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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Guerfel, Mohamed Abdelwahed, Imen Ben Jaffel, Ines Bouzrara, Kais Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Electronics department Tunisia National Engineering School of Monastir University of Monastir Laboratory of Automatic Control Signal and Image Processing Tunisia Higher Institute of Applied Sciences and Technology of Kairouan University of Kairouan Tunisia

ISBN: (纸本)9798350389838

This paper explores the application of genetic algorithms to optimize ARX-Laguerre model parameters. It outlines the ARX-Laguerre modeling process and genetic algorithm principles. A fitness function is chosen and the impact of noise on it is addressed. A new methodology for optimizing Laguerre poles using genetic algorithms is proposed. It is used in modeling a real 2-Degree-of-Freedom helicopter offering valuable insights for real-world applications. © 2024 IEEE.

关键词： Genetic algorithms

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On Cognitive Radar Jamming for countering Cognitive Electromagnetic Protection systems

On Cognitive Radar Jamming for countering Cognitive Electrom...

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Technologies for Defense and Security (TechDefense), IEEE International Workshop on

作者： Hemanga Banerjee Christos Ilioudis Carmine Clemente Chris Williams Centre for Signal and Image Processing University of Strathclyde Glasgow Defence Science and Technology Laboratory Porton Down Wiltshire United Kingdom

ISBN: (数字)9798331505585

ISBN: (纸本)9798331505592

The paper introduces a novel model of a fully adaptive cognitive radar jamming approach, designed for the purpose of countering modern cognitive radars. Cognitive radar systems have been widely used in modern electromagnetic warfare and also serve as an effective counter to traditional jamming techniques. Cognitive jamming makes use of machine learning and adaptive signal processing strategies to dynamically adapt jamming strategies in response to real-time changes in target radar transmissions. The proposed model may be used to incorporate advanced reinforcement learning algorithms, signal feature extraction techniques, and waveform optimization techniques to create a cohesive system that can optimize jamming strategies with minimal prior knowledge of the electromagnetic environment. The paper also discusses the state-of-the-art in cognitive radar jamming technologies, and their efficiency in the face of intelligent radar equipped with advanced anti-jamming measures. Furthermore, it addresses current challenges and future directions in the field with considerations for defense and security applications.

关键词： Electromagnetic scattering signal processing algorithms Cognitive radar Feature extraction Real-time systems Security Jamming Electromagnetics Protection Optimization

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Bacterial community in response to packaging conditions in farmed gilthead seabream

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Aquaculture and Fisheries 2023年第4期8卷 410-421页

作者： Apollon Thomas Spyros J.Konteles Sotiris Ouzounis Spyros Papatheodorou Aliki Tsakni Dimitra Houhoula Theofania Tsironi Department of Food Science and Technology Faculty of Food SciencesUniversity of West AtticaAthens11521Greece Medical Image and Signal Processing Laboratory Department of Biomedical EngineeringUniversity of West AtticaAthens11521Greece Laboratory of Food Process Engineering Department of Food Science and Human NutritionAgricultural University of AthensAthens11855Greece

The objective of this study was to evaluate the effect of modified atmosphere packaging(MAP)on fish skin,gills and intestines bacterial *** gilthead seabream was packed aerobically or under modified atmospheres(60%CO_(2),30%N_(2),10%O_(2))and stored isothermally at 0℃.Next Generation Sequencing(NGS)analysis was applied for the characterization of fish microbiome on fish skin,gills,and intestines initially(time of packaging,1 day after harvesting)and after 10 days of isothermal storage at 0℃.NGS results indicated statistically significant differences in families’richness and diversity in the tested fish tissues between aerobic and MAP packaging during storage at 0℃.The most persistent bacteria were Proteobacteria for both packaging *** fish skin microbiota,the initially prevailing families were Comamonadaceae,Enterobacteriaceae and Moraxellaceae while in the intestines Comamonadaceae,Anaplasmataceae,Bacillaceae and Enterobacteriaceae were the dominant *** the end of storage period,the fish microbiota was dominated by psychotropic and psychrophilic families(Pseudoalteromonadaceae,Psychromonadaceae,and Shewanellaceae),while families such as Comamonadaceae were persistent under MAP *** 8 days of fish storage at 0℃,МАРsamples exhibited higher sensory scorings than the respective aerobically stored fish,indicating better retention of fish freshness and fish quality attributes under *** on the results of the study,MAP modified significantly the microbiological status and extended the shelf life of *** was a powerful tool that provided a more complete assessment compared to a culture-based analysis.

