Symbiotic Organisms Search Optimization to Predict Optimal Thread Count for Multi-threaded Applications

Multicore systems have emerged as a cost-effective option for the growing demands for high-performance, low-energy computing. Thread management has long been a source of concern for developers, as overheads associated with it reduce the overall throughput of the multicore processor systems. One of the most complex problems with multicore processors is determining the optimal number of threads for the execution of multithreaded programs. To address this issue, this paper proposes a novel solution based on a modified symbiotic organism search (MSOS) algorithm which is a bio-inspired algorithm used for optimization in various engineering domains. This technique uses mutualism, commensalism and parasitism behaviours seen in organisms for searching the optimal solutions in the available search space. The algorithm is simulated on the NVIDIA DGX Intel-Xeon E5-2698-v4 server with PARSEC 3.0 benchmark suit.  The results show that keeping the thread count equal to the number of processors available in the system is not necessarily the best strategy to get maximum speedup when running multithreaded programs. It was also observed that when programs are run with the optimal thread count, the execution time is substantially decreased, resulting in energy savings due to the use of fewer processors than are available in the system

Improving Quality of Watermarked Medical Images Using Symmetric Dilated Convolution Neural Networks, Journal of Telecommunications and Information Technology, 2023, nr 2

Rapid development of online medical technologies raises questions about the security of the patient’s medical data.When patient records are encrypted and labeled with a watermark, they may be exchanged securely online. In order to avoid geometrical attacks aiming to steal the information, image quality must be maintained and patient data must be appropriately extracted from the encoded image. To ensure that watermarked images are more resistant to attacks (e.g. additive noise or geometric attacks), different watermarking methods have been invented in the past. Additive noise causes visual distortion and render the potentially harmful diseases more difficult to diagnose and analyze. Consequently, denoising is an important pre-processing method for obtaining superior outcomes in terms of clarity and noise reduction and allows to improve the quality of damaged medical images. Therefore, various publications have been studied to understand the denoising methods used to improve image quality. The findings indicate that deep learning and neural networks have recently contributed considerably to the advancement of image processing techniques. Consequently, a system has been created that makes use of machine learning to enhance the quality of damaged images and to facilitate the process of identifying specific diseases. Images, damaged in the course of an assault, are denoised using the suggested technique relying on a symmetric dilated convolution neural network. This improves the system’s resilience and establishes a secure environment for the exchange of data while maintaining secrecy

Catalytic transesterification of beta-ketoesters with zeolite H-FER under solvent free conditions

Zeolite H-FER catalyzes the transesterification of ??-ketoesters with variety of alcohols under solvent-less condition in excellent yields. The catalyst can be reused without any loss of activity. ?? ARKAT

Three wafer stacking for 3D integration.

Vertical wafer stacking will enable a wide variety of new system architectures by enabling the integration of dissimilar technologies in one small form factor package. With this LDRD, we explored the combination of processes and integration techniques required to achieve stacking of three or more layers. The specific topics that we investigated include design and layout of a reticle set for use as a process development vehicle, through silicon via formation, bonding media, wafer thinning, dielectric deposition for via isolation on the wafer backside, and pad formation

Task scheduling and resource allocation in cloud computing using a heuristic approach

Abstract Cloud computing is required by modern technology. Task scheduling and resource allocation are important aspects of cloud computing. This paper proposes a heuristic approach that combines the modified analytic hierarchy process (MAHP), bandwidth aware divisible scheduling (BATS) + BAR optimization, longest expected processing time preemption (LEPT), and divide-and-conquer methods to perform task scheduling and resource allocation. In this approach, each task is processed before its actual allocation to cloud resources using a MAHP process. The resources are allocated using the combined BATS + BAR optimization method, which considers the bandwidth and load of the cloud resources as constraints. In addition, the proposed system preempts resource intensive tasks using LEPT preemption. The divide-and-conquer approach improves the proposed system, as is proven experimentally through comparison with the existing BATS and improved differential evolution algorithm (IDEA) frameworks when turnaround time and response time are used as performance metrics

Improving Quality of Watermarked Medical Images Using Symmetric Dilated Convolution Neural Networks

