Mohamed Mohandes

Estimation of global solar radiation using artificial neural networks

This paper introduces a neural network technique for the estimation of global solar radiation. There are 41 radiation data collection stations spread all over the kingdom of Saudi Arabia where the radiation data and sunshine duration information are being collected since 1971. The available data from 31 locations is used for training the neural networks and the data from the other 10 locations is used for testing. The testing data was not used in the modeling to give an indication of the performance of the system in unknown locations. Results indicate the viability of this approach for spatial modeling of solar radiation.

A neural networks approach for wind speed prediction

This paper introduces neural networks technique for wind speed prediction and compares its performance with an autoregressive model. First, we studied the statistical characteristics of mean monthly and daily wind speed in Jeddah, Saudi Arabia. The autocorrelation coefficients are computed and the correlogram is found compatible with the real diurnal variation of mean wind speed. The stochastic time series analysis is found to be suitable for the description of autoregressive model that involves a time lag of one month for the mean monthly prediction and one day for the mean daily wind speed prediction. The results on a testing data indicate that the neural network approach outperforms the AR model as indicated by the prediction graph and by the root mean square errors.

Wind power cost assessment at twenty locations in the kingdom of Saudi Arabia

The Kingdom of Saudi Arabia has vast open land and hence has great potential of harnessing solar and wind energy sources for domestic and industrial use. This study proposes to assess wind power cost per kWh of electricity produced using three types of wind electric conversion systems at 20 locations within the Kingdom. These sites cover the eastern, central, and western regions. Hourly values of wind speed recorded for periods of 5.5–13 years (between 1970–1982, in most cases) were used for all 20 locations. Wind duration curves were developed and utilized to calculate the cost per kWh of electricity generated from three chosen wind-machines.

Radial basis function networks for contingency analysis of bulk power systems

Radial basis function networks (RBFNs) are used for contingency evaluation of bulk power system. The motivation behind this work is to exploit the nonlinear mapping capabilities of RBFN in estimating line loading and bus voltage of a bulk power system following a contingency. Unlike most of the available neural networks based techniques, the proposed method utilizes the potential of RBFN in planning studies. The performance of the RBFN is compared with a standard AC load flow algorithm.

Arabic sign language recognition using the leap motion controller

Sign language is important for facilitating communication between hearing impaired and the rest of society. Two approaches have traditionally been used in the literature: image-based and sensor-based systems. Sensor-based systems require the user to wear electronic gloves while performing the signs. The glove includes a number of sensors detecting different hand and finger articulations. Image-based systems use camera(s) to acquire a sequence of images of the hand. Each of the two approaches has its own disadvantages. The sensor-based method is not natural as the user must wear a cumbersome instrument while the imagebased system requires specific background and environmental conditions to achieve high accuracy. In this paper, we propose a new approach for Arabic Sign Language Recognition (ArSLR) which involves the use of the recently introduced Leap Motion Controller (LMC). This device detects and tracks the hand and fingers to provide position and motion information. We propose to use the LMC as a backbone of the ArSLR system. In addition to data acquisition, the system includes a preprocessing stage, a feature extraction stage, and a classification stage. We compare the performance of Multilayer Perceptron (MLP) neural networks with the Nave Bayes classifier. Using the proposed system on the Arabic sign alphabets gives 98% classification accuracy with the Nave Bayes classifier and more than 99% using the MLP.

Image-Based and Sensor-Based Approaches to Arabic Sign Language Recognition

Sign language continues to be the preferred method of communication among the deaf and the hearing-impaired. Advances in information technology have prompted the development of systems that can facilitate automatic translation between sign language and spoken language. More recently, systems translating between Arabic sign and spoken language have become popular. This paper reviews systems and methods for the automatic recognition of Arabic sign language. Additionally, this paper highlights the main challenges characterizing Arabic sign language as well as potential future research directions.

GSM-based wireless home appliances monitoring & control system

This paper presents the emerging applications of the GSM technology. Using the public GSM networks, a home automation system has been proposed, designed, implemented and tested. The design of a stand-alone embedded system that can monitor and control home appliances locally using built-in input and output peripherals is presented. Remotely, the system allows the homeowner monitoring and controlling the house appliances via the mobile phone set by sending commands in the form of SMS messages and receiving the appliances status. The GSM modem provides the communication media between the homeowner and the system by means of SMS messages. The system software driver is also developed using an interactive C programming language platform.

Image based arabic sign language recognition

In this paper we propose an image based system for Arabic Sign Language recognition. The recognition stage is performed using a Hidden Markov Model. We have used a Gaussian skin color model to detect the signer’s face. The detected face region is then used as a reference to track the hands movement using region growing from the sequence of images comprising the signs. A number of features are then selected from the detected hand regions across the sequence of images. Such features are then used as input to the HMM. The proposed system achieved a recognition accuracy of 98% for a data set of 50 signs.

Arabic Sign Language Recognition an Image-Based Approach

In this paper we propose an image based system for Arabic sign language recognition. A Gaussian skin color model is used to detect the signers face. The centroid of the detected face is then used as a reference to track the hands movement using region growing from the sequence of images comprising the signs. A number of features are then selected from the detected hand regions across the sequence of images. The recognition stage is performed using a hidden Markov model. The proposed system achieved a recognition accuracy of about 93% for a data set of 300 signs with leave one out method.

Wireless Sensor Networks for Pilgrims Tracking

Every year, and for five days, about three million pilgrims gather in the small city of Makkah, Saudi Arabia, to perform the rituals of Hajj (Pilgrimage). Tracking the movement of such a large number of people is crucial to the pilgrims themselves and the authorities managing the whole event. This letter reports a real-time pilgrim tracking system that has been designed and implemented. The system relies on a dedicated delay-tolerant wireless sensor network (WSN). This WSN is interfaced to the Internet through gateway(s) available from an internet service provider (ISP). Energy efficiency, robustness, and reliability are key factors in the design of the system. Each pilgrim is given a mobile sensor unit which includes a GPS chip, a microcontroller, and antennas. A network of fixed units is installed in the Holy area for receiving and forwarding data. Periodically, each mobile unit sends its user identification (UID), latitude, longitude, and a time stamp. A central server maps the latitude and longitude information on a geographical information system (GIS). The developed system can be used to track a specific or a group of pilgrims. The developed system was tested during the last two pilgrim seasons. The pilot system was able to successfully track all pilgrims who participated in the experiment.