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This work aimed at developing a CRONE Controller based Speed Control of Permanent Magnet Direct Current Motors in order to achieve a robust and more effective speed control. Torque is one of the fundamental factors that affect PMDC motor’s speed control, which generate uncertainty known as disturbance torque on shaft of the motor due to increase in temperature and decrease in frequency of the system. Proportional Integral Derivative (PID) controllers are commonly used in the speed control of PMDC motors, but PID controllers often do not fully reject external disturbances in the system. As such, the CRONE controller based speed control of PMDC motors was developed to address the limitations of PID control. To achieve this, a PMDC motor model with the following system response characteristics was adopted; settling time of 6.2s, rise time of 0.79s and overshot of 30.4%. A second generation CRONE controller was developed and then applied for speed control of the PMDC motor. The performance of the developed controller was then compared with that of a Proportional Integral Derivative – Particle Swamp Algorithm (PIDPSO) controller for speed control of PMDC motor. The result of the second generation CRONE controller for the speed control of PMDC motor obtained a settling time of 0.018s and overshoot of 0.0001%. The PID-PSO controller based speed control of the PMDC motor obtained a settling time and overshoot of 0.2s and 1.87% respectively. The effectiveness of CRONE controller was visualized in frequency domain, with the stability margin of 34.5dB and 900. Simulations were carried out using MATLAB 2016a.

The monitoring, observation and security of a modern-day smart grid system is highly dependent on the accuracy of the selected state estimator. State estimation provides accurate states of a power system in a real-time frame. The conventional meters, which are mostly installed in current smart grid systems are prone to provide wrong measurements to the estimator, which reduces the efficiency of the state estimation results. Over the last two decades, new and reliable meters have been introduced to electric utilities. For example, phasor measurement units (PMU), which are devices that can provide voltage and current phasors with a very high accuracy are readily available. However, they are expensive and need to be optimally placed on the smart grids. This paper presents a technique for optimizing the placement of PMUs, with the objective of improving the estimation performance using Genetic Algorithm (GA). The weighted least absolute value (WLAV) estimator, which is one of the well-established robust estimators, is being selected for simulation. The IEEE-14 and 30-bus power systems are chosen to demonstrate the effectiveness of proposed optimal PMU placement.

A new enhanced reversible logic circuit synthesis method was developed using reversible gates that include NOT, CNOT (Feynman), Toffoli, Fredkin, Swap, and Peres gates. The synthesis method was done using newly developed genetic programming. Usually previous synthesis methods that uses genetic algorithms or other similar evolutionary algorithms suffers a problem known as blotting which is a sudden uncontrolled growth of an individual (circuit), which may render the synthesis inefficient because of memory utilization, making the algorithm difficult to continue running and eventually stack in a local minima, there for an optimized reversible circuit may not be generated. In this method the algorithm used was blot free, the blotting was carefully controlled by fixing a suitable length and size of the individuals in the population. Following this approach, the cost of generating circuits was greatly reduced giving the algorithm to reach the end of the last designated generation to give out optimal or near optimal results. The results of the circuits generated using this method were compared with some of the results already in the literature, and in many cases, our results appeared to be better in terms of gate count and quantum cost metrics.

The unique and rich details present in human fingerprints has nowadays made fingerprint verification common in a number of authentication systems. However, fraudulent fingerprints made in order to imitate the live ones with a view to deceiving these systems are now common. It is therefore important to develop a robust authentication system capable of detecting or recognizing liveness in any fingerprints presented to the authentication system. This paper therefore developed a software-based fingerprint spoof detection model using deep convolutional neural network (DCNN) features extracted from a set of live and spoof fingerprint images. The extracted features are then separately fed to two trainable classifiers namely logistic regression (LR) and support vector machine (SVM). Three variants of the SVM were used based on the training kernel: radial basis function (RBF), linear and polynomial kernels). Experiments performed on the Biometrika datasets obtained from the LivDet2009 database yielded a true accept rate (TAR) of 97.96%, a true reject rate (TRR) of 85.95%, and an average classification accuracy (ACA) of 91.94% for the LR classifier. An accuracy of 100% was obtained for the SVM trained using RBF kernel while an accuracy of 96.16% was obtained using the linear kernel. SVM trained using polynomial kernels of orders 2 and 3 yielded accuracies of 99.6163% and 99.4244% respectively. The model's accuracy was observed to decrease as the polynomials order increased. All experiments were carried out in MATLAB programming environment.

This paper analyzed the conventional one-vehicle look-ahead convoy with the view to improve the overall operation of the system. A vehicle was first modelled from first principle taking the effect of aerodynamic drag into consideration to mitigate the effects of disturbance. The proposed vehicle model was used to develop the improved topology of two-vehicle look-ahead and rear-vehicle convoy. To maximize the operating region of the convoy system, constant headway time (CHT) policy was utilized for the inter-vehicular spacing. Homogeneous vehicle convoy was assumed and it was compared with the conventional one-vehicle look-ahead to ascertain the improvement achieved. It was observed that the proposed model provides 5 m of inter-vehicular spacing with no chattering over the period of 80 s. The control vehicle's speed depends on the neighboring vehicles speeds with an improvement in reduction on acceleration and jerk of 0. 0213 ms−2 and 0.02109 ms−3 respectively.

We newly formed universal gate library, that includes NOT, CNOT (Feyman), Toffoli, Fredkin, Swap, Peres gates and a special gate called G gate. The gate G on its own is a universal gate, but using it alone in a library will result in large circuit realization. G gate combines the operations of Generalized Toffoli gates. For example a gate called G3 combines the operations of NOT, CNOT and T3 (3 - bit Toffoli) gates all in one place. The new library was used in synthesizing reversible circuits. The experiment was done using Genetic programming algorithm that is capable of allowing the choice of any type of gate library and optimizing the circuit. The results were promising because the gate complexity in the circuits were drastically reduced compared to previously attempted synthesis.

The work presents in this paper proposes to solve the existing issue of initial qubit (primary key) lost due to an attack by eavesdropper, causing the quantum bit error rate (QBER) to be high which may leaked enough information to the eavesdropper during secret key sharing in network communication. We intend to greatly reduce the QBER to a reasonable percentage that will make the key sharing communication more secured and effective. We use the dual quantum channels against the traditional single quantum channel. The dual channels give an upper hand to reduce the chances of error caused by eavesdropper. Simulations were conducted for varying the noise factor which is a measure of the presence of an eavesdropper. The results where compared between our proposed method and the one quantum channel model. Our method shows an almost half of the QBER reduced during the secret key sharing session.

The current public key infrastructure (pki) encryption/decryption methods are still vulnerable to attacks. The main issue is, the public key algorithm suffers no effective solutions which are natural within particular integer factorization, distinct logarithm and also elliptic curve interactions. It might be computationally possible for an individual to create public and private key set computationally for attacking purposes. The current single channel quantum key distribution (qkd) technique suffers high quantum bit error rates (qber) in the presence of eavesdropping attacks. Therefore this high error rate needs to be reduced for better security using two channels qkd. With the current emergence of quantum computers many security algorithm that could take classical computer years to break could now be broken in matter of seconds. Therefore stronger quantum cryptography is needed for high security networking. This paper is proposed to provide a way for solving the key transmission issues using qkd, as a new method, by providing two quantum channels to improve qber. In addition also to develop an algorithm for integrating pki and qkd in solving the secrete key sharing issues in grid environment. The research methodology will require the use of qkd devices to be placed at the end of the sender and the receiver nodes for quantum channel secrete key sharing. The outcome of the research shall provide highly enhanced network cryptography mechanism for cyber security.