Assessment of relative impacts of various geo-mining factors on methane dispersion for safety in gassy underground coal mines: an artificial neural networks approach

Neural Computing and Applications - Dispersing methane to a safer level is crucial for mines safety as methane has been the greatest contributor of explosion hazard in underground coal mines...     

Optimization techniques for spectrum handoff in cognitive radio networks using cluster based cooperative spectrum sensing

Wireless Networks - Spectrum handoff has an undesirable effect in utilizing the space for Secondary user (SU) in the spectrum, which causes a handoff delay in cognitive radio network. The SU...     

Sleepy keeper style based Low Power VLSI Architecture of a Viterbi Decoder applying for the Wireless LAN Operation sustainability

Analog Integrated Circuits and Signal Processing - Remarkable progress in the field of wireless communication has created a research interest for Viterbi decoder with long duration of battery life,...     

Survey on various architectures of preamplifiers for electroencephalogram (EEG) signal acquisition

Microsystem Technologies - Modern-day biomedical science and technology have progressed with implantable neural recording systems. There is a demand for miniaturised devices that can be emplaced...     

Isaacs syndrome: long-term improvement with intravenous polyvalent immunoglobulins

A 35 year-old man developed a syndrome with muscle cramp, myokimia, generalized, fasciculations, excessive sweating, sleep disorders and severe impairment. It was a syndrome of continuous muscle fiber activity--or Isaacs syndrome--with central disorders (this may be called "Maladie de Morvan"). Previous reports have suggested that Isaac's syndrome might be an autoimmune disorder. Moreover, high doses intravenous immunoglobulins were given resulting in a substantial improvement six months after the onset of this treatment.     

A cluster prediction model-based data collection for energy efficient wireless sensor network

The Journal of Supercomputing - Wireless sensor networks (WSN) are expected to cover the major portion of the earth’s surface in the coming years. In the era of IoT, the WSN is the major data...     

Enhancing multi-document summarization using concepts

In this paper we propose a methodology to mine concepts from documents and use these concepts to generate an objective summary of all relevant documents. We use the conceptual graph (CG) formalism as proposed by Sowa to represent the concepts and their relationships in the documents. In the present work we have modified and extended the definition of the concept given by Sowa. The modified and extended definition is discussed in detail in section 2 of this paper. A CG of a set of relevant documents can be considered as a semantic network. The semantic network is generated by automatically extracting CG for each document and merging them into one. We discuss (i) generation of semantic network using CGs and (ii) generation of multi-document summary. Here we use restricted Boltzmann machines, a deep learning technique, for automatically extracting CGs. We have tested our methodology using MultiLing 2015 corpus. We have obtained encouraging results, which are comparable to those from the state of the art systems.     

Metal ion sensing and light activated antimicrobial activity of aloe-vera derived carbon dots

Carbon dots (CDs) have emerged out as a potential material amongst the carbon family for a wide range of applications including chemical/biological sensing, photocatalysis, bioimaging, etc. The green synthesis of these CDs from natural sources is gaining the significant interest of peer community for their wide utility. Herein, we present a facile one-step pyrolysis method for CDs synthesis from Aloe-Vera extract, which show bright blue luminescence under UV light with a quantum yield of 12.3%. Further, ex-situ morphological, structural and optical characterizations reveal their high quality and excitation independent emission behavior with the presence of carboxyl, hydroxyl functional groups. Furthermore, these CDs were studied for Fe(III) sensing in water without any surface modifications and assessed for their light activated antibacterial activity against E.Coli and Staphylococcus aureus.     

Synthesis,defect characterization and photocatalytic degradation efficiency of Tb doped CuO nanoparticles

Monoclinic undoped and Tb doped CuO are prepared by solution combustion method and annealed at different temperatures. The effect of annealing and doping on their structural and optical properties of CuO are examined using XRD, FTIR and DRS. The surface and lattice defects in CuO and Tb doped CuO is analyzed qualitatively and quantitatively using positron lifetime and Doppler broadening spectroscopy. The average positron lifetime and electron momentum (energy) S parameter increases owing to the number of vacancies in the CuO lattice upon doping and decreases with increasing temperature. The migration of vacancies from grain to grain boundary region is observed at 600 °C annealed samples. At 800 °C, the overall behavior of lifetime value denotes that the vacancy type defect is recovered, cluster vacancy and microvoids exists with reducing size. The photocatalytic performance of undoped and Tb doped CuO on degradation of methylene blue (MB) and methyl orange (MO) is investigated under visible light for two different lamp power and dye concentration. The influence of annealing temperature and dopant ion on the efficiency is also elaborated. Enhanced photocatalytic efficiency in Tb doped CuO is observed upon annealing. X-ray photoelectron spectroscopy (XPS) result indicates that the valence states of Cu, O and Tb ions exist at the surface of the particles. Brunauer–Emmett–Teller N₂ adsorption–desorption analyses were employed to characterize specific surface area and porosity of Tb doped CuO. The doped CuO with pore size of about ～34 nm have a surface area of 16–28 m²/g. The surface area effect plays an important role in the enhanced catalytic performance on Tb doped catalysts.     

Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models

Diabetes is associated with many complications that could lead to death. Diabetic retinopathy, a complication of diabetes, is difficult to diagnose and may lead to vision loss. Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians. Because clinical testing involves complex procedures and is time-consuming, an automated system would help ophthalmologists to detect DR and administer treatment in a timely manner so that blindness can be avoided. Previous research works have focused on image processing algorithms, or neural networks, or signal processing techniques alone to detect diabetic retinopathy. Therefore, we aimed to develop a novel integrated approach to increase the accuracy of detection. This approach utilized both convolutional neural networks and signal processing techniques. In this proposed method, the biological electro retinogram (ERG) sensor network (BSN) and deep convolution neural network (DCNN) were developed to detect and classify DR. In the BSN system, electrodes were used to record ERG signal, which was pre-processed to be noise-free. Processing was performed in the frequency domain by the application of fast Fourier transform (FFT) and mel frequency cepstral coefficients (MFCCs) were extracted. Artificial neural network (ANN) classifier was used to classify the signals of eyes with DR and normal eye. Additionally, fundus images were captured using a fundus camera, and these were used as the input for DCNN-based analysis. The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images, non-proliferative DR (NPDR) or early-stage DR images, and proliferative DR (PDR) or advanced-stage DR images. Furthermore, it classified NPDR according to microaneurysms, hemorrhages, cotton wool spots, and exudates, and the presence of new blood vessels indicated PDR. The accuracy, sensitivity, and specificity of the ANN classifier were found to be 94%, 95%, and 93%, respectively. Both the accuracy rate and sensitivity rate of the DCNN classifier was 96.5% for the images acquired from various hospitals as well as databases. A comparison between the accuracy rates of BSN and DCNN approaches showed that DCNN with fundus images decreased the error rate to 4%.