and demonstrate an electric field sensor based on LNOI.The sensor consists of an asymmetric Mach–Zehnder interferometer(MZI)and a tapered dipole antenna array.The measured fiber-to-fiber loss is less than−6.7 dB,while the MZI structure exhibits an extinction ratio of greater than 20 dB.Moreover,64-QAM signals at 2 GHz were measured,showing an error vector magnitude(EVM)of less than 8%.
基金Supported by the National High Technology Research and Development Program of China(863 Program,2011AA-040405)the National Natural Science Foundation of China(Nos.61101049,61201078,61302032,61327810)
文摘This paper presents a high performance electric field micro sensor with combined differential structure.The sensor consists of two backward laid micro-machined chips,each packaged by polymer and metal.The novel combined differential structure effectively reduces various environmental affections,such as thermal drift,humidity drift and electrostatic charge accumulation.The sensor is tested in near-ground place as well as balloon-borne sounding.In different weather conditions,the measurement results showed good agreement with those of the commercial electric field mill.
