The image of a finite-size object defined by a general reflectivity function is found when the object is illuminated and is viewed through the same extended random medium modeling atmospheric turbulence. It is found t...
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The image of a finite-size object defined by a general reflectivity function is found when the object is illuminated and is viewed through the same extended random medium modeling atmospheric turbulence. It is found that the average image spectrum contains diffraction-limited information about the object spectrum for cases of both a nondeterministic object and a smooth object. The results are found analytically and are supported by numerical calculations.
A multiple-valued relation is a relation between inputs and outputs in which the input variables can assume more than two discrete values. Multiple-valued relations arise quite naturally in many contexts. Using charac...
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A multiple-valued relation is a relation between inputs and outputs in which the input variables can assume more than two discrete values. Multiple-valued relations arise quite naturally in many contexts. Using characteristic functions to represent relations, we can handle the problem of minimizing multiple-valued relations as a generalization of the conventional minimization problem of regular logic functions. Our approach is based on a state-of-the-art paradigm for the two level minimization of functions. We clarify some special properties of relations, in contrast to functions, which must be carefully considered in realizing a high quality procedure for solving the minimization problem. An efficient heuristic method to find an optimal sum-of-products representation for a multiple-valued relation is proposed and implemented in the program GYOCRO. It uses multiple-valued decision diagrams (MDD's) to represent the characteristic functions for the relations. Experimental results are presented and compared with previous exact and heuristic Boolean relation minimizers to demonstrate the effectiveness of the proposed method.
Control of nonlinear systems is challenging in *** making,performed many times per second,must ensure system *** input to perform a task often involves solving a nonlinear system of differential equations,which is a c...
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Control of nonlinear systems is challenging in *** making,performed many times per second,must ensure system *** input to perform a task often involves solving a nonlinear system of differential equations,which is a computationally intensive,if not intractable *** article proposes sampling-based task learning for controlaffine nonlinear systems through the combined learning of both state and action-value functions in a model-free approximate value iteration setting with continuous inputs.A quadratic negative definite state-value function implies the existence of a unique maximum of the action-value function at any *** allows the replacement of the standard greedy policy with a computationally efficient policy approximation that guarantees progression to a goal state without knowledge of the system *** policy approximation is consistent,i.e.,it does not depend on the action samples used to calculate *** method is appropriate for mechanical systems with high-dimensional input spaces and unknown dynamics performing Constraint-Balancing *** verify it both in simulation and experimentally for an Unmanned Aerial Vehicles(UAVs) carrying a suspended load,and in simulation,for the rendezvous of heterogeneous robots.
One of the main objectives in the process of mapping a digital circuit onto a LUT-based FPGA structure is minimizing the total number of lookup tables needed to implement the circuit. This will increase the size of th...
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One of the main objectives in the process of mapping a digital circuit onto a LUT-based FPGA structure is minimizing the total number of lookup tables needed to implement the circuit. This will increase the size of the circuit that can be implemented using the available FPGA structure. In this paper, we show that even restricted cases of the lookup-table minimization for FPGA technology mapping are NP-complete (even when K is a small constant), and that it can be solved optimally for all values of K on a tree input in O(min{nK, nlogn}) time where n is the number of nodes in the network and K is the input capacity of the LUT's. Based on our algorithm for trees, we present a polynomial time heuristic algorithm for general Boolean networks. Experimental results confirm substantial decrease on the number of LUT's on a number of MCNC logic synthesis benchmarks compared to the algorithms that allow no or just local exploitation of Boolean properties of the circuit. We obtain 10% to 80% improvement on the number of LUT's compared to the previous algorithms (even though we allow very limited operations, e.g., we do not exploit Boolean properties of the circuits or decompose nodes).
Platoon formation of highway vehicles is a critical foundation for autonomous or semiautonomous vehicle control for enhanced safety,improved highway utility,increased fuel economy,and reduced emission toward intellige...
