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Kanhira Kadavath Mujeeb Rahman
- Senior Lecturer
- Ext: 6791
Dr. Mujeeb Rahman is an accomplished educator and researcher specializing in Artificial Intelligence (AI) and Machine Learning (ML) within the field of Biomedical Engineering. He holds a Ph.D. in Biomedical Engineering and Machine Learning from Vellore Institute of Technology (VIT), India, and further enhanced his expertise with a postgraduate degree in ML and AI from the University of Texas at Austin, USA. Dr. Rahman’s academic excellence was recognized with a gold medal for achieving the top rank in the M.Tech. program in Biomedical Instrumentation at Visvesvaraya Technological University (VTU), India. With over two decades of teaching experience at Ajman University, he has made significant contributions to the advancement of biomedical engineering in the GCC region, particularly in the UAE. His research focuses on leveraging ML and AI to drive innovations in healthcare technology, with an emphasis on accessibility and affordability. Beyond his professional work, Dr. Rahman has a passion for teaching mathematics, physics, and programming, inspiring the next generation of students and innovators.
Education
- Doctor of Philosophy, Biomedical Engineering and Machine Learning, Vellore Institute of Technology, India, 2023.
- Post Graduate Program in Machine Learning & Artificial Intelligence, TEXAS McCombs, The University of Texas at Austin, 2020.
- Master of Technology in Biomedical Instrumentation, Viswesaraya Technological University, India, 2002.
- Bachelor’s Degree (Double Major) in Electronics & Communication Engineering and Electrical Engineering, AICTE, India, 2000.
Doctor of Philosophy, Biomedical Engineering and Machine Learning, Vellore Institute of Technology, India, 2023.
Post Graduate Program in Machine Learning & Artificial Intelligence, TEXAS McCombs, The University of Texas at Austin, 2020.
Master of Technology in Biomedical Instrumentation, Viswesaraya Technological University, India, 2002.
Bachelor’s Degree (Double Major) in Electronics & Communication Engineering and Electrical Engineering, AICTE, India, 2000.
Experience
- Senior Lecturer, Department of Biomedical Engineering, Ajman University, August 2004 – Present.
- Lecturer, Department of Electronics & Biomedical Engineering, Sahrdaya College of Engineering & Technology, India, July 2003 – May 2004.
- Visiting Professor, Department of Biomedical Engineering, Ajman University, Abu Dhabi, UAE, March 2003 – June 2003.
- Biomedical Engineer, Gulf Medical College Hospital, Ajman, UAE, September 2002 – March 2003.
Senior Lecturer, Department of Biomedical Engineering, Ajman University, August 2004 – Present.
Lecturer, Department of Electronics & Biomedical Engineering, Sahrdaya College of Engineering & Technology, India, July 2003 – May 2004.
Visiting Professor, Department of Biomedical Engineering, Ajman University, Abu Dhabi, UAE, March 2003 – June 2003.
Biomedical Engineer, Gulf Medical College Hospital, Ajman, UAE, September 2002 – March 2003.
Teaching Area
- Biomedical Signal Processing
- Computer Programming
- Directed Studies in Biomedical Engineering
- Medical Image Processing and Algorithms
- Introduction to Biomedical Engineering
- Biomedical Design Project
- Technical Report Writing and Presentation
- Medical Electronics
- Biomedical Design
- Electrophysiology
- Artificial Neural Networks and Fuzzy Logic
Biomedical Signal Processing
Computer Programming
Directed Studies in Biomedical Engineering
Medical Image Processing and Algorithms
Introduction to Biomedical Engineering
Biomedical Design Project
Technical Report Writing and Presentation
Medical Electronics
Biomedical Design
Electrophysiology
Artificial Neural Networks and Fuzzy Logic
Research
- EEG Feature Extraction for Early Detection and Grading of Autism Spectrum Disorder (ASD).
- Development of Algorithms for ECG Arrhythmia Analysis (Time, Frequency, and Wavelet-Based)
- Design and Development of a Low-Cost DAQ System for Accurate Biomedical Signal Acquisition.
- Development of Conventional Machine Learning Algorithms, DNN- and CNN-Based Supervised and Unsupervised Learning Algorithms for Biomedical Image and Signal Analysis.
