Abdul Ghani Naim

Discrete-time analogues of integrodifferential equations modelling bidirectional neural networks

We formulate discrete-time analogues of integrodifferential equations modelling bidirectional neural networks studied by Gopalsamy and He. The discrete-time analogues are considered to be numerical discretizations of the continuous-time networks and we study their dynamical characteristics. It is shown that the discrete-time analogues preserve the equilibria of the continuous-time networks. By constructing a Lyapunov-type sequence, we obtain easily verifiable sufficient conditions under which every solution of the discrete-time analogue converges exponentially to the unique equilibrium. The sufficient conditions are identical to those obtained by Gopalsamy and He for the uniqueness and global asymptotic stability of the equilibrium of the continuous-time network. By constructing discrete-time versions of Halanay-type inequalities, we obtain another set of easily verifiable sufficient conditions for the global exponential stability of the unique equilibrium of the discrete-time analogue. The latter sufficient conditions have not been obtained in the literature of continuous-time bidirectional neural networks. Several computer simulations are provided to illustrate the advantages of our discrete-time analogue in numerically simulating the continuous-time network with distributed delays over finite intervals.

Performance evaluation and optimization of nested high resolution weather simulations

Weather models with high spatial and temporal resolutions are required for accurate prediction of meso-micro scale weather phenomena. Using these models for operational purposes requires forecasts with sufficient lead time, which in turn calls for large computational power. There exists a lot of prior studies on the performance of weather models on single domain simulations with a uniform horizontal resolution. However, there has not been much work on high resolution nested domains that are essential for high-fidelity weather forecasts. In this paper, we focus on improving and analyzing the performance of nested domain simulations using WRF on IBM Blue Gene/P. We demonstrate a significant reduction (up to 29%) in runtime via a combination of compiler optimizations, mapping of process topology to the physical torus topology, overlapping communication with computation, and parallel communications along torus dimensions. We also conduct a detailed performance evaluation using four nested domain configurations to assess the benefits of the different optimizations as well as the scalability of different WRF operations. Our analysis indicates that the choice of nesting configuration is critical for good performance. To aid WRF practitioners in making this choice, we describe a performance modeling approach that can predict the total simulation time in terms of the domain and processor configurations with a very high accuracy (<8%) using a regression-based model learned from empirical timing data.

Effect of Sine-Squared Thermal Boundary Condition on Augmentation of Heat Transfer in a Triangular Solar Collector Filled with Different Nanofluids

Numerical study of heat transfer phenomena has become a major field of research nowadays. In engineering applications, different boundary conditions arise which have various effects on heat transfer characteristics. For the present work, a triangular-shape cavity has been analyzed for the sine-squared thermal boundary condition which is common in practical cases. The augmentation of heat transfer has been done by introducing a nanofluid inside the cavity. Different solid volume fractions (φ = 0, 0.05, 0.1, 0.2) of water-CuO, water-Al2O3, and water-TiO2 nanofluid have been tested for the cavity with a wide range of Rayleigh number (Ra = 105–108) and for dimensionless time (τ = 0.1 to 1). The Galerkin weighted residual finite-element method has been applied for the numerical solution, and numerical accuracy has been checked by code validation. The heat transfer augmentation for different nanofluids has been done in the light of local (NuL) and overall Nusselt number (Nuav), and the results have been presented with streamline, isotherm, and related contours, in graphs and charts. It has been found that variable boundary condition has significant effect on flow and thermal fields and increase of solid volume fraction enhances the heat transfer.

Magnetohydrodynamic time-dependent computational natural convection flow, heat and mass transfer in inclined semi-circular enclosures

Purpose The purpose of this paper is to make a numerical analysis on unsteady analysis of natural convection heat and mass transfer to obtain flow field, temperature distribution, and concentration distribution. Design/methodology/approach A finite element method is applied to solve governing equations of natural convection in curvilinear-shaped system for different parameters as thermal Rayleigh numbers (103=RaT=106), inclination angle (0°=φ=60°) and Hartmann numbers (0=Ha=100). Findings Both magnetic field and inclination angle can be used as control parameter on heat and mass transfer. Flow strength decreases almost 100 percent between Ha=0 and Ha=100 on behalf of the higher values of thermal Rayleigh number. Originality/value The originality of this work is to application of magnetic field on time-dependent natural convection flow, heat and mass transfer for curvilinear geometry.

