Interest point detection using imbalance oriented selection

Interest point detection has a wide range of applications, such as image retrieval and object recognition. Given an image, many previous interest point detectors first assign interest strength to each image point using a certain filtering technique, and then apply non-maximum suppression scheme to select a set of interest point candidates. However, we observe that non-maximum suppression tends to over-suppress good candidates for a weakly textured image such as a face image. We propose a new candidate selection scheme that chooses image points whose zero-/first-order intensities can be clustered into two imbalanced classes (in size), as candidates. Our tests of repeatability across image rotations and lighting conditions show the advantage of imbalance oriented selection. We further present a new face recognition application—facial identity representability evaluation—to show the value of imbalance oriented selection.     

Solubility and thermodynamic properties of Y2O3 in LiF-YF3 melts

A systematic investigation was undertaken to measure the solubility of Y₂O₃ in several LiF-YF₂ melts, with the YF₃ composition ranging from 20 to 50 mole pct, in the temperature range of 998 to 1273 K. Experimental results showed that the solubility of Y₂O₄ in the melts increased with increase in temperature and also with increased YF₃ content. The activity of Y₂O₃ was calculated using the free energy of fusion of Y₂O₃, . The was deduced from the values of enthalpy, heat of fusion, and melting point of Y₂O₃. From the known values of activity, the activity coefficients of Y₂O₃ as a function of temperature and melt composition were calculated. Considering the ionic nature of the melt, activity coefficients were also calculated using Temkin’s ideal mixing and electrically equivalent fraction methods. The thermodynamic data, such as integral molar enthalpy, entropy, and free energy of formation, were calculated as a function of composition and temperature. The calculated thermodynamic data showed that the melt exhibited a negative deviation from ideal conditions.     

Throughput analysis in censoring-based cooperative cognitive radio network with energy harvesting

An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied.     

Circular Fractal Antenna Design with Inscribed Square for Ultra Wide Band Applications

Journal of Communications Technology and Electronics - In this article, an ultra-wide band circular fractal antenna inscribed with square is presented. The prototype of the antenna is fabricated on...     

Image super resolution model enabled by wavelet lifting with optimized deep convolutional neural network

This paper plans to develop an intelligent super resolution model with the linkage of Wavelet lifting scheme and Deep learning algorithm. Before initiating the resolution procedure, the entire HR images are converted into Low Resolution (LR) images using bicubic interpolation-based downsampling and upsampling. Further, the Wavelet lifting scheme helps to generate the four subbands of each image like LR wavelet Sub-Bands for LR images, and High Resolution (HR) wavelet Sub-Bands for HR images. The residual image is generated by taking the difference between the LR wavelet Sub-Bands and HR wavelet Sub-Bands images. The proposed model involves two main phases: Training phase and Testing. The training phase trains the residual image of all images by Deep Convolutional Neural Network with LR wavelet Sub-Bands as input and residual image as target. On the other hand, in testing phase, the LR wavelet Sub-Bands query image is subjected to Deep Convolutional Neural Network, which outputs the concerned residual image. This generated residual image is summed with LR wavelet Sub-Bands image, followed by inverse wavelet lifting scheme to obtain the final super resolution image. The main contribution of this paper is to improve the conventional Deep Convolutional Neural Network by optimizing the number of hidden layer, and hidden neurons using modified Whale Optimization Algorithm called Average Fitness Enabled Whale Optimization Algorithm by considering the objective of maximizing the Peak Signal-to-Noise Ratio. Finally, the proposed method achieves an improved quality of the results which is comparable the existing models.     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

Microstructure and Magnetic Properties of Sn<Subscript>1 − <Emphasis Type="Italic">x</Emphasis></Subscript>Ni<Subscript><Emphasis Type="Italic">x</Emphasis></Subscript>O<Subscript>2</Subscript> Thin Films Prepared by Flash Evaporation Technique

