Design & development of a novel house hold compressing device for a wheelie bin

This project consists of researching, designing, developing, prototyping and testing a device that clamps onto a wheelie bin and easily compresses the waste inside. Once attached, by pulling on the lever, the device will compress the contents inside allowing for disposal of more waste. The design is easily assembled, light and sturdy enough so that any household individual can use it. Prototype testing was performed to identify the required forces necessary to compress a sufficient amount of waste and identifying which materials or types of waste can be compressed. The prototype testing showed that the device is capable of compressing the normal household wastes (compressing plastic bottles, cardboards, tin cartons) up to 45% off the original volume. The paper discusses the design, development and testing of this device to appreciate the proof of the concept as well as discussing it in terms of saving the cost of recycling as well

Multi-step derivative-free preconditioned Newton method for solving systems of nonlinear equations

Preconditioning of systems of nonlinear equations modifies the associated Jacobian and provides rapid convergence. The preconditioners are introduced in a way that they do not affect the convergence order of parent iterative method. The multi-step derivative-free iterative method consists of a base method and multi-step part. In the base method, the Jacobian of the system of nonlinear equation is approximated by finite difference operator and preconditioners add an extra term to modify it. The inversion of modified finite difference operator is avoided by computing LU factors. Once we have LU factors, we repeatedly use them to solve lower and upper triangular systems in the multi-step part to enhance the convergence order. The convergence order of m-step Newton iterative method is m + 1. The claimed convergence orders are verified by computing the computational order of convergence and numerical simulations clearly show that the good selection of preconditioning provides numerical stability, accuracy and rapid convergence.Peer ReviewedPostprint (author's final draft

Feature Representation for Online Signature Verification

Biometrics systems have been used in a wide range of applications and have improved people authentication. Signature verification is one of the most common biometric methods with techniques that employ various specifications of a signature. Recently, deep learning has achieved great success in many fields, such as image, sounds and text processing. In this paper, deep learning method has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information Forensics and Securit

Machine Learning with Abstention for Automated Liver Disease Diagnosis

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.Comment: Preprint version before submission for publication. complete version published in proc. 15th International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan. http://ieeexplore.ieee.org/document/8261064

Achieving Robust Self-Management for Large-Scale Distributed Applications

Autonomic managers are the main architectural building blocks for constructing self-management capabilities of computing systems and applications. One of the major challenges in developing self-managing applications is robustness of management elements which form autonomic managers. We believe that transparent handling of the effects of resource churn (joins/leaves/failures) on management should be an essential feature of a platform for self-managing large-scale dynamic distributed applications, because it facilitates the development of robust autonomic managers and hence improves robustness of self-managing applications. This feature can be achieved by providing a robust management element abstraction that hides churn from the programmer. In this paper, we present a generic approach to achieve robust services that is based on finite state machine replication with dynamic reconfiguration of replica sets. We contribute a decentralized algorithm that maintains the set of nodes hosting service replicas in the presence of churn. We use this approach to implement robust management elements as robust services that can operate despite of churn. Our proposed decentralized algorithm uses peer-to-peer replica placement schemes to automate replicated state machine migration in order to tolerate churn. Our algorithm exploits lookup and failure detection facilities of a structured overlay network for managing the set of active replicas. Using the proposed approach, we can achieve a long running and highly available service, without human intervention, in the presence of resource churn. In order to validate and evaluate our approach, we have implemented a prototype that includes the proposed algorithm

Simultaneous detection of the nonlinear restoring and excitation of a forced nonlinear oscillation: an integral approach

We address in this article, how to calculate the restoring characteristic and the excitation of a nonlinear forced oscillating system. Under the assumption that the forced nonlinear oscillator has a periodic solution with period, we constructed a system of linear equations by introducing time-dependent multipliers. The periodicity assumption helps simplify the system of linear equations. The stability and uniqueness are also presented for the inverse problem. Numerical testing is conducted to show the effectiveness of our presented methodology.Peer ReviewedPostprint (author's final draft

Wormhole Structures in Logarithmic-Corrected R2R^2 Gravity

This paper is devoted to find the feasible shape functions for the construction of static wormhole geometry in the frame work of logarithmic-corrected $R^2$ gravity model. We discuss the asymptotically flat wormhole solutions sustained by the matter sources with anisotropic pressure, isotropic pressure and barotropic pressure. For anisotropic case, we consider three shape functions and evaluate the null energy conditions and weak energy conditions graphically along with their regions. Moreover, for barotropic and isotropic pressures, we find shape function analytically and discuss its properties. For the formation of traversable wormhole geometries, we cautiously choose the values of parameters involved in $f(R)$ gravity model. We show explicitly that our wormhole solutions violates the non-existence theorem even with logarithmic corrections. We discuss all physical properties via graphical analysis and it is concluded that the wormhole solutions with relativistic formalism can be well justified with logarithmic corrections.Comment: 12 pages, 8 figure

A Parameterized multi-step Newton method for solving systems of nonlinear equations

We construct a novel multi-step iterative method for solving systems of nonlinear equations by introducing a parameter. to generalize the multi-step Newton method while keeping its order of convergence and computational cost. By an appropriate selection of theta, the new method can both have faster convergence and have larger radius of convergence. The new iterative method only requires one Jacobian inversion per iteration, and therefore, can be efficiently implemented using Krylov subspace methods. The new method can be used to solve nonlinear systems of partial differential equations, such as complex generalized Zakharov systems of partial differential equations, by transforming them into systems of nonlinear equations by discretizing approaches in both spatial and temporal independent variables such as, for instance, the Chebyshev pseudo-spectral discretizing method. Quite extensive tests show that the new method can have significantly faster convergence and significantly larger radius of convergence than the multi-step Newton method.Peer ReviewedPostprint (author's final draft