Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

Cancer disease is a deadliest disease cause more dangerous one. By identifying the disease through Artificial intelligence to getting the mage features directly from patients. This paper presents the lung knob division and disease characterization by proposing an enhancement calculation. Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification. This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy. To resolve this problem, to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor. The general technique of the created approach includes four stages, such as pre-processing, segmentation, highlight extraction, and the order. From the outset, the Computerized Tomography (CT) image of the lung is taken care of to the division. When the division is done, the highlights are extricated through morphological factors for feature observation. By getting the features are analysed and the characterization is done dependent on the Deep Belief Network (DBN) which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm (CSCA) which distinguish the lung tumour, giving two classes in particular, knob or non-knob. The proposed system produce high performance as well compared to the other system. The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specificity, precision, affectability, and the explicitness.     

Applicability of time-of-flight-based ground and multispectral aerial imaging for grapevine canopy vigour monitoring under direct root-zone deficit irrigation

ABSTRACT

This study investigates the applicability of ground-based 3D time-of-flight (ToF) imaging and small unmanned aerial system (UAS) integrated multispectral imaging as a rapid grapevine canopy vigour mapping tool for decision support during crop production management. Direct root-zone deficit irrigation was applied to grapevines with continuous and pulse irrigation techniques at 15%, 30%, and 60% rates as that of standard irrigation rate (100%) established by the grower in a commercial production. The control block was irrigated continuously at 100% standard irrigation rate. Field plots were imaged using ground platform integrated with 3D ToF imaging sensor and small UAS-integrated multispectral camera at 128 and 65 days before the harvest to estimate the canopy vigour variability associated with irrigation treatments. Customized as well as standard methods (convex hull and voxel grid) were utilized to extract canopy attributes (e.g. volume) from the 3D ToF imaging sensor data. The multispectral images were processed to extract normalized and green normalized difference vegetation index images. Resulting data were used to estimate canopy area ratio i.e. the ratio between the grapevine canopy area with respect to the total area in selected region of interest. A significant correlation (r = 0.34) between canopy volume estimated by customized algorithm and aerial canopy area ratio was observed. Custom canopy volume estimates were also highly correlated (r = 0.79) with voxel grid derived canopy volume data. Overall, 60% continuous direct root zone deficit irrigation appeared to produce canopy volume/vigour comparable with those under control treatment. Results also suggest that grower can utilize either or both (ground and aerial) grapevine canopy mapping techniques for effective management.     

Double Stage Gaussian Filter for Better Underwater Image Enhancement

Wireless Personal Communications - Major issue in marine environment imaging is the expulsion of hazy scenes caused by natural phenomena such as absorption and scattering in underwater images. The...     

Development and evaluation of ZnO-Fe2O3 based nanocomposite sensors for butanol detection

Olfactory sensing of specific volatile organic compounds released by bacterial pathogens is one of the unique ways for determining microbial contamination in packaged food products. This study reports the development and evaluation of zinc oxide-iron oxide (ZnO-Fe2O3) nanocomposite sensors to detect low concentrations of butanol, one of the VOCs specific to Salmonella contamination in packaged beef, at low operating temperature (100 degrees C). The ZnO-Fe2O3 sensor was developed using modified Sol-gel method on an interdigitated alumina substrate. The sensor thin film characterization confirmed a uniform layer of ZnO-Fe2O3 thin film formation with ZnO nanorods of 100 nm height. Also, ZnO-Fe2O3 nanocomposite sensor demonstrated repeatable responses and good sensitivity to butanol with an estimated lower detection limit of about 26 ppm at 100 degrees C.     

Olfactory receptor based piezoelectric biosensors for detection of alcohols related to food safety applications

Our major goal in developing intelligent quality sensors is to detect bacterial pathogens such as Salmonella in the packaged beef. Olfactory sensing of specific volatile organic compounds released by the bacterial pathogens is one of the unique ways for determining contamination in food products. This work aims at developing a biomimetic piezoelectric olfactory sensor for detecting specific gases (alcohols) at low concentrations.The computational simulation was used to determine the biomimetic peptide-based sensing material to be deposited on the quartz crystal microbalance (QCM) sensor. Tripos/Sybyl^®8.0 was used to predict the binding site of an olfactory receptor and determine the binding affinity as well as orientation of the selected ligands (specific molecules) to the olfactory receptor. The designed polypeptide sequence based on the simulation program was synthesized and used as a sensing layer in the QCM crystal. The developed QCM sensors were sensitive to 1-hexanol as well as 1-pentanol as predicted by the simulation algorithm. The estimated lower detection limits of the QCM sensors for detecting 1-hexanol and 1-pentanol were 2-3 ppm and 3-5 ppm, respectively. This study demonstrates the applicability of simulation-based peptide sequence that mimics the olfactory receptor for sensing specific gases.     

