Wi-Wheat+:Contact-free wheat moisture sensing with commodity WiFi based on entropy

In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time monitoring.The proposed system is based on Commodity WiFi and is easy to deploy.Leveraging WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel entropy.The feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.

Hybrid spectrum access model for cognitive femtocell networks

In order to mitigate the interference for macrocell users caused by deploying femtocells （home base-station） in a long term evolution （LTE） system, a hybrid spectrum access model is proposed by means of applying the cognitive radio technology to the femtocell. The femtocell periodically senses the radio environment and opportunistically accesses the usable frequency band so that the frequency spectrum resource is used intelligently. The sensing process is performed in two stages, which are principal sensing in the downlink and assisted sensing in the uplink, respectively. Based on the information obtained from the sensing results, the frequency spectrum can be used flexibly in underlay or overlay modes in the femtocell. Simulation results show that by using the proposed model, the throughput of a femtocell is greatly improved with tolerable interference to macrocell users.

PTX-symmetric metasurfaces for sensing applications

In this paper,we introduce an ultra-sensitive optical sensing platform based on the parity-time-reciprocal scaling(PT^-symmetric non-Hermitian metasurfaces,which leverage exotic singularities,such as the exceptional point(EP)and the coherent perfect absorber-laser(CPAL)point,to significantly enhance the sensitivity and detectability of photonic sensors.We theoretically studied scattering properties and physical limitations of the PTX-symmetric metasurface sensing systems with an asymmetric,unbalanced gain-loss profile.The PTLY-symmetric metasurfaces can exhibit similar scattering properties as their Pr-symmetric counterparts at singular points,while achieving a higher sensitivity and a larger modulation depth,possible with the reciprocal-scaling factor(i.e.,X transformation).Specifically,with the optimal reciprocalscaling factor or near-zero phase offset,the proposed PTX-symmetric metasurface sensors operating around the EP or CPAL point may achieve an over 100 dB modulation depth,thus paving a promising route toward the detection of small-scale perturbations caused by,for example,molecular,gaseous,and biochemical surface adsorbates.

Multiscale Feature Fusion for Gesture Recognition Using Commodity Millimeter-Wave Radar

Gestures are one of the most natural and intuitive approach for human-computer interaction.Compared with traditional camera-based or wearable sensors-based solutions,gesture recognition using the millimeter wave radar has attracted growing attention for its characteristics of contact-free,privacy-preserving and less environmentdependence.Although there have been many recent studies on hand gesture recognition,the existing hand gesture recognition methods still have recognition accuracy and generalization ability shortcomings in shortrange applications.In this paper,we present a hand gesture recognition method named multiscale feature fusion(MSFF)to accurately identify micro hand gestures.In MSFF,not only the overall action recognition of the palm but also the subtle movements of the fingers are taken into account.Specifically,we adopt hand gesture multiangle Doppler-time and gesture trajectory range-angle map multi-feature fusion to comprehensively extract hand gesture features and fuse high-level deep neural networks to make it pay more attention to subtle finger movements.We evaluate the proposed method using data collected from 10 users and our proposed solution achieves an average recognition accuracy of 99.7%.Extensive experiments on a public mmWave gesture dataset demonstrate the superior effectiveness of the proposed system.

RFPose-OT:RF-based 3D human pose estimation via optimal transport theory

This paper introduces a novel framework,i.e.,RFPose-OT,to enable three-dimensional(3D)human pose estimation from radio frequency(RF)signals.Different from existing methods that predict human poses from RF signals at the signal level directly,we consider the structure difference between the RF signals and the human poses,propose a transformation of the RF signals to the pose domain at the feature level based on the optimal transport(OT)theory,and generate human poses from the transformed features.To evaluate RFPose-OT,we build a radio system and a multi-view camera system to acquire the RF signal data and the ground-truth human poses.The experimental results in a basic indoor environment,an occlusion indoor environment,and an outdoor environment demonstrate that RFPose-OT can predict 3D human poses with higher precision than state-of-the-art methods.