Article
Chemistry, Analytical
Woohyuk Jang, Chaewon Lee, Dae Sik Jeong, Kunyoung Lee, Eui Chul Lee
Summary: The objective of this study was to develop an automated system for recognizing banknote serial numbers using a deep learning-based optical character recognition framework. A model was developed for recognizing the serial numbers of banknotes from South Korea, the United States, India, and Japan. The model's accuracy and generalization performance were improved through data augmentation and fine-tuning. The proposed method achieved real-time processing of less than 30 ms per image and character recognition with 99.99% accuracy.
Article
Engineering, Electrical & Electronic
Guijun Chen, Kejia Bai, Zhengchun Lin, Xiuxiu Liao, Shaopeng Liu, Zhiyong Lin, Qian Zhang, Xiping Jia
Summary: This paper proposes a practical and efficient method based on image processing to address the issues of low character recognition rate and precision in calculating water level in automatic reading of water level ruler (WLR). Experimental results demonstrate that the proposed method achieves a high character recognition rate and small measurement error.
SIGNAL IMAGE AND VIDEO PROCESSING
(2021)
Article
Computer Science, Information Systems
Sakinat O. Folorunso, Sulaimon A. Afolabi, Adeoye B. Owodeyi
Summary: This study focuses on automatic genre classification of Nigerian songs, building the ORIN dataset and training on four different classifiers, with results indicating XGBoost classifier performing the best. Further analysis reveals the similarity in timbral properties between some genres.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Kyuwon Park, Jueun Jeon, Sihyun Park, Young-Sik Jeong
Summary: This study proposes an unknown music genre classification scheme that accurately classifies both known and unknown music genres. By generating mel-spectrogram images and using the EfficientNet-B3 model for classification, the accuracy of music genre classification is improved.
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Dariusz Kania, Paulina Kania, Tomasz Lukaszewicz
Summary: This article introduces a new concept of using the trajectory of fifths to represent the content of a music piece, which enhances music data mining techniques based on music signature analysis. The trajectory of fifths reflects the variability of music signature over time and can provide valuable information about the harmonic structure of a music piece. Experiments confirm the usability of this approach in music tonality evaluation and genre classification.
Article
Computer Science, Artificial Intelligence
David Sosa-Trejo, Antonio Bandera, Martin Gonzalez, Santiago Hernandez-Leon
Summary: Since the 19th century, scientists have tried to quantify species distributions using techniques such as direct counting and microscopes. Automatic image processing and classification methods are now being utilized to avoid manual procedures for classifying marine plankton. This article summarizes the techniques proposed for classifying marine plankton from the beginning of this field to the present day, focusing on automatic methods that utilize image processing.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Shuo Zhu, Yu Wang, Zongyang Wang
Summary: This paper proposes an improved lightweight YOLOv5s model for license plate detection, using various strategies to improve the accuracy and speed of recognition. Experimental results on the CCPD dataset demonstrate that the model achieves excellent performance and is an effective approach for license plate detection.
IET IMAGE PROCESSING
(2023)
Article
Construction & Building Technology
Amir Hossein Asjodi, Mohammad Javad Daeizadeh, Mohammadjavad Hamidia, Kiarash M. Dolatshahi
Summary: A new image-based method called the Arc Length method is introduced for accurately extracting crack width and length, showing high efficiency in crack monitoring in laboratory experiments. The method has significant potential applications in crack monitoring of infrastructures like concrete bridges and tunnels.
STRUCTURAL CONTROL & HEALTH MONITORING
(2021)
Review
Computer Science, Artificial Intelligence
Shuo Meng, Ruru Pan, Weidong Gao, Benchao Yan, Yangyang Peng
Summary: This paper provides a comprehensive review of recent research on automatic recognition of woven fabric structural parameters, highlighting the drawbacks of manual operations based on human eyes and experiences and the advantages of computer-vision-based automatic methods. It offers insights for researchers in the textile industry to understand and utilize automated methods effectively.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Information Systems
Benjamin Luna-Benoso, Jose Cruz Martinez-Perales, Jorge Cortes-Galicia, Rolando Flores-Carapia, Victor Manuel Silva-Garcia
Summary: This study proposes a methodology for the detection of three types of diseases in tomato leaves using image analysis and pattern recognition. The methodology involves segmentation, feature extraction, and classification. The results of the methodology are compared with other classifiers using accuracy values and validated with cross validation.
