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Smart Self-Checkout Carts Based on Deep Learning for Shopping Activity Recognition
Hong-Chuan Chi,Muhammad Atif Sarwar,Yousef-Awwad Daraghmi,Kuan-Wen Liu,Tsi-Ui ?k,Yih-Lang Li 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
Fast and reliable communication plays a major role in the success of smart shopping applications. In a ”Just Walk Out” shopping scenario, a video camera is installed on the cart to monitor shopping activities and transmit images to the cloud for processing so that items in the cart can be tracked and checked out. This paper proposes a prototype of a smart shopping cart based on image-based action recognition. Firstly, deep learning networks such as Faster R-CNN, YOLOv2, and YOLOv2-Tiny are utilized to analyze the content of each video frame. Frames are classified into three classes: No Hand, Empty Hand, and Holding Items. The classification accuracy based on Faster RCNN, YOLOv2, or YOLOv2-Tiny is between 93.0% and 90.3%, and the processing speed of the three networks can be up to 5 fps, 39 fps, and 50 fps, respectively. Secondly, based on the sequence of frame classes, the timeline is divided into No Hand intervals, Empty Hand intervals, and Holding Items intervals. The accuracy of action recognition is 96%, and the time error is 0.119s on average. Finally, we categorize the events into four cases: No Change, placing, Removing, and Swapping. Even including the correctness of the item recognition, the accuracy of shopping event detection is 97.9%, which is higher than the minimal requirement to deploy such a system in a smart shopping environment. A demo of the system and a link to download the data set used in the paper are in Smart Shopping Cart Prototype or found at this URL: https://hackmd.io/abEiC83rQoqxz7zpL4Kh2w.
Exact Sum-Rate Analysis of MIMO Broadcast Channels with Random Unitary Beamforming
Hong-Chuan Yang,Peng Lu,Hyung-Ki Sung,Young-Chai Ko IEEE 2011 IEEE TRANSACTIONS ON COMMUNICATIONS Vol.59 No.11
<P>Random unitary beamforming (RUB) is an attractive transmission scheme for MIMO broadcast channels because of its ability to achieve high sum-rate capacity with limited feedback. In this letter, we derive the exact analytical expressions for the ergodic sum rate of MIMO broadcast channels with RUB. The analysis is facilitated by the development of the complete statistical characterization of ordered beam SINRs for a user, which can find their application in many related problems.</P>
Hong-Chuan Yan,Zhuo Li,Yong-Jian Shuai,Hong-Qiang Xie,Ming-Li Xiao,Ming-Guang Cai 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.6
To investigate the potential use of the complex function theory in displacement back analysis, a novel method for parameter identification is proposed by combining the complex function theory and the back-propagation neural network optimized by the particle swarm optimization algorithm. The finite element method is replaced with the complex function method to establish a nonlinear relationship between parameters and deformations of the surrounding rock around a complex underground cavern. As demonstrated by a virtual example of an arched tunnel, the deformation parameters were identified by the proposed method and demonstrated to be approximately equal to the predefined parameters. Subsequently, the proposed method is further applied to the inverse analysis of mechanical parameters for the surrounding rock in the underground powerhouse of the Baihetan Hydropower Station. The computed displacements based on the back-analyzed parameters show excellent agreement with the monitoring displacements. The average error between them is about 4.17%, so the proposed method provides the potential to viably enhance the back-analysis technique.