Abdel-Rahman Al-Qawasmi

Efficient performance of COFDM-based DVB-T

European terrestrial digital video broadcasting (DVB-T) standard has been launched based on the well knows modulation technique; Orthogonal Frequency Division Multiplexing (OFDM), which when used in conjunction with channel coding is known as COFDM. This paper will show the efficiency of COFDM in different DVB-T environments based on a compliant DVB-T simulation. An efficiency performance study of COFDM systems-based DVB-T over three different channels has introduced. During this study, there were two different channel coding schemes used with two different coding rates, in addition to the use of two different modulation techniques. Also, using a strong modulation technique, such as 16QAM, will show better performance than the less power ones, such as the QPSK.

Speaker Identification Wavelet Transform based method

One of the most important signal processing method in digital signal processing discipline is speaker identification method (SIM). Because of the difficult nature of speech signals and their fast variation with time, the wavelet transform is used to reduce the complexity of such signals. In this paper two identification methods are presented based on Continuous Wavelet Transform CWT. The first method is the male and female identification method and the second is the SIM. The obtained results showed a high recognition rate about 98%, which makes the speaker identification wavelet transform based method to be very important in digital security systems applications.

Speaker identification system using Wavelet Transform and neural network

The speech enhancement that is concerned with the processing of corrupted or noisy speech signal in order to improve the quality of speaker recognition system is presented. This idea of noise cancellation for the speech signal is processed to increase the speaker recognition system robustness. The presented system is divided into two blocks: 1. Discrete Wavelet Transform DWT and Adaptive Linear Neuron (Adaline) Enhancement Method (DWADE). 2. Wavelet Gender Discrimination (WGD) and Speaker Recognition using Discrete Wavelet Transform (DWT) Power Spectrum Density (PSD). The tested signal is enhanced up to 15 dB by Wavelet Transform and Adaline Enhancement Method that increases the speaker recognition rate. The accomplished speaker recognition rate is about 95%. Back Propagation Feed Forward Neural Network (BPFFNN) perceptron classification methods are used.

QRS complex detection based on Symmlets wavelet function

Wavelet theory is inspired the development of a strong methodology for signal processing and can be used as a good tool for non-stationary electrocardiogram (ECG signal) detection. In this paper a QRS complex detection method is proposed based on wavelet transform (WT) with Symmlets function. The proposed method show sharp results for ECG detection parameters. The fiducial points are easily detected and the results show that the sensitivity of the proposed detector is 99.8% and the specificity is 98.6%. The results obtained in this paper are based on real ECG signal.

A novel islanding detection method for three-phase photovoltaic generation systems

The increasing penetration of distributed generation (DG) into distribution systems may lead to unacceptable system conflicts. This is due to the fact that the present distribution system is designed as a passive and radial network configuration. One of these serious problems is that of the islanding phenomenon. This paper presents a new anti-islanding method that based on a periodical current magnitude variation. The proposed method leads to a voltage magnitude variation when islanding occurs in a three-phase grid-connected photovoltaic generation systems. Validation of models and control algorithms is carried out through computer simulations PSIM software.

Wavelet Transform based multistage speaker feature tracking identification system using Linear Prediction Coefficient

In this paper Wavelet Transform (WT) in its two forms Continuous and Discrete are used to create text-dependent robust to noise speaker recognition system. The research intends to investigate a high accuracy of identification the speech signal of very difficult nature that is non- stationary. Three methods are used to extract the essential speaker features based on Continuous, Discrete Wavelet Transform and Linear Prediction Coefficient (LPC). To have better identification rate three measurement methods are used: Percentage rms Difference (PRD), Correlation Coefficient (CC), and Statically Deformation Determination Coefficient (SDDC). 95% identification rate is accomplished. The presented system in this paper depends on multi-stage features extracting due to its better accuracy. The system works with excellent capability of features tracking even when the tested signals are very noisy with −32dB SNR. This is accomplished because of multistage features tracking based system using Wavelet Transform, which is suitable for non-stationary signal.

