A Novel Approach for Quaternion Algebra Based JSEG Color Texture Segmentation

In this work, a novel colour quantization approach has been applied to the JSEG colour texture segmentation using quaternion algebra. As a rule, the fundamental vectors of the colour space are derived by inverting the three RGB colour directions in the complex hyperplanes. In the proposed system, colour is represented as a quaternion because quaternion algebra provides a very intuitive means of working with homogeneous coordinates. This representation views a colour pixel as a point in the three-dimensional space. A novel quantization approach that makes use of projective geometry and level set methods has been produced as a consequence of the suggested model. The JSEG colour texture segmentation will use this technique. The new colour quantization approach utilises the binary quaternion moment preserving thresholding methodology, and is therefore a splintering clustering method. This method is used to segment the colour clusters found inside the RGB cube and the colour consistency throughout the spectrum and in the space are both considered. The results of the segmentation are compared with JSEG as well as with the most recent standard segmentation techniques. These comparisons show that the suggested quantization technique makes JSEG segmentation more robust

Methods of obtaining smooth surface in 2D/3D surface reconstruction

Surface reconstruction is an emergent research area in the field of computer aided design and manufacturing. There are various methods / algorithms which are working considerably well for surface reconstruction problem but we cannot say to the best of our knowledge that we got all the solutions. Missing surface can be repaired either by surface patch or by extending boundary curves. However, in both cases, surface smoothening problem arises in form of flat surface. The present paper has been tried to offer a solution to above problem which makes the curve smoother

Methods of obtaining smooth surface in 2D/3D surface reconstruction

Surface reconstruction is an emergent research area in the field of computer aided design and manufacturing. There are various methods / algorithms which are working considerably well for surface reconstruction problem but we cannot say to the best of our knowledge that we got all the solutions. Missing surface can be repaired either by surface patch or by extending boundary curves. However, in both cases, surface smoothening problem arises in form of flat surface. The present paper has been tried to offer a solution to above problem which makes the curve smoother

Federated Learning for the Internet-of-Medical-Things: A Survey

Recently, in healthcare organizations, real-time data have been collected from connected or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end devices, named the Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital in driving healthcare analytics (HA) toward extracting meaningful data-driven insights. Recently, concerns have been raised over data sharing over IoMT, and stored electronic health records (EHRs) forms due to privacy regulations. Thus, with less data, the analytics model is deemed inaccurate. Thus, a transformative shift has started in HA from centralized learning paradigms towards distributed or edge-learning paradigms. In distributed learning, federated learning (FL) allows for training on local data without explicit data-sharing requirements. However, FL suffers from a high degree of statistical heterogeneity of learning models, level of data partitions, and fragmentation, which jeopardizes its accuracy during the learning and updating process. Recent surveys of FL in healthcare have yet to discuss the challenges of massive distributed datasets, sparsification, and scalability concerns. Because of this gap, the survey highlights the potential integration of FL in IoMT, the FL aggregation policies, reference architecture, and the use of distributed learning models to support FL in IoMT ecosystems. A case study of a trusted cross-cluster-based FL, named Cross-FL, is presented, highlighting the gradient aggregation policy over remotely connected and networked hospitals. Performance analysis is conducted regarding system latency, model accuracy, and the trust of consensus mechanism. The distributed FL outperforms the centralized FL approaches by a potential margin, which makes it viable for real-IoMT prototypes. As potential outcomes, the proposed survey addresses key solutions and the potential of FL in IoMT to support distributed networked healthcare organizations

Protein–protein interaction network for differentially expressed genes involved in KEGG pathways in spleen, liver and lung at late stage of infection.

<p>All the upregulated genes are shown in green, and downregulated genes in red, with the gradient showing the extent of expression. The diameter of the node represents the connectivity/degree of the node among the DE genes.</p

Level of TNF and IL-6 in spleen and lung by ELISA.

<p>Concentration (ng/ml) of TNF and IL6 was plotted against the control, early infection and late infection in spleen and lung. Significant difference (P ≤ 0.05) was calculated using Tukey’s HSD in JMP 12.0 (n = 3). Levels not connected by same letter are significantly different.</p

Workflow of the experiment.

<p>Workflow of the experiment.</p

Venn diagram showing the genes involved across different tissues and time points in the enriched KEGG pathways.

<p>Venn diagram showing the genes involved across different tissues and time points in the enriched KEGG pathways.</p

Quantitative real-time PCR to validate the RNA-seq experiment.

<p>Log₂ fold change is plotted with the standard error of difference.</p