This work investigates and contrasts two approaches for trajectory tracking control strategies for robotic operating systems: model-free adaptive algorithm and radial basis function (RBF) neural network adaptive algor...
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Epilepsy is a debilitating neurological condition characterized by intermittent paroxysmal states called fits or seizures. Especially, the major motor seizures of a convulsive nature, such as tonic-clonic seizures, ca...
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Epilepsy is a debilitating neurological condition characterized by intermittent paroxysmal states called fits or seizures. Especially, the major motor seizures of a convulsive nature, such as tonic-clonic seizures, can cause aggravating consequences. Timely alerting for these convulsive epileptic states can therefore prevent numerous complications, during, or following the fit. Based on our previous research, a non-contact method using automated video camera observation and optical flow analysis underwent field trials in clinical settings. Here, we propose a novel adaptive learning paradigm for optimization of the seizure detection algorithm in each individual application. The main objective of the study was to minimize the false detection rate while avoiding undetected seizures. The system continuously updated detection parameters retrospectively using the data from the generated alerts. The system can be used under supervision or, alternatively, through autonomous validation of the alerts. In the latter case, the system achieved self-adaptive, unsupervised learning functionality. The method showed improvement of the detector performance due to the learning algorithm. This functionality provided a personalized seizure alerting device that adapted to the specific patient and environment. The system can operate in a fully automated mode, still allowing human observer to monitor and override the decision process while the algorithm provides suggestions as an expert system.
This paper studies the problem of fast and accurate fault estimation for a class of descriptor systems with time-varying delays based on an adaptive finite time robust observer. This descriptor system is detectable, a...
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The article investigates the problem of synthesizing an adaptive UAV spatial restructuring algorithm that implements the control vector of unmanned vehicles. The synthesis of the algorithm is based on the implementati...
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This research addresses the critical challenge of path planning for autonomous vehicles in dynamic environments, where obstacles may unpredictably change positions or emerge. The purpose of this study is to introduce ...
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Adam is a famous adaptive optimization algorithm for model training of deep learning. However, its weak generalization capability under non-convex conditions is still an open problem. To tackle this problem, we propos...
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We present a fast, hierarchical, and adaptive algorithm for Metropolis Monte Carlo simulations of systems with long-range interactions that reproduces the dynamics of a standard implementation exactly, i.e., the gener...
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We present a fast, hierarchical, and adaptive algorithm for Metropolis Monte Carlo simulations of systems with long-range interactions that reproduces the dynamics of a standard implementation exactly, i.e., the generated configurations and consequently all measured observables are identical, allowing in particular for nonequilibrium studies. The method is demonstrated for the power-law interacting long-range Ising and XY spin models with nonconserved order parameter and a Lennard-Jones particle system, all in two dimensions. The measured run times support an average complexity O(N log N), where N is the number of spins or particles. Importantly, prefactors of this scaling behavior are small, which in practice manifests in speedup factors larger than 104. The method is general and will allow the treatment of large systems that were out of reach before, likely enabling a more detailed understanding of physical phenomena rooted in long-range interactions.
The adaptive array with a rectangular aperture, whose antennas are placed in the nodes of the rectangular grid, is considered in the paper. The partial adaptability is provided via the base-band combination of the sig...
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This paper proposes a distributed dynamic event-triggered control that addresses the flocking control problem with a virtual leader for multiple unmanned surface vehicle (USV) systems. In contrast to existing flocking...
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The persistent improvement of the hybrid adaptive algorithms and the swift growth of signal processing chip enhanced the performance of signal processing technique exalted mobile transceiver systems. The proposed arti...
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