Longitudinal and lateral slip control of autonomous wheeled mobile robot for trajectory tracking

This research formulates a path-following control problem subjected to wheel slippage and skid and solves it using a logic-based control scheme for a wheeled mobile robot (WMR). The novelty of the prop...     

Effect of Al-doping on lithium nickel oxides

The electronic structures of LiNiO₂, LiAlO₂ and LiNi_0.5Al_0.5O₂ are investigated using density-functional theory (DFT) in the local-density approximation (LDA). The effect of lithium intercalation and the influence of aluminium doping on the structure and electrochemical properties of LiNiO₂ are discussed. An increase in the open circuit voltage is observed with Al doping in LiNi_0.5Al_0.5O₂ compound.     

Numerical damage prediction in deep-drawing of sheet metals

This paper presents an efficient damage model based on the strong coupling of both anisotropic elasto-plasticity and isotropic ductile damage. The elasto-plasticity is modeled with mixed non-linear work hardening considering both isotropic and kinematic effects. The developed model is then coupled with the finite element method using ABAQUS finite element code. First, the formulation of the proposed damage model is presented. A comparison between numerical and experimental results is presented, in the context of the square cup deep-drawing benchmark test of Numisheet 1993. The results demonstrate the capability of the model to predict where and when the damaged zones will appear in the workpiece during the forming operation.     

Design of an intelligent prediction-based neural network controller for multi-scroll chaotic systems

An indispensable part of the precise control of multi-scroll chaotic systems, model identification has received increasing attention in recent years. Because of plant uncertainty and unmodeled dynamics, conventional control methods cannot guarantee a sufficiently high-performance for stabilizing multi-scroll chaotic systems. In an effort to tackle the matter better, we propose an intelligent controller called the adaptive neural network prediction-based controller (NN-PbC ). The specified neural network is trained with the system model, which is extracted from a time series. In actual practice, the data are divided into two sets. One set is used for training and the other set for testing. In fact, a generalized NN will perform well for both training and testing data. The prediction-based control method is then applied to the obtained neural network model to stabilize the multiple equilibrium points. The stability of the closed-loop system is proven. In addition, simulation examples on two typical multi-scroll chaotic systems are presented to demonstrate the effectiveness of the proposed controller.     

Constructing accurate and robust HMM/GMM models for an Arabic speech recognition system

Conventional Hidden Markov Model (HMM) based Automatic Speech Recognition (ASR) systems generally utilize cepstral features as acoustic observation and phonemes as basic linguistic units. Some of the most powerful features currently used in ASR systems are Mel-Frequency Cepstral Coefficients (MFCCs). Speech recognition is inherently complicated due to the variability in the speech signal which includes within- and across-speaker variability. This leads to several kinds of mismatch between acoustic features and acoustic models and hence degrades the system performance. The sensitivity of MFCCs to speech signal variability motivates many researchers to investigate the use of a new set of speech feature parameters in order to make the acoustic models more robust to this variability and thus improve the system performance. The combination of diverse acoustic feature sets has great potential to enhance the performance of ASR systems. This paper is a part of ongoing research efforts aspiring to build an accurate Arabic ASR system for teaching and learning purposes. It addresses the integration of complementary features into standard HMMs for the purpose to make them more robust and thus improve their recognition accuracies. The complementary features which have been investigated in this work are voiced formants and Pitch in combination with conventional MFCC features. A series of experimentations under various combination strategies were performed to determine which of these integrated features can significantly improve systems performance. The Cambridge HTK tools were used as a development environment of the system and experimental results showed that the error rate was successfully decreased, the achieved results seem very promising, even without using language models.     

