Search results for: Ramy Hussein

Items from 1 to 18 out of 18 results

chapter

Robust detection of acoustic partial discharge signals in noisy environments

Ramy Hussein, Khaled Bashir Shaban, Ayman H. El-Hag

2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) > 1 - 6

2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

Partial discharge (PD) can be used to predict insulation failures in power transformers. Accurate detection of particular PD types has a significant role in anticipating forthcoming outages. However, the noise encountered with PD measurements negatively affects the detection accuracy. In this paper, we propose a robust PD detection technique that is immune to noise through efficient frequency domain-based...

article

Robust Feature Extraction and Classification of Acoustic Partial Discharge Signals Corrupted With Noise

Ramy Hussein, Khaled Bashir Shaban, Ayman H. El-Hag

IEEE Transactions on Instrumentation and Measurement > 2017 > 66 > 3 > 405 - 413

Partial discharge (PD) can be used as an indicator of impending failure in electrical plant insulation making the accurate classification of particular occurrence patterns useful for anticipating forthcoming outages. In this paper, we propose a feature extraction method that is robust to noise and can effectively select the most discriminant features of PD signals. Specifically, we follow three main...

article

Comparison of different fourth order Hilbert fractal antennas for partial discharge measurement

Abd Almonam Zahed, Ayman H. El-Hag, Nasser Qaddoumi, Ramy Hussein, more

IEEE Transactions on Dielectrics and Electrical Insulation > 2017 > 24 > 1 > 175 - 182

Three Hilbert fractal antenna designs are proposed in this work to capture and classify common types of partial discharge (PD) in an oil insulated system. Each antenna design shows unique characteristics in terms of resonant frequencies, inception voltage, classification capabilities and noise performance. Three types of PD signals are artificially generated; namely, corona, surface and sharp PD....

chapter

Ll-regularization based EEG feature learning for detecting epileptic seizure

Ramy Hussein, Z. Jane Wang, Rabab Ward

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP) > 1171 - 1175

2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Most electroencephalogram (EEG) monitoring-based seizure detection methods reported in the literature are not robust in the noisy environments, while the detection accuracy decays along with poor signal-to-noise ratios in EEG signals. In this paper, a feature learning method, based on the L1 regularization, is adopted to recognize the most discriminant features pertinent to seizures. Specifically,...

chapter

Energy Efficient EEG Monitoring System for Wireless Epileptic Seizure Detection

Ramy Hussein, Rabab Ward, Z. Jane Wand, Amr Mohamed

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) > 294 - 299

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)

Wireless EEG monitoring systems have been successfully used for seizure detection outside clinical settings. The wireless EEG sensor nodes consume a considerable amount of battery energy to acquire, encode and transmit the data to the server side. In this paper, we introduce energy-efficient monitoring systems to increase the sensors' battery lifetime. Specifically, we propose a feature extraction...

article

Energy conservation-based thresholding for effective wavelet denoising of partial discharge signals

Ramy Hussein, Khaled Bashir Shaban, Ayman H. El-Hag

IET Science, Measurement & Technology > 2016 > 10 > 7 > 813 - 822

Recent studies have shown that wavelet transform can effectively be used for noise reduction in the context of partial discharge (PD) signal detection and classification. Several thresholding approaches for wavelet denoising have been reported in the literature. In this study, a novel wavelet threshold estimation method, named energy conservation-based thresholding (ECBT), is introduced. The proposed...

article

Denoising of acoustic partial discharge signals corrupted with random noise

Ramy Hussein, Khaled BashirShaban, Ayman H. El-Hag

IEEE Transactions on Dielectrics and Electrical Insulation > 2016 > 23 > 3 > 1453 - 1459

Power transformers are one of the most important and expensive electrical equipment that require online condition monitoring. Partial discharge (PD) measurement is considered the most effective and non-destructive approach to observe the condition of power transformers in service. However, many sources of noise interfere with the captured PD signals leading to waveform deformation. Thus, it is important...

