Moncef Gabbouj

Moncef Gabbouj
Tampere University | UTA · Department of Signal Processing

Ph.D.

About

1,029
Publications
275,235
Reads
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26,757
Citations
Additional affiliations
January 2011 - December 2015
Tampere University of Technology
Position
  • Professor
January 2002 - July 2007
Tampere University of Technology
Position
  • Head of Department
January 1998 - present
Tampere University
Position
  • Professor (Full)
Education
January 1987 - December 1989
Purdue University
Field of study
  • Electrical Engineering
August 1985 - December 1986
Purdue University
Field of study
  • Electrical Engineering
August 1981 - May 1985
Oklahoma State University - Stillwater
Field of study
  • Electrical Engineering

Publications

Publications (1,029)
Preprint
Full-text available
Objective: Many studies on radar signal restoration in the literature focus on isolated restoration problems, such as denoising over a certain type of noise, while ignoring other types of artifacts. Additionally, these approaches usually assume a noisy environment with a limited set of fixed signal-to-noise ratio (SNR) levels. However, real-world r...
Article
Full-text available
Robust and real-time detection of faults has become an ultimate objective for predictive maintenance on rotating machinery. Vibration-based Deep Learning (DL) methodologies have become the de facto standard for bearing fault detection as they can produce state-of-the-art detection performances under certain conditions. Despite such particular foc...
Preprint
Full-text available
Vibration sensors are essential in acquiring seismic activity for an accurate earthquake assessment. The state-of-the-art sensors can provide the best signal quality and the highest bandwidth; however, their high cost usually hinders a wide range of applicability and coverage, which is otherwise possible with their basic and cheap counterparts. But...
Preprint
Full-text available
Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage. This study introduces a novel method for early MI detection using a one-class classification (OCC) algorithm in echocardiography. Our study overcomes the challenge of limited echocardiography...
Article
Full-text available
In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the locations of non-zero elements in sparse signals. By its very nature, the mapping between the measurement and sparse signal is a non-linear operation. Traditional support...
Article
Full-text available
Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the r...
Preprint
Full-text available
Deep learning-based informative band selection methods on hyperspectral images (HSI) recently have gained intense attention to eliminate spectral correlation and redundancies. However, the existing deep learning-based methods either need additional post-processing strategies to select the descriptive bands or optimize the model indirectly, due to t...
Preprint
Full-text available
Robust and real-time detection of faults has become an ultimate objective for predictive maintenance on rotating machinery. Studies have indicated that around half of all motor failures are attributed to bearing faults alone. Vibration-based Deep Learning (DL) methodologies have become the de facto standard for bearing fault detection as they can p...
Preprint
Full-text available
Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns. Bearing faults are primary contributors to machine vibrations, and analyzing their signal morphology offers insights into their health status. Unfortunately, existing approaches are optimized for controlled environments, neglecting realistic condition...
Article
This article proposes a novel multichannel fault recognition strategy for planetary gearboxes using difference-average symbol transition entropy (DASTE) and twin support higher-order tensor machine (TwSHTM). In the proposed hybrid framework, a new feature representation method, namely DASTE is first proposed to capture rich information inherent in...
Article
Full-text available
In marine search and rescue missions, the objective is to find a missing person in the water. Time is a critical factor in the identification of the missing person, as any delay in locating them can have life-threatening consequences. Autonomous unmanned aerial vehicles (UAVs) possess the potential to help in the search task by providing a bird’s-e...
Article
Full-text available
The Just Noticeable Difference (JND) refers to the smallest distortion in an image or video that can be perceived by Human Visual System (HVS), and is widely used in optimizing image/video compression. However, accurate JND modeling is very challenging due to its content dependence, and the complex nature of the HVS. Recent solutions train deep lea...
Article
Full-text available
Detecting malware is crucial for ensuring the security of computer systems. Traditional machine learning models face challenges in effectively detecting malware, mainly due to the class imbalance problem, where the number of malware samples is significantly smaller than that of non-malware samples. Additionally, malware’s dynamic and evolving natur...
Article
Full-text available
The emerging Learned Compression (LC) replaces the traditional codec modules with Deep Neural Networks (DNN), which are trained end-to-end for rate-distortion performance. This approach is considered as the future of image/video compression, and major efforts have been dedicated to improving its compression efficiency. However, most proposed works...