EEG Feature Extraction for Early Detection and Grading of Autism Spectrum Disorder (ASD).
Development of Algorithms for ECG Arrhythmia Analysis (Time, Frequency, and Wavelet-Based)
Design and Development of a Low-Cost DAQ System for Accurate Biomedical Signal Acquisition.
Development of Conventional Machine Learning Algorithms, DNN- and CNN-Based Supervised and Unsupervised Learning Algorithms for Biomedical Image and Signal Analysis.
Publications
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JournalK.K. Mujeeb Rahman; Sedra Zulaikha; Banan Dhafer; Rawan Ahmed, Advancing tuberculosis screening: A tailored CNN approach for accurate chest X-ray analysis and practical clinical integration, Elsevier-Intelligence-Based Medicine, Vol: 11, Jan 2025, doi: https://doi.org/10.1016/j.ibmed.2024.100196
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JournalMujeeb Rahman K K, Mohamed Nasor and Ahmed Imran, Enhanced Hand Gesture Recognition with Surface Electromyogram and Machine Learning, Sensors, Vol: 24 (16), Aug 2024, doi: https://doi.org/10.3390/s24165231
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ConferenceMujeeb Rahman K K*; Mohamed Nasor M; Rayan Zidan; Ibrahim Alsarraj; Besher Has, IoT-Based Wireless Patient Monitor Using ESP32Microcontroller, 24th International Arab Conference on Information Technology (ACIT'2023), Ajman
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Book ChapterMujeeb Rahman K. K and Mohamed Nasor Khalil, Classification of Mental State Using a Muse Headband and Machine Learning Algorithm, Proceedings of ICASPACE 2023, A Springer book series Lecture Notes in Electrical Engineering., Springer
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Book ChapterMujeeb Rahman K K, Khawla Ahmed, Tala Moh’d, and Amina Wali Alrahman, A Low-Cost Diabetic Retinopathy Screening Tool Using a Smartphone and Machine Learning Algorithm, Proceedings of ICASPACE 2023 - A Springer book series Lecture Notes in Electrical Engineering., Springer
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JournalK. K. Mujeeb Rahman, Mohamed Nasor, and Ahmed Imran, Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms, Diagnostics, Sep 2022, doi: 10.3390/diagnostics12092262
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JournalMujeeb Rahman, K. K. and Subashini, M.M, Identification of autism in children using static facial features and deep neural networks, Brain Sciences, Vol: 12, Jan 2022, doi: 10.3390/brainsci12010094
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JournalMujeeb Rahman Kanhirakadavath and Monica Subashini Mohan Chandran, Investigation of Eye-Tracking Scan Path as a Biomarker for Autism Screening Using Machine Learning Algorithms, Diagnostics, Feb 2022
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JournalMujeeb Rahman, K. K. and Monica Subashini, M. , A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT), Journal of Autism and Developmental Disorders, Vol: 52, pp.2732 - 46, Jun 2022, doi: 10.1007/s10803-021-05141-2
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Book ChapterMujeeb Rahman K. K, Mohamed Nasor K, and Praveen Kumar Reddy Yelampalli, Classification of Finger Movements Using Multi-channel EMG and Machine Learning, Select Proceedings of ICASPACE 2023, A Springer book series Lecture Notes in Electrical Engineering., Springer
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ConferenceK K Mujeeb Rahman; M Monica Subashini; Mohamed Nasor; Ayman Tawfik , Development of bio-shields for Arduino Uno, Advances in Science and Engineering Technology International Conferences (ASET), Sharjah, 10.1109/ICASET.2018.8376901
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ConferenceK K Mujeeb Rahman; M. Monica Subashini, ECG Signal Processing and Analysis for Accurate Features Extraction, Published in: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), AJMAN, United Arab Emirates
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ConferenceMohamed Nasor;K K Mujeeb Rahman;Maryam Mohamed Zubair;Haya Ansari;Farida Mohamed, Eye-controlled mouse cursor for physically disabled individual, 2018 Advances in Science and Engineering Technology International Conferences (ASET), Sharjah, United Arab Emirates
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JournalGeorge K. Varotsos, Hector E. Nistazakis, Konstantinos Aidinis, Fadi Jaber. and K. K. Mujeeb Rahman , Signal Intensity Estimation in Transdermal Optical Wireless Links with Stochastic Pointing Errors Effect, Technologies, Jan 2020, doi: 10.3390/technologies8040060
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JournalG. K. Varotsos,H. E. Nistazakis,K. Aidinis,F. Jaber &K. K. Mujeeb Rahman, Transdermal subcarrier L-PSK or DBPSK optical wireless links with time diversity, skin attenuation and spatial jitter, Journal of Modern optics, pp.1233 - 1240, Oct 2020, doi: 10.1080/09500340.2020.1825848