An effort was made to develop semiconductor oxide-based room temperature dilute magnetic semiconductor (DMS) thin films based on wide band gap and transparent host lattice with transition metal substitution. The Sn\(_{\mathrm {1}-x}\)Ni\(_{x}\textit {O}_{\mathrm {2}}\) (\(x\,= \mathrm {0.00, 0.03, 0.05, 0.07, 0.10, and \,0.15}\)) thin film samples were prepared on glass substrates by flash evaporation technique. All the samples were shown single phase crystalline rutile structure of host SnO\(_{\mathrm {2}}\) with dominant (110) orientation. The Ni substitution promotes reduction of average crystallite size in SnO\(_{\mathrm {2}}\) as evidenced from the reduction of crystallite size from 40 (SnO\(_{\mathrm {2}}\)) to 20 nm (Sn\(_{\mathrm {0.85}}\)Ni\(_{\mathrm {0.15}}\textit {O}_{\mathrm {2}}\)). In the energy dispersive spectra as well as X-ray photoelectron spectra of all the samples show, the chemical compositions are close to stoichiometric with noticeable oxygen deficiency. The crystalline films were formed by coalescence of oval-shaped polycrystalline particles of 100 nm size as evidenced from the electron micrographs. The energy band gap of DMS films decreases from 4 (SnO\(_{\mathrm {2}}\)) to 3.8 eV (x \(=\) 0.05) with increase of Ni content. The magnetic hysteresis loops of all the samples at room temperature show soft ferromagnetic nature except for SnO\(_{\mathrm {2}}\) film. The SnO\(_{\mathrm {2}}\) films show diamagnetic nature and it converts into ferromagnetic upon substitution of 3 % Sn\(^{\mathrm {4+}}\) by Ni\(^{\mathrm {2+}}\). The robust intrinsic ferromagnetism (saturation magnetization, 21 emu/cm\(^{\mathrm {3}}\)). Further increase of Ni content weakens ferromagnetic strength due to Ni-O antiferromagnetic interactions among the nearest neighbour Ni ions via O\(^{\mathrm {2-}}\) ions. The observed magnetic properties were best described by bound magnetic polarons model.     

Impact of Zinc oxide nanoparticles on eggplant (S. melongena): studies on growth and the accumulation of nanoparticles

The increasing use of nanoparticles and their occurrence in the environment has made it imperative to elucidate their impact on the environment. Although several studies have advanced the authors’ understanding of nanoparticle–plant interactions, their knowledge of the exposure of plants to nanoparticles and their effects on edible crop plants remain meager and is often paradoxical. The aim of this study was to increase their knowledge on the effect of zinc oxide (ZnO) nanoparticles on eggplant seed germination and seedling growth. ZnO nanoparticles had a negative effect on the growth of eggplant in plant tissue‐culture conditions, as the growth of seedlings decreased with the increase in the concentration of ZnO nanoparticles. In contrast, ZnO nanoparticles enhanced eggplant growth under greenhouse conditions. The accumulation of ZnO nanoparticles in various parts of eggplant was observed through scanning electron microscopy of both plant tissue‐culture and greenhouse‐raised eggplant seedlings. To the best of their knowledge, this is the first study to report on ZnO nanoparticle accumulation in eggplant and its effect on seed germination and seedling growth.Inspec keywords: crops, zinc compounds, scanning electron microscopy, II‐VI semiconductors, nanoparticles, agriculture, cellular biophysics, nanofabricationOther keywords: plant tissue‐culture, greenhouse‐raised eggplant seedlings, ZnO nanoparticle accumulation, seedling growth, ZnO nanoparticles, nanoparticle–plant interactions, zinc oxide nanoparticles, eggplant seed germination, eggplant growth, ZnO     

Synthesis and properties analysis of char-reinforced Al–13.5Si–2.5Mg alloy composites

The re-evaluation of previous and existing methods in materials processing is becoming ever more critical because of processing and starting materials cost factors. A study on the synthesis and properties investigation of hypereutectic Al–13.5Si–2.5Mg alloy reinforced with carbon chars using coconut shell as the organic precursor has been carried out. The low-cost, double compaction solid-state technique was used. Reinforcing the hypereutectic alloy with coconut shell char particles (size:<140 m) at 2 vol % and consolidating by reaction sintering at 600 °C in vacuum for 15 min, followed by near net-shape compaction at 250 MPa, increased the hardness of the alloy 6% while reducing its strength (UTS) by only 3%. The use of palm kernel shell char as the dispersed phase was found to yield identical results. At 2 vol % char, the mechanical properties, sintered density and dimensional changes were optimally found to be suitable for lightweight anti-friction electromechanical applications. Attempts to reinforce the alloy with 2 vol % coconut shell chars activated in CO2 reduced its strength in the range of 19 to 26% at different burn-off percentages. This is attributed to the higher amount of oxide products formed during the activation process. At 600 °C, formation of the brittle Al4C3 phase in the different sintered composites containing activated and unactivated chars was identified by X-ray studies. © 1998 Chapman & Hall