Article
Electrochemistry
Sarathy K. Gopalakrishnan, Akash Ganesh, Chun-Chieh Wang, Matthew Mango, Kirk J. Ziegler, Ranga Narayanan
Summary: Small disturbances at an interface can lead to well-defined patterns, but nonlinear interactions can obscure the dominant wavelength in diffusion-limited system instabilities. A microfluidic cell design is presented to mitigate nonlinear effects, enabling enhanced diffusional effects and quasi-1D electrodeposition of patterns. An automated image analysis routine converts instability patterns into height profiles and objectively extracts wavelengths, with a custom filter used to identify noise and select dominant wavelengths. The results show that the dominant wavelengths in nearly all experiments were within 5% of theoretical values, with two-thirds within 1%.
ELECTROCHIMICA ACTA
(2021)
Review
Computer Science, Artificial Intelligence
Qinghua Huang, Jiakang Zhou, ZhiJun Li
Summary: Robot-assisted medical ultrasound imaging systems leverage the accuracy and stability of robotic motion to standardize medical ultrasound imaging. Compared to free-hand ultrasound, these systems can interpret image information more quantitatively and acquire stable images over a long period of time, reducing the operator's workload and providing better results. However, there are challenges to overcome before large-scale clinical application can be achieved.
Article
Agriculture, Multidisciplinary
Arnab Banerjee, Arijit Das, Samarendra Behra, Debotosh Bhattacharjee, Nagesh Talagunda Srinivasan, Mita Nasipuri, Nibaran Das
Summary: The fisheries industry heavily relies on automatic fish species identification. This study proposes the use of autoencoder network models and various classifiers to automatically identify major carps. The results show that the deep convolutional autoencoder outperforms other models in accurately identifying these fish species.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Chemistry, Multidisciplinary
Ann-Marie Parrey, Daniel Gleichauf, Michael Sorg, Andreas Fischer
Summary: The presence of defects on the leading edges of wind turbine rotor blades can result in the formation of turbulent flow regions, which can negatively impact energy production. Infrared thermography is a useful tool for visualizing the transition from laminar to turbulent flow, and a model-based algorithm has been proposed to reliably detect turbulence wedges and reduce measurement errors in additional turbulent flow regions.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Electrical & Electronic
Andres Eduardo Coca Salazar
Summary: This paper introduces a music genre classification system that utilizes hierarchical mining and a multi-hybrid feature strategy. Experimental results on three datasets demonstrate that the system achieves an accuracy of over 90%, outperforming other state-of-the-art methods.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Computer Science, Information Systems
Alexandre R. Mello, Marcelo R. Stemmer, Alessandro L. Koerich
INFORMATION SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
Jefferson G. Martins, Luiz E. S. Oliveira, Daniel Weingaertner, Andersson Barison, Gerlon A. R. Oliveira, Luciano M. Liao
Summary: Forests are being exploited disorderly and many species are endangered, prompting the need for a spatial distribution plan. Researchers facing a lack of representative databases can benefit from introducing new databases and proposing selection strategies to improve outcomes.
Review
Computer Science, Information Systems
Jonathan de Matos, Steve Tsham Mpinda Ataky, Alceu de Souza Britto, Luiz Eduardo Soares de Oliveira, Alessandro Lameiras Koerich
Summary: This paper reviews machine learning methods for histopathological image analysis, including shallow and deep learning methods, covering common tasks and datasets used in HI research.
Article
Computer Science, Artificial Intelligence
Bernardo B. Gatto, Eulanda M. dos Santos, Alessandro L. Koerich, Kazuhiro Fukui, Waldir S. S. Junior
Summary: This paper introduces a new method for multi-dimensional data classification, utilizing tensor representation and subspace concept to enhance classification accuracy. The use of generalized difference subspace (GDS) and n-mode GDS for data dimensionality reduction and discriminative feature extraction, along with the introduction of n-mode Fisher score and an improved metric based on geodesic distance for better tensor data classification performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Marine
Rafael Aguiar, Gianluca Maguolo, Loris Nanni, Yandre Costa, Carlos Silla
Summary: Passive acoustic monitoring (PAM) is a noninvasive technique for wildlife surveillance, where machine learning is useful for identifying species based on audio recordings. The experimental protocols using PAM filters were not intended to improve accuracy rates, but rather to provide more reliable results in the classification system.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Voncarlos M. Araujo, Alceu S. Britto Jr, Luiz S. Oliveira, Alessandro L. Koerich
Summary: This study proposed a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained plant species recognition, achieving effective results in identifying plant genus and species by using botanical taxonomy as a basis.
Article
Chemistry, Analytical
Lucas O. Teixeira, Rodolfo M. Pereira, Diego Bertolini, Luiz S. Oliveira, Loris Nanni, George D. C. Cavalcanti, Yandre M. G. Costa
Summary: The study demonstrated the impact of lung segmentation in COVID-19 identification using CXR images, achieving good Jaccard distance and Dice coefficient for segmentation. It investigated the generalization of COVID-19 from images created from different sources, finding a strong bias introduced by underlying factors from different sources even after segmentation.