Article
Full-text available
Spatio-temporal information plays an important role in compressed video quality enhancement. Most advanced studies use deformable convolution or Swin transformer to explore spatio-temporal information. However, deformable convolution based methods may incur inaccurate motion compensation due to the compression artifacts and limited receptive fields...
Preprint
Full-text available
Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected failures. However, many existing techniques are developed and tested under strictly controlled conditions, limiting their adaptability to the diverse and dynamic settings...
Article
Full-text available
The efforts in compressive sensing (CS) literature can be divided into two groups: finding a measurement matrix that preserves the compressed information at its maximum level, and finding a robust reconstruction algorithm. In the traditional CS setup, the measurement matrices are selected as random matrices, and optimization-based iterative solutio...
Preprint
Full-text available
Controller Area Network bus systems within vehicular networks are not equipped with the tools necessary to ward off and protect themselves from modern cyber-security threats. Work has been done on using machine learning methods to detect and report these attacks, but common methods are not robust towards unknown attacks. These methods usually rely...
Preprint
Full-text available
In an increasingly digitalized commerce landscape, the proliferation of credit card fraud and the evolution of sophisticated fraudulent techniques have led to substantial financial losses. Automating credit card fraud detection is a viable way to accelerate detection, reducing response times and minimizing potential financial losses. However, addre...
Preprint
Full-text available
In this paper, we present an adaptation of Newton's method for the optimization of Subspace Support Vector Data Description (S-SVDD). The objective of S-SVDD is to map the original data to a subspace optimized for one-class classification, and the iterative optimization process of data mapping and description in S-SVDD relies on gradient descent. H...
Preprint
Full-text available
Myocardial infarction (MI) is a severe case of coronary artery disease (CAD) and ultimately, its detection is substantial to prevent progressive damage to the myocardium. In this study, we propose a novel view-fusion model named self-attention fusion network (SAF-Net) to detect MI from multi-view echocardiography recordings. The proposed framework...
Chapter
We study the diversity of the features learned by a two-layer neural network trained with the least squares loss. We measure the diversity by the average \(L_2\)-distance between the hidden-layer features and theoretically investigate how learning non-redundant distinct features affects the performance of the network. To do so, we derive novel gene...
Preprint
Full-text available
In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the locations of non-zero elements in a sparse signal. By its very nature, the mapping between the measurement and sparse signal is a non-linear operation. Traditional support...
Conference Paper
Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification. Due to the significant amount of spectral information from recent HSI sensors, analyzing the acquired images is challenging using traditional Machine Learning (ML) meth...
Article
Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one. At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy. In thi...
Preprint
Full-text available
Energy-based learning is a powerful learning paradigm that encapsulates various discriminative and generative approaches. An energy-based model (EBM) is typically formed of inner-model(s) that learn a combination of the different features to generate an energy mapping for each input configuration. In this paper, we focus on the diversity of the pro...
Preprint
Full-text available
Automatic sensor-based detection of motor failures such as bearing faults is crucial for predictive maintenance in various industries. Numerous methodologies have been developed over the years to detect bearing faults. Despite the appearance of numerous different approaches for diagnosing faults in motors have been proposed, vibration-based methods...
Preprint
As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide. Warmer temperatures and drier conditions can cause quickly spreading fires and make them harder to control; therefore, early detection and accurate locating of active fires are crucial in environmental monitoring. Us...
Preprint
Full-text available
Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification. Due to the significant amount of spectral information from recent HSI sensors, analyzing the acquired images is challenging using traditional Machine Learning (ML) meth...
Preprint
High-frequency trading requires fast data processing without information lags for precise stock price forecasting. This high-paced stock price forecasting is usually based on vectors that need to be treated as sequential and time-independent signals due to the time irregularities that are inherent in high-frequency trading. A well-documented and te...
Preprint
Full-text available
