Integrated micro Pirani gauge based hermetical package monitoring for uncooled VO x bolometer FPAs

This paper presents the design and fabrication of a micro Pirani gauge using VO _x as the sensitive material for monitoring the pressure inside a hermetical package for micro bolometer focal plane arrays (FPAs). The designed Pirani gauge working in heat dissipating mode was intentionally fabricated using standard MEMS processing which is highly compatible with the FPAs fabrication. The functional layer of the micro Pirani gauge is a VO _x thin film designed as a 100 × 200 μm pixel, suspended 2 μm above the substrate. By modeling of rarefied gas heat conduction using the Extended Fourier’s law, finite element analysis is used to investigate the sensitivity of the pressure gauge. Also the thermal interactions between the micro Pirani gauge and bolometer FPAs are verified. From the fabricated prototype, the measured device TCR is about −0.8% K⁻¹ and the sensitivity about 1.84 × 10⁻³ W K⁻¹ mbar⁻¹.     

Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.     

Automatic Task-Based Code Generation for High Performance Domain Specific Embedded Language

Providing high level tools for parallel programming while sustaining a high level of performance has been a challenge that techniques like Domain Specific Embedded Languages try to solve. In previous works, we investigated the design of such a DSEL—NT\(^2\)—providing a Matlab -like syntax for parallel numerical computations inside a C++ library. In this paper, we show how NT\(^2\!\) has been redesigned for shared memory systems in an extensible and portable way. The new NT\(^2\!\) design relies on a tiered Parallel Skeleton system built using asynchronous task management and automatic compile-time taskification of user level code. We describe how this system can operate various shared memory runtimes and evaluate the design by using two benchmarks implementing linear algebra algorithms.     

SVM for English semantic classification in parallel environment

Semantic analysis is very important and very helpful for many researches and many applications for a long time. SVM is a famous algorithm which is used in the researches and applications in many different fields. In this study, we propose a new model using a SVM algorithm with Hadoop Map (M)/Reduce (R) for English document-level emotional classification in the Cloudera parallel network environment. Cloudera is also a distributed system. Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews. Our English training data set has 90,000 English sentences, including 45,000 English positive sentences and 45,000 English negative sentences. Our new model is tested on the English testing data set and we achieve 63.7% accuracy of sentiment classification on this English testing data set.     

A decision tree using ID3 algorithm for English semantic analysis

Natural language processing has been studied for many years, and it has been applied to many researches and commercial applications. A new model is proposed in this paper, and is used in the English document-level emotional classification. In this survey, we proposed a new model by using an ID3 algorithm of a decision tree to classify semantics (positive, negative, and neutral) for the English documents. The semantic classification of our model is based on many rules which are generated by applying the ID3 algorithm to 115,000 English sentences of our English training data set. We test our new model on the English testing data set including 25,000 English documents, and achieve 63.6% accuracy of sentiment classification results.     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.     

Efficient Multimedia Distribution in Source Constraint Networks

In recent years, the number of peer-to-peer (P2P) applications has increased significantly. One important problem in many P2P applications is how to efficiently disseminate data from a single source to multiple receivers on the Internet. A successful model used for analyzing this problem is a graph consisting of nodes and edges, with a capacity assigned to each edge. In some situations however, it is inconvenient to use this model. To that end, we propose to study the problem of efficient data dissemination in a source constraint network. A source constraint network is modeled as a graph in which, the capacity is associated with a node, rather than an edge. The contributions of this paper include (a) a quantitative data dissemination in any source constraint network, (b) a set of topologies suitable for data dissemination in P2P networks, and (c) an architecture and implementation of a P2P system based on the proposed optimal topologies. We will present the experimental results of our P2P system deployed on PlanetLab nodes demonstrating that our approach achieves near optimal throughput while providing scalability, low delay and bandwidth fairness among peers.     

Unsupervised active contours driven by density distance and local fitting energy with applications to medical image segmentation

This study presents an efficient variational region-based active contour model for segmenting images without priori knowledge about the object or background. In order to handle intensity inhomogeneities and noise, we propose to integrate into the region-based local intensity model a global density distance inspired by the Bhattacharyya flow. The local term based on local information of segmented image allows the model to deal with bias field artifact, which arises in data acquisition processes. The global term, which is based on the density distance between the probability distribution functions of image intensity inside and outside the active contour, provides information for accurate segmentation, keeps the curve from spilling, and addresses noise in the image. Intensive 2D and 3D experiments on many imaging modalities of medical fields such as computed tomography, magnetic resonance imaging, and ultrasound images demonstrate the effectiveness of the model when dealing with images with blurred object boundary, intensity inhomogeneities, and noise.     

Experimental and numerical investigation of the debonding interface between an old concrete and an overlay

The paper focuses on debonding propagation along an interface, notably on the major influence of the interlocking between the two faces of the debonding interface. The aim of the study is to obtain the data necessary for relevant and efficient debonding modelling. The work associates experiment and simulation with the purpose of quantifying the interlocking along the interface. The overlay material investigated was a fibre reinforced mortar (FRM). Direct tension tests of notched FRM specimens were firstly conducted to obtain the tensile strength and the residual normal stress—crack width relationship. Its Young's modulus was determined from compression tests. The substrate-overlay interface was investigated by direct tension tests and flexure tests performed on composite substrate-overlay specimens. The direct tension tests provided the interface tensile strength and the relationship between debonding-opening and residual normal tensile stress. Three point flexural static tests informed on the structural behaviour of the interface. The debonding interface propagation was monitored using a video-microscope with a maximum enlargement of ×175. Using the identified and quantified parameters, modelling of the above mentioned static tests was carried out by the finite elements method using CAST3M code developed in France by CEA (Centre for Atomic Energy). The comparison of modelling and experiment results shows a good coherence and proves the important role of interlocking on the debonding mechanism.     

Design and optimization of electrostatic fluid accelerators

Electrostatic air propulsion is a promising technology with such potential applications as energy-efficient ventilation, air sterilization, cooling of electronics, and dehumidification. The challenges of existing designs include the need to increase air speed, backpressure, energy efficiency, and heat exchange capability. The ultimate goal of this direction of research is to create multi-channel energy efficient ionic pumps. In the described project, a single cell analysis is conducted in this study as a building block of future designs. This paper presents the numerical simulation and experimental results of electrostatic fluid accelerators. This study was conducted for the purpose of optimizing device characteristics through the control of the electric field distribution. Simulations were performed for multiple collector electrode voltage distributions. A method to quantify the change in pump performance between different voltage distributions is presented. The influence of space charge on pump performance is also discussed. A significant improvement of air velocity generated by optimized electrostatic fluid accelerators has been achieved using the proposed approach.