Bassam Al‐Naami

Hashemite University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Paolo Visconti

Innovation Engineering (Italy)

Roberto De Fazio

University of Salento

Jamal Al-Nabulsi

Al-Ahliyya Amman University

Hamza Abu Owida

Al-Ahliyya Amman University

Abdel-Razzak Al-Hinnawi

Isra University

Hossam Fraihat

Al-Ahliyya Amman University

Nasr Y. Gharaibeh

Al-Balqa Applied University

Bassam Jamil Mohd

Hashemite University

Feras Alnaimat

Al-Ahliyya Amman University

Sa’ed Abed

Kuwait University

Cooperative Institutions

Hashemite University

Al-Ahliyya Amman University

University of Salento

Innovation Engineering (Italy)

Yarmouk University

Al-Balqa Applied University

Center for Biomolecular Nanotechnologies

Italian Institute of Technology

Damascus University

Universidad Panamericana

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Assessment of Dual-Tree Complex Wavelet Transform to Improve SNR in Collaboration with Neuro-Fuzzy System for Heart-Sound Identification

Electronics (2022)

Bassam Al‐Naami Hossam Fraihat Jamal Al-Nabulsi Nasr Y. Gharaibeh Paolo Visconti

The research paper proposes a novel denoising method to improve the outcome of heart-sound (HS)-based heart-condition identification by applying the dual-tree complex wavelet transform (DTCWT) together with the adaptive neuro-fuzzy inference System (ANFIS) classifier. The method consists of three steps: first, preprocessing to eliminate 50 Hz noise; second, applying four successive levels of DTCWT to denoise and reconstruct the time-domain HS signal; third, to evaluate ANFIS on a total of 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The results show that the signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was an 11% to many decibel (dB)-fold increase in SNR after DTCWT, representing a significant improvement in denoising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc compared to other attempts on the same dataset. Therefore, DTCWT is a successful technique in removing noise from biosignals such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.

Complex wavelet transform

10.3390/electronics11060938

Cite

Citations (10)

A New Technique for Connecting a Dual Excitation Synchronous Generator to the Power Grid

Energies (2023)

Roberto De Fazio Ayman Alerksousi Lorenzo Spongano Bassam Al‐Naami Abdullah Al-Odienat

Due to an increasing demand for electric power and changes in the typology of loads, stability has become a major concern in power systems. As the system stability is directly related to the response of the connected generator, recent research has focused on enhancing generators’ stability and improving their response to load variations. This study focuses on adding another excitation winding on to the q-axis, perpendicular to the conventional excitation winding on the d-axis, to control both active and reactive power. This paper studies and compares the performance of the dual excitation synchronous generator (DESG) to conventional synchronous generators. The mathematical equations are derived, and a mathematical model is then developed. The experimental tests have been conducted using a laboratory model consisting of a two-phase synchronous generator driven by a DC motor with different loads. The obtained results and radial diagrams for the different loading types are presented and evaluated. Therefore, a new approach has been designed to connect the DESG directly to the power grid without any electronic components using a special coupling that works in one direction. Two perpendicular excitation coils, d and q, were formed from the existing coils, and the tests were carried out on all loads, ensuring that the revolving angle (i.e., the stability angle φ) was fixed. The results show that the proposed method offers significant cost savings, potentially amounting to 15–20% of the unit price. The experimental results confirm that the DESG significantly improves the generator stability by maintaining a constant rotor angle δ, which requires using an automatic angle regulator (AAR) in addition to the conventional automatic voltage regulator (AVR).

Synchronous motor

10.3390/en16247936

Cite

Citations (0)

A New Prototype of Smart Wearable Monitoring System Solution for Alzheimer’s Patients

Medical Devices Evidence and Research (2021)