关键词： Fish Preservation Next generation sequencing Modified atmosphere packaging Microbiome

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Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm

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Energy Reports 2023年 10卷 3113-3124页

作者： Attouri, Khadija Dhibi, Khaled Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Mohamed Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Qatar Chemical Engineering Program Texas A&M University at Qatar Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir 5019 Tunisia

This paper introduces a pioneering fault diagnosis technique termed Interval Ensemble Learning based on Sine Cosine Optimization Algorithm (IEL- SCOA), tailored to tackle uncertainties prevalent in wind energy conversion (WEC) systems. The approach unfolds in three integral phases. Firstly, the establishment of interval centers and ranges, employing upper and lower bounds, effectively manages the inherent uncertainties arising from noise and measurement errors intrinsic to the wind system. Subsequently, the dataset undergoes processing via the Sine-Cosine Optimization Algorithm (SCOA), enabling the extraction of the most pertinent attributes. The culmination of predictive precision and classification performance is achieved through the integration of the refined dataset into an ensemble learning paradigm, harmonizing bagging, boosting techniques, and an artificial neural network classifier. The principal aim of the IEL-SCOA approach is to discern the spectrum of operational conditions within WEC systems, encompassing a healthy mode alongside six distinct faulty modes. These anomalies, encompassing short circuits, open circuits, and wear-out incidents, are deliberately induced at diverse locations and facets of the system, notably the generator and grid sides. Empirical results underscore the robustness and efficiency of the proposed methodology, showcasing an exceptional accuracy rate of 99.76 %. These outcomes definitively establish the IEL-SCOA approach as a potent and efficacious tool for precise fault diagnosis in uncertain WEC systems. © 2023

关键词： Fault detection

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Evaluation of Classifiers for the Identification of Multiple Sclerosis Lesions in Neural MRI Scans with Attributes Extracted from Pre-trained Neural Networks 9th

Evaluation of Classifiers for the Identification of Multiple...

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9th Latin American Congress on Biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical Engineering, CBEB 2022

作者： Vital, D.A. Kudo, M.S. Marconatto, L.P. Moraes, M.C. Abdala, N. Department of Diagnostic Imaging Paulista School of Medicine Federal University of São Paulo UNIFESP São Paulo Brazil Laboratory of Image and Signal Processing Institute of Science and Technology Federal University of São Paulo UNIFESP São José dos Campos Brazil

ISBN: (纸本)9783031494031

About 2.8 million people worldwide have multiple sclerosis, and around 250,000 new cases are diagnosed annually. This disease damages the myelin sheath of neurons, injuring electrical signal conduction, and causing impairment and loss of senses, movement, and other neurological functions. Although the disease has no cure, early diagnosis is essential for the initiation of adequate treatment, controlling eventual outbreaks, delaying the advance, and improving the quality of life of patients. Neuroaxis magnetic resonance imaging is used to investigate this disease, identifying and following up lesions in the brain and spinal cord tissues;however, the diagnosis only through the visual evaluation of these exams may lack information for a quick and accurate analysis. Recent studies present computational methods based on artificial intelligence that allow the identification of lesions caused by the disease, aiming to overcome these visual limitations, but still have limitations in terms of accuracy and scope. The objective of this work was to evaluate the potential of classifiers based on machine learning algorithms in the identification of multiple sclerosis lesions in brain tissue. Axial FLAIR MRI brain exams were used from two databases combining filtering, normalization, and enhancement image pre-processing methods to extract the exam attributes. The algorithms evaluated were Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine and Logistic Regression, through training with attributes extracted from 3 pre-trained neural models: SqueezeNet, Inception V3 and VGG-19. The classifiers were evaluated by the classification of images from the studies as "with lesion" and "without lesion", and the SVM classifier trained with attributes extracted from the pre-trained neural model Inception V3 provided the best result, obtaining AUC = 0.988, accuracy = 0.980, sensitivity = 0.990 and F1-Score = 0.979. © The Author(s), under exclusive license to Springer Natur

关键词： Magnetic resonance imaging

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ConvNextUNet: A small-region attentioned model for cardiac MRI segmentation

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Computers in Biology and Medicine 2024年 177卷 108592-108592页

作者： Zhang, Huiyi Cai, Zemin The Department of Electronic Engineering Shantou University Guangdong Shantou515063 China Key Laboratory of Digital Signal and Image Processing of Guangdong Province Guangdong Shantou515063 China

Cardiac MRI segmentation is a significant research area in medical image processing, holding immense clinical and scientific importance in assisting the diagnosis and treatment of heart diseases. Currently, existing cardiac MRI segmentation algorithms are often constrained by specific datasets and conditions, leading to a notable decrease in segmentation performance when applied to diverse datasets. These limitations affect the algorithm's overall performance and generalization capabilities. Inspired by ConvNext, we introduce a two-dimensional cardiac MRI segmentation U-shaped network called ConvNextUNet. It is the first application of a combination of ConvNext and the U-shaped architecture in the field of cardiac MRI segmentation. Firstly, we incorporate up-sampling modules into the original ConvNext architecture and combine it with the U-shaped framework to achieve accurate reconstruction. Secondly, we integrate Input Stem into ConvNext, and introduce attention mechanisms along the bridging path. By merging features extracted from both the encoder and decoder, a probability distribution is obtained through linear and nonlinear transformations, serving as attention weights, thereby enhancing the signal of the same region of interest. The resulting attention weights are applied to the decoder features, highlighting the region of interest. This allows the model to simultaneously consider local context and global details during the learning phase, fully leveraging the advantages of both global and local perception for a more comprehensive understanding of cardiac anatomical structures. Consequently, the model demonstrates a clear advantage and robust generalization capability, especially in small-region segmentation. Experimental results on the ACDC, LVQuan19, and RVSC datasets confirm that the ConvNextUNet model outperforms the current state-of-the-art models, particularly in small-region segmentation tasks. Furthermore, we conducted cross-dataset training and testin