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ConferenceG.K. Varotsos; H.E. Nistazakis; K. Aidinis; G.D. Roumelas; F. Jaber; K.K.Mujeeb Rahman, Modulated Retro-Reflector Transdermal Optical Wireless Communication Systems with Wavelength Diversity over Skin-Induced Attenuation and Pointing Errors, IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Ajman, United Arab Emirates
K.K. Mujeeb Rahman; Sedra Zulaikha; Banan Dhafer; Rawan Ahmed, Advancing tuberculosis screening: A tailored CNN approach for accurate chest X-ray analysis and practical clinical integration, Elsevier-Intelligence-Based Medicine, Vol: 11, Jan 2025, doi: https://doi.org/10.1016/j.ibmed.2024.100196
Pulmonary tuberculosis (PTB) is a chronic infectious disease claiming approximately 1.5 million lives annually, emphasizing the need for timely diagnosis to improve survival and limit its spread. Chest X-rays are effective for identifying TB-related lung abnormalities, often before symptoms arise, making early detection crucial. Our project enhances PTB screening by leveraging a CNN model trained on 12,848 images from reliable open-access datasets. The system achieves 99.72 % accuracy in binary classification (normal vs. abnormal) and 99.61 % in distinguishing healthy, TB, and non-TB cases, outperforming existing solutions. This ML-driven tool enables swift, cost-effective, and precise PTB detection, ensuring targeted treatment and addressing medicolegal needs through reliable and accountable diagnostics.
About the journal
Mujeeb Rahman K K, Mohamed Nasor and Ahmed Imran, Enhanced Hand Gesture Recognition with Surface Electromyogram and Machine Learning, Sensors, Vol: 24 (16), Aug 2024, doi: https://doi.org/10.3390/s24165231
This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human–computer interaction technologies.
About the journal
Mujeeb Rahman K K*; Mohamed Nasor M; Rayan Zidan; Ibrahim Alsarraj; Besher Has, IoT-Based Wireless Patient Monitor Using ESP32Microcontroller, 24th International Arab Conference on Information Technology (ACIT'2023), Ajman
This paper discusses the details of an IoT-based wireless patient monitoring system designed to monitor patient electrocardiogram, oxygen saturation, and heart rate. Patients need to be physically present inside a hospital ward to be monitored by a doctor or physician using a patient monitor. This challenges individuals who cannot afford hospitalization fees, reside in remote areas, or are elderly. It also risks hospital staff when dealing with patients infected with highly contagious diseases like COVID-19. To address these issues, we propose developing a wireless patient monitoring system that enables doctors and nurses to monitor patients from the comfort of their homes. This system incorporates an ECG recording module and a SpO2 recording module, both interfaced with a microcontroller capable of Wi-Fi connection (ESP32). The microcontroller transmits the data acquired by the sensors to an online, real-time database called Firebase. Furthermore, our project includes a mobile application designed to retrieve data from the online realtime database and display it on the device’s screen for healthcare professionals to monitor. This innovative solution aims to provide access to healthcare for a larger segment of the population, irrespective of their income or social status.
About the journal
Mujeeb Rahman K. K and Mohamed Nasor Khalil, Classification of Mental State Using a Muse Headband and Machine Learning Algorithm, Proceedings of ICASPACE 2023, A Springer book series Lecture Notes in Electrical Engineering., Springer
This paper describes an experimental study on the automatic classification of mental states using electroencephalogram (EEG) signals and a machine learning algorithm. We adopted the statistical and frequency domain features of EEG signals captured by Muse Headband, which correspond to three classes of mental state that include relaxation, concentration, and neutral states. We experimented with different lightweight machine learning models to get an optimum classification. The paper includes descriptions of the Muse Headband, feature extraction techniques, and machine learning algorithm development. We found the Random Forest and XGBoost algorithms achieved over 99% accuracy for the three classes. The study’s findings demonstrate that the Muse headband and XGBoost algorithm can predict three mental states with remarkably high levels of accuracy, providing promise for the development of novel methods to identify mental health disorders and the ability to detect complex mental states.
