ASP Transactions on Pattern Recognition and Intelligent Systems <p><em><strong>The ASP Transactions on Pattern Recognition and Intelligent Systems </strong></em><strong>(TPRIS)</strong> publishes articles on all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence, with a particular emphasis on machine learning for pattern recognition. Areas such as techniques for visual search, document and handwriting analysis, medical image recognition, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition and relevant specialized hardware and/or software architectures are also covered.</p> en-US (Marley Vellasco) (Marley Vellasco) Sat, 29 May 2021 00:00:00 +0800 OJS 60 Deep Learning based Cell Classification in Imaging Flow Cytometer <p>Deep learning is an idea technique for image classification. Imaging flow cytometer enables high throughput cell image acquisition and some have integrated with real-time cell sorting. The combination of deep learning and imaging flow cytometer has changed the landscape of high throughput cell analysis research. In this review, we focus on deep learning technologies applied in imaging flow cytometer for cell classification and real-time cell sorting. This article describes some recent research, challenges and future trend in this area. </p> Yi Gu, Aiguo Chen, Xin Zhang, Chao Fan, Kang Li, Jinsong Shen Copyright (c) 2021 ASP Transactions on Pattern Recognition and Intelligent Systems Sun, 13 Jun 2021 00:00:00 +0800 A New Location Sensing Algorithm Based on DV-Hop and Quantum-Behaved Particle Swarm Optimization in WSN <p>In wireless sensor network, the location sensing of the sensor nodes is important. If there is no location information of the sensor nodes, the perceived data would have no meaning. In recent years, the range-free location sensing algorithms have got great attention. DV-Hop localization algorithm is one of the important algorithm in range-free location algorithms. It has high efficiency, convenient operation and low energy consumption. However, the localization accuracy cannot meet the requirements in some applications. In this paper, a new localization method is proposed, which is based on DV-Hop and Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, it deals with the high influence of average single jumping distance and then modifies the calculation of it in the DV-Hop algorithm. Second, in order to solve the problem of the coordinate optimization in the DV-Hop algorithm, the paper chooses QPSO algorithm to optimize the unknown nodes’ coordinates. Simulation results show that the new method can improve the localization accuracy of the unknown nodes obviously in WSN.</p> Dan Zhang, Xiaohuan Zhang, Hai Qi Copyright (c) 2021 ASP Transactions on Pattern Recognition and Intelligent Systems Sat, 29 May 2021 00:00:00 +0800