In this paper, we consider the problem of tracking a desired trajectory for an uncertain robot in the presence of constraints and uncertainties. The dynamics of the uncertain robot are represented by an n-link rigid r...
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The recent deployment of very large-scale camera networks consisting of fixed/moving surveillance cameras and vehicle video recorders, has led to a novel field in object tracking problem. The major goal is to detect a...
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This paper presents ERELT (Enhanced Radial Edgeless Tree), a tree visualization approach on modern mobile devices. ERELT is designed to offer a clear visualization of any tree structure with intuitive interaction. We ...
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This paper presents a new multi-scale place recognition system inspired by the recent discovery of overlapping, multi-scale spatial maps stored in the rodent brain. By training a set of Support Vector Machines to reco...
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ISBN:
(纸本)9780980740448
This paper presents a new multi-scale place recognition system inspired by the recent discovery of overlapping, multi-scale spatial maps stored in the rodent brain. By training a set of Support Vector Machines to recognize places at varying levels of spatial specificity, we are able to validate spatially specific place recognition hypotheses against broader place recognition hypotheses without sacrificing localization accuracy. We evaluate the system in a range of experiments using cameras mounted on a motorbike and a human in two different environments. At 100% precision, the multiscale approach results in a 56% average improvement in recall rate across both datasets. We analyse the results and then discuss future work that may lead to improvements in both robotic mapping and our understanding of sensory processing and encoding in the mammalian brain.
Images/videos captured from optical devices are usually degraded by turbid media such as haze, smoke, fog, rain and snow. Haze is the most common problem in outdoor scenes because of the atmosphere conditions. This pa...
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Recently, a method for estimating permittivity distributions was reported. The method combined a numerical technique (Finite Element Method) with Genetic Algorithm (GA). However, methods for estimating the distributio...
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The paper presents the implementation of a quantum cryptography protocol for secure communication. As computing power increases, classical cryptography and key management schemes based on computational complexity beco...
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Contemporary implementations of Quantum Key Distribution are based on BB84, first proposed in 1984, and commercially implemented for limited market applications in the early 2000s. A major limitation of BB84 is that i...
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In this paper,adaptive neural network control is designed for a robotic manipulator with unknown *** networks are used to compensate for the unknown deadzone effect faced by the manipulator's ***-feedback control ...
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ISBN:
(纸本)9781479900305
In this paper,adaptive neural network control is designed for a robotic manipulator with unknown *** networks are used to compensate for the unknown deadzone effect faced by the manipulator's ***-feedback control is proposed first and high-gain observer is then designed to make the proposed control scheme more *** deadzone effect is approximated by a Radial Basis Function Neural Network(RBFNN) and the tracking error for the deadzone effect is bounded and *** unknown dynamics of the robotic manipulator is estimated with another *** for the estimated deadzone effect in the control law then leads to our proposed *** proposed control is then verified on a two-joint rigid manipulator via numerical simulations.
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