About the journal
Mujeeb Rahman K K, Khawla Ahmed, Tala Moh’d, and Amina Wali Alrahman, A Low-Cost Diabetic Retinopathy Screening Tool Using a Smartphone and Machine Learning Algorithm, Proceedings of ICASPACE 2023 - A Springer book series Lecture Notes in Electrical Engineering., Springer
Diabetes can cause diabetic retinopathy (DR), an eye condition that can ultimately lead to blindness. The DR is rising as a result of the rising prevalence of diabetes worldwide. Routine eye check-ups at the hospital are suggested to maintain the health of a diabetic eye. The greatest method to prevent complications with DR is early diagnosis. Therefore, the availability of trustworthy screening techniques that are simple to use is essential for the early detection of DR. The main goal of this study is to develop a compact machine-learning (ML) DR screening tool that delivers accurate results without the use of a powerful computer or specialized software. A secondary goal is to evaluate the usability of smartphone-based fundus imaging as a low-cost alternative for fundus image acquisition. Using MATLAB software and the GLCM features derived from 560 previously recorded fundus images, we constructed a DR classifier that had a 96.45 % training accuracy and a 95.99% test accuracy. A user can feed fundus photos received from a smartphone into the user-friendly graphical user interface (GUI) we created to do early screening in less than a second. We expect the classifier to work much better if we combine the suggested model with a large number of fundus images taken with the image-capturing device.
About the journal
K. K. Mujeeb Rahman, Mohamed Nasor, and Ahmed Imran, Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms, Diagnostics, Sep 2022, doi: 10.3390/diagnostics12092262
Diabetic Retinopathy is a vision impairment caused by blood vessel degeneration in the retina. It is becoming more widespread as it is linked to diabetes. Diabetic retinopathy can lead to blindness. Early detection of diabetic retinopathy by an ophthalmologist can help avoid vision loss and other complications. Diabetic retinopathy is currently diagnosed by visually recognizing irregularities on fundus pictures. This procedure, however, necessitates the use of ophthalmic imaging technologies to acquire fundus images as well as a detailed visual analysis of the stored photos, resulting in a costly and time-consuming diagnosis. The fundamental goal of this project is to create an easy-to-use machine learning model tool that can accurately predict diabetic retinopathy using pre-recorded digital fundus images. To create the suggested classifier model, we gathered annotated fundus images from publicly accessible data repositories and used two machine learning methods, support vector machine (SVM) and deep neural network (DNN). On test data, the proposed SVM model had a mean area under the receiver operating characteristic curve (AUC) of 97.11%, whereas the DNN model had a mean AUC of 99.15%.
About the journal
Mujeeb Rahman, K. K. and Subashini, M.M, Identification of autism in children using static facial features and deep neural networks, Brain Sciences, Vol: 12, Jan 2022, doi: 10.3390/brainsci12010094
Autism spectrum disorder (ASD) is a complicated neurological developmental disorder that manifests itself in a variety of ways. The child diagnosed with ASD and their parents' daily lives can be dramatically improved with early diagnosis and appropriate medical intervention. The applicability of static features extracted from autistic children's face photographs as a biomarker to distinguish them from typically developing children is investigated in this study paper. We used five pre-trained CNN models: MobileNet, Xception, EfficientNetB0, EfficientNetB1, and EfficientNetB2 as feature extractors and a DNN model as a binary classifier to identify autism in children accurately. We used a publicly available dataset to train the suggested models, which consisted of face pictures of children diagnosed with autism and controls classed as autistic and non-autistic. The Xception model outperformed the others, with an AUC of 96.63%, a sensitivity of 88.46%, and an NPV of 88%. EfficientNetB0 produced a consistent prediction score of 59% for autistic and non-autistic groups with a 95% confidence level.