Supercapacitive properties of activated carbon electrode using ammonium based proton conducting electrolytes

In this study, we demonstrated the usefulness of proton conducting electrolytes (such as ammonium thiocyanate (NH₄SCN) and ammonium nitrate (NH₄NO₃)) for electrochemical energy storage devices using activated carbon (AC) as the electrode material. The cyclic voltammetry analysis revealed the presence of rectangular shaped cyclic voltammograms indicating the presence of electrical double layer capacitance in AC electrode using NH₄SCN and NH₄NO₃ electrolytes. The mechanism of charge-storage in AC electrode using the proton conducting electrolytes has been studied in detail using electrochemical impedance spectroscopy (Nyquist and Bode plots). The galvanostatic charge-discharge analysis revealed that a maximum specific capacitance of AC electrode using NH₄SCN and NH₄NO₃ electrolytes was found to be 136.75 mF cm^?2 and 113.38 mF cm^?2 at a current density of 0.5 mA cm^?2. This study would open a new avenue for the use of ammonium based proton conducting electrolytes for supercapacitor applications.     

A UNIFIED ENGINEERING MODEL FOR STEADY AND QUASI-STEADY SHEAR-DRIVEN GAS MICROFLOWS

We analyze one-dimensional plane Couette flows in the entire Knudsen regime with the objective of modeling shear-driven rarefied gas flows encountered in various microelectromechanical system (MEMS) components. Using the linearized Boltzmann solutions available in the literature and hard sphere direct simulation Monte Carlo (DSMC) results, we develop a unified empirical model that includes analytical expressions for the velocity distribution and shear stress for steady plane Couette flows. We also present extension of this model to time-periodic oscillatory Couette flows. Comparisons between the extended model and ensemble averaged unsteady DSMC computations show good agreements in the quasi-steady flow limit, where the Stokes number (β) based on the plate separation distance and oscillation frequency is ≤ 0.25. Overall, the new model accurately predicts the velocity distribution and shear stress for steady and quasi-steady (β ≤ 0.25) flows in a wide Knudsen number range (K_n ≤ 12), and it is strictly valid for low subsonic flows with Mach number ≤ 0.3.     

Interferences contributed by leaching from filters on measurements of collective organic constituents

Membrane and glass fiber filters are widely used in laboratory water and wastewater analyses. During sample filtration, some filters release organic compounds, which may interfere with organic analyses. This research investigated the interferences due to organic leaching in the determinations of dissolved organic carbon (DOC), biodegradable dissolved organic carbon (BDOC), soluble biochemical oxygen demand (BOD), and soluble chemical oxygen demand (COD) and the appropriate cleaning method for the filters. Nineteen filters studied included 16 membrane filters and 3 glass fiber filters. A wide variation was observed on the amount and characteristics of organics leaching from the filters. Some filters showed no organic leaching while some others had very high organic leaching. Soaking the filters in deionized distilled water (DDW) resulted in more leachable organics from the filters compared with filtering DDW through them. Certain filters should be avoided for use in the above analyses and soaking in DDW is a more suitable cleaning method than discarding initial filtration volumes for most filters.     

Olfactory receptor-based polypeptide sensor for acetic acid VOC detection

Rapid detection of food-borne pathogens in packaged food products can prevent the spread of infectious diseases. This study investigates the application of novel sensing material that is sensitive to specific indicator volatile organic compound (VOC) related to Salmonella contamination in packaged meat. Specifically, the objective was to develop an olfactory receptor-based synthetic polypeptide sensor for the detecting acetic acid in low concentrations and at room temperature. Synthetic polypeptide was deposited on a quartz crystal microbalance (QCM) electrode and was evaluated for detecting acetic acid at 10–100 ppm. Developed sensor exhibited repeatable response to a particular concentration of acetic acid and displayed reproducibility among multiple sensors during acetic acid detection. Mean estimated lower detection limits of these sensors were about 1–3 ppm and linear calibration models showed linear relationships. Thus, the QCM sensors exhibited a great potential for detecting low concentrations of acetic acid at room temperature and can be used in the sensor array for packaged meat spoilage and contamination detection.     

Effect of incorporation of amaranth flour on the quality of cookies

Studies were carried out on composite flour cookies by incorporating amaranth seed (Amaranthus gangeticus) flour, which is rich in protein at 0–35% levels, showed a small reduction in water absorption capacity from 58.0 to 56.5%, a considerable reduction in Farinograph stability from 3.0 to 1.5 min and increase in the mixing tolerance index from 40 to 120 BU of the dough. The pasting characteristics of the blends using rapid visco analyser (RVA) showed a reduction in peak viscosity, cold paste viscosity as well as break down viscosity from 261 to 192, 204 to 153 and 151 to 108 RVU, respectively. Incorporation of amaranth flour improved the colour of the cookies from pale cream to golden brown. The cookies became crispier which is evident from the reduction in the breaking strength value from 6.2 to 4.02 kg. Considering the colour, taste, flavour, surface appearance of the cookies, 25% incorporation of amaranth flour was found to be optimum. By the addition of glycerol mono stearate (GMS) and soy lecithin in combination at 0.5% level, the quality of the cookies further improved.