Dynamic analysis and drilling degrees of freedom

Finite element methods for dynamic analysis employing elements with drilling degrees of freedom are presented. The formulation is based on a variational principle in which displacements and rotations are interpolated independently. The issue of zero masses corresponding to rotational degrees of freedom is addressed and techniques for defining consistent and lumped rotational mass matrices are presented.     

A knowledge-based system for sensor selection

This paper presents a Knowledge-based system (KBS) developed to allow users, who may not be knowledgeable about sensors, to select sensors suitable for their specific needs. The KBS runs on a micro-computer. The selection criteria are user specified and are based on the desired measurement parameters. The system output includes all of the operational and dimensional parameters of the recommended sensor, price, and vendor information.     

Reusable silicon IP cores for discrete wavelet transform applications

Architectures and methods for the rapid design of silicon cores for implementing discrete wavelet transforms over a wide range of specifications are described. These architectures are efficient, modular, scalable, and cover orthonormal and biorthogonal wavelet transform families. They offer efficient hardware utilization by exploiting a number of core wavelet filter properties and allow the creation of silicon designs that are highly parameterized, including in terms of wavelet type and wordlengths. Control circuitry is embedded within these systems allowing them to be cascaded for any desired level of decomposition without any interface glue logic. The time to produce chip designs for a specific wavelet application is typically less than a day and these are comparable in area and performance to handcrafted designs. They are also portable across a wide range of silicon foundries and suitable for field programmable gate array and programmable logic data implementation. The approach described has also been extended to wavelet packet transforms.     

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.     

Measurement of birefringence for optical recording of disk substrates

We experimentally investigate the birefringence of bare and coated substrates for magneto-optical recording, using ellipsometry at wavelengths of 632.8 and 780 nm. The polarization rotation and ellipticity of reflected or transmitted light are measured for different incident angles and for different orientations of the incident linear polarization. The measured data are then fitted by the MULTILAYER computer program, which solves the Maxwell equations for a plane wave propagating in a multilayer structure. This approach makes it possible to determine, with high accuracy, the orientations of the principal axes of the substrate and the corresponding refractive indices. The results show that one of the principal axes is always along the substrate's normal direction, but the orientations of the in-plane principal axes can be much different from the radial and track directions. A special feature of the ellipsometers that were used is that a glass hemisphere is placed in contact with the substrate to eliminate refraction of the incident beam. This enables a maximum propagation angle of 70° (with respect to the normal) in the substrate and hence increases the measurement sensitivity. Certain anomalies have been observed, which we believe are associated with the variation of birefringence properties along the thickness direction.     

Retroreflecting ellipsometer for measuring the birefringence of optical disk substrates

A retroreflecting ellipsometer has been constructed for measuring the birefringence of optical disk substrates. In contrast to conventional ellipsometers with two mechanical arms, this system has only one arm along which both the incident and reflected beams travel. This construction eliminates the mechanical limitations of conventional ellipsometers, thereby permitting normal incidence on the sample. In addition, the single arm is adjustable in two dimensions, with the polar incident angle, θ(inc), varying from 0° to 70°, and the azimuthal incident angle, Φ(inc), varying from 0° to 360°. The condition of normal incidence permits accurate measurement of in-plane birefringence. The adjustability of both θ(inc) and Φ(inc) is necessary for the measurement of possible tilts of the index ellipsoid, and also for the variation of birefringence through the substrate thickness. Measurement results showing the useful features of the equipment are presented. The optics of the hemispherical assembly used for retroreflection as well as for the elimination of undesirable refractions are also studied by use of the ZEMAX lens design program.     

FSE2R: An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocol in USN

The 3D Underwater Sensor Network (USNs) has become the most optimistic medium for tracking and monitoring underwater environment. Energy and collision are two most critical factors in USNs for both sparse and dense regions. Due to harsh ocean environment, it is a challenge to design a reliable energy efficient with collision free protocol. Diversity in link qualities may cause collision and frequent communication lead to energy loss; that effects the network performance. To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing (FSE2R) is proposed. Our proposal’s key idea is based on computation of node distance from the sink, Residual Energy (RE) of each node and Signal to Interference Noise Ratio (SINR). The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision. The novel proposal compares with existing protocols like H2AB, DEEP, and E2LR to achieve Quality of Service (QoS) in terms of throughput, packet delivery ratio and energy consumption. The comparative analysis shows that FSE2R gives on an average 30% less energy consumption, 24.62% better PDR and 48.31% less end-to-end delay compared to other protocols.     

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.     

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.     

Measurements and simulations of differential phase-tracking signals in optical disk data storage

The tracking-error signal generated in differential phase detection (DPD) is theoretically analyzed and numerically simulated. Experimental measurements of the DPD signal versus the tracking offset obtained on compact read-only and phase-change disks are also reported. The signal is sensitive to the geometry of the marks, intersymbol interference along the track, and cross-track cross talk. A characteristic parameter is introduced to relate the DPD signal to the reflectivities of the mark and the spacer. For read-only disks such as CD-ROM and DVD-ROM, the magnitude of the DPD signal does not seem to depend on the reflectivity of the disks, nor does it depend on the pit depth. As for the influence of the various aberrations on the DPD signal, coma in the cross-track direction is shown to give rise to significant tracking offset, whereas defocus and spherical aberrations reduce the magnitude of the DPD signal appreciably.