PLASMONIC PROPERTIES OF METALLIC NANOPARTICLES

In this study, the scattering properties of three different gold nano-particles have been studied. The proposed nano-particles are spherical, conical and cylindrical. The simulation results indicate that as the parameter of these nano-particles are changed so different LSPR peaks and shifts achieved in the scattering spectra. So this shows that the resonance modes are strongly reliant on the parameters of the proposed nano-particles. Moreover we have compared the scattering spectra of all the three nano-particles on the basis of their volume. The spherical nano-particle got wide spectral width, shift and high amplitude in the scattering spectra due to which it can be used for biomedical applications

Motion Planning of UAV Swarm: Recent Challenges and Approaches

The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work

PLASMONIC PROPERTIES OF METALLIC NANOPARTICLES

In this study, the scattering properties of three different gold nano-particles have been studied. The proposed nano-particles are spherical, conical and cylindrical. The simulation results indicate that as the parameter of these nano-particles are changed so different LSPR peaks and shifts achieved in the scattering spectra. So this shows that the resonance modes are strongly reliant on the parameters of the proposed nano-particles. Moreover we have compared the scattering spectra of all the three nano-particles on the basis of their volume. The spherical nano-particle got wide spectral width, shift and high amplitude in the scattering spectra due to which it can be used for biomedical applications

Optical Transmission Plasmonic Color Filter withWider ColorGamut Based on X-Shaped Nanostructure

Extraordinary Optical Transmission Plasmonic Color Filters (EOT-PCFs) with nanostructures have the advantages of consistent color, small size, and excellent color reproduction, making them a suitable replacement for colorant-based filters. Currently, the color gamut created by plasmonic filters is limited to the standard red, green, blue (sRGB) color space, which limits their use in the future. To address this limitation, we propose a surface plasmon resonance (SPR) color filter scheme, which may provide a RGB-wide color gamut while exceeding the sRGB color space. On the surface of the aluminum film, a unique nanopattern structure is etched. The nanohole functions as a coupled grating that matches photon momentum to plasma when exposed to natural light. Metals and surfaces create surface plasmon resonances as light passes through the metal film. The plasmon resonance wavelength can be modified by modifying the structural parameters of the nanopattern to obtain varied transmission spectra. The International Commission on Illumination (CIE 1931) chromaticity diagram can convert the transmission spectrum into color coordinates and convert the spectrum into various colors. The color range and saturation can outperform existing color filters.Funding: This project has received funding from Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538

Optical Transmission Plasmonic Color Filter with Wider Color Gamut Based on X-Shaped Nanostructure

Extraordinary Optical Transmission Plasmonic Color Filters (EOT-PCFs) with nanostructures have the advantages of consistent color, small size, and excellent color reproduction, making them a suitable replacement for colorant-based filters. Currently, the color gamut created by plasmonic filters is limited to the standard red, green, blue (sRGB) color space, which limits their use in the future. To address this limitation, we propose a surface plasmon resonance (SPR) color filter scheme, which may provide a RGB-wide color gamut while exceeding the sRGB color space. On the surface of the aluminum film, a unique nanopattern structure is etched. The nanohole functions as a coupled grating that matches photon momentum to plasma when exposed to natural light. Metals and surfaces create surface plasmon resonances as light passes through the metal film. The plasmon resonance wavelength can be modified by modifying the structural parameters of the nanopattern to obtain varied transmission spectra. The International Commission on Illumination (CIE 1931) chromaticity diagram can convert the transmission spectrum into color coordinates and convert the spectrum into various colors. The color range and saturation can outperform existing color filters

Performance Evaluation of Spectral Efficiency for Uplink and Downlink Multi-Cell Massive MIMO Systems