Hyperspectral Imaging is a crucial tool in remote sensing which captures far more spectral information than standard color images. However, the increase in spectral information comes at the cost of spatial resolution. Super-resolution is a popular technique where the goal is to generate a high-resolution version of a given low-resolution input. The...
Preprint
Full-text available
Camera calibration involves estimating camera parameters to infer geometric features from captured sequences, which is crucial for computer vision and robotics. However, conventional calibration is laborious and requires dedicated collection. Recent efforts show that learning-based solutions have the potential to be used in place of the repeatabili...
Article
Full-text available
With over 17 million annual deaths, cardiovascular diseases (CVDs) dominate the cause of death statistics. CVDs can deteriorate the quality of life drastically and even cause sudden death, all the while inducing massive healthcare costs. This work studied state-of-the-art deep learning techniques to predict increased risk of death in CVD patients,...
Preprint
Full-text available
The health and safety hazards posed by worn crane lifting ropes mandate periodic inspection for damage. This task is time-consuming, prone to human error, halts operation, and may result in the premature disposal of ropes. Therefore, we propose using deep learning and computer vision methods to automate the process of detecting damaged ropes. Speci...
Chapter
Classification of SAR images has been an interesting task considering its major role in environmental and natural research areas. Existing studies proposed for Land use/land cover (LU/LC) classification using SAR data can be grouped into two categories: traditional Machine Learning (ML) approaches and approaches that are based on deep Convolutional...
Preprint
Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one. At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy. In thi...
Article
Full-text available
No-Reference Image Quality Assessment aims to evaluate the perceptual quality of an image, according to human perception. Many recent studies use Transformers to assign different self-attention mechanisms to distinguish regions of an image, simulating the perception of the human visual system (HVS). However, the quadratic computational complexity c...
Article
Full-text available
Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neurons that yield an elegant level of diversity; however, like its predecessor, conventional Convolutional Neural Networks (CNNs), they still have a common drawback: localiz...
Article
Full-text available
Hyperspectral Imaging is a crucial tool in remote sensing which captures far more spectral information than standard color images. However, the increase in spectral information comes at the cost of spatial resolution. Super-resolution is a popular technique where the goal is to generate a high-resolution version of a given low-resolution input. The...
Article
Full-text available
Convolutional neural networks have achieved excellent results in compressed video quality enhancement task in recent years. State-of-the-art methods explore the spatio-temporal information of adjacent frames mainly by deformable convolution. However, the CNN-based methods can only exploit local information, thus lacking the exploration of global in...
Preprint
Full-text available
Objective: Despite numerous studies proposed for audio restoration in the literature, most of them focus on an isolated restoration problem such as denoising or dereverberation, ignoring other artifacts. Moreover, assuming a noisy or reverberant environment with limited number of fixed signal-to-distortion ratio (SDR) levels is a common practice. H...
Preprint
Full-text available
Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the r...
Preprint
Full-text available
Learned Compression (LC) is the emerging technology for compressing image and video content, using deep neural networks. Despite being new, LC methods have already gained a compression efficiency comparable to state-of-the-art image compression, such as HEVC or even VVC. However, the existing solutions often require a huge computational complexity,...
Article
Full-text available
In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional Support Estimator Networks (CSENs). CSENs are designed to compute a direct mapping for the Support Estimation (SE) task in a representation-based classification problem....
Preprint
Convolutional neural networks have achieved excellent results in compressed video quality enhancement task in recent years. State-of-the-art methods explore the spatiotemporal information of adjacent frames mainly by deformable convolution. However, offset fields in deformable convolution are difficult to train, and its instability in training ofte...
Conference Paper
Application of domain adaptation techniques to predictive maintenance of modern electric rotating machinery (RM) has significant potential with the goal of transferring or adaptation of a fault diagnosis model developed for one machine to be generalized on new machines and/or new working conditions. The generalized nonlinear extension of convention...
Conference Paper
Full-text available
Coronavirus disease 2019 (COVID-19) has been diagnosed automat-ically using Machine Learning algorithms over chest X-ray (CXR)images. However, most of the earlier studies used Deep Learningmodels over scarce datasets bearing the risk of overfitting. Addi-tionally, previous studies have revealed the fact that deep networksare not reliable for classi...