Kabiru O. Akande

Application of computational intelligence technique for estimating superconducting transition temperature of YBCO superconductors

We developed CIM for estimating TC of doped YBCO superconductors.The developed CIM is characterized with high degree of accuracy.The results of the developed CIM agree well with the experimental results.TC of any doped YBCO superconductor can be accurately estimated using CIM. Yttrium barium copper oxide (YBCO) is a high temperature superconductor with excellent potential for long distance power transmission applications as well as other applications involving generation of high magnetic field such as magnetic resonance imaging machines in hospitals. Among the uniqueness of this material is its perpetual current carrying ability without loss of energy. Practical applications of YBCO superconductor depend greatly on the value of the superconducting transition temperature (TC) attained by YBCO superconductor upon doping with other external materials. The number of holes (i.e. doping) present in an atom of copper in CuO2 planes of YBCO superconductor controls its TC. Movement of the apical oxygen along CuO2 planes due to doping gives insight to the way of determining the effect of doping on TC using the bound related quantity (lattice parameter) that is easily measurable with reasonable high precision. This work employs excellent predictive and generalization ability of computational intelligence technique via support vector regression (SVR) to develop a computational intelligence-based model (CIM) that estimates the TC of thirty-one different YBCO superconductors using lattice parameters as the descriptors. The estimated superconducting transition temperatures agree with the experimental values with high degree of accuracy. The developed CIM allows quick and accurate estimation of TC of any fabricated YBCO superconductor without the need for any sophisticated equipment.

Estimation of average surface energies of transition metal nitrides using computational intelligence technique

Several properties of transition metal nitrides (TMN) that make them useful in many applications are closely related to the state of their surfaces. Meanwhile, high melting points which characterize these materials make the determination of their surface energies experimentally difficult. This work presents a computational intelligence technique using support vector regression (SVR) to establish, for the first time, a complete database of average surface energies of all members of TMN series. SVR-based model was developed by training and testing SVR with best parameters obtained through test-set–cross-validation technique using thirty-five experimental data of periodic metals. The developed SVR-based model was used to estimate average surface energies of 3d, 4d and 5d-TMN, and the obtained results agree well with the existing theoretical values. Simple and effective computational approach of the developed model together with its accurate estimation of average surface energies of all the members of TMN series contributes to the uniqueness of this developed model over the existing theoretical methods.

Impact of timing jitter on the performance of carrier amplitude and phase modulation

Carrier amplitude and phase modulation (CAP) is a highly spectral-efficient multilevel modulation scheme renowned for its implementation simplicity as it offers a low complexity approach to increase spectral efficiency and system throughput. These characteristics has seen CAP being implemented as an alternative to the well-known discrete multitone (DMT). However, the sensitivity of CAP to timing jitter is high due to its use of pulse shaping which eliminate the need for carrier modulation and carrier frequency recovery in its tranceiver architecture. In this paper, the impact of timing jitter on the performance of CAP technique is investigated. It is shown that with increasing timing jitter, the bit error rate (BER) performance of CAP-4 degrades and reaches an error floor when the timing jitter (τ) reaches 20% of the symbol duration, T. Although the timing jitter effect is observed to reduce with increasing roll-off factor, α, the resulting performance degradation increases with the data constellation size. With a 0.02T timing jitter, CAP with 64 data constellation points (CAP-64) and 0.15 roll-off factor reaches an error floor at a BER of 2 × 101. The results of this paper show that although CAP offer implementation simplicity, timing synchronization is a critical requirement in its receiver design.

Estimation of melting points of fatty acids using homogeneously hybridized support vector regression

This work develops a hybridized support vector regression (HSVR)-based model for accurate estimation of melting points of fatty acids using their molecular weights and the number of carbon–carbon double bond as descriptors. The development of HSVR-based model is characterized with two stages. The first stage involves training and testing SVR using test-set-cross validation technique with molecular weights and the number of carbon–carbon double bond as descriptors, while the second stage utilizes the estimated melting points obtained from the first stage as descriptor for further training and testing of SVR. The proposed hybrid system therefore demonstrates a better predictive and generalization ability than ordinary SVR. Furthermore, the melting points of sixty-two fatty acids estimated using the proposed HSVR-based model show persistence closeness with the experimental values than the results of other existing predictive models for fatty acids melting points estimation such as Guijie et al. model and Guendouzi model. The developed HSVR-based model is also characterized with higher value of coefficient of correlation and lower value of mean absolute error than that of the existing predictive models. Superiority of the developed HSVR-based model over the existing predictive models in terms of the ease of obtaining its descriptors and the accuracy of its estimates is advantageous to unravel estimation challenges associated with determination of fatty acids melting points.

Modeling of Curie temperature of manganite for magnetic refrigeration application using manual search and hybrid gravitational-based support vector regression

Magnetic refrigeration (MR) combines many unique features such as low cost, high efficiency and environmental friendliness which make it preferred to the conventional gas compression system of refrigeration. MR employs manganite-based material due to its high magnetocaloric effect as well as tunable Curie temperature (

Synchronization of carrierless amplitude and phase modulation in visible light communication

{T}_{\mathrm{C}}

A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir

TC). For effective utilization of this technology,