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.     

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     

Electrical properties of ZnSe-CdS alloy films

Thin films of ZnSe _x CdS_1–x (t 0.6 m) over the entire range of x, were deposited on glass substrates at two temperatures, T _s (350 and 470 K) by vacuum evaporation. X-ray diffraction studies showed that all the films were polycrystalline in nature. Films prepared at 470 K were nearly stoichiometric. Grain size increased with substrate temperature, T _s. The electrical conductivity and Hall measurements were carried out by d.c. van der Pauw technique. Hall effect studies/hot probe test showed that all the films were of n-type conductivity. Hall mobility increased with T _s. In addition, mobilities increased with temperature in films of all compositions, indicating the dominance of grain-boundary scattering. Grain-boundary potentials were in range 0.03–0.06 eV.     

Helicobacter pylori does not migrate from the antrum to the corpus in response to omeprazole

BACKGROUND: Omeprazole is known to have an effect on Helicobacter pylori in vivo. One opinion is that H. pylori "migrates" from the antrum to the corpus in response to omeprazole therapy. METHODS: To determine whether H. pylori migrates in response to omeprazole, we assessed the presence of H. pylori in the antrum and corpus in duodenal ulcer patients receiving omeprazole for 4 wk. Culture and histological examination of antral biopsies (Genta stain) were performed before patients received omeprazole, at the end of therapy, and 4-6 wk later. The end points were presence or absence of H. pylori and the number of H. pylori colonies per biopsy. RESULTS: Seventy-two patients had H. pylori in both the antrum and corpus at entry and 4-6 wk after ending therapy. Three general patterns were prevalent at the end of omeprazole therapy: antrum- and corpus-positive (54%), antrum-negative and corpus-positive (24%), both antrum- and corpus-negative (21%), and one patient had antrum-positive with corpus-negative (1%). Evaluation of the number of colonies per biopsy in those who remained H. pylori-positive in both the antrum and corpus throughout showed that the number of H. pylori decreased in both the antrum and corpus during therapy (507 +/- 60 vs. 225 +/- 51, p < 0.01 and 415 +/- 58 vs. 290 +/- 46 0.1) for antrum and corpus, respectively, and tended to return to pre-therapy levels 4-6 wk later. The number of H. pylori in the corpus also decreased in the antrum-negative and corpus-positive group during therapy with omeprazole (433 +/- 87 vs. 185 +/- 61, p < 0.05). In most of the patients studied, the number of H. pylori in the corpus was less posttreatment than it was pretreatment. The decrease in H. pylori load was also reflected in the development of false-negative urea breath tests. CONCLUSIONS: Omeprazole is detrimental to H. pylori in both the antrum and the corpus; migration from the antrum to the corpus in response to omeprazole is a myth.     

Concept-based evidential reasoning for multimodal fusion in human–computer interaction

Considerable research has been done on using information from multiple modalities, like hands, facial gestures or speech, for better interaction between humans and computers, and many promising human–computer interfaces (HCI) have been developed in recent years. However, most of the current HCI systems have a few drawbacks: firstly, they are highly dependent on the performance of individual sensors. S econdly, the information fusion process from these sensors tends to ignore the semantic nature of the modalities, which may reinforce or clarify each other over time. Finally, they are not robust enough at representing the imprecise nature of human gestures, since individual gestures are highly ambiguous in themselves. In this paper, we propose an approach for the semantic fusion of different input modalities, based on transferable belief models. We show that this approach allows for a better representation of the ambiguity involved in recognizing gestures. Ambiguity is resolved by combining the beliefs of the individual sensors on the input information, to form new extended concepts, based on a pre-defined domain specific knowledge base, represented by conceptual graphs. We apply this technique to a multimodal system consisting of a hand gesture recognition sensor and a brain computing interface. It is shown that the technique can successfully combine individual gestures obtained from the two sensors, to form meaningful concepts and resolve ambiguity. The advantage of this approach is that it is robust even if one of the sensors is inefficient or has no input. Another important feature is its scalability, wherein more input modalities, like speech or facial gestures, can be easily integrated into the system at minimal cost, to form a comprehensive HCI interface.     

Hybrid signal processing and machine intelligence techniques for detection,quantification and classification of power quality disturbances

This paper presents an advanced signal processing technique known as S-transform (ST) to detect and quantify various power quality (PQ) disturbances. ST is also utilized to extract some useful features of the disturbance signal. The excellent time–frequency resolution characteristic of the ST makes it an attractive candidate for analysis of power system disturbance signals. The number of features required in the proposed approach is less than that of the wavelet transform (WT) for identification of PQ disturbances. The features extracted by using ST are used to train a support vector machine (SVM) classifier for automatic classification of the PQ disturbances. Since the proposed methodology can reduce the features of disturbance signal to a great extent without losing its original property, it efficiently utilizes the memory space and computation time of the processor. Eleven types of PQ disturbances are considered for the classification purpose. The simulation results show that the combination of ST and SVM can effectively detect and classify different PQ disturbances.