article

Wavelet Transform With Histogram-Based Threshold Estimation for Online Partial Discharge Signal Denoising

Ramy Hussein, Khaled Bashir Shaban, Ayman H. El-Hag

IEEE Transactions on Instrumentation and Measurement > 2015 > 64 > 12 > 3601 - 3614

Online condition assessment of the power system devices and apparatus is considered vital for robust operation, where partial discharge (PD) detection is employed as a diagnosis tool. PD measurements, however, are corrupted with different types of noises such as white noise, random noise, and discrete spectral interferences. Hence, the denoising of such corrupted PD signals remains a challenging problem...

chapter

Acoustic partial discharge signal denoising using power spectral subtraction

Ramy Hussein, Khaled Bashir Shaban, Ayman H. El-Hag

2015 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP) > 330 - 333

2015 IEEE Conference on Electrical Insulation and Dielectric Phenomena - (CEIDP)

Measuring partial discharge (PD) phenomena in power transformers is often conducted by acoustic emission (AE) method. However, many interference sources are usually encountered with the captured PD signals which negatively affect the PD detection and classification. Thus, an effective and efficient denoising technique is required to suppress such environmental noises. Most denoising attempts aim to...

article

Scalable real-time energy-efficient EEG compression scheme for wireless body area sensor network

Ramy Hussein, Amr Mohamed, Masoud Alghoniemy

Biomedical Signal Processing and Control > 2015 > 19 > Complete > 122-129

Recent technological advances in wireless body sensor networks have made it possible for the development of innovative medical applications to improve health care and the quality of life. By using miniaturized wireless electroencephalography (EEG) sensors, it is possible to perform ambulatory EEG recording and real-time healthcare applications. One master consideration in using such battery-powered...

chapter

Histogram-based thresholding in discrete wavelet transform for partial discharge signal denoising

Ramy Hussein, Khaled Bashir Shaban, Ayman H. El-Hag

2015 International Conference on Communications, Signal Processing, and their Applications (ICCSPA'15) > 1 - 5

2015 International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

White noise is a major interference source that affects the partial discharge (PD) signal detection and recognition. Wavelet shrinkage denoising methods can efficiently reject the white noise embedded in the PD signal acquisition and measurement processes. The wavelet threshold determination is a key factor in the quality of noise suppression from signals. A novel threshold estimation technique, namely...

chapter

Energy-efficient on-board processing technique for wireless epileptic seizure detection systems

Ramy Hussein, Amr Mohamed, Masoud Alghoniemy

2015 International Conference on Computing, Networking and Communications (ICNC) > 1116 - 1121

2015 International Conference on Computing, Networking and Communications (ICNC)

The growth of wireless body area sensor networks (WBASNs) has led the way to advancements In healthcare applications and patient monitoring systems; epileptic seizure lies at the heart of these promising technologies. For real-time epileptic seizure detection, wireless EEG sensors have been utilized for the purpose of data acquisition, pre-processing and transmission to the server side. The dilemma...

chapter

Design and analysis of an adaptive compressive sensing architecture for epileptic seizure detection

Ramy Hussein, Amr Mohamed, Masoud Alghoniemy, Alaa Awad

2013 4th Annual International Conference on Energy Aware Computing Systems and Applications (ICEAC) > 141 - 146

2013 Fourth Annual International Conference on Energy Aware Computing Systems and Applications (ICEAC)

Epileptic detection techniques rely heavily on the Electroencephalography (EEG) as a representative signal carrying valuable information pertaining to the current brain state. In this work, we investigate the stability of time domain EEG features while varying the channel conditions. We identify the feature sets that would provide the most robust EEG classification accuracy. Moreover, an embedded...

chapter

Energy-aware cross-layer optimization for EEG-based wireless monitoring applications

