New adaptive algorithms for real-time registration and fusion of multimodal imagery

2010 ◽  
Author(s):  
J. P. Heather ◽  
M. I. Smith
Keyword(s):  
Real Time ◽  
Adaptive Algorithms ◽  
Multimodal Imagery

scholarly journals Artificial Immune Systems: An Overview for Faulting Actuators

Actuators ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 53
Author(s):  
Kidd
Keyword(s):  
Real Time ◽  
Artificial Immune Systems ◽  
Adaptive Algorithms ◽  
Actuator Faults ◽  
Artificial Immune ◽  
Immune Systems ◽  
Limited Success ◽  
Fault Compensation

This paper reviews Artificial Immune Systems (AIS) that can be implemented to compensate for actuators that are in a faulted state or operating abnormally. Eventually, all actuators will fail or wear out, and these actuator faults must be managed if a system is to operate safely. The AIS are adaptive algorithms which are inherently well-suited to these situations by treating these faults as infections that must be combated. However, the computational intensity of these algorithms has caused them to have limited success in real-time situations. With the advent of distributed and cloud-based computing these algorithms have begun to be feasible for diagnosing faulted actuators and then generating compensating controllers in near-real-time. To encourage the application of AIS to these situations, this work presents research for the fundamental operating principles of AIS, their applications, and a brief case-study on their applicability to fault compensation by considering an overactuated rover with four independent drive wheels and independent front and rear steering.

Adaptive Algorithms for Intelligent Geometric Computing

2012 ◽  
pp. 97-104
Author(s):  
M. L. Gavrilova
Keyword(s):  
Real Time ◽  
Adaptive Algorithms ◽  
Adaptive Mesh ◽  
Complex Data ◽  
Geometric Computing ◽  
Adaptive Computation ◽  
Image Capturing ◽  
Complex Data Sets ◽  
Very Large Datasets ◽  
Primary Focus

This chapter spans topics from such important areas as Artificial Intelligence, Computational Geometry and Biometric Technologies. The primary focus is on the proposed Adaptive Computation Paradigm and its applications to surface modeling and biometric processing. Availability of much more affordable storage and high resolution image capturing devices have contributed significantly over the past few years to accumulating very large datasets of collected data (such as GIS maps, biometric samples, videos etc.). On the other hand, it also created significant challenges driven by the higher than ever volumes and the complexity of the data, that can no longer be resolved through acquisition of more memory, faster processors or optimization of existing algorithms. These developments justified the need for radically new concepts for massive data storage, processing and visualization. To address this need, the current chapter presents the original methodology based on the paradigm of the Adaptive Geometric Computing. The methodology enables storing complex data in a compact form, providing efficient access to it, preserving high level of details and visualizing dynamic changes in a smooth and continuous manner. The first part of the chapter discusses adaptive algorithms in real-time visualization, specifically in GIS (Geographic Information Systems) applications. Data structures such as Real-time Optimally Adaptive Mesh (ROAM) and Progressive Mesh (PM) are briefly surveyed. The adaptive method Adaptive Spatial Memory (ASM), developed by R. Apu and M. Gavrilova, is then introduced. This method allows fast and efficient visualization of complex data sets representing terrains, landscapes and Digital Elevation Models (DEM). Its advantages are briefly discussed. The second part of the chapter presents application of adaptive computation paradigm and evolutionary computing to missile simulation. As a result, patterns of complex behavior can be developed and analyzed. The final part of the chapter marries a concept of adaptive computation and topology-based techniques and discusses their application to challenging area of biometric computing.

scholarly journals Adaptive algorithms for real-time filtering of electrocardiogram with multilevel noise estimation

Radiotekhnika ◽  
2020 ◽  
Vol 2 (201) ◽  
pp. 201-214
Author(s):  
Н.О. Тулякова ◽  
О.М. Трофимчук
Keyword(s):  
Real Time ◽  
Adaptive Algorithms ◽  
Noise Estimation

Adaptive Algorithms for Intelligent Geometric Computing

2011 ◽  
pp. 8-15 ◽  
Author(s):  
M. L. Gavrilova
Keyword(s):  
Real Time ◽  
Adaptive Algorithms ◽  
Adaptive Mesh ◽  
Complex Data ◽  
Geometric Computing ◽  
Adaptive Computation ◽  
Image Capturing ◽  
Complex Data Sets ◽  
Very Large Datasets ◽  
Primary Focus

This chapter spans topics from such important areas as Artificial Intelligence, Computational Geometry and Biometric Technologies. The primary focus is on the proposed Adaptive Computation Paradigm and its applications to surface modeling and biometric processing. Availability of much more affordable storage and high resolution image capturing devices have contributed significantly over the past few years to accumulating very large datasets of collected data (such as GIS maps, biometric samples, videos etc.). On the other hand, it also created significant challenges driven by the higher than ever volumes and the complexity of the data, that can no longer be resolved through acquisition of more memory, faster processors or optimization of existing algorithms. These developments justified the need for radically new concepts for massive data storage, processing and visualization. To address this need, the current chapter presents the original methodology based on the paradigm of the Adaptive Geometric Computing. The methodology enables storing complex data in a compact form, providing efficient access to it, preserving high level of details and visualizing dynamic changes in a smooth and continuous manner. The first part of the chapter discusses adaptive algorithms in real-time visualization, specifically in GIS (Geographic Information Systems) applications. Data structures such as Real-time Optimally Adaptive Mesh (ROAM) and Progressive Mesh (PM) are briefly surveyed. The adaptive method Adaptive Spatial Memory (ASM), developed by R. Apu and M. Gavrilova, is then introduced. This method allows fast and efficient visualization of complex data sets representing terrains, landscapes and Digital Elevation Models (DEM). Its advantages are briefly discussed. The second part of the chapter presents application of adaptive computation paradigm and evolutionary computing to missile simulation. As a result, patterns of complex behavior can be developed and analyzed. The final part of the chapter marries a concept of adaptive computation and topology-based techniques and discusses their application to challenging area of biometric computing.

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