A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors

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.

HIGH PERFORMANCE ELECTRIC FIELD MICRO SENSOR WITH COMBINED DIFFERENTIAL STRUCTURE

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.

QAM signal with electric field sensor based on thin-film lithium niobate [Invited]

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%.

Nanocrystalline Gd_(1–x)Ca_xFeO_3 sensors for detection of methanol gas

The sol-gel method was used to prepare the nanocrystalline Gd_（1–x）Ca_xFeO_3 （x=0–0.4） powders. The XRD results showed that all the Gd_（1–x）Ca_xFeO_3 （x=0–0.4） compounds crystallized as perovskite phase with orthorhombic structure. The doping of Ca in GdFeO_3 not only reduced the resistance, but also enhanced the response to methanol. The Gd_（0.9）Ca_（0.1）FeO_3 showed the best response to methanol among Gd_（1–x）Ca_xFeO_3 sensors. Besides, it showed good selectivity to methanol among methanol, ethanol, CO and formaldehyde gases. The responses at 260 oC for Gd_（0.9）Ca_（0.1）FeO_3-based sensor to 600 ppm methanol, ethanol and CO gases were 117.7, 72.7 and 31.9, respectively. Even at quite low gas concentrations, Gd_（0.9）Ca_（0.1）FeO_3-based sensor had an obvious response. At 260 °C, the response of 1.54 was obtained to be 45 ppm methanol. The experimental results showed that nanocrystalline Gd_（0.9）Ca_（0.1）FeO_3 based sensor can be used to detect methanol gas.

Focus on using nanopore technology for societal health,environmental,and energy challenges

With an increasing global population that is rapidly ageing,our society faces challenges that impact health,environment,and energy demand.With this ageing comes an accumulation of cellular changes that lead to the development of diseases and susceptibility to infections.This impacts not only the health system,but also the global economy.As the population increases,so does the demand for energy and the emission of pollutants,leading to a progressive degradation of our environment.This in turn impacts health through reduced access to arable land,clean water,and breathable air.New monitoring approaches to assist in environmental control and minimize the impact on health are urgently needed,leading to the development of new sensor technologies that are highly sensitive,rapid,and low-cost.Nanopore sensing is a new technology that helps to meet this purpose,with the potential to provide rapid point-of-care medical diagnosis,real-time on-site pollutant monitoring systems to manage environmental health,as well as integrated sensors to increase the efficiency and storage capacity of renewable energy sources.In this review we discuss how the powerful approach of nanopore based single-molecule,or particle,electrical promises to overcome existing and emerging societal challenges,providing new opportunities and tools for personalized medicine,localized environmental monitoring,and improved energy production and storage systems.

E-field measurement of a pulse line ion accelerator

The E-field of pulse line ion accelerator （PLIA） is unique with high frequency （~MHz）, large magni- tude （~MV/m）, and limited measuring space （~cm）. The integrated optical E-field sensor （IOES） has remarkable advantages and has been used for PLIA E-field measurement. Firstly, the transfer function of the IOES has been calibrated to ensure measurement accuracy. The time-domain response illustrates that the sensor has a fast dynamic performance to effectively follow a 4 ns rising edge. Then, the E-field distribution along the axis and near the insula- tor surface of the PLIA was measured, showing that propagation of the E-field is almost lossless and the E-field near the insulation surface is about 1.1 times larger than that along the axis, which is in accordance with the simulation result.