Bassam Al‐Naami Hamza Abu Owida Mohammed Abu Mallouh Feras Alnaimat Moh'd Agha

The daily life management of patients with Alzheimer's disease (AD) constitutes a significant and rapidly expanding health-care responsibility. In this study, an innovative prototype of a wireless-sensing smart wearable medical device (SWMD) is proposed as a multi-functions solution for Alzheimer patients. The SWMD is aimed to assemble three main biomedical engineering advances: 1) use of a Wi-Fi microcontroller, 2) simultaneous monitoring of a set of vital biomarkers, and 3) cautions of fall down conditions, in addition to GPS location indicator.The SWMD employs a Wi-Fi controller that is incorporated with electronic circuits to monitor three vital signals (temperature, heart rate, and oxygen saturation), fall down conditions in three directions (X, Y, and Z axis), and GPS location. The SWMD was connected to the Firebase Service (database hosted on the Internet Cloud). The proposed device was tested on 13 normal volunteers. The left side, right side, forward, and backward fall down conditions were assessed. The prototype's functions during daily activity such as rising hand, sitting down or standing up, and walking conditions were also assessed.The three assembled functions were all successfully incorporated to build the SWDM device as a suggested solution offering real-time alerts during daily activity to AD patients. The Bland-Altman statistical test showed no significant difference (p-value >0.05) between the SWMD biomarkers' acquisition and the reference methods. The gyro/accelerator sensor yielded 93% sensitivity in fall down detection and 95% specificity during daily activities. The GPS yielded correct positioning of the SWDM holder, while the internet cloud allowed saving and managing all vital biomarkers daily.The SWMD is a possible solution for daily life support for AD patients. It incorporates three functions in one single device, GPS location indicator, monitoring set of biomarkers, and fall down alert, which are all controlled via a Wi-Fi micro controller on-line connected to Internet Cloud. It successfully would allow the management of the daily records as well as the real-time alerts to remote persons.

10.2147/mder.s339855

Cite

Citations (11)

Optic nerve head slope-based quantitative parameters for identifying open-angle glaucoma on SPECTRALIS OCT images

International Ophthalmology (2016)

Abdel-Razzak Al-Hinnawi Bassam Al‐Naami Motasem Al-Latayfeh

10.1007/s10792-016-0362-9

Cite

Citations (4)

Quantitative analysis signal-based approach using the dual tree complex wavelet transform for studying heart sound conditions

Bassam Al‐Naami HA Owida Hossam Fraihat

Heart sound signal is an important sign about the mechanical performance of the cardiac valves. Enhancement of heart sound signal is a crucial issue to identify cardiac disease relevant to valve disorder. This study, presents a new approach based on the use of Dual Tree Complex Wavelet transform (DTCWT). The current approach has been employed in order to identify the normal heart sound from the pathological disorder. Twenty analyzed signals obtained from the PhysioNet database. After the preprocessing procedure the DTCWT has been implemented and the reconstructed signal were employed to extract five statistical features for both sounds. The result of the implementation and box plot showing the robust of DTCWT with apparent significance amongst the traditional discreet wavelet transform (DWT).

Complex wavelet transform

SIGNAL (programming language)

10.1109/mecbme47393.2020.9265121

Cite

Citations (2)

Assessment of Dual Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

Research Square (Research Square) (2021)

Bassam Al‐Naami Hossam Fraihat Jamal Al-Nabulsi Abdel-Razzak Al-Hinnawi

Abstract Here we propose a novel de-noising method to improve the outcome of heart sound (HS)-based heart condition identification. We applied Dual Tree Complex Wavelet Transform (DTCWT) in collaboration with Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The method consisted of three steps. First, preprocess to eliminate 50 Hz noise. Second, application of DTCWT to de-noise and reconstruct time-domain HS signal. Third, evaluation of ANFIS on total 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was a 11% increase in SNR after DTCWT, representing a significant improvement in de-noising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc in comparison to other attempts on the same dataset. Therefore, DTCWT is a successful technique in de-noising information such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.

Complex wavelet transform

10.21203/rs.3.rs-628636/v1

Cite

Citations (0)

Advanced Low-Pass Filters for Signal Processing: A Comparative Study on Gaussian, Mittag-Leffler, and Savitzky-Golay Filters

Mathematical Modelling and Engineering Problems (2024)

Yahia Z. Rawash Bassam Al‐Naami Ausilah Alfraihat Hamza Abu Owida

Signal processing plays a crucial role in biomedical applications, facilitating accurate health monitoring and clinical diagnoses.This study presents a comparative analysis of Gaussian, Mittag-Leffler, and Savitzky-Golay filters, evaluating their effectiveness in noise reduction and signal enhancement for electrocardiogram (ECG) signals.These filters offer adjustable parameters, making them adaptable to various applications.Our findings demonstrate that the Savitzky-Golay smoothing filter outperforms the others in smoothing data and computing derivatives of noisy data, despite its limitations in suppressing noise at higher frequencies.On the other hand, the adaptive Gaussian and Mittag-Leffler filters excel in noise reduction but may compromise fine signal details.Through MATLAB simulations and mean squared error (MSE) comparisons as well as Signal to Nosie Ratio (SNR), we evaluate the filters' performance in denoising realworld ECG signals.The results indicate that both the Savitzky-Golay smoothing and Mittag-Leffler filters hold promise for noise reducing in other biomedical signals, such as medical EEG and medical EMG signals.This research serves as a foundational exploration of the application and enhancement of these filters in biomedical signal processing.

