Estimation of Defects Based on Defect Decay Model: ED^{3}M

An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product's required maintenance. Here, a new approach called ED³M is presented that computes an estimate of the total number of defects in an ongoing testing process. ED³M is based on estimation theory. Unlike many existing approaches the technique presented here does not depend on historical data from previous projects or any assumptions about the requirements and/or testers' productivity. It is a completely automated approach that relies only on the data collected during an ongoing testing process. This is a key advantage of the ED³M approach, as it makes it widely applicable in different testing environments. Here, the ED³M approach has been evaluated using five data sets from large industrial projects and two data sets from the literature. In addition, a performance analysis has been conducted using simulated data sets to explore its behavior using different models for the input data. The results are very promising; they indicate the ED³M approach provides accurate estimates with as fast or better convergence time in comparison to well-known alternative techniques, while only using defect data as the input.     

直觉线性μ-演算中的合成推理

讨论了以基于前缀封闭集合的Heyting代数的直觉解释的线性μ-演算(IμTL)作为描述“假设-保证”的逻辑基础的问题,提出了一个基于IμTL的“假设-保证”规则.该规则比往常应用线性时序逻辑(LTL)作为规范语言的那些规则具有更好的表达能力,扩展了对形如“always ?”等安全性质的“假设-保证”的范围,具备更一般的“假设-保证”推理能力及对循环推理的支持.     

Power efficient scheme for self localization in wireless sensor networks

With the tremendous applications of the wireless sensor network, self-localization has become one of the challenging subject matter that has gained attention of many researchers in the field of wireless sensor network. Localization is the process of assigning or computing the location of the sensor nodes in a sensor network. As the sensor nodes are deployed randomly, we do not have any knowledge about their location in advance. As a result, this becomes very important that they localize themselves as manual deployment of sensor node is not feasible. Also, in WSN the main problem is the power as the sensor nodes have very limited power source. This paper provides a novel solution for localizing the sensor nodes using controlled power of the beacon nodes such that we will have longer life of the beacon nodes which plays a vital role in the process of localization as it is the only special nodes that has the information about its location when they are deployed such that the remaining ordinary nodes can localize themselves in accordance with these beacon node. We develop a novel model that first finds the distance of the sensor nodes then it finds the location of the unknown sensor nodes in power efficient manner. Our simulation results show the effectiveness of the proposed methodology in terms of controlled and reduced power.     

Structural study of titanium oxide films synthesized by ion beam-assisted deposition

The application of titanium dioxide (TiO(2)) films as surgical implant coatings for antibiotic attachment depends crucially on their available surface area and thus their surface morphology and crystallinity. Here, we report our fabrication of high Wenzel ratio TiO(2) films targeted to increase the film surface area using the ion beam-assisted deposition (IBAD) technique at high-deposition temperatures (approximately 610 degrees C). The modulation of the films' surface morphology was accomplished by varying the chemical identity of the concurrent ion beams bombarded on the films during the e-beam evaporation process. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) were employed to investigate the surface morphology of the as-deposited films. X-ray diffractometry (XRD) revealed that these nanocrystalline films primarily consist of anatase phase TiO(2). Wenzel ratio, the ratio of the actual surface area to the projected area, of IBAD films prepared with argon, oxygen, and nitrogen ion beams was measured to be 1.52, 1.31 and 1.49, respectively. The effect of the differences in chemical reactivity and ion size of these three type ion beams are discussed to explain the present results.     

Towards a standard in assessment of bone cutting for total knee replacement

Arthroplasty outcome is influenced by the 'quality' of bone preparation for implant insertion. Surgeons face increasing choices of technique and instrumentation, yet clinical scoring methods assess the overall outcome and patient satisfaction but not the bone cuts directly. 'Quality management' of bone reshaping is needed to evaluate different bone cutting methods and computer assisted orthopaedic surgery (CAOS) systems. Analyses and experiments in this study were formulated for measurement and computation of four quantitative characteristics of bone preparation 'quality' and produced a highly condensed index for each. These represented (a) surface finish of cuts, (b) implant fit/looseness possible with the cut shape, (c) implant location/misalignment, and (d) accuracy of individual planar cuts. Assessment of synthetic bone cuts verified the robustness of the method for wide application in arthroplasty intraoperatively, in vitro and for comparing navigation systems.     

Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In-Sensor Neuromorphic Computation

Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra-thin (<3 nm) doped indium oxide (In₂O₃) layers are engineered to demonstrate a monolithic two-terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof-of-concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real-time, in-sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand.     

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.     

Robust Multimodal Biometric System Based on Feature Level Fusion of Optimiseddeepnet Features

Wireless Personal Communications - Multimodal biometric systems combine feature knowledge from multiple traits to overcome shortcomings of unimodal systems. However, most of the traditional...     

Unital Design Based Location Service for Subterranean Network Using Long Range Topology

Wireless Personal Communications - Wireless Underground Networks comprise the ability to constantly monitor several physical parameters such as ground temperature, water level and soil condition,...