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Image Quality Assessment (IQA) remains a complex and challenging problem that has garnered great interest by the research community as well as industry. A majority of no-reference IQA metrics leverage handcrafted features which discriminates distortions from true image structures. Recently, deep neural networks (DNN) has been successfully applied on local image patches to estimate image quality, However,...
Biotechnological processes are very complex systems for modeling, due to the presence of live microorganisms, nonlinear behavior and time-variant features. Many parameters of the process are uncertain and the industrial operating conditions are determined empirically for them. This paper presents a robust dynamic simulator in Matlab/Simulink environment from the work of Scaglia and the study of experimental...
Automated Visual Inspection (AVI) systems for metal surface inspection is increasingly used in industries to aid human visual inspectors for classification of possible anomalies. For classification, the challenge lies in having a small and specific dataset that may easily result in over-fitting. As a solution, we propose to use deep Convolutional Neural Networks (ConvNets) learnt from the large ImageNet...
Knowing the force exerted by a human operator while he/she is performing a specific task is important in many different field. For instance, limiting or optimizing the effort in sport activities allows for the development of specific training patterns for athletes, while knowing the effort made by a worker when he/she lifts a weight is important from the point of view of the safety. The effective...
We always use CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) to prevent automated bot for data entry. Although there are various kinds of CAPTCHAs, text-based scheme is still applied most widely, because it is one of the most convenient and user-friendly way for daily user [1]. The fact is that segmentations of different types of CAPTCHAs are not always the same,...
A typical direct neural-adaptive control of an n-link rigid manipulator uses n neural network outputs in tandem with n proportional-derivative controls. However, previous approaches to rigid-link, flexible-joint robot control using 3 steps of backstepping have required knowledge of the spring constants, extra adaptive parameters, extra neural network outputs (beyond 3n), and/or robust control terms...
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