M. B. Mua’zu

Bio: M. B. Mua’zu is an academic researcher from Ahmadu Bello University. The author has contributed to research in topics: Weighting & Inverted pendulum. The author has an hindex of 3, co-authored 8 publications receiving 28 citations.

Weighted Artificial Fish Swarm Algorithm with Adaptive Behaviour Based Linear Controller Design for Nonlinear Inverted Pendulum

Abstract: The Linear Quadratic Regulator (LQR) performance depends largely on the design choice of state and control weighting matrices (Q and R). However, these matrices are usually selected by the designer through several trial and error iterative processes. This might not guarantee robustness and may increase computational time. This paper proposes a new approach for the optimal determination of the LQR weighting matrices based on weighted artificial fish swarm algorithm (wAFSA), which is then used to obtain an optimal controller for a dynamic nonlinear Inverted Pendulum. In this paper, we first introduce an approach called inertial weight into the standard Artificial Fish Swarm Algorithm (AFSA) to adaptively select its parameters (visual and step sizes) thereafter, the modified algorithm was used to determine the optimal values of LQR weighting matrices which was then used to stabilize a non-linear inverted pendulum. Simulation results showed that the proposed method is efficient in determining the weighting matrices of LQR in comparison with the conventional trial-and error approach.

Stabilization of inverted pendulum system using intelligent Linear Quadratic Regulator controller

Abstract: One of the classical problem in dynamics and control theory, which has being widely used as a benchmark for testing control algorithms, such as Linear Quadratic Regulator (LQR) is the balancing of inverted pendulum. The performance of LQR depends largely on the design choice of state and control weighting matrices (Q & R). However, these matrices are usually selected by the designer through a trial and error iterative process which might not guarantee robustness and may increase computational time. To overcome this, we propose a new approach for the optimal determination of the LQR weighting matrices based on weighted artificial fish swarm algorithm (wAFSA). The designed controller is then used to obtain an optimal controller for a dynamic nonlinear Quadruple Inverted Pendulum (QIP). In this paper, we first introduce an approach called inertial weight into the standard Artificial Fish Swarm Algorithm (AFSA) to adaptively select its parameters (visual & step sizes) thereafter, the modified algorithm was used to determine the optimize values of LQR weighting matrices randomly. The optimized values of the weighting matrices were also determined using the standard AFSA and the standard Artificial Bee Colony (ABC) algorithm. This was then used to stabilize the QIP system. Simulation results showed that the proposed method is efficient in determining the weighting matrices of LQR and minimizes time-to-solution in comparison with the conventional trial-&-error approach.

Development of a modified bat algorithm using elite opposition — Based learning

Abstract: This research work presents the development of a modified bat algorithm (mBA) using elite opposition — based learning. The bat algorithm (BA), which is a nature inspired meta-heuristic algorithm, works on the basis of the echolocation behavior of bat. It, however, has a poor exploration capability leading to it easily getting stuck in local optima. The mBA is developed by modifying the BA with elite opposition — based learning (EOBL) in order to diversify the solution search space and the inertial weight in order to improve its exploitation capability. The performance of the proposed mBA was compared with that of the standard BA using seven benchmark optimization test functions. The simulation results showed that the mBA is superior to the standard BA by obtaining global optimal result of most of the test functions. All simulations were carried out using MATLAB R2013b.

RMWSaug: Robust Multi-window Spectrogram Augmentation Approach for Deep Learning based Speech Emotion Recognition

Abstract: Data scarcity and speech degradation due to environmental noise are two significant issues in the modelling and deployment speech emotion recognition (SER) systems. Deep learning-based SER systems overfits during modelling because of scarce training samples. Although recent attempts to tackle these issues, simultaneously, using data augmentation have yielded promising results, they are not robust enough to handle speech degradation due to real environmental noise. Thus, there is the need to further improve the classification performance of deployed SER systems. This work proposes an SER system based on a novel robust multi-window spectrogram augmentation (RMWSaug) scheme and, transfer learning to handle these aforementioned issues simultaneously. First, the RMWSaug scheme utilizes the concept of multi-window and multi-noise conditioning of clean speech samples to create additional speech spectrograms required for training. Then, pretrained networks are adapted for speech emotion recognition and finetuned with the generated training datasets to develop a model robust to speech degradation due to noise. Thereby, improving the classification performance in the wild. The Interactive Emotional Dyadic Motion Capture (IEMOCAP) database was selected as benchmark dataset for evaluating the proposed SER system. Experimental results show that the proposed SER system outperformed existing methods when deployed in the wild. The proposed SER system can be deployed to predict the emotions of speakers conversing virtually on online platforms.

Bacteria Interactive Cost and Balanced-Compromised Approach to Clustering and Transmission Boundary-Range Cognitive Routing In Mobile Heterogeneous Wireless Sensor Networks.

Abstract: The improvement of stable, energy-efficient mobile-based clustering and routing protocols in wireless sensor networks (WSNs) has become indispensable so as to develop large-scale, versitale, and adaptive applications. Data is gathered more efficiently and the total path length is shortened optimally by means of mobile sink (MS). Two algorithms as bacterial interaction based cluster head (CH) selection and energy and transmission boundary range cognitive routing algorithm with novel approach for heterogeneous mobile networks are proposed in this study. The more reliable and powerful CH selection is made with the greedy approach that is based on the interaction fitness value, energy node degree, and distance to adjacent nodes in a compromised manner. The best trajectories, thanks to intersection edge points of the visited CHs, are obtained in the proposed routing algorithm. In this way, the MS entry to transmission range boundaries of the CH has been a sufficient strategy to collect information. As in energy model, we adopt energy consumption costs of listening and sensing channel as well as transmit and receive costs. Comprehensive performance analyzes have been seriously carried out via the Matlab 2016a environment. We validate that the proposed scheme outperforms existing studies in terms of several performance metrics as simulations.

Road Traffic Signal Control and Management System : A Survey

Abstract: From the past few decades, technology is out breaking with its significant application in each and every field. Gradually, technology is becoming integral part of human life and effectively used to address many societal issues. One such issue is traffic in urban areas that leads to elongated waiting time at road intersections. An Intelligent Traffic Signal System (ITSS) can mitigate the urban traffic congestion. Already many researchers are working towards and proposed various solutions or techniques. The solution involves combination of different technologies such as Fuzzy Logic, Wireless Sensor Network, Machine Learning, Artificial Neural Networks, Internet of Things, Microcontrollers and high level computing environments. The proposed ideas are capable of adapting to the traffic demands based on real time situations on the road, resulting in efficient utilization of the available road infrastructure. In this paper, an attempt has been made to classify the traffic control signal system deployed at the intersections based on technology adopted and various research contribution for developing ITSS are also summarised.