Modeling a shallow water acoustic communication channel using environmental data for seafloor sensor networks

Development of communication channels for underwater sensor networks holds many unique challenges. Communication near the bottom of the ocean is no exception as the effects of reflection and refraction greatly affect how acoustic waves travel between a source and an intended receiver. Deployment and testing in the ocean are difficult and expensive; thus there is a strong reliance on models to aid in design and development of a potential network. Since each ocean region can present very unique challenges, it is of great value to model an environment based on real environmental parameters whenever available. A well prepared channel model will provide the ability to show channel capacity as it relates to node positions, as well as showing the performance of modulation techniques to an environment with propagation characteristics and path arrivals. This channel model will also be implementable into a simulation package to allow for high quality simulation of higher level protocols. The proposed method has proved to be a useful tool in modeling a particular environment and provides insight into underwater sensor node placement and modulation. Copyright © 2009 John Wiley & Sons, Ltd.     

Fabrication of room temperature H2 gas sensor using pure and La: ZnO with novel nanocorn morphology prepared by sol–gel dip coating method

Nanostructured ZnO thin films with different lanthanum concentration (0, 1, 3, 5 and 10 wt%) were fabricated by sol–gel dip coating method on glass substrates. The effect of La incorporation on structural, morphological and H₂ gas sensing (room temperature) properties of the ZnO thin films was studied. Thermal behaviour of the xerogel of pure ZnO was studied by thermo gravimetric analysis/differential thermal analysis. The structural property of the films was analyzed by powder X-ray diffraction method and which revealed the presence of hexagonal structure. It emphasized that the film became (002) textured upon the 3 wt% of La incorporation onwards. The surface morphology was examined by field emission scanning electron microscopy equipped with energy dispersive X-ray spectroscopy (EDX) and it substantiated that all the films have uniform distribution of particles with novel corn like morphology. Obviously, the length of nanocorn increases with the increase of La. The elemental composition was studied by EDX spectroscopy. Photoluminescence (PL) spectra revealed that La: ZnO thin films showed a blue shift and an enhanced PL intensity over that of the pure ZnO. The H₂ gas sensitivity of the pure and La: ZnO thin films were studied towards the concentration of 1,000 ppm. 1 wt% of La incorporated ZnO thin film showed a high sensitivity (51 %) than the pure and the higher La concentration. The increase and decrease of H₂ gas sensitivity of pure ZnO upon La incorporation is also discussed in this paper.     

果胶酶处理对各种棉的影响

果胶质存在于棉纤维的表层,由于其中85%的羧基被甲基化,故它的存在影响着原棉的吸湿性.棉纤维果胶酶处理是利用局部水解破坏表层连续性的原理.过去,棉纱通常由不同种类的棉混合加工而成.分析了果胶酶活性对棉纤维的影响以及酶处理对不同棉纤维、棉纤维重量损失以及棉纤维束纤维强度的影响.     

Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning

Roller bearing is one of the most widely used elements in rotary machines. Condition monitoring of such elements is conceived as pattern recognition problem. Pattern recognition has three main phases: feature extraction, feature selection and feature classification. Histogram features can be used for fault diagnosis of roller bearing. This paper presents the use of decision tree for selecting best few histogram features (bin ranges) that will discriminate the fault conditions of the bearing from given train samples. These features are extracted from vibration signals. A rule set is formed from the extracted features and fed to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper also presents the usage of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezoelectric transducer is captured for the following conditions – good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race fault. The histogram features were extracted and good features that discriminate the different fault conditions of the bearing were selected using decision tree. The rule set for fuzzy classifier is obtained by once using the decision tree again. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging.     

The Effect of Acoustic Field on the Thermal Conductivity of composite propellants

A study is conducted to determine the effect of acoustic field on the thermal conductivity of composite propellants based on poly- butadiene and polyurethane binders. The thermal conductivity of the propellant is determined by a specially designed equipment. The thermal conductivity measurements are made in steady state conditions and in an acoustic field of constant amplitude at various frequencies ranging from 1 kHz to 6 kHz and at different temperatures ranging from 30°C to 50°C. The results indicate that the thermal conductivities of the composite propellants increase when they are subjected to an acoustic field. The data obtained are presented in the paper. Since the burning behaviour of solid propellants is influenced also by its thermal conductivity, the present work may be of direct relevance in improving upon some of the existing burning rate and combustion models.     

Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver ailment. Signs of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s condition. Those changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis, suggesting that the cause may be alcohol instead of liver disease. Existing Two-Dimensional (2D) ultrasound data sets contain an accuracy rate of 85.9% and a 2D Computed Tomography (CT) data set of 91.02%. The most recent work on designing a Three-Dimensional (3D) ultrasound imaging system in or close to real-time is examined. In this article, a Deep Learning (DL) model is implemented and modified to fit liver CT segmentation, and a semantic pixel classification of road scenes is recommended. The architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers. A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training phase. For the normal class, we obtained 100% precision for chronic cirrhosis hepatitis (73%), offset cirrhosis (59.26%), and offensive cirrhosis (91.67%) for chronic hepatitis or cirrhosis (73,0%). The aim is to develop a Computer-Aided Detection (CAD) screening tool to detect steatosis. The results proved 98.33% exactness, 94.59% sensitivity, and 92.11% case with Convolutional Neural Networks (CNN) classification. Although the classifier’s performance did not differentiate so clearly at this level, it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve (AUC) and accuracy.