Article
Chemistry, Multidisciplinary
Thomas Teixeira, Eric Granger, Alessandro Lameiras Koerich
Summary: This paper investigates the use of deep learning architectures for continuous emotion recognition, extending 2D CNN models to learn spatiotemporal information from videos. Experimental results on the SEWA-DB dataset show that these architectures can effectively encode spatiotemporal information and achieve state-of-the-art results.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Steve Tsham Mpinda Ataky, Alessandro Lameiras Koerich
Summary: This paper proposes a novel approach to quantifying complex systems of diverse patterns in texture, using species diversity, richness, and taxonomic distinctiveness. The method takes advantage of ecological patterns' invariance to build a permutation, rotation, and translation invariant descriptor. Experimental results show the advantages of this method.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Mateus F. T. Carvalho, Sergio A. Silva Jr, Carla Cristina O. Bernardo, Franklin Cesar Flores, Juliana Vanessa C. M. Perles, Jacqueline Nelisis Zanoni, Yandre M. G. Costa
Summary: This study investigates automatic detection of cancer in laboratory animals using preclinical microphotograph images of liver tissue. Two different texture descriptors were explored to capture texture properties and their complementarity was evaluated. Results showed that both descriptors performed well in this scenario.
Article
Computer Science, Artificial Intelligence
Thiago M. Paixao, Rodrigo F. Berriel, Maria C. S. Boeres, Alessandro L. Koerich, Claudine Badue, Alberto F. De Souza, Thiago Oliveira-Santos
Summary: Advances in machine learning, especially deep learning, have improved the accuracy of automatically reconstructing shredded documents. However, there is still room for improvement in fully automatic reconstruction. To address this issue, we propose a human-in-the-loop reconstruction framework that allows users to verify the adjacency of adjacent shreds in the solution. Introducing human involvement can reduce errors by over 40%.
PATTERN RECOGNITION LETTERS
(2022)
Article
Computer Science, Theory & Methods
Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich
Summary: This paper introduces a defense approach against adversarial attacks on speech-to-text systems. The proposed algorithm utilizes short-time Fourier transform, spectrogram subspace projection, and a novel GAN architecture trained with Sobolev integral probability metric. Experimental results demonstrate that it outperforms other defense algorithms in terms of accuracy and signal quality.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Proceedings Paper
Acoustics
Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich
Summary: This paper introduces a novel defense approach against end-to-end adversarial attacks by finding the optimal input vector through minimizing the relative chordal distance adjustment and reconstructing the signal. Experimental results show that this approach significantly outperforms conventional defense algorithms.
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
(2021)
Article
Engineering, Electrical & Electronic
Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich
Summary: This letter introduces a new defense approach utilizing a cyclic generative adversarial network to reconstruct signals for countering state-of-the-art white and black-box adversarial attack algorithms. Experimental results show the effectiveness of this defense method in various adversarial attack scenarios.
IEEE SIGNAL PROCESSING LETTERS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Sevegni Odilon Clement Allognon, Alceu de S. Britto Jr, Alessandro L. Koerich
2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2020)
Article
Engineering, Electrical & Electronic
Alam Abbas Syed, Hassan Foroosh
Summary: This paper presents effective methods using spherical polar Fourier transform data for two different applications: 3D volumetric registration and machine learning classification network. The proposed method for registration offers unique and effective techniques, handling arbitrary large rotation angles and showing robustness. The modified classification network achieves robust classification results in processing spherical data.
Article
Engineering, Electrical & Electronic
Ruibo Fan, Mingli Jing, Jingang Shi, Lan Li, Zizhao Wang
Summary: In this study, a new low-rank sparse decomposition algorithm named TVRPCA+ is proposed for foreground-background separation. The algorithm combines spectral norm, structured sparse norm, and total variation regularization to suppress noise and obtain cleaner foregrounds. Experimental results demonstrate that TVRPCA+ achieves high performance in complex backgrounds and noise scenarios.
Article
Engineering, Electrical & Electronic
Omair Aldimashki, Ahmet Serbes
Summary: This paper proposes a coarse-to-fine FrFT-based algorithm for chirp-rate estimation of multi-component LFM signals, which achieves improved performance and a reduced signal-to-noise breakdown threshold by utilizing mathematical models for coarse estimation and a refined estimate-and-subtract strategy. Extensive simulation results demonstrate that the proposed algorithm performs very close to the Cramer-Rao lower bound, with the advantages of eliminating leakage effect, avoiding error propagation, and maintaining acceptable computational cost compared to other state-of-the-art methods.