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Platoon formation of highway vehicles is a critical foundation for autonomous or semiautonomous vehicle control for enhanced safety,improved highway utility,increased fuel economy,and reduced emission toward intelligent transportation *** control encounters great challenges from vehicle control,communications,team coordination,and *** paper introduces a new method for coordinated control of platoons by using integrated network consensus decisions and vehicle *** achieve suitable coordination of the team vehicles based on terrain and environmental conditions,the emerging technology of network consensus control is modified to a weighted and constrained consensus-seeking *** are introduced and their convergence properties are *** methodology employs neighborhood information through on-board sensors and V2 V or V2 I communications,but achieves global coordination of the entire *** ability of the methods in terms of robustness,disturbance rejection,noise attenuation,and cyber-physical interaction is analyzed and demonstrated with simulated case studies.
Metasurfaces have pioneered significant improvements in sensing technology by tailoring strong optical responses to weak signals. When designed with anisotropic subwavelength geometries, metasurfaces can tune response...
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In recent years, hardware acceleration in large-scale computing fields such as Artificial Intelligence and High Performance Computing, faces hardware resource shortages. To overcome this problem, we propose Reconfigur...
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In recent years there has been a great increase in the use of computer modeling in many fields of science and engineering. The success of such modeling is evident in computer-aided design and manufacturing where much ...
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In recent years there has been a great increase in the use of computer modeling in many fields of science and engineering. The success of such modeling is evident in computer-aided design and manufacturing where much of the testing process of the final product can be done without resort to actual construction of intermediate attempts. However, there is another area of computer modeling whose results have been widely studied without recourse to considering the caveats that computer modeling itself may entail. This area is the computer modeling of nonlinear vector fields. To model a nonlinear vector field by the use of a computer introduces a number of errors intrinsic to computer modeling. This paper considers one such source of computer generated error and its interpretation from the viewpoint of the user who wishes to understand the original vector field. The approach taken to investigate the pragmatic results of computer modeling in consideration of the properties of the underlying nonlinear vector field will be essentially topological. Generic properties, as well as properties of denseness and openness (to be discussed), will be interpreted from their effects on computer modeling. As a byproduct of this work the class of continuous piecewise-liner vector fields has shown to be very amenable to theoretical analysis. This suggests the use of piecewise-linear vector fields to be the preferred modeling technique from both a practical and theoretical viewpoint. The theoretical results justifying the practical observations.
The problem of encoding the states of a synchronous finite state machine (FSM) so that the area of a two-level implementation of the combinational logic is minimized is addressed. As in previous approaches, the proble...
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The problem of encoding the states of a synchronous finite state machine (FSM) so that the area of a two-level implementation of the combinational logic is minimized is addressed. As in previous approaches, the problem is reduced to the solution of the combinatorial optimization problems defined by the translation of the cover obtained by a multiple-valued logic minimization or by a symbolic minimization into a compatible Boolean representation. The authors present algorithms for this solution, based on a novel theoretical framework that offers advantages over previous approaches to develop effective heuristics. The algorithms are part of NOVA, a program for optimal encoding of control logic. Final areas averaging 20% less than other state assignment programs and 30% less than the best random solution have been obtained. Literal counts averaging 30% less than the best random solutions have been obtained.< >
This study explores energy-efficient machine learning approaches for intrusion detection in SCADA systems, addressing the dual challenges of cybersecurity and sustainability. A comprehensive evaluation of models, incl...
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ISBN:
(数字)9798331507022
ISBN:
(纸本)9798331507039
This study explores energy-efficient machine learning approaches for intrusion detection in SCADA systems, addressing the dual challenges of cybersecurity and sustainability. A comprehensive evaluation of models, including Decision Trees, Random Forests, and LightGBM, highlights their performance across SCADA, IIoT, and Edge IoT environments. Decision Trees achieve exceptional efficiency scores, with 83.85 in Edge IoT systems, demonstrating high accuracy with minimal energy consumption. Random Forests and LightGBM balance scalability, computational cost, and resilience, supporting robust deployment in resource-constrained environments. Integrating lightweight and high-performing models provides a roadmap for achieving eco-secure SCADA systems, advancing the synergy between green cybersecurity and machine learning reliability.
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