Image processing is basically the use of computer algorithms to perform image processing on digital images. Digital image processing is a part of digital signal processing. Digital image processing has many significant advantages over analog image processing. Image processing allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing of images. Wavelet transforms have become a very powerful tool for de-noising an image.

The present study aims to design stochastic intelligent computational heuristics for the numerical treatment of a nonlinear SITR system representing the dynamics of novel coronavirus disease 2019 (COVID-19). The mathematical SITR system using fractal parameters for COVID-19 dynamics is divided into four classes; that is, susceptible (S), infected (I), treatment (T), and recovered (R). The comprehensive details of each class along with the explanation of every parameter are provided, and the dynamics of novel COVID-19 are represented by calculating the solution of the mathematical SITR system using feed-forward artificial neural networks (FF-ANNs) trained with global search genetic algorithms (GAs) and speedy fine tuning by sequential quadratic programming (SQP)—that is, an FF-ANN-GASQP scheme. In the proposed FF-ANN-GASQP method, the objective function is formulated in the mean squared error sense using the approximate differential mapping of FF-ANNs for the SITR model, and learning of the networks is proficiently conducted with the integrated capabilities of GA and SQP. The correctness, stability, and potential of the proposed FF-ANN-GASQP scheme for the four different cases are established through comparative assessment study from the results of numerical computing with Adams solver for single as well as multiple autonomous trials. The results of statistical evaluations further authenticate the convergence and prospective accuracy of the FF-ANN-GASQP method.

Cyclops is a new architecture for high performance parallel computers being developed at the IBM T. J. Watson Research Center. The basic cell of this architecture is a single-chip SMP system with multiple threads of execution, embedded memory, and integrated communications hardware. Massive intra-chip parallelism is used to tolerate memory and functional unit latencies. Large systems with thousands of chips can be built by replicating this basic cell in a regular pattern. In this paper we describe the Cyclops architecture and evaluate two of its new hardware features: memory hierarchy with flexible cache organization and fast barrier hardware. Our experiments with the STREAM benchmark show that a particular design can achieve a sustainable memory bandwidth of 40 GB/s, equal to the peak hardware bandwidth and similar to the performance of a 128-processor SGI Origin 3800. For small vectors, we have observed in-cache bandwidth above 80 GB/s. We also show that the fast barrier hardware can improve the performance of the Splash-2 FFT kernel by up to 10%. Our results demonstrate that the Cyclops approach of integrating a large number of simple processing elements and multiple memory banks in the same chip is an effective alternative for designing high performance systems.

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