A lightweight attention deep learning method for human-vehicle recognition based on wireless sensing technology.

Wireless sensing-based human-vehicle recognition (WiHVR) methods have become a hot spot for research due to its non-invasiveness and cost-effective advantages. However, existing WiHVR methods shows limited performance and slow execution time on human-vehicle classification task. To address this issue, a lightweight wireless sensing attention-based deep learning model (LW-WADL) is proposed, which consists of a CBAM module and several depthwise separable convolution blocks in series.

An Improved Roadside Parking Space Occupancy Detection Method Based on Magnetic Sensors and Wireless Signal Strength.

Smart Parking Management Systems (SPMSs) have become a research hotspot in recent years. Many researchers are focused on vehicle detection technology for SPMS which is based on magnetic sensors. Magnetism-based wireless vehicle detectors (WVDs) integrate low-power wireless communication technology, which improves the convenience of construction and maintenance.

L1-ORF1p and Ago2 are involved in a siRNA-mediated regulation for promoter activity of L1-5'UTR.

Introduction: Long interspersed nuclear elements-1 (L1), as the only one self-active retrotransposon of the mobile element, was found to be generally activated in tumor cells. The 5'UTR of L1 (L1-5'UTR) contains both sense and antisense bidirectional promoters, transcription products of which can generate double-strand RNA (dsRNA). In addition, L1-ORF1p, a dsRNA binding protein encoded by L1, is considered to engage in some RNA-protein (RNP) formation.

A Novel Vehicle Detection Method Based on the Fusion of Radio Received Signal Strength and Geomagnetism.

A geomagnetic signal blind zone exists between the front and rear axle of high-chassis vehicle such as trucks and buses, which leads to multiple-detection problem in detecting those vehicles running at low speed on roads or error-detection problem in the case of the stopping position of the vehicle is not standard when waiting for the traffic light to change. In order to improve the detection accuracy of any type of vehicle running at any speed, a novel two-sensors data fusion vehicle detection method through combining received signal strength from radio stations with geomagnetism around vehicles is designed and verified in the paper. Experimental results show that the accuracy of our proposed method can reach 95.