About the journal
Mujeeb Rahman Kanhirakadavath and Monica Subashini Mohan Chandran, Investigation of Eye-Tracking Scan Path as a Biomarker for Autism Screening Using Machine Learning Algorithms, Diagnostics, Feb 2022
Autism spectrum disorder is a group of disorders marked by difficulties with social skills, repetitive activities, speech, and nonverbal communication. Deficits in paying attention to, and processing, social stimuli are common for children with autism spectrum disorders. It is uncertain whether eye-tracking technologies can assist in establishing an early biomarker of autism based on the children’s atypical visual preference patterns. In this study, we used machine learning methods to test the applicability of eye-tracking data in children to aid in the early screening of autism. We looked into the effectiveness of various machine learning techniques to discover the best model for predicting autism using visualized eye-tracking scan path images. We adopted three traditional machine learning models and a deep neural network classifier to run experimental trials. This study employed a publicly available dataset of 547 graphical eye-tracking scan paths from 328 typically developing and 219 autistic children. We used image augmentation to populate the dataset to prevent the model from overfitting. The deep neural network model outperformed typical machine learning approaches on the populated dataset, with 97% AUC, 93.28% sensitivity, 91.38% specificity, 94.46% NPV, and 90.06% PPV (fivefold cross-validated). The findings strongly suggest that eye-tracking data help clinicians for a quick and reliable autism screening.
About the journal
Mujeeb Rahman, K. K. and Monica Subashini, M. , A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT), Journal of Autism and Developmental Disorders, Vol: 52, pp.2732 - 46, Jun 2022, doi: 10.1007/s10803-021-05141-2
About the journal
Mujeeb Rahman K. K, Mohamed Nasor K, and Praveen Kumar Reddy Yelampalli, Classification of Finger Movements Using Multi-channel EMG and Machine Learning, Select Proceedings of ICASPACE 2023, A Springer book series Lecture Notes in Electrical Engineering., Springer
This paper describes an experimental study on decoding of finger movements using surface electromyography (EMG) signals obtained from Myo-armband and machine learning techniques. The study set out to determine whether machine learning algorithms and EMG signals could be used to precisely decode finger movements. The paper includes descriptions of the EMG dataset used in the study, preprocessing steps, feature extraction techniques, and machine learning algorithm development. The proposed model recognized seven pre-defined finger movements, with an overall cross-validated AUC of 95.29%. The study’s results, which show that myo-bands and a support vector machine algorithm can predict finger movements with impressive accuracy, could have a big impact on how prosthetics and other tools to help people with disabilities are made.
About the journal
K K Mujeeb Rahman; M Monica Subashini; Mohamed Nasor; Ayman Tawfik , Development of bio-shields for Arduino Uno, Advances in Science and Engineering Technology International Conferences (ASET), Sharjah, 10.1109/ICASET.2018.8376901
The main purpose of this project is to design and implement a low cost Arduino bio-shield. Bio-shields are modular circuit boards that can be mounted on top of the Arduino kit in order to enhance its capabilities. Proposed shield can be used for the acquisition of bio-electric signals such ECG, EMG, EEG and EOG with the help of an Arduino Uno. Scope of this project includes the design of bio-amplifiers according to the bio-signal characteristics and practical implementation including fabrication of printed circuit boards. Three bio-shields (ECG, EMG and EOG) have been designed and implemented using surface mounted components.
About the journal
K K Mujeeb Rahman; M. Monica Subashini, ECG Signal Processing and Analysis for Accurate Features Extraction, Published in: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), AJMAN, United Arab Emirates
Heart is the most essential organ of the body which maintain the circulation of blood to various parts of the human body. Arrhythmia, a rhythmic disorder of the heart due to which heart cannot pump blood properly. The main purpose of this work to extract potential biomarkers of arrhythmia using two algorithms: Pan-Tompkins Algorithm and Wavelet based algorithm. ECG signals used in the system were downloaded from the MIT-BIH arrhythmia database. These signals were then imported to MATLAB and pre-processed. QRS complexes of the ECG signal are accurately detected by using the abovementioned algorithms. Features including QRS duration, RR interval and PR interval were accurately calculated for the entire dataset. Performance of the two algorithms done based on accuracy of the QRS detector.