关键词： Diagnosis

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Pyramidal deep neural network for classification of retinal OCT images

Pyramidal deep neural network for classification of retinal ...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Mohammad Almasganj Emad Fatemizadeh Department of Electrical Engineering Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran

Retinal optical coherence tomography (OCT) images are widely used to diagnose and grade macular diseases, such as age-related macular degeneration (AMD). However, manual interpretation of OCT images is time-consuming and subjective. Therefore, automated and accurate classification of OCT images is essential for assisting ophthalmologists in clinical decision-making. This paper proposes a pyramidal deep neural network that can diagnose normal and two types of AMD (dry and wet) in OCT images. Our network leverages features from different scales of a pre-trained convolutional neural network (CNN) and integrates them with two advanced versions of feature pyramid networks: bidirectional feature pyramid network (BiFPN) and path aggregation network (PANet). We evaluate our network on the NEH dataset and compare it with its predecessor. Our results show that our BiFPN-VGG16 and PAN-VGG16 models achieve accuracies of 94.S% and 95.0%, respectively, which are 2.8 to 3% higher than the previous models. Our approach demonstrates the potential of multi-scale feature networks for OCT image classification and can serve as an auxiliary diagnostic tool for ophthalmologists.

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Research on state monitoring of bridge cable based on optical fiber sensing technology

Research on state monitoring of bridge cable based on optica...

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International Conference on Optical Communications and Networks (ICOCN)

作者： Chunying Xu Xinjie Wu Ruixin Liang Yu Zhou Chengyu Yang Chuliang Wei College of Enineering Shantou Guangdong Province China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing

In this paper, the strain and vibration of the bridge cable and its compensation device were monitored using fiber optic grating (FBG) sensing technology on the Zhoushan-Daishan Bridge in Zhoushan, Zhejiang Province. During the monitoring period, the bridge experienced two typhoon events. The strain and vibration signals were analyzed. The results showed that the typhoon 'Muifa' had a greater impact on the Zhoushan-Daishan Bridge than typhoon 'Hinnamnor'. And there was no movement of the expansion compensation device during the monitoring period.

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Rooftop Surfaces and Types Identification Using VHR Satellite images for Electrification Potentiel in Africa

Rooftop Surfaces and Types Identification Using VHR Satellit...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ferdaous Chaabane Safa Réjichi Florence Tupin COSIM Laboratory SUP’COM Carthage University Tunisia Department of Image and Signal Processing Telecom ParisTech France

Automatic extraction and recognition of roof structures, surfaces and types from remotely sensed data is one of the most notable challenges for installing urban photovoltaics panels which is of great importance for policymakers planning and investing in distributed energy infrastructures and electrification. DL techniques applied on VHR satellite images, allows to overcome the limitations of roofs surveys in providing this mapping at large *** paper proposes a DL based approach for mapping the location and identifying the type of roof surfaces starting from VHR images. The originality of this work is the automatization of roof types classification (metal, concrete, wood, etc.) independently from the country style (Africa, Europe, etc.). Indeed, to classify roof types using DL techniques you need a dataset covering all roof types. Data collection and labelling is usually done manually which is very time consuming. The proposed approach constructs a new dataset of roof types adapted for every region of interest using DL features extraction and clustering. It overcomes the appearance of new roof types especially in developing *** experimental results show that proposed approach can effectively and accurately detect and recognize roof types and has competitive performance. .

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Exoskeleton Dynamic Modeling and Identification with Load and Temperature-Dependent Friction Model 2

Exoskeleton Dynamic Modeling and Identification with Load an...

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2nd IEEE International Conference on signal, Control and Communication, SCC 2021

作者： Hassen, Mouna Dali Laamiri, Imen Messaoud, Hassani University of Monastir Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir 5019 Tunisia

ISBN: (纸本)9781665402705

Frictional losses within the transmission components of robotic manipulators are often modeled as a constant Coulomb friction force or/and a viscous friction force proportional to the sliding velocity. However, the variation in contact forces of lubricated surfaces of the mechanical transmission system influences the friction force. The joint temperature also has an effect on the variation of the latter. Hence, the importance of considering the parameters mentioned in the dynamic model of the robotic manipulator. This paper presents a new load and temperature-dependent model that will be subsequently identified from experimental measurements carried out on two-link robotic manipulator with different loads. © 2021 IEEE.

关键词： Friction

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