Heart rate is closely related to physiological and psychological states, and video-based techniques such as Imaging Photoplethysmography (IPPG) have been developed for heart rate detection. Although there have been some methods based on IPPG that are used to address the impact of illumination changes on heart rate detection, these methods perform poorly in environments with intense or complex illumination. This study proposes a framework that uses normalized least mean square adaptive filtering and singular spectrum analysis to combat the effects of illumination changes on heart rate detection. Experimental results on a dataset comprising 13 men and women aged 20 to 28 demonstrate the feasibility of our method under illumination changes.
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%.
End-to-end task-oriented dialogue systems rely heavily on an understanding of the dialog history. This often faces the challenge of inferring which dialog history information is critical to generating responses. In this paper, we address this challenge by leveraging a dialog history manager component that dynamically focuses on dialog history memory. It performs multiple add and forget operations by fusing an enhanced entity representation of dialog history and Knowledge Base (KB) information as queries, remembering entities relevant to responses and filtering out unimportant information. Experimental results on an open task-oriented dialogue dataset show that our model outperforms the baseline system in terms of effectiveness and produces contextually consistent responses.
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