Mohammed A. Khasawneh

A biometric-secure e-voting system for election processes

In this paper we propose a multifaceted online e-voting system. The proposed system is capable of handling electronic ballots with multiple scopes at the same time, e.g., presidential, municipal, parliamentary, amongst others. The system caters for integrity of an election process in terms of the functional and non-functional requirements. The functional requirements embedded in the design of the proposed system warrant well-secured identification and authentication processes for the voter through the use of combined simple biometrics. The design of the system guarantees that no votes in favor of a given candidate are lost, due to improper tallying of the voting counts, with the proper incorporation of system FLAGpsilas. Transparency of voting follows through in all phases of an election process to assure the voter that his/her vote went in favor of his/her candidate of choice. Besides its main functional properties, the proposed system is designed to cater for several essential nonfunctional requirements. Of utmost importance are the requirements for correctness, robustness, coherence, consistency, and security. To verify the robustness and reliability of the proposed system, intensive computer simulations were run under varying voting environments, viz. voter density, voter inter-arrival times, introduced acts of malice, etc. Results of the simulations show that security and performance of the system are according to expectations. These results provide the proper grounds that would guide the decision maker in customizing the proposed system to fit his particular voting needs.

Analytical development of the MMAXNLMS algorithm

In this paper an adaptive algorithm with reduced complexity is analysed for the white Gaussian input case. The new analysis is extended for the proposed case where updating includes more than one component of the weight vector. The new algorithm, which updates the weights corresponding to the element sizes of the data vector with the largest magnitude, is compared with the case where the updated weights are chosen randomly according to a uniform density function. Analysis is performed for both cases and the results are verified via computer simulations.

A modified variable degree variable step size LMS algorithm

In this paper, the data reusing technique which utilizes the LMS algorithm is further investigated. It is shown that a near-optimal performance can be achieved if the number of data reusing runs, M, is made variable. Two modifications are proposed in this paper. The first one is based on updating each individual component of the weight vector with a different step size. This makes the modified algorithm more capable of tracking weight variations than the original algorithm. The second modification makes the algorithm behave initially as normalized LMS with unity step and at the steady state the algorithm behaves like the LMS with a constant step size. Computer simulations show that the first modification gives the algorithm the capacity to perform better for correlated signals than the original algorithm. Also the second modification is found to give the algorithm the ability to perform very close to optimal for uncorrelated signals.

A secure novel sensor fusion architecture for nuclear applications

We propose a novel architecture to fuse and synthesize data from multiple sensors. This architecture, based on wireless communication of data, can be applied to monitor system integrity, to help in system control, and for personnel guidance through potentially hazardous radiation areas in nuclear applications. The proposed architecture employs sensor fusion in a way that would lead to improved decision making. The sensor suites used are interconnected serially to warrant more robust sensing strategies while leveraging spatially correlated data. They are also fed in parallel into a data fusion center using wireless technologies to ensure enhanced system reliability. While the proposed architecture can readily be tailored for specific applications in the nuclear industry such as for plant monitoring and automated decision and control, it is also designed to track and guide personnel away from radiation-contaminated zones.

A new forced LMS-based adaptive algorithm utilizing the principle of potential energy

Abstract In this paper, we propose a new gradient-based adaptive algorithm, in which the conventional LMS update recursion is modified by introducing a damping factor, which emulates the force acting on a free-moving object in a gravitational field. The contribution of the damping factor being introduced is controlled by an exponential function, which eliminates its effect whenever the squared error signal is less than a given threshold. Furthermore, we show via stability analysis that the control parameter can assume both positive and negative values. Implementing a negative control parameter transforms our algorithm to a time-varying Leaky-type LMS algorithm, which exhibits bias-free performance, unlike that for the conventional Leaky-LMS algorithm. With positive contribution, the algorithm exhibits improved convergence speeds with a smoothing property in stationary, nonstationary power and correlated noisy environments. Several simulation examples are persented to verify the validity of the new algorithm compared with the LMS, including echo cancellation in telephone networks and noise cancellation in transient and PCG signals.

System identification using the fast LMS-sine algorithm

The authors extend the desirable features inherent in the gradient LMS (least-mean-square) algorithm and explore an approach aimed at improving its convergence rate. They modify the LMS algorithm by adding a nonlinear term in the update recursion. The resulting algorithm emulates the dynamics of a planar pendulum, and in the steady-state it reduces to an LMS algorithm with much smoother learning curves. Additionally, the new algorithm has a much faster convergence rate than existing gradient algorithms.<<ETX>>

Uranium fuel as byproduct of phosphate fertilizer production

The world has substantial unconventional energy resources in phosphate rock deposits and sea water which contain vast amounts of dilute uranium.

A security-embedded infrastructure for Tele-Traffic Speed Control

Abstract In this paper we introduce a novel approach, relying on intelligent engineering, whereby the maximum speed limit at which vehicles on the road can cruise is controlled from some central or distributed facility. The system, as designed, leverages the use of the ubiquitous cellular infrastructure to cut down the costs involved that would otherwise accrue as a result of the need to build a dedicated traffic control system. In the new system maximum speed limits are transmitted from a Central Control Facility (CCF) to all stretches of roads and highways dispersed across urban areas in a highly dynamic manner. The system uses information arriving via a dynamic feedback system on prevailing weather conditions, road conditions, and bulk of traffic, amongst others, before it would broadcast maximum speed limit information to various destinations. When deployed in its final release, this will be done using a highly automated system with little human intervention. The work presented in this paper elaborates the TTC Network Design and RSU Network Distribution for the Tele-Traffic Speed Control System, to remotely manage, enforce, and control the maximum speed limit allowed on road stretches in rural and urban areas for different vehicle categories. The new system has been deployed successfully in a laboratory controlled environment on three vehicle categories; one category representing passenger vehicles, another buses and commuter transport, while the third representing trucks and freight traffic. It is expected that when the system is fully deployed on the wireless infrastructure, speed-related traffic accidents will witness a drop by more than 65–70% according to known accident statistics.

The application of polynomial discriminant function classifiers to isolated Arabic speech recognition

We apply polynomial discriminant function classifiers for isolated-word speaker-independent Arabic digit recognition. The performance of the polynomial classifier is evaluated for different implementations. We also provide a performance comparison between the polynomial classifier and dynamic time warping (DTW). The polynomial classifier is found to outperform DTW in many aspects such as recognition rate, and computational and memory requirements.

TIME-FREQUENCY DISTRIBUTIONS BASED ON GENERALIZED CONE-SHAPED KERNELS FOR THE REPRESENTATION OF NONSTATIONARY SIGNALS

Abstract A generalized category of cone-shaped kernels is proposed. Analysis of the kernel in the 2-D time, 2-D frequency, and ambiguity domains is performed. The shape of this kernel in the 2-D time plane is bow-tie, which effectively suppresses cross-terms especially in the frequency direction. In the 2-D frequency plane, the shape of the kernel is of a lateral inhibition form, which enhances spectral peaks when convolved with the instantaneous spectral correlation. By investigating the new kernel in the ambiguity domain, it is shown that the resulting distribution has many desirable properties that encourage its use as a time-frequency signal analysis tool. By proper selection of kernel parameters, a family of kernels is obtained, hence, providing many kernels which may be used in the analysis of different types of signals, the most important of which are the Born-Jordan and ZAM (Zhao, Atlas and Marks) kernels. Experimental results on a simulated test signal that represents two short-duration Gaussian pulses show the advantage of the proposed distribution in comparison with ZAM and Born-Jordan distributions.