About the journal
Mohamed Nasor;K K Mujeeb Rahman;Maryam Mohamed Zubair;Haya Ansari;Farida Mohamed, Eye-controlled mouse cursor for physically disabled individual, 2018 Advances in Science and Engineering Technology International Conferences (ASET), Sharjah, United Arab Emirates
This paper presents a novel algorithm for controlling the movement of a computer screen cursor using the iris movement. By accurately detecting the position of the iris in the eye and mapping that to a specific position on the computer screen, the algorithm enables physically disabled individuals to control the computer cursor movement to the left, right, up and down. The algorithm also enables the person to open and close folders or files or applications through a clicking mechanism.
About the journal
George K. Varotsos, Hector E. Nistazakis, Konstantinos Aidinis, Fadi Jaber. and K. K. Mujeeb Rahman , Signal Intensity Estimation in Transdermal Optical Wireless Links with Stochastic Pointing Errors Effect, Technologies, Jan 2020, doi: 10.3390/technologies8040060
Transdermal optical wireless (TOW) communication links have recently gained particular research and commercial attention as a viable alternative for establishing high speed and effective implantable data transmissions, which is vital for a variety of neuroprosthetic and other medical applications. However, the development of this optical telemetry modality with medical implanted devices (IMDs) is adversely affected by skin-induced photon absorption, scattering and pointing errors effects. Thus, in this work a minimum mean-square error (MMSE) criterion is proposed for the estimation of the optical signal intensity in a typical TOW link of varying path loss and misalignment-induced fading characteristics. In this context, the stochastic nature of the transmitter–receiver misalignment has been considered and jointly modeled with transdermal path loss. Additionally, the link is assumed to employ the suitable On–Off Keying (OOK) with intensity modulation and direct detection scheme as well as a PIN photodiode at the receiver side for signal detection. Under these assumptions the results demonstrate that the stochastic amount of pointing mismatch strongly affects the received irradiance estimation.
About the journal
G. K. Varotsos,H. E. Nistazakis,K. Aidinis,F. Jaber &K. K. Mujeeb Rahman, Transdermal subcarrier L-PSK or DBPSK optical wireless links with time diversity, skin attenuation and spatial jitter, Journal of Modern optics, pp.1233 - 1240, Oct 2020, doi: 10.1080/09500340.2020.1825848
About the journal
G.K. Varotsos; H.E. Nistazakis; K. Aidinis; G.D. Roumelas; F. Jaber; K.K.Mujeeb Rahman, Modulated Retro-Reflector Transdermal Optical Wireless Communication Systems with Wavelength Diversity over Skin-Induced Attenuation and Pointing Errors, IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Ajman, United Arab Emirates
In this work the potential for the enhancement of the outage performance of a common transdermal optical wireless (TOW) communication link is investigated by utilizing both retro-reflective modulation and wavelength diversity techniques. In this respect, an external multi-laser transceiver emits at the same time the interrogating laser signal through different optical wavelengths towards the implanted modulated retro-reflector (MRR), which modulates and reflects the arriving optical beams back to the transceiver unit equipped with the corresponding photo detector apertures, recovering data signals originated from the in-body retro-modulator. Additionally, taking into consideration the stochastic nature of pointing errors effects along with the transdermal path loss, an outage analysis is performed for the proposed TOW architecture. Under these circumstances, by jointly estimating both skin-induced attenuation and misalignment-induced fading, novel, accurate and compact mathematical expressions are derived for the evaluation of outage probability of the total TOW system, using various wavelength diversity retro-reflective configurations. The results demonstrate the feasibility of our suggestions and that the wavelength diversity is as a very efficient method for significantly enhancing the availability of a common retro-reflective TOW link.
About the journal
Conference Presentation
- Optimizing Deep Neural Networks for Categorical Variables: A Practical Case Study 3rd International Conference on Advances in Signal Processing and Communication Engineering (ICASPACE-2025, 30 & 31 January 2025).
- Mitigation of Baseline Wander in Electrooculogram Using Wavelets 3rd International Conference on Advances in Signal Processing and Communication Engineering (ICASPACE-2025, 30 & 31 January 2025).
- Classification of Mental State Using a Muse Headband and Machine Learning Algorithms, ICASPACE 2023.
