Many studies have shown that wireless sensing would be a promising method for liquid identification, but existing methods still have limitations for fine-grained liquid sensing. In this paper, we propose a liquid identification method based on multiple transceiver pairs, which effectively improves the sensing resolution of liquid identification. We implement our method with a commercially FMCW millimeter wave radar and evaluate its performance. Our result shows that for concentrations as low as 0.5% in alcohol solutions, our method can achieve an accuracy of more than 95%.
KEYWORDS: Signal detection, Ultrasonics, Defense and security, Signal attenuation, Accelerometers, Frequency response, Signal processing, Environmental sensing, Detection and tracking algorithms, Design and modelling
Inaudible attack has brought growing concerns over security of voice assistants. With a well-designed inaudible signal, an adversary can force the voice assistant to execute commands inaudibly like “Siri, open the door”. It is challenging to defend against ultrasonic attacks without modifying the hardware. In this paper, we proposed a light-weight system named IMUSHIELD to defend voice assistant against inaudible attack. By comparing the different response of signal from microphone and inertial measurement units (IMUs) to different frequencies on smartphones. IMUSHIELD is able to detect the attacks without modifying the hardware. We have prototyped our method on a number of smartphones and test the performance of IMUSHIELD comprehensively in the real world, the result shows that our average detection accuracy exceeded 90%.
To achieve non-invasive, non-contact, and real-time heart rate monitoring, proposed a pulse signal acquisition system using PVDF (Polyvinylidene Fluoride) piezoelectric film. In order to address the issue of errors in heart rate extraction caused by differences in the morphology of pulse signals across individuals or in different states, the K-means clustering algorithm was innovatively used to locate the peak of pulse waveforms in different states and constructed a heart rate data set. Real-time heart rate monitoring by training a large number of pulse signal samples with the proposed CNN-LSTM network model. Experimental results demonstrated that the performance metrics of this model, including the MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and R2 (Coefficient of Determination), are 0.2517, 0.3395, and 0.9863, respectively. the maximum error between the proposed system and the standard instrument within three minutes was only 1.55 beats/minute, indicating that the system exhibits high accuracy and reliability, and holds great potential for applications in heart rate detection.
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