Data-stream-based global event monitoring using pairwise interactions

The problem of global state observation is fundamental to distributed systems and to the analysis of data streams. Many interactions in distributed systems can be analyzed in terms of the building block formed by the pairwise interactions of intervals at two processes. Considering causality-based pairwise interactions by which two processes may interact with each other, there are 40 orthogonal interaction types. For each pair of processes (_Pi,_Pj)$(P_i,P_j)$, let interaction type _ri,j$r_{i,j}$ be of interest. This paper examines the problem: “If a global state of interest to an application is specified in terms of such pairwise interaction types, one per pair of processes, how can such a global state be detected?” A solution identifies a global state in which the interaction type specified for each process pair is satisfied. This paper formulates the specific conditions on the communication structures to determine which of the intervals being examined at any time may never satisfy the stipulated interaction type for that pair of processes, and therefore that interval(s) need no longer be considered as forming a part of any solution. Based on this theory, the paper proposes two on-line distributed algorithms to solve the problem.     

Characterizing the nonlinear behaviour of double walled carbon nanotube based nano mass sensor

This work deals with the evaluation of nonlinear behaviour of a curved double walled carbon nanotube (DWCNT) when used for mass sensing applications. The DWCNT is considered to be doubly clamped at a source and a drain. To judge the nonlinear behaviour, equations of motion have been derived using Euler beam theory and Hamilton principle, considering nonlinear van der Waals interaction nonlinear oscillations of a double walled carbon nanotube excited harmonically near its primary resonance are considered. The nonlinear free vibration of double-walled carbon nanotubes based on the elasticity theory is studied in this paper. Modelling of the weak van der Waals force of attraction between the inner and outer tubes is represented using a spring element. The equation of motion involves four nonlinear terms due to the curved geometry and the stretching of the central plane. The dynamic response of the double walled carbon nanotube based mass sensor is analyzed in the context of the time response, Poincaré maps, and fast Fourier transformation diagrams. The results show the appearance of instability and chaos in the dynamic response as the mass on carbon nanotube is changed. The appearance of regions of periodic, subharmonic, and chaotic behavior is observed to be strongly dependent on mass, inner and outer tubes and the geometric imperfections of double walled carbon nanotube.

     

Information intermediaries for emergency preparedness and response: A case study from public health

Information intermediaries play a critical role in information supply chains for emergency preparedness. Yet, their responsibilities have not been adequately examined in the literature. Using a state public health department as an exemplar, we explore the roles and challenges experienced by one intermediary organization as it faced the unique challenges of deploying a public health emergency preparedness system. We further discuss the influence of stakeholder participation and commitment, inter-organizational collaboration, issues related to organizational structure and resources, and the challenges specific to developing and institutionalizing an IT system for emergency preparedness. Based on the public health case, a set of propositions focused on trust, coordination, information sharing and incentive alignment are developed to illustrate the role of information intermediaries.     

Unsupervised hyperspectral band selection using ranking based on a denoising error matching approach

In this paper, we focus on utilizing the image denoising method for ranking of significant bands in hyperspectral imagery. We make use of the fact that the denoising error of bands varies with the significant information content of the bands in hyperspectral imagery. The denoising error is computed for each band individually and compared using a matching parameter with the denoising error of a reference image. The reference image is selected to be the first principal component corresponding to the maximum information. Three matching parameters including mutual information (MI), correlation coefficient (r) and the structural similarity index (SSIM) were used for ranking the bands based on the match with the denoising error of the reference image. The proposed algorithm is tested using three datasets, namely, Indian Pines, Salinas and Dhundi. The Indian Pines and Salinas datasets were acquired from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor and comprised rural and agricultural area. The Dhundi dataset of Hyperion comprises mostly of features corresponding to snow-covered mountainous regions. To assess the accuracy of the proposed method, a supervised classification was carried out using a random forest classifier with 20% training pixels selected randomly from the ground reference. The proposed method yielded significantly better results determined by the kappa coefficient (κ) of 0.756, 0.910 and 0.996 for the Indian Pines, Salinas and Dhundi datasets, respectively, over several other state of the art methods. The classification results of the proposed method also yielded better results than those obtained by the state-of-the-art methods for hyperspectral band selection.     

Four-legged intelligent mobile autonomous robot

This paper presents the design and development of a four-legged mobile robot with intelligent sensing and decision-making capabilities. Multiple sensors with embedded knowledge bases and learning capabilities are used in a novel approach towards environmental perception and reaction. These sensors continuously monitor the environment as well as their own operating parameters. Priority is given to any one or a group of sensors based on prevailing environmental conditions. Intelligent sensing is shown to be the key towards a high degree of autonomy for a mobile robot. Nicknamed Flimar, this robot has the ability to function at varying degrees of intelligence made possible by an object-oriented architecture with embedded intelligence at various levels. This architecture is shown to be conducive towards incremental learning. Each of the four legs has three degrees of freedom, i.e. Flimar has a total of 12 motors on its four legs. Flimar can walk and turn without dragging or skidding, and also turns about its center of gravity with a zero radius. Flimar responds to light, sound and touch in different ways, based on prevailing environmental conditions. The overall goal of the paper is to present a novel walking principle and control architecture for a walking robot.     