Energy Efficiency Audit Based on Wireless Sensor and Actor Networks: Air-Conditioning Investigation

This paper investigates the energy performance of new design air-conditioning system combined with energy audit in KSA governmental building since air conditioning is responsible for more than 60 % of electricity consumption. Alternative solutions are provided. Based on the results, the old conditioners will be replaced for 113 rooms in three floors of the MUAB. The applied methodology uses Wireless Sensor and Actuator Networks (WSAN) for sensing and measuring technique characteristics of the conditioners and compares them with the ones available on the market, which were selected according to ASHRAE Standard 62.1-2007 specified by the manufacturers. Energy balance examination proves that operational conditioners have greater electric consumption and power factor comparing to the known standards. Energy audit applied in the MUAB provided a saving of 10% in the average consumption of electric energy. More than that an exploration is performed to insert Stirling engine in air conditioning cycle proving that it can be a promoted solution and can reduce significantly energy consumption; an alpha Stirling engine has been tested numerically for the same temperature and pressure reigned in the refrigeration cycle proving that it can work with energy efficiency around 42%.

C6. Noise Immune Spectrum Sensing Algorithm for Cognitive Radio

One promising approach to achieving high precision spectrum sensing in a cognitive radio (CR) system is the energy detector based on Wavelet Packet Transform (WPT). However, energy detector spectrum sensing is affected by the Signal to Noise Ratio (SNR). When the SNR is lower than 5 dB, an energy detector may falsely determine some unoccupied sub-channels as occupied [12]. In this paper, a new noise immune algorithm for spectrum sensing is introduced. This algorithm combines two powerful tools: the wavelet packet analysis and Higher-Order-Statistics (HOS). The use of the proposed technique makes spectrum sensing possible in very low SNR conditions. This allows better utilization of the unoccupied spectrum and high spectrum efficiency usage.

New FEC encoding technique based parity selected codes for 4-ary PAM signal

To ensure reliable communications,(an algorithm has been built for bit error detection and correction). To achieve this goal, special codeword combinations and their related parity codes are used as powerful detection and correction codes. All codeword combinations are divided into four main groups, where each code in a group has a common parity code. In this paper, we used the distance feature between special selected codeword combinations and unique combinations from a fixed set to improve the BER in digital communications systems. The results of using such algorithm show that 100% correction of two errors and 66% of three errors. The probability of detection is very high and up to 8 errors in different positions. All correction and detection processes are achieved with minimum number of transmitted bits representing 4- ary PAM symbols with compression ratio equals to 76% comparing to traditional distance parity check codes.

Discrete Wavelet Transform & Linear Prediction Coding Based Method for Speech Recognition via Neural Network

In the proposed work, the techniques of wavelet transform (WT) and neural network were introduced for speech based text-independent speaker identification and Arabic vowel recognition. The linear prediction coding coefficients (LPCC) of discrete wavelet transform (DWT) upon level 3 features extraction method was developed. Feature vector fed to probabilistic neural networks (PNN) for classification. The functions of features extraction and classification are performed using the wavelet transform and neural networks (DWTPNN) expert system. The declared results show that the proposed method can make an powerful analysis with average identification rates reached 93. Two published methods were investigated for comparison. The best recognition rate selection obtained was for framed DWT. Discrete wavelet transform was studied to improve the system robustness against the noise of 0dB. Our investigation of speaker-independent Arabic vowels classifier system performance is performed via several experiments depending on vowel type. The declared results show that the proposed method can make an effectual analysis with identification rates may reach 93%. In general, a speaker identification system can be implemented by observing the voiced/unvoiced components or through analyzing the energy distribution of utterances. A number of digital signal processing algorithms, such as LPC technique (Adami & Barone, 2001; Tajima, Port, & Dalby, 1997), Mel frequency cepstral coefficients (MFCCs) (Mashao & Skosan, 2006; Sroka & Braida, 2005; Kanedera, Arai, Hermansky & Pavel, 1999; Daqrouq & Al-Faouri, 2010), DWT (Fonseca, Guido, Scalassara, Maciel, & Pereira, 2007) and wavelet packet transform (WPT) (Lung, 2006; Zhang & Jiao, 2004) are extensively utilized. In the beginning of 1990s, Mel frequency cepstral technique became the most widely used technique for recognition purposes due to its aptitude to represent the speech spectrum in a compacted form (Sarikaya & ansen, 2000). Actually, MFCCs simulate the model of umans’ auditory perception and have been proven to be very effective in automatic speech recognition system and modeling the individual frequency components of speech signals.