With the recent development of small radars with high resolution, various human–computer interaction (HCI) applications using them have been developed. In particular, a method of applying a user’s hand gesture recognition using a short-range radar to an electronic device is being actively studied. In general, the time delay and Doppler shift characteristics that occur when a transmitted signal that is reflected off an object returns are classified through deep learning to recognize the motion. However, the main obstacle in the commercialization of radar-based hand gesture recognition is that even for the same type of hand gesture, recognition accuracy is degraded due to a slight difference in movement for each individual user. To solve this problem, in this paper, the domain adaptation is applied to hand gesture recognition to minimize the differences among users’ gesture information in the learning and the use stage. To verify the effectiveness of domain adaptation, a domain discriminator that cheats the classifier was applied to a deep learning network with a convolutional neural network (CNN) structure. Seven different hand gesture data were collected for 10 participants and used for learning, and the hand gestures of 10 users that were not included in the training data were input to confirm the recognition accuracy of an average of 98.8%.
This paper address a new scheme that alleviates the packet collision problem caused by contentions among nearby coordinators (CNs) in IEEE 802.15.4 meshed sensor networks. In existing studies, the number of retransmissions is reduced by adjusting the proper backoff period (BP) of sensor nodes, or unnecessary energy consumption is diminished by increasing channel utilization efficiently based on traffic load. In contrast, the proposed scheme avoids contentions among nearby CNs, thereby it enhances the energy efficiency of sensor nodes. To achieve this, the proposed scheme separates the starting points of CNs' contention periods and reduces contentions and collisions among overlapping CNs. According to our simulation results, the proposed scheme
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.