- A Low-Cost Diabetic Retinopathy Screening Tool Using a Smartphone and Machine Learning Algorithm, ICASPACE 2023
- Classification of Finger Movements Using Multi-channel EMG and Machine Learning, ICASPACE 2023.
- ECG Signal Processing and Analysis for Accurate Features Extraction, International Symposium On Signal Processing and Information Technology (IEEE-ISSPIT), December - 2019.
- "Development of Bio-shield for Arduino Uno," ASET International Conference on Engineering Innovations in Healthcare (IEEE), Abu Dhabi, UAE, Feb 6-7, 2018.
- "Eye Controlled Mouse Cursor for Physically Disabled Individuals," ASET International Conference on Engineering Innovations in Healthcare (IEEE), Abu Dhabi, UAE, Feb 6-7, 2018.
- “Low Cost Bio-Electric signal acquisition system for Biomedical research applications”, 4th International conference on Bio-sensing Technology (ELSEVIER), Lisbon, Portugal, 10 – 13 May 2015.
- “Multipurpose low cost Bio-Daq system for real time Biomedical applications”, IEEE ICTRC Conference, Abu Dhabi, UAE, 17 – 19 May 2015.
- “An Algorithm for Arrhythmia Detection”, 1st Middle East Conference on Biomedical Engineering, IEEE-MECBME, Sharjah, UAE, 2011.
Optimizing Deep Neural Networks for Categorical Variables: A Practical Case Study 3rd International Conference on Advances in Signal Processing and Communication Engineering (ICASPACE-2025, 30 & 31 January 2025).
Mitigation of Baseline Wander in Electrooculogram Using Wavelets 3rd International Conference on Advances in Signal Processing and Communication Engineering (ICASPACE-2025, 30 & 31 January 2025).
Classification of Mental State Using a Muse Headband and Machine Learning Algorithms, ICASPACE 2023.
A Low-Cost Diabetic Retinopathy Screening Tool Using a Smartphone and Machine Learning Algorithm, ICASPACE 2023
Classification of Finger Movements Using Multi-channel EMG and Machine Learning, ICASPACE 2023.
ECG Signal Processing and Analysis for Accurate Features Extraction, International Symposium On Signal Processing and Information Technology (IEEE-ISSPIT), December - 2019.
"Development of Bio-shield for Arduino Uno," ASET International Conference on Engineering Innovations in Healthcare (IEEE), Abu Dhabi, UAE, Feb 6-7, 2018.
"Eye Controlled Mouse Cursor for Physically Disabled Individuals," ASET International Conference on Engineering Innovations in Healthcare (IEEE), Abu Dhabi, UAE, Feb 6-7, 2018.
“Low Cost Bio-Electric signal acquisition system for Biomedical research applications”, 4th International conference on Bio-sensing Technology (ELSEVIER), Lisbon, Portugal, 10 – 13 May 2015.
“Multipurpose low cost Bio-Daq system for real time Biomedical applications”, IEEE ICTRC Conference, Abu Dhabi, UAE, 17 – 19 May 2015.
“An Algorithm for Arrhythmia Detection”, 1st Middle East Conference on Biomedical Engineering, IEEE-MECBME, Sharjah, UAE, 2011.
Memberships, Awards and Honors
- Certificate of participation and honor (cash award) – Final round qualified research poster, Mechatronics Dept., American University of Sharjah, May 2022.
- Best Project Supervisor Award, Students Scientific Conference, Ajman University, 2010, 2014, 2019, and 2022.
- IEEE member.
- Distinguished Reviewer Certificate, Biomedical Signal Processing and Control, Elsevier Journal, 2016 and 2022.
- Best Project Live Demonstration award, 9th Annual Undergraduate Research Conference on Applied Computing (URC-2017) Organized by Zayed University, Dubai, April 26 - 27, 2017.
- Second Prize, Common Design project, IEEE Student's Day, Ajman University, Ajman, 2014.
- Recipient of Honorarium from the Institution of Engineers (India) since 2010.
- Chartered Engineer, Institution of Engineers (India), Kolkata, 2002.
- Qualified GATE-2000 and received a scholarship for M.Tech study from MHRD, India.
- M.Tech Gold Medalist (University Topper) from Visvesvaraya Technological University, 2002.
- Corporate Member of Institution of Engineers (India), Kolkata, 2000