Article
Engineering, Electrical & Electronic
Xinlei Shi, Xiaofei Zhang, Yuxin Sun, Yang Qian, Jinke Cao
Summary: In this paper, a low-complexity localization approach for multiple sources using two-dimensional discrete Fourier transform (2D-DFT) is proposed. The method computes the cross-covariance and utilizes phase offset method and total least square solution to obtain accurate position estimates.
Article
Engineering, Electrical & Electronic
Prabhanjan Mannari, Ratnasingham Tharmarasa, Thiagalingam Kirubarajan
Summary: This paper discusses the problem of extended target tracking for a single 2D extended target with a known convex polytope shape and dynamics. It proposes a framework based on the existing point multitarget tracking framework to address the challenges of uncertainty in shape and kinematics, as well as self-occlusion. The algorithm developed using this framework is capable of dynamically changing the number of parameters used to describe the shape and estimating the whole target shape even when different parts of the target are visible at different frames.
Article
Engineering, Electrical & Electronic
Yongsong Li, Zhengzhou Li, Jie Li, Junchao Yang, Abubakar Siddique
Summary: This paper proposes a weighted adaptive ring top-hat transformation (WARTH) for extracting infrared small targets in complex backgrounds. The WARTH method effectively measures local and global feature information using an adaptive ring-shaped structural element and a target awareness indicator, resulting in accurate detection of small targets with minimized false alarms.
Article
Engineering, Electrical & Electronic
Yu Wang, Zhen Qin, Jun Tao, Yili Xia
Summary: In this paper, an enhanced sparsity-aware recursive least squares (RLS) algorithm is proposed, which combines the proportionate updating (PU) and zero-attracting (ZA) mechanisms, and introduces a general convex regularization (CR) function and variable step-size (VSS) technique to improve performance.
Article
Engineering, Electrical & Electronic
Neil J. Bershad, Jose C. M. Bermudez
Summary: This paper analyzes the impact of processing delay on the Least Mean Squares (LMS) algorithm in system identification, highlighting bias issues in the resulting weight vector.
Article
Engineering, Electrical & Electronic
Kanghui Jiang, Defu Jiang, Mingxing Fu, Yan Han, Song Wang, Chao Zhang, Jingyu Shi
Summary: In this paper, a novel method for velocity estimation using multicarrier signals in a single dwell is proposed, which effectively addresses the issue of Doppler ambiguity in pulse Doppler radars.
Article
Engineering, Electrical & Electronic
Xiao-Jun Zhang, Peng-Lang Shui, Yu-Fan Xue
Summary: This paper proposes a method for low-velocity small target detection in maritime surveillance radars. It models sea clutter sequences using the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and inverse Gamma distributed texture. The proposed detector, which is a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD), shows competitive detection performance in experiments.
Article
Engineering, Electrical & Electronic
Aiyi Zhang, Fulai Liu, Ruiyan Du
Summary: This paper proposes an adaptive weighted robust data recovery method with total variation regularization for hyperspectral image. The method models the HSI recovery problem as a tensor robust principal component analysis optimization problem, decomposing the data into low-rank HSI data, outliers, and noise component. An adaptive weighted strategy is then defined to impose on the tensor nuclear norm and outliers, using the priori information of singular values and strengthening the sparsity of outliers.
Article
Engineering, Electrical & Electronic
Hamid Asadi, Babak Seyfe
Summary: This paper presents a novel approach for estimating the model order in the presence of observation errors. The proposed method is based on correntropy estimation of eigenvalues in the observation space, which is further enhanced by resampling the observations using the bootstrap method. The algorithm partitions the observation space into signal and noise subspaces using the covariance matrix of mixtures, and determines the model order based on a correntropy estimator with kernel functions. Theoretical analysis and comparative evaluations demonstrate the superiority of this information-theoretic approach.
Article
Engineering, Electrical & Electronic
Buket colak Guvenc, Engin Cemal Menguc
Summary: In this paper, a novel family of online censoring based complex-valued least mean kurtosis (CLMK) algorithms is proposed. The algorithms censor less informative complex-valued data streams and reduce the costs of data processing without affecting accuracy. Robust algorithms are also developed to handle outliers. The simulation results confirm the attractive features of the proposed algorithms in large-scale system identification and regression scenarios.
Article
Engineering, Electrical & Electronic
Yun Su, Weixian Tan, Yifan Dong, Wei Xu, Pingping Huang, Jianxin Zhang, Diankun Zhang
Summary: In this study, a novel method for detecting low-resolution and small targets in millimeter wave radar images is proposed. The Wavelet-Conv structure and Wavelet-Attention mechanism are introduced to overcome the limitations of existing detectors. Experimental results demonstrate that the proposed method improves recall and mean average precision while maintaining competitive inference