Alaa Awad, Ramy Hussein, Amr Mohamed, Amr A. El-Sherif

38th Annual IEEE Conference on Local Computer Networks > 356 - 363

2013 IEEE 38th Conference on Local Computer Networks (LCN 2013)

Body Area Sensor Networks (BASNs) for healthcare applications have gained significant research interests recently due to the growing number of patients with chronic diseases requiring constant monitoring. Because of the limited power source and small form factors, BASNs have distinguished design and operational challenges, particularly focusing on energy optimization. In this paper, an Energy-Delay-Distortion...

chapter

Adaptive compression and optimization for real-time energy-efficient wireless EEG monitoring systems

Ramy Hussein, Amr Mohamed, Masoud Alghoniemy

The 6th 2013 Biomedical Engineering International Conference > 1 - 5

2013 6th Biomedical Engineering International Conference (BMEiCON)

Recent technological advances in wireless body sensor networks (WBSN) have made it possible for the development of innovative medical applications to improve health care and the quality of life. Electroencephalography (EEG)-based applications lie at the heart of this promising technologies. However, excessive power consumption may render some of these applications inapplicable. Hence, intelligent...

chapter

Adaptive energy-aware encoding for DWT-based wireless EEG tele-monitoring system

Ramy Hussein, Alaa Awad, Amr A. El-Sherif, Amr Mohamed, more

2013 IEEE Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE) > 245 - 250

2013 IEEE Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE)

Recent technological advances in wireless body sensor networks (WBSN) have made it possible for the development of innovative medical applications to improve health care and the quality of life. Electroencephalography (EEG)-based applications lie at the heart of these promising technologies. However, excess power consumptions may render some of these applications inapplicable. Wireless (EEG) tele-monitoring...

chapter

Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring

Khalid Abualsaud, Massudi Mahmuddin, Ramy Hussein, Amr Mohamed

2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC) > 231 - 236

2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC 2013)

Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different physiological brain states. In this paper, we propose an efficient framework for evaluating the power-accuracy trade-off for EEG-based compressive sensing and classification techniques...

chapter

EEG feature extraction and selection techniques for epileptic detection: A comparative study

Ramy Hussein, Amr Mohamed, Khaled Shahan, Abduljalil A. Mohamed

2013 IEEE Symposium on Computers & Informatics (ISCI) > 170 - 175

2013 IEEE Symposium on Computers & Informatics (ISCI)

. Epileptic detection techniques rely heavily on the Electroencephalography (EEG) as representative signal carrying valuable information pertaining to the current brain state. For these techniques to be efficient and reliable, a set of discriminant, epileptic-related features has first to be obtained. Furthermore, depending on the classifier model used, a subset of these features is identified and...

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INFONA - science communication portal

Search results for: Ramy Hussein

Robust detection of acoustic partial discharge signals in noisy environments

Robust Feature Extraction and Classification of Acoustic Partial Discharge Signals Corrupted With Noise

Comparison of different fourth order Hilbert fractal antennas for partial discharge measurement

Ll-regularization based EEG feature learning for detecting epileptic seizure

Energy Efficient EEG Monitoring System for Wireless Epileptic Seizure Detection

Energy conservation-based thresholding for effective wavelet denoising of partial discharge signals

Denoising of acoustic partial discharge signals corrupted with random noise

Wavelet Transform With Histogram-Based Threshold Estimation for Online Partial Discharge Signal Denoising

Acoustic partial discharge signal denoising using power spectral subtraction

Scalable real-time energy-efficient EEG compression scheme for wireless body area sensor network

Histogram-based thresholding in discrete wavelet transform for partial discharge signal denoising

Energy-efficient on-board processing technique for wireless epileptic seizure detection systems

Design and analysis of an adaptive compressive sensing architecture for epileptic seizure detection

Energy-aware cross-layer optimization for EEG-based wireless monitoring applications

Adaptive compression and optimization for real-time energy-efficient wireless EEG monitoring systems

Adaptive energy-aware encoding for DWT-based wireless EEG tele-monitoring system

Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring

EEG feature extraction and selection techniques for epileptic detection: A comparative study

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