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. LW-WADL takes raw channel state information (CSI) as input, and extracts the advanced features of CSI by jointly using depthwise separable convolution and convolutional block attention mechanism (CBAM). Experimental results show that the proposed model achieves 96.26% accuracy on the constructed CSI-based dataset, and the model size is only 5.89% of the state of the art (SOTA) model. The results demonstrate that the proposed model achieves better performance on WiHVR tasks while reducing the model size compared to SOTA model.

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. However, the magnetic signals are not only susceptible to the adjacent vehicles, but also affected by the magnetic signal dead zone of high-chassis vehicles, resulting in a decrease in vehicle detection accuracy. In order to improve the vehicle detection accuracy of the magnetism-based WVDs, the paper introduces an RF-based vehicle detection method based on the characteristics analysis of received signal strengths (RSSs) generated by the wireless transceivers. Since wireless transceivers consume more energy than magnetic sensors, the proposed RF-based method is only activated to extract the data characteristics of RSSs to further judge the states of vehicles when the data feature of magnetic signals is not sufficient to provide accurate judgment on parking space status. The proposed method was evaluated in an actual roadside parking lot and experimental results show that when the sampling rate of magnetic sensor is 1 Hz, the vehicle detection accuracy is up to 99.62%. Moreover, compared with machine-learning-based vehicle detection method, the experimental results show that our method has achieved a good compromise between detection accuracy and power consumption.

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. Ago2, one of the RISC components, can bind to dsRNA to form RISC complex, but its role in L1 regulation still remains unclear. Due that the 5'UTR of L1 (L1-5'UTR) contains both sense and antisense bidirectional promoters, so the activities in both string were identified. A dsRNA-mediated regulation of L1-5'UTR, with the feedback regulation of L1-ORF1p as well as other key molecules engaged (Ago1-4) in this process, was also investigated. MATERIAL AND METHODS: Genomic DNA was extracted from HEK293 cells and subjected to L1-5'UTR prepa-ration by PCR. Report gene system pIRESneo with SV40 promoter was employed. The promoter activities of different regions in L1-5'UTR were identified by constructing these regions into pIRESneo, which SV40 region was removed prior, to generate different recombinant plasmids. The promoter activities in recombinant plasmids were detected by the luciferase expression assay. Western blot and co-immunoprecipitation were employed to identify proteins expression and protein-protein interaction respectively. RESULTS: Ago2 is a member of Agos family, which usually forms a RISC complex with si/miRNA and is involved in post- transcriptional regulation of many genes. Here L1-ORF1p and Ago2 conducts a regulation as a negative feedback for L1-5'UTR sense promoter. L1-ORF1p could form the immune complexes with Ago1, Ago2 and Ago4, respectively. CONCLUSIONS: L1-5'UTR harbors both sense and antisense promoter activity and a dsRNA-mediated regulation is responsible for L1-5'UTR regulation. Agos proteins and L1-ORF1p were engaged in this process.

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 95.4% and traditional single magnetism-based detection method was only 83.4% in the detection of high-chassis vehicles.