Low power highly sensitive platform for gas sensing application

This paper reports a low power miniaturized MEMS based integrated gas sensor with 36.84 % sensitivity (ΔR/R₀) for as low as 4 ppm (NH₃) gas concentration. Micro-heater based gas sensor device presented here consumes very low power (360 °C at 98 mW/mm²) with platinum (Pt) micro-heater. Low powered micro-heater is an essential component of the metal oxide based gas sensors which are portable and battery operated. These micro-heaters usually cover less than 5 % of the gas sensor chip area but they need to be thermally isolated from substrate, to reduce thermal losses. This paper elaborates on design aspects of micro fabricated low power gas sensor which includes ‘membrane design’ below the microheater; the ‘cavity-to-active area ratio’; effect of silicon thickness below the silicon dioxide membrane; etc. using FEM simulations and experimentation. The key issues pertaining to process modules like fragile wafer handling after bulk micro-machining; lift-off of platinum and sensing films for the realization of heater, inter-digitated-electrodes (IDE) and sensing film are dealt with in detail. Low power platinum microheater achieving 700 °C at 267 mW/mm² are fabricated. Temperature calculations are based on experimentally calculated thermal coefficient of resistance (TCR) and IR imaging. Temperature uniformity and localized heating is verified with infrared imaging. Reliability tests of the heater device show their ruggedness and repeatability. Stable heater temperature with standard deviation (σ) of 0.015 obtained during continuous powering for an hour. Cyclic ON–OFF test on the device indicate the ruggedness of the micro-heater. High sensitivity of the device for was observed for ammonia (NH₃), resulting in 40 % response for ~4 ppm gas concentration at 230 °C operating temperature.     

Classification of complex environments using pixel level fusion of satellite data

The present study reports classification and analysis of composite land features using fusion images obtained by fusing two original hyperspectral and multispectral datasets. The high spatial-spectral resolution, multi-instrument and multi-period satellite images were used for fusion. Three pixel level fusion based techniques, Color Normalized Spectral Sharpening (CNSS), Principal Component Spectral Sharpening Transform (PCSST) and Gram-Schmidt Transform (GST), were implemented on the datasets. Performance evaluations of three fusion algorithms were done using classification results. The Support Vector Machine (SVM) and Gaussian Maximum Likelihood Classification (MLC) were used for classification using five types of images, viz. hyperspectral, multispectral and three fused images. Number of classes considered was eight. Sufficient number of ground field data for each class has also been acquired which was needed for supervise based classification. The accuracy was improved from 74.44 to 97.65% when the fused images were considered with SVM classifier. Similarly, the results were improved from 69.25 to 94.61% with original and fused data using MLC classifier. The fusion image technique was found to be superior to the single original image and the SVM is better than the MLC method.

     

A classification and systematic review of product line feature model defects

Product line (PL)-based development is a thriving research area to develop software-intensive systems. Feature models (FMs) facilitate derivation of valid products from a PL by managing commonalities and variabilities among software products. However, the researchers in academia as well as in the industries experience difficulties in quality assessment of FMs. The increasing complexity and size of FMs may lead to defects, which outweigh the benefits of PL. This paper provides a systematic literature review and key research issues related to the FM defects in PL. We derive a typology of FM defects according to their level of importance. The information on defects’ identification and explanations are provided with formalization. Further, corrective explanations are presented which incorporates various techniques used to fix defects with their implementation. This information would help software engineering community by enabling developers or modelers to find the types of defects and their causes and to choose an appropriate technique to fix defects in order to produce defect-free products from FMs, thereby enhancing the overall quality of PL-based development.

     

High performance a‐IGZO thin‐film transistors with mf‐PVD SiO2 as an etch‐stop‐layer

In this work, we report on high‐performance bottom‐gate top‐contact (BGTC) amorphous‐Indium‐Gallium‐Zinc‐Oxide (a‐IGZO) thin‐film transistor (TFT) with SiO₂ as an etch‐stop‐layer (ESL) deposited by medium frequency physical vapor deposition (mf‐PVD). The TFTs show field‐effect mobility (μ_FE) of 16.0 cm²/(V.s), sub‐threshold slope (SS^?1) of 0.23 V/decade and off‐currents (I_OFF) < 1.0 pA. The TFTs with mf‐PVD SiO₂ ESL deposited at room temperature were compared with TFTs made with the conventional plasma‐enhanced chemical vapor deposition (PECVD) SiO₂ ESL deposited at 300 °C and at 200 °C. The TFTs with different ESLs showed a comparable performance regarding μ_FE, SS^?1, and I_OFF, however, significant differences were measured in gate bias‐stress stability when stressed under a gate field of +/?1 MV/cm for duration of 10⁴ s. The TFTs with mf‐PVD SiO₂ ESL showed lower threshold‐voltage (V_TH) shifts compared with TFTs with 300 °C PECVD SiO₂ ESL and TFTs with 200 °C PECVD SiO₂ ESL. We associate the improved bias‐stress stability of the mf‐PVD SiO₂ ESL TFTs to the low hydrogen content of the mf‐PVD SiO₂ layer, which has been verified by Rutherford‐Back‐Scattering‐Elastic‐Recoil‐Detection technique.     

Optimizing reliability-based robust design model using multi-objective genetic algorithm

Reliability-based robust design optimization (RBRDO) is one of the most important tools developed in recent years to improve both quality and reliability of the products at an early design stage. This paper presents a comparative study of different formulation approaches of RBRDO models and their performances. The paper also proposes an evolutionary multi-objective genetic algorithm (MOGA) to one of the promising hybrid quality loss functions (HQLF)-based RBRDO model. The enhanced effectiveness of the HQLF-based RBRDO model is demonstrated by optimizing suitable examples.