Optimal barrier coverage for critical area surveillance using wireless sensor networks

Intrusion detection using barrier coverage is one of many applications existed in wireless sensor networks. The main purpose of using barrier coverage is to monitor the borders of a specific area against the intruders that are trying to penetrate this critical area by ensuring the total coverage with a low cost and extending the lifetime of the network, many solutions have been proposed in the literature in order to solve the coverage problem in wireless sensor networks, which became a vital field of research. In this paper, we present a new technique based on geometric mathematical models, in order to guarantee the total coverage of our deployed barriers with a minimum possible number of sensors. The idea is to calculate the number of sensors adequate to cover a barrier before deployment. We then formulate the problem to minimize the number of sensors to be deployed to properly cover a barrier; the calculation makes it possible to solve this problem in polynomial using our own heuristic. Additionally, we propose a new mechanism for ensuring a fault‐tolerant network by detecting the faulty sensors and select other suited sensors to close the existing gaps inside the barriers and detecting the sensors whose energy is low before the failure. The obtained simulation results prove the effectiveness of the proposed algorithms.     

Structural and optical properties of MoO3 and V2O5 thin films prepared by Spray Pyrolysis

MoO₃ and V₂O₅ thin films were prepared on glass substrates by Spray Pyrolysis technique at a substrate temperature of 423 K. The precursor solutions were obtained by varying the concentrations of MoCl₅ and VCl₃ in bi-distilled water. The structural investigation conducted by X-ray diffraction showed that MoO₃ and V₂O₅ thin films were polycrystalline with orthorhombic structure. The optical properties studied in the 300–2500 nm range suggest that the thin film behaviours are related to bound electronic states. The optical band gaps have been estimated from slopes of ln(hν) versus hν plots of MoO₃ and V₂O₅ films were 3.35 and 2.44 eV, respectively. The electrical conductivity was measured using four probes method.     

An Energy-Efficient Protocol Using an Objective Function & Random Search with Jumps for WSN

Wireless Sensor Networks (WSNs) have hardware and software limitations and are deployed in hostile environments. The problem of energy consumption in WSNs has become a very important axis of research. To obtain good performance in terms of the network lifetime, several routing protocols have been proposed in the literature. Hierarchical routing is considered to be the most favorable approach in terms of energy efficiency. It is based on the concept parent-child hierarchy where the child nodes forward their messages to their parent, and then the parent node forwards them, directly or via other parent nodes, to the base station (sink). In this paper, we present a new Energy-Efficient clustering protocol for WSNs using an Objective Function and Random Search with Jumps (EEOFRSJ) in order to reduce sensor energy consumption. First, the objective function is used to find an optimal cluster formation taking into account the ratio of the mean Euclidean distance of the nodes to their associated cluster heads (CH) and their residual energy. Then, we find the best path to transmit data from the CHs nodes to the base station (BS) using a random search with jumps. We simulated our proposed approach compared with the Energy-Efficient in WSNs using Fuzzy C-Means clustering (EEFCM) protocol using Matlab Simulink. Simulation results have shown that our proposed protocol excels regarding energy consumption, resulting in network lifetime extension.     

Bandgap-assisted surface-plasmon sensing

Surface-plasmon resonance (SPR) is a sensing technique widely used for its label-free feature. However, its sensitivity is contingent on the divergence angle of the excitation beam. The problem becomes pronounced for compact systems when a low-cost LED is used as the light source. When the Kretschmann configuration with a periodically modulated surface is used, a bandgap appears in the surface plasmon dispersion relation. We recognize that the high density of modes on the edge of the surface-plasmon bandgap permits the coupling of a wider range of incidence angles of excitation photons to surface-plasmon polaritons than what is possible in the traditional Kretschmann configuration. Here, the numerical simulation illustrates that the sensitivity, detection limit, and reflectivity minimum of an amplitude-based SPR bandgap-assisted surface-plasmon sensor are almost independent of the divergence angle. Two different bandgap structures are compared with the Kretschmann configuration using the rigorous coupled-wave analysis technique. The results indicate that the bandgap-assisted sensing outperforms traditional SPR sensing when the angular standard deviation of the excitation beam is above 1 degree.