Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-...
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Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.
Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no ...
Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no ...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no guarantee the closedloop system has finite incremental gain. It has been suggested in the literature that without this guarantee such a system may be critically sensitive to time-varying exogenous signals including noise. We show that multipliers guarantee the power gain of the system to be bounded and quantifiable. Furthermore power may be measured about an appropriate steady state bias term, provided the multiplier does not require the nonlinearity to be odd. Hence dynamic multipliers can be used to guarantee Lurye systems have low sensitivity to noise, provided other exogenous systems have constant steady state. We illustrate the analysis with an example where the exogenous signal is a power signal with non-zero mean.
Visual-based object detection has become a crucial component in the realm of autonomous vehicles. However, conducting reliable testing for such systems remains unresolved. In this paper, we advocate for the applicatio...
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ISBN:
(数字)9798350348811
ISBN:
(纸本)9798350348828
Visual-based object detection has become a crucial component in the realm of autonomous vehicles. However, conducting reliable testing for such systems remains unresolved. In this paper, we advocate for the application of causal inference to investigate the pivotal environmental factors influencing detection accuracy. Through the integration of diffusion models, we address the specialized conditional generalization of hazardous testing images. Our approach involves the construction of observational data to attribute key factors and fine-tune the diffusion model. Additionally, we introduce an optimal prompt words search method that strikes a balance between test coverage and level of challenge. Subsequently, leveraging these optimal prompts, we propose a cost-effective testing image generation through both "Text2Scene" and "Image2Scene" fashions. The experimental results indicate that, on the generalized dataset, the performance of object detection algorithms is the poorest, with the average detection accuracy decreasing from 0.81 to 0.285. Moreover, retraining object detection models on our generalized critical test cases can ultimately enhance algorithm performance, achieving a median accuracy improvement of up to 8.13%. Overall, our research proposes a novel approach to generalize test cases, thereby contributing to the advancement and deployment of safer autonomous vehicles.
In this work we propose a novel computer fovea model based on hexagonal-type cellular neural networks (hCNN). The hCNN represents a new image processing architecture that is motivated by the overwhelming evidence for ...
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In this work we propose a novel computer fovea model based on hexagonal-type cellular neural networks (hCNN). The hCNN represents a new image processing architecture that is motivated by the overwhelming evidence for hexagonal image processing in biological systems. The necessary new coupling templates and basic hCNN image operators are introduced. The fovea model includes the biological mechanisms of the photoreceptors, the horizontal cells, the ganglions, the bipolar cells, and their cooperation. Thus the model describes the signal processing from the optical stimulation at retina to the output of the ganglion cells. Different building blocks of the model turned out to be useful for practical image enhancement algorithms. Two such applications are considered in this work, namely the image sharpness improvement and the color constancy algorithm.
作者：
HERR, DONALDBLUMENSTOCK, NORMANHONORARY MEMBERTHE AUTHORS MR. HERR
Honorary Member of the A.S.N.E. has the B.S. in E.E. M.S. in E.E. and E.E. degrees. He was National Coffin Foundation Fellow of the General Electric Company National Tau Beta Pi Fellow and National Sigma Tau Fellow at the Moore School of Electrical Engineering University of Pennsylvania and at M.I.T. prior to World War II. He was also awarded a National Gordon McKay Fellowship by Harvard University and received the A. Atwater Kent Award in Electrical Engineering from the University of Pennsylvania. A licensed radio amateur at 12 Mr. Herr first worked summers at RCA and Bell Laboratories and was with the General Electric Company in 1939 and 1940 as development engineer before volunteering for over five years of active Naval duty. He served as Officer-in-Charge Electrical Minesweeping Group Bureau of Ships December 1940 to April 1943 as Acting Design Superintendent and Officer-in-Charge
Los Angeles-Long Beach Harbor Surge Investigation U. S. Naval Shipyard Terminal Island to November 1944 and as Research-Patents Liaison Officer
Office of Naval Research to January 1946 returning to inactive duty as lieutenant commander U.S.N.R. Mr. Herr received two Navy letters of commendation. Since 1946 he was assistant to vice president in charge of the engineering division of Control Instrument Company Brooklyn New York and is project engineer at the Reeves Instrument Corporation responsible for new servo and computer component developments. Mr. Herr has been associated with Dean Harold Pender and Professor Ernst Guillemin in advanced network theory and has specialized for 12 years in development and design of servomechanisms differential analyzers computers and fire control systems utilizing advanced network analysis and synthesis methods. Mr. Herr is also presently teaching servomechanisms network-synthesis and feedback amplifier design in the Graduate School of the Polytechnic Institute of Brooklyn. He has contributed frequently to the JOURNAL OF THE AMERICA
作者：
HERR, DONALD HONORARYMEMBERTHE AUTHOR:Mr.Herr
Honorary Member of the A.S.N.E. has the B.S. in E.E. M.S. in E.E. and E.E. degrees. He was National Coffin Foundation Fellow of the General Electric Company National Tau Beta Pi Fellow and National Sigma Tau Fellow at the Moore School of Electrical Engineering University of Pennsylvania and at M.I.T. prior to World War II. He was also awarded a National Gordon McKay Fellowship by Harvard University and received the A. Atwater Kent Award in Electrical Engineering from the University of Pennsylvania. A licensed radio amateur at 12 Mr. Herr first worked summers at RCA and Bell Laboratories and was with the General Electric Company in 1939 and 1940 as development engineer before volunteering for over five years of active Naval duty. He served as Officer-in-Charge Electrical Minesweeping Group Bureau of Ships December 1940 to April 1943 as Acting Design Superintendent and Officer-in-Charge
Los Angeles-Long Beach Harbor Surge Investigation U. S. Naval Shipyard Terminal Island to November 1944 and as Research-Patents Liaison Officer
Office of Naval Research to January 1946 returning to inactive duty as lieutenant commander U.S.N.R. Mr. Herr received two Navy letters of commendation. Since 1946 he was assistant to vice president in charge of the engineering division of Control Instrument Company Brooklyn New York and is project engineer at the Reeves Instrument Corporation responsible for new servo and computer component developments. Mr. Herr has been associated with Dean Harold Pender and Professor Ernst Guillemin in advanced network theory and has specialized for 12 years in development and design of servomechanisms differential analyzers computers and fire control systems utilizing advanced network analysis and synthesis methods. He has contributed frequently to the JournalOF THE AmericanSocietyOF NavalEngineersand was the Society's 1945 Prize Essayist on the subject: “Engineering in the Navy as seen by an Active Reserve Officer.” He is senior member of the I.R.E
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