Smart Sensing and Biofeedback for Vertical Jump in Sports

Vertical jump activity is a measurement of an athlete for different movements in sports required jumping. The analysis of this activity is subject to athlete’s capability to Drop Vertical Jump (DVJ) landing upon achieved optimal Vertical Jump Height (VJH). This article discusses applying smart sensing mechanisms for DVJ and VJH as assistive tools to analyze DVJ and VJH and provides biofeedback for athlete during DVJ for safe landing and VJH to reach desired target. While optimization of VJH is the primary target of any sport, DVJ causes injuries on lower extremity. Hence, appropriate vibrotactile feedback during DVJ allows injury prevention and smart watches as IoTs can be used for biofeedback visualization during VJH monitoring for performance enhancement. Thus, different smart sensing mechanisms are required to analyze lower extremity kinematics jointly with the influence of muscles during DVJ and VJH. In this research, Inertia Measurement Units (IMUs), vibrotactors, Qualisys Motion Capture System and Bio-capture system consisted of muscle activity measurement sensors are integrated for DVJ and VJH measurements. The overall system design presented allows athletes to customize and to re-configure for different sport regimes requirements involving different DVJ and VJH. Integrated low powered wearable IMUs, bio-capture system, vibrotactors and smart watch are cost effective and require little infrastructure with no influence on natural human movement due to their total weight less than 500 g. This chapter will give a comprehensive system architecture of wearable embedded devices for DVJ and VJH measurements and for biofeedback during DVJ and VJH.

Array of Things for Smart Health Solutions Injury Prevention, Performance Enhancement and Rehabilitation

Data visualization on wearable devices using cloud servers can provide solutions for personalized healthcare monitoring of general public leading to smart nation. The objective of this research is to develop personalized healthcare IoT assistive devices/tools for injury prevention, performance enhancement and rehabilitation using an Intelligent User Interfacing System. It consists of Array of Things (AoT) which interconnects hybrid prototypes built using different wearable measurement and instrumentations multimodel sensor system for transient and actual health status and classification. Android platforms have been used to prove the success of AoT using national athletes and soldiers with whom were permitted the implementation of a knowledge base encapsulated reference/benchmarking massive retrieve, retain, reuse and revise health pattern sets accessible via case base reasoning cloud storage. Two case studies were conducted for injury prevention and rehabilitation and performance enhancement of soldiers and athletes using smart health algorithms. Validation and testing were carried out using Samsung Gear S3 smart watches in real time.

A database-driven neural computing framework for classification of vertical jump patterns of healthy female netballers using 3D kinematics–EMG features

Classification of athletes’ performance in vertical jump (VJ) tests is a recommended practice at various stages of athlete fitness development and performance enhancement. The practice is, however, currently conducted subjectively and mainly based on measures of the vertical jump height (VJH) attained in standardized VJ tests. The current study presents an intelligent integrated classification framework (IICF) for classification of athlete performance in single-leg (SL) and double-leg (DL) standing VJ tests based on lower extremity (LE) biomechanical data. Biomechanical data consisted of three-dimensional (3D) kinematics and electromyography (EMG) features generated from ankle–knee joints and eight LE muscles of 13 healthy female national netball players (subjects) obtained during six trials of SL-left leg (SLLL), SL-right leg (SLRL), and DL VJ tests. Each participating subject had prior VJ classification by the trainer as either excellent or very good in each of the three VJ tests. IICF introduced in this work utilizes an integration of the scalable and interoperable relational database management system (MySQLDB) and artificial neural networks (ANNs). Integrated pattern sets containing the extracted features (EF) obtained from first three VJ trials data were randomly partitioned into design and test data sets and ANN implemented using fully connected multilayer perceptron feedforward neural networks (MLP-FFNN) with three different training algorithms. Subjects’ prior VJ classifications were used as MLP-FFNN target outputs. A second classifier is trained using support vector machine (SVM) using three different kernel mapping functions and performances between MPL-FFNN and SVM classifiers compared on both training and independent test pattern sets. The test pattern sets comprise of EF generated from the latter three VJ trials data stored in MySQLDB. The average classification accuracy ( F -measure) achieved by the optimally trained MLP-FFNN classifier on independent test pattern sets across all the three VJ activities was 93.33% (86.67–96.77%), whereas SVM classifier’s was 82.5% (73.33–87.5%). Through a custom-made web-based interface having backend integration of MySQLDB and the optimally trained MLP-FFNN, classification of individual subjects’ pattern sets from either VJ activities is also enabled. Individual subjects’ classification results can then be compared with prior classifications by the trainer, and differences were highlighted. IICF introduced in this work has demonstrated its feasibility as an objective complementary assessment tool for trainers during conducting of SL and DL vertical jump tests for netball players and athletes in general. Descriptive statistics for the entire experimental data sets’ features used in this study are also presented.