Efficient implementation of 90 degrees phase shifter in FPGA

In this article, we present an efficient way of implementing 90 phase shifter using Hilbert transformer with canonic signed digit (CSD) coefficients in FPGA. It is implemented using 27-tap symmetric finite impulse response (FIR) filter. Representing the filter coefficients by CSD eliminates the need for multipliers and the filter is implemented using shifters and adders/subtractors. The simulated results for the frequency response of the Hilbert transformer with infinite precision coefficients and CSD coefficients agree with each other. The proposed architecture requires less hardware as one adder is saved for the realization of every negative coefficient compared to convectional CSD FIR filter implementation. Also, it offers a high accuracy of phase shift

An Efficient Visual Analysis Method for Cluster Tendency Evaluation, Data Partitioning and Internal Cluster Validation

Visual methods have been extensively studied and performed in cluster data analysis. Given a pairwise dissimilarity matrix D of a set of n objects, visual methods such as Enhanced-Visual Assessment Tendency (E-VAT) algorithm generally represent D as an n times n image I( overlineD) where the objects are reordered to expose the hidden cluster structure as dark blocks along the diagonal of the image. A major constraint of such methods is their lack of ability to highlight cluster structure when D contains composite shaped datasets. This paper addresses this limitation by proposing an enhanced visual analysis method for cluster tendency assessment, where D is mapped to D' by graph based analysis and then reordered to overlineD' using E-VAT resulting graph based Enhanced Visual Assessment Tendency (GE-VAT). An Enhanced Dark Block Extraction (E-DBE) for automatic determination of the number of clusters in I( overlineD') is then proposed as well as a visual data partitioning method for cluster formation from I( overlineD') based on the disparity between diagonal and off-diagonal blocks using permuted indices of GE-VAT. Cluster validation measures are also performed to evaluate the cluster formation. Extensive experimental results on several complex synthetic, UCI and large real-world data sets are analyzed to validate our algorithm

Replenish the source within: Rescuing tumor-infiltrating lymphocytes by double checkpoint blockade.

We have recently reported that the PD-1 and CTLA4 signaling pathways are active in both effector and regulatory T cells, causing profound immune dysfunctions in the tumor microenvironment. In line with this notion, the dual blockade of PD-1- and CTLA4-conveyed signals may exert robust therapeutic effects. Here, we discuss the mechanisms possibly underlying such a synergic interaction

Embryonic Architecture with Built-in Self-test and GA Evolved Configuration Data

The embryonic architecture, which draws inspirationfrom the biological process of ontogeny, has built-inmechanisms for self-repair. The entire genome is stored in theembryonic cells, allowing the data to be replicated in healthycells in the event of a single cell failure in the embryonic fabric.A specially designed genetic algorithm (GA) is used to evolve theconfiguration information for embryonic cells. Any failed embryoniccell must be indicated via the proposed Built-in Self-test(BIST) the module of the embryonic fabric. This paper recommendsan effective centralized BIST design for a novel embryonic fabric.Every embryonic cell is scanned by the proposed BIST in casethe self-test mode is activated. The centralized BIST design usesless hardware than if it were integrated into each embryoniccell. To reduce the size of the data, the genome or configurationdata of each embryonic cell is decoded using Cartesian GeneticProgramming (CGP). The GA is tested for the 1-bit adder and2-bit comparator circuits that are implemented in the embryoniccell. Fault detection is possible at every function of the cell due tothe BIST module’s design. The CGP format can also offer gate-levelfault detection. Customized GA and BIST are combinedwith the novel embryonic architecture. In the embryonic cell, self-repairis accomplished via data scrubbing for transient errors

