Deep learning solutions in big data applications can benefit cloud centres and can also lead to network communication overhead. Typically, data collected from traffic are sent to the traffic management centre for anal...
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Deep learning solutions in big data applications can benefit cloud centres and can also lead to network communication overhead. Typically, data collected from traffic are sent to the traffic management centre for analysis. However, this process can worsen the network route to the traffic management centre. A two-tier mechanism has been developed to address this issue, which performs vehicle speed estimation and traffic congestion detection for efficient traffic management. The real-time traffic video data are captured and the video frames are initially processed through a foreground extraction process, which extracts the temporarily stopped vehicles on the road by removing background pixels from the frames. The video frames are then wrapped in an up-down view to remove the influence of the observation angle. The traffic congestion is then detected accurately based on the traffic characteristics using the proposed Ensemble Random Forest-basedgradientoptimization (ERF-GO) algorithm. The generalization error occurs when learning complex features on frames is minimized using a gradient-basedoptimization (GO) algorithm. Finally, the learned information on traffic conditions is forwarded to the cloud and edge computing environments based on network connection speed. The efficiency of the proposed ERF-GO is investigated in terms of performance metrics, namely root mean square error, speed detection error, execution time, computational cost, accuracy, latency, workload balance, precision, recall, f-measure, and congestion detection error rate. The analytic result displays that the proposed ERF-GO algorithm attains a greater accuracy rate of about 98.65% in detecting traffic congestion which is comparably higher than state-of-the-art methods.
In this study, we introduce a novel real-time measurement and correction method for time-varying wavefront aberrations. Central to this method is a graphics processing unit (GPU)-accelerated parallel algorithmbased o...
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In this study, we introduce a novel real-time measurement and correction method for time-varying wavefront aberrations. Central to this method is a graphics processing unit (GPU)-accelerated parallel algorithmbased on phase diversity images. We apply an approximate model for the point spread function (PSF) to reduce the computational load of error metric minimization. We forge a parallel framework that independently measures each aberration mode by deriving an object-independent error metric and its gradient. Numerical experiments with actual Kolmogorov model-based data were conducted to assess the measurement performance and real-time feasibility of the proposed method. When juxtaposed with the global optimizationalgorithm, the proposed method improved the computation speed by up to 1300x, while maintaining measurement accuracy. Moreover, we executed benchmark tests on diverse hardware configurations, thereby verifying the real-time viability of GPU acceleration. The GPU achieved a 6.8x improvement in computational speed compared to the CPU. Seamlessly integrating a linear quadratic regulator (LQR) controller into the adaptive optics (AO) system, we zeroed in on the real-time correction of dynamic aberrations. The empirical results exhibited an operational speed of 90 Hz in a realistic environment for correcting only three types of aberrations (astigmatism, defocus, and coma). Furthermore, we demonstrated the correction capability for large-scale aberrations, proving that the proposed method is scalable relative to the intensity of aberrations. In conclusion, this study paves the way for a combination of real-time execution and precise wavefront aberration correction in a sensorless AO, establishing a novel standard for future development and enhancements in wavefront sensing technology.
The paper presents a physical structure optimization of an integrated semiconductor device: a power MOSFET and other vertical transistors are integrated within the same die, introducing a novel self supplied power tra...
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The paper presents a physical structure optimization of an integrated semiconductor device: a power MOSFET and other vertical transistors are integrated within the same die, introducing a novel self supplied power transistor. This integrated optimal design leads to complex optimization problems with close constraints. The main constraint model deals with the avalanche phenomenon that is formulated by multiple integral expressions of implicit functions. The paper focuses on two aspects: the integral formulation of the avalanche model and more specifically its gradient computation in the aim of applying a gradient-based optimization algorithm, and the comparisons of several optimization methods on this problem.
For PET transmission imaging, the conventional iterative algorithms based on expectation maximization type algorithms, could not effectively converge to optimal image solution. In this study, we suggest a statistical ...
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For PET transmission imaging, the conventional iterative algorithms based on expectation maximization type algorithms, could not effectively converge to optimal image solution. In this study, we suggest a statistical model PET transmission data, and then investigate a class of gradient-based optimization algorithms for transmission image reconstruction including steepest ascent, conjugate gradient, and preconditioned conjugate gradient. From phantom studies, the preconditioned conjugate algorithms can converge to good image results within limited number of iteration. Combined with the suggested statistical model of transmission data. the preconditioned conjugate algorithms can also produce attenuation maps with accurate linear attenuation coefficients for clinical data.
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