Massive multiple-input and multiple-output (MIMO) systems have become the most persuasive technology for 5G as it increased the energy efficiency gigantically as compared to other wireless communication systems. Being the most vibrant research technology in the communication sector, this research work is based on the optimal model development of energy-efficient massive MIMO systems. The proposed model is a realistic model that augmented the spectral efficiency (SE) of massive MIMO systems where a multi-cell model scenario is considered. Channel estimation is carried out at the base stations (BSs) based on uplink (UL) transmission while the minimum mean-squared error (MMSE), Element-wise MMSE, and Least-square (LS) estimators are used for the estimation. We analyze the achievable SE of the UL based on the MMSE channel estimator with different receive combining schemes. Moreover, the downlink (DL) transmission model is also modelled with different precoding schemes by taking the same vectors used in combining schemes. The simulation results show a significant improvement in spectral efficiency by developing UL and DL transmission models and also realized that the average sum of SE per cell can be improved by optimized MMSE channel estimation, installing multiple BS antennas, and serving multiple UEs per cell. The findings of this work specify that the massive MIMO system can be developed by optimizing the channel estimation for the augmentation of SE in UL and DL transmissions. Conclusively, it can be summarized that some complex computations of MMSE channel estimators can enhance the average sum of SE per cell as per the results verified in this model

Quantitative protein expression of Malaysian Phaleriamacrocarpa

Herbal plants are the best alternative for synthetic medicines which are costly and proved to be double-edged source. P. macrocarpais the traditional herb compatible in curing diseases like diabetes, inflammation, and even cancer. People have gained lots of benefits from most parts of the plant with largest usage of seeds. This study aimed to analyze protein expressions in three major parts of the plant; leaf, seed and fruit by using SDS PAGE and high-throughput two-dimensional gel electrophoresis as proteomics approached. There were 42 spots detected in a pH range of 3-10 with 3 spots have been analyzed by MALDI-TOF mass spectrometry. Most identified proteins were expressed in seeds with highest intensity of bands and spots. The identified spots are homologs to putative clathrin assembly protein and ferredoxin NADP+ reductase which are characterized as ‘housekeeping agent’ in plant system

Quantitative protein expression of Malaysian Phaleria macrocarpa

Herbal plants are the best alternative for synthetic medicines which are costly and proved to be double-edged source. P. macrocarpa is the traditional herb compatible in curing diseases like diabetes, inflammation, and even cancer. People have gained lots of benefits from most parts of the plant with largest usage of seeds. This study aimed to analyze protein expressions in three major parts of the plant; leaf, seed and fruit by using SDS PAGE and high-throughput two-dimensional gel electrophoresis as proteomics approached. There were 42 spots detected in a pH range of 3-10 with 3 spots have been analyzed by MALDI-TOF mass spectrometry. Most identified proteins were expressed in seeds with highest intensity of bands and spots. The identified spots are homologs to putative clathrin assembly protein and ferredoxin-NADP+ reductase which are characterized as ‘housekeeping agent’ in plant system

Early-Stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains

Alzheimer’s Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD

On Disharmony in Batch Normalization and Dropout Methods for Early Categorization of Alzheimer’s Disease

Alzheimer’s disease (AD) is a global health issue that predominantly affects older people. It affects one’s daily activities by modifying neural networks in the brain. AD is categorized by the death of neurons, the creation of amyloid plaques, and the development of neurofibrillary tangles. In clinical settings, an early diagnosis of AD is critical to limit the problems associated with it and can be accomplished using neuroimaging modalities, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Deep learning (DL) techniques are widely used in computer vision and related disciplines for various tasks such as classification, segmentation, detection, etc. CNN is a sort of DL architecture, which is normally useful to categorize and extract data in the spatial and frequency domains for image-based applications. Batch normalization and dropout are commonly deployed elements of modern CNN architectures. Due to the internal covariance shift between batch normalization and dropout, the models perform sub-optimally under diverse scenarios. This study looks at the influence of disharmony between batch normalization and dropout techniques on the early diagnosis of AD. We looked at three different scenarios: (1) no dropout but batch normalization, (2) a single dropout layer in the network right before the softmax layer, and (3) a convolutional layer between a dropout layer and a batch normalization layer. We investigated three binaries: mild cognitive impairment (MCI) vs. normal control (NC), AD vs. NC, AD vs. MCI, one multiclass AD vs. NC vs. MCI classification problem using PET modality, as well as one binary AD vs. NC classification problem using MRI modality. In comparison to using a large value of dropout, our findings suggest that using little or none at all leads to better-performing designs