This paper is focused on a comparison between Model-Free control and Active Disturbance Rejection control methods. These two techniques do not require a detailed mathematical description of the system, since they base...
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Differential Ant Stigmergy Algorithm (DASA) is a recent meta-heuristic method which represents an adaptation of Ant Colony Optimization (ACO) to continuous optimization problems. Other adaptations of ACO to continuous...
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This paper gives an overview of both specification and implementation details for integration of DTV services into the Android OS, using Java programming language. Since Java programming language is the language of ch...
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This paper presents a solution for visualization of DTV related data on devices with Android OS. The main goal was to display DTV related data on Android graphical layer using native C/C++ code. We have visualized thr...
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This paper describes development of the module, which analyzes medical images and its use is also possible in sectors other than medicine. A software module can base on events captured on the ECG graph store images, w...
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This paper describes the processing of photographs in the newly created module for 2D modeling, which will be integrated into the system FOTOM NG. This is a usable application in medicine to analyze images of probes a...
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Research into conventional therapy and motor learning theory provides evidence that the intensity of practice of a task and feedback are important for iterative control of health care. Iterative learning control (ILC)...
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Research into conventional therapy and motor learning theory provides evidence that the intensity of practice of a task and feedback are important for iterative control of health care. Iterative learning control (ILC) is one model-based approach to stroke rehabilitation that has progressed to a program of clinical trials, which constitute the first major stage towards the eventual transfer into practice. ILC exploits the repeating nature of the patients' tasks to improve performance by learning from past experience as compared with other approaches employed to control FES. ILC is able to respond to physiological changes in the system, such as spasticity and the presence of a patient's voluntary effort by updating the control input using data collected over previous attempts at the task. ILC can also closely regulate the amount of stimulation supplied, ensuring that minimum assistance is provided, promoting the patient's maximum voluntary contribution to the task completion.
The interconnection network is a crucial part of high-performance computersystems. It significantly determines parallel system performance as well as the development and the operating cost. In this paper we suggest e...
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The interconnection network is a crucial part of high-performance computersystems. It significantly determines parallel system performance as well as the development and the operating cost. In this paper we suggest efficient and scalable hierarchical multi-ring interconnection network architecture. For building up the interconnection network we have designed adequate switch architecture and implemented “step-back-on-blocking” flow control algorithm. The architectural model has been verified and communicational performance parameters have been evaluated on the basis of numerous simulation experiments conducted in the OMNeT++ simulation environment.
Animals use stochastic search strategies to find a potential nest or to localize the food source. Animals do not rely much on the environmental cues or their sensory inputs. Since their cognitive capabilities are rath...
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作者：
Marko BunicStjepan BogdanUniversity of Zagreb
Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering LARICS–Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia
This paper presents the extension of the previously proposed method for multi-agent formation control based on potential function. The method derived for 2D space is extended to 3D. It has been shown that the control ...
This paper presents the extension of the previously proposed method for multi-agent formation control based on potential function. The method derived for 2D space is extended to 3D. It has been shown that the control algorithm keeps all the properties of the original scheme in case of multi-agent formation moving in 3D. An adaptation mechanism that assures avoidance of unwanted stable equilibria, used in 2D, is implemented in the same form for 3D formations. The obtained simulation results demonstrate stable behavior of the system for various sets of parameters - the desired 3D formation is reached in finite time and maintained during trajectory execution.
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