Hand gesture recognition is an essential Human–Computer Interaction (HCI) mechanism for users to control smart devices. While traditional device-based methods support acceptable recognition performance, the recent advance in wireless sensing could enable device-free hand gesture recognition. However, two severe limitations are serious environmental interference and high-cost hardware, which hamper wide deployment. This paper proposes the novel system TaGesture, which employs an inaudible acoustic signal to realize device-free and training-free hand gesture recognition with a commercial speaker and microphone array. We address unique technical challenges, such as proposing a novel acoustic hand-tracking-smoothing algorithm with an Interaction Multiple Model (IMM) Kalman Filter to address the issue of localization angle ambiguity, and designing a classification algorithm to realize acoustic-based hand gesture recognition without training. Comprehensive experiments are conducted to evaluate TaGesture. Results show that it can achieve a total accuracy of 97.5% for acoustic-based hand gesture recognition, and support the furthest sensing range of up to 3 m.
Using Kansei engineering method, this paper discussed the principle of Kansei status evaluation of consumer electronics products, and analyzed the approach to quantify the inner perception of consumers. Based on the technology of artificial neural network, this paper established neural network model of perceptual status evaluation of consumer electronics products. Using investigation samples to train this neural network, we got the optimized neural evaluation network.
Hand gesture recognition is an essential Human Computer Interaction (HCI) mechanism for users to control smart devices. While traditional device-based methods support acceptable recognition performance, the recent advance in wireless sensing could enable device-free hand gesture recognition. However, two severe limitations are serious environmental interference and high-cost hardware, which hamper the wide deployment. This paper proposes a novel system TaGesture, which employ the inaudible acoustic signal to realize device-free and training-free hand gesture recognition with a pair of commercial speaker and microphone array. We address unique technical challenges, such as proposing a novel acoustic hand tracking smoothing algorithm with Interaction Multiple Model (IMM) Kalman Filter to address the issue of localization angle ambiguity, and designing a classification algorithm to realize acoustic-based hand gesture recognition without training. Comprehensive experiments are conducted to evaluate TaGesture. Results show that it can achieve a total accuracy of 97.5% for acoustic-based hand gesture recognition, and support the furthest sensing range of up to 3 m.
In this article, a Session Initiation Protocol (SIP)-based instant messaging system is proposed to give an open protocol for instant messaging software to communicate with multi-platform and multi-service interoperability. The system will solve the issue of compatibility between current, mainstream, communication software. The system is based mainly on a multi-media scheduling server. This article is focused on the design of the SIP channel module, the instant messaging module, and presence service module with the goal of eventually achieving the function of instant communication between clients.
An investigation of risk factors has been identified as a crucial aspect of the routine management of rockburst. However, the identification methods for principal impact factors and the examination of the relationship between seismic energy and other source parameters have not been extensively explored to conduct dynamic risk management. This study aims to quantify impact risk factors and discriminate hazardous high-energy seismic events. The analytic hierarchy process (AHP) and entropy weight method (EWM) are utilized to ascertain the primary control factors based on geotechnical data and nearly two months of seismic data from a longwall panel. Furthermore, the distribution law and correlation relationship among seismic source parameters are systematically analyzed. Results show that the effect of coal depth, coal seam thickness, coal dip, and mining speed covers the entire mining process, while the fault is only prominent in localized areas. There are varying degrees of log-positive correlations between seismic energy and other source parameters, and this positive correlation is more pronounced for hazardous high-energy seismic events. Utilizing the linear logarithmic relationship between seismic energy and other source parameters, along with the impact weights of dynamic risks, the comprehensive energy index for evaluating high-energy seismic events is proposed. The comprehensive energy index identification method proves to be more accurate by comparing with the high-energy seismic events based on energy criteria. The limitations and improvements of this method are also synthesized to obtaining a wide range of applications.
Wireless multimedia sensor networks (WMSNs) have been proposed for use in many challenging applications, such as military surveillance, wildlife tracking, and security monitoring. Reducing energy and storage space consumption are challenging in WMSNs. To address this issue, the authors propose a novel low‐duty‐cycle WMSN (LWMSN) based on Wi‐Fi. This special WMSN is especially suitable for security monitoring, which involves long invalid times in which humans do not appear in the target area. The LWMSN can be intelligently switched on and off by human detection based on channel state information provided by Wi‐Fi products. The authors evaluated their design with a simulation and an experiment. Results show that the accuracy of human detection can be >97% on average. In addition, energy and storage space consumption are reduced by 53 and 40%, respectively.
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