DenseHyper: an automatic recognition system for detection of hypertensive retinopathy using dense features transform and deep-residual learning

2020 ◽  
Vol 79 (41-42) ◽  
pp. 31595-31623
Author(s):  
Qaisar Abbas ◽  
Mostafa E. A. Ibrahim
2020 ◽  
Vol 11 (1) ◽  
pp. 10
Author(s):  
Muchun Su ◽  
Diana Wahyu Hayati ◽  
Shaowu Tseng ◽  
Jiehhaur Chen ◽  
Hsihsien Wei

Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.


Robotica ◽  
1995 ◽  
Vol 13 (6) ◽  
pp. 591-598 ◽  
Author(s):  
Yagmur Denizhan

SummaryIn disassembly tasks, due to the large variety of objects and the different positions and orientations in which they appear, the disassembly trajectories supplied on-line by a human operator or an automatic recognition system can contain large errors. The classical compliant control methods turn out to be insufficient to eliminate sticking which is due to these errors. This paper presents a compliant control method for disassembly of non-elastic parts in non-elastic environments which adopts the trajectories according to realised motion. In case of sticking a new direction of motion is searched for until the manipulated part is set into motion.


2019 ◽  
Vol 62 (6) ◽  
pp. 1755-1765
Author(s):  
Sunan Zhang ◽  
Jianyan Tian ◽  
Amit Banerjee ◽  
Jiangli Li

Abstract. With the rapid development of large-scale breeding, manual long-term monitoring of the daily activities and health of livestock is costly and time-consuming. Therefore, the application of bio-acoustics to automatic monitoring has received increasing attention. Although bio-acoustical techniques have been applied to the recognition of animal sounds in many studies, there is a dearth of studies on the automatic recognition of abnormal sounds from farm animals. In this study, an automatic detection and recognition system based on bio-acoustics is proposed to hierarchically recognize abnormal animal states in a large-scale pig breeding environment. In this system, we extracted the mel-frequency cepstral coefficients (MFCC) and subband spectrum centroid (SSC) as composite feature parameters. At the first level, support vector data description (SVDD) is used to detect abnormal sounds in the acoustic data. At the second level, a back-propagation neural network (BPNN) is used to classify five kinds of abnormal sounds in pigs. Furthermore, improved spectral subtraction is developed to reduce the noise interference as much as possible. Experimental results show that the average detection accuracy and the average recognition accuracy of the proposed system are 94.2% and 95.4%, respectively. The effectiveness of the proposed sound detection and recognition system was also verified through tests at a pig farm. Keywords: Abnormal sounds, MFCC, SSC, States of pigs, SVDD.


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