Rapid development of online medical technologies raises questions about the security of the patient’s medical data.When patient records are encrypted and labeled with a watermark, they may be exchanged securely online. In order to avoid geometrical attacks aiming to steal the information, image quality must be maintained and patient data must be appropriately extracted from the encoded image. To ensure that watermarked images are more resistant to attacks (e.g. additive noise or geometric attacks), different watermarking methods have been invented in the past. Additive noise causes visual distortion and render the potentially harmful diseases more difficult to diagnose and analyze. Consequently, denoising is an important pre-processing method for obtaining superior outcomes in terms of clarity and noise reduction and allows to improve the quality of damaged medical images. Therefore, various publications have been studied to understand the denoising methods used to improve image quality. The findings indicate that deep learning and neural networks have recently contributed considerably to the advancement of image processing techniques. Consequently, a system has been created that makes use of machine learning to enhance the quality of damaged images and to facilitate the process of identifying specific diseases. Images, damaged in the course of an assault, are denoised using the suggested technique relying on a symmetric dilated convolution neural network. This improves the system’s resilience and establishes a secure environment for the exchange of data while maintaining secrecy

CFD based condition monitoring of centrifugal pumps

Pumps are an integral part of engineering systems used in power and process industries. Flow through the centrifugal pump is very complex mainly due to the rotation imposed by the impeller and its interaction with the volute casing. Performance and condition monitoring of pumps are important to identify decreased performance, to avoid unplanned shut downs, to predict and plan preventive maintenance, and to enhance service life. Cavitation has direct influence on operating stability and service life of centrifugal pump. The Computational Fluid Dynamics (CFD) technique is emerging as a useful computational tool for the prediction of pump performance and occurrence of cavitation at different flow conditions. In this work, three-dimensional steady-state analysis of a single-stage centrifugal pump with three backward curved blades and a double volute casing has been carried out using Ansys-CFX. The computational results are validated with the experimental results of head developed and the overall efficiency of the centrifugal pump over a wide range of flow rates. The main focus of this study is to predict cavitation inside the centrifugal pump at different flow conditions. The computed results will be useful in knowing the flow conditions favorable to prevent cavitation. This would also enable development of cavitation predicting tools for the centrifugal pump as a part of condition monitoring exercise

Pre-Sowing Treatments, Seed Components and Water Imbibition Aids Seed Germination of Gloriosa superba

Gloriosa superba L. is a horticulturally and medicinally important plant. Its seeds have poor, erratic, and deferred germination. The detailed seed structure components and water imbibition mechanism facilitating the process of seed germination in G. superba remain unexplored. Therefore, it is essential to develop methods to ensure consistent and enhanced seed germination in G. superba. Various pre-sowing treatments along with the Brunauer-Emmett-Teller (BET) surface area analysis and 3D X-ray micro-tomography (micro-T) were employed to elucidate seed structure components, porosity network, and the water imbibition mechanism during germination in G. superba. The study revealed that consistent and significantly improved seed germination (>85%) was observed using the pre-sowing treatment mechanical scarification followed by 24 h water soaking in G. superba. BET and micro-T showed that the tegmen of the seed coat exhibited porosity (21%) with a well-connected porosity network (17.50%) that helped in water movement through hilum, which was confirmed by phosphotungstic acid staining. However, the sarcotesta and endosperm were water-impermeable due to their negligible porosity. Multidisciplinary techniques such as BET and micro-T along with conventional methodologies can be employed to address the seed coat structure, porosity, and water imbibition mechanism aiding seed germination. Mechanical scarification enabled the water to penetrate internal seed layers through the permeable tegmen via the reticulate pore network, which significantly improved seed germination. The developed seed germination method can produce a large number of plants in less time and conserve the natural populations of this high-value medicinally important species

Three wafer stacking for 3D integration.

Vertical wafer stacking will enable a wide variety of new system architectures by enabling the integration of dissimilar technologies in one small form factor package. With this LDRD, we explored the combination of processes and integration techniques required to achieve stacking of three or more layers. The specific topics that we investigated include design and layout of a reticle set for use as a process development vehicle, through silicon via formation, bonding media, wafer thinning, dielectric deposition for via isolation on the wafer backside, and pad formation