Classification of hyper-scale multimodal imaging datasets

Algorithms that classify hyper-scale multi-modal datasets, comprising of millions of images, into constituent modality types can help researchers quickly retrieve and classify diagnostic imaging data, accelerating clinical outcomes. This research aims to demonstrate that a deep neural network that is trained on a hyper-scale dataset (4.5 million images) composed of heterogeneous multi-modal data can be used to obtain significant modality classification accuracy (96%). By combining 102 medical imaging datasets, a dataset of 4.5 million images was created. A ResNet-50, ResNet-18, and VGG16 were trained to classify these images by the imaging modality used to capture them (Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and X-ray) across many body locations. The classification accuracy of the models was then tested on unseen data. The best performing model achieved classification accuracy of 96% on unseen data, which is on-par, or exceeds the accuracy of more complex implementations using EfficientNets or Vision Transformers (ViTs). The model achieved a balanced accuracy of 86%. This research shows it is possible to train Deep Learning (DL) Convolutional Neural Networks (CNNs) with hyper-scale multimodal datasets, composed of millions of images. Such models can find use in real-world applications with volumes of image data in the hyper-scale range, such as medical imaging repositories, or national healthcare institutions. Further research can expand this classification capability to include 3D-scans.Publisher PDFPeer reviewe

Fluoride - an adjunctive therapeutic agent for periodontal disease? Evidence from a cross-sectional study

Objective: To assess the influence of the water fluoride level on periodontal status, by determining the periodontal health status of subjects residing in low, optimum and high fluoride areas. Study design: A cross sectional survey was carried out on 967 adults aged 35-44 years old, from the Udaipur district of India. A stratified cluster random sampling technique was implemented in order to collect a representative sample from low (3ppm) areas, based on the fluoride concentration in drinking water. Periodontal status was assessed in accordance to WHO criteria. The Chi-square test was used to compare proportions, and logistic regression analysis was used to determine the contribution of water fluoride levels to periodontal disease. Results: Those residing in areas of low fluoride levels were more likely to present periodontal pockets than those living in high fluoride areas 1.3 (95 % CI 1.11±1.86). Subjects living in areas of low fluoride were noted to have a higher risk of periodontal attachment loss of more than 8mm (OR = 1.94, 95% CI 1.67±3.85). The risk for presence of periodontal pockets and attachment loss of more than 8mm increased by 1.17 (95 % CI 1.02±1.69) and 1.59 (95 % CI 1.27±3.29) respectively for those residing in areas of optimum fluoride levels. Deep periodontal pockets were more prevalent (6.3%) among those residing in areas of low fluoride, followed by optimum (5.2%) and high (3.1%). Conclusions: As the fluoride concentrations increased, the prevalence of shallow and deep periodontal pockets decreased. The severity of periodontal disease was significantly associated with fluoride levels, with cases of loss of attachment gradually decreasing when moving from low fluoride areas to high fluoride areas. It appears that longitudinal studies need to be conducted in order to ascertain the benefits; and microbiological analysis of dental plaque and periodontium should be carried out in order to confirm the effects of fluoride on periodontal conditions

Influence of Roller Ball Tool in Single Point Incremental Forming of Polymers

Polymers defend the metals in making complex geometries because of their strength to weight ratio. Utilizing the conventional process has become a challenge in manufacturing customized products. Increase in demand of tailored products in minimum quantities with preferred quality creates the need for developing new techniques. Incremental forming process is an emerging flexible technology that can obtain pre-defined profiles through deformation of metals and polymers in desired thickness at a reasonable cost. In this work, single point incremental forming (SPIF) of different polymer materials is done using roller ball tool and modest fixture system. Materials such as polyvinyl chloride (PVC), polypropylene (PP), polycarbonate (PC), high density poly ethylene (HDPE) are considered for this investigation due to high applications in automobile and biomedical area. The experiments are designed to analyse the influence of variable process parameters such as tool diameter, step size, spindle speed and sheet thickness. The analysis is carried out by characterizing the formability with depth of failure, thickness distribution, surface roughness and microstructure evaluation. Based on the result, the spindle speed and sheet thickness show high response in formability, surface roughness and depth of failure. The tool diameter has a significant effect on the surface roughness. PVC shows the springback resistance and cracks are observed in the circumferential route on the transition area among the bottom and side wall portion

Microfluidic Processes for Synthesis of Plasmonic Nanomaterials

Ph.DDOCTOR OF PHILOSOPH