Binary Golay code

SIGNAL (programming language)

10.18280/mmep.110713

Cite

Citations (3)

IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic

Sensors (2023)

Jamal Al-Nabulsi Nidal Turab Hamza Abu Owida Bassam Al‐Naami Roberto De Fazio

A global health emergency resulted from the COVID-19 epidemic. Image recognition techniques are a useful tool for limiting the spread of the pandemic; indeed, the World Health Organization (WHO) recommends the use of face masks in public places as a form of protection against contagion. Hence, innovative systems and algorithms were deployed to rapidly screen a large number of people with faces covered by masks. In this article, we analyze the current state of research and future directions in algorithms and systems for masked-face recognition. First, the paper discusses the importance and applications of facial and face mask recognition, introducing the main approaches. Afterward, we review the recent facial recognition frameworks and systems based on Convolution Neural Networks, deep learning, machine learning, and MobilNet techniques. In detail, we analyze and critically discuss recent scientific works and systems which employ machine learning (ML) and deep learning tools for promptly recognizing masked faces. Also, Internet of Things (IoT)-based sensors, implementing ML and DL algorithms, were described to keep track of the number of persons donning face masks and notify the proper authorities. Afterward, the main challenges and open issues that should be solved in future studies and systems are discussed. Finally, comparative analysis and discussion are reported, providing useful insights for outlining the next generation of face recognition systems.

Pandemic

Limiting

Face masks

10.3390/s23167193

Cite

Citations (5)

Developing custom signal processing algorithm with labview FPGA and compact RIO to detect the Aortic Stenosis disease

Computing in Cardiology Conference (2006)

Bassam Al‐Naami Jalel Chebil B. Trabsheh H.M. Mgdob

FPGAs provide an ideal template for run-time reconfigurable (RTR) designs. Only recently have RTR enabling design tools that bypass the traditional synthesis and bit stream generation process for FPGAs become available. Heart auscultation which is the interpretation of sounds produced by the heart is a fundamental tool in the diagnosis of heart disease. It is the most commonly used technique for screening and diagnosis in primary health care. This study aims at utilizing the discrete wavelet packet transforms in early detection of an Aortic Stenosis (AS) using heart sound data collected at Sussex University Hospital in England. From the data analysis, a criteria has been proposed for the detection of the AS disease from the heart sound data.

Auscultation

Source

Cite

Citations (11)

An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images

International Journal of Signal and Imaging Systems Engineering (2018)

Obaida M. Al-hazaimeh Khalid M.O. Nahar Bassam Al‐Naami Nasr Y. Gharaibeh

Diabetic retinopathy (i.e., DR), is an eye disorder caused by diabetes, diabetic retinopathy detection is an important task in retinal fundus images due the early detection and treatment can potentially reduce the risk of blindness. Retinal fundus images play an important role in diabetic retinopathy through disease diagnosis, disease recognition (i.e., by ophthalmologists), and treatment. The current state-of-the-art techniques are not satisfied with sensitivity and specificity. In fact, there are still other issues to be resolved in state-of-the-art techniques such as performances, accuracy, and easily identify the DR disease effectively. Therefore, this paper proposes an effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images that will satisfy the performance metrics (i.e., sensitivity, specificity, accuracy). The proposed automatic screening system for diabetic retinopathy was conducted in several steps: Pre-processing, optic disc detection and removal, blood vessel segmentation and removal, elimination of fovea, feature extraction (i.e., Micro-aneurysm, retinal hemorrhage, and exudates), feature selection and classification. Finally, a software-based simulation using MATLAB was performed using DIARETDB1 dataset and the obtained results are validated by comparing with expert ophthalmologists. The results of the conducted experiments showed an efficient and effective in sensitivity, specificity and accuracy.

Fundus (uterus)

10.1504/ijsise.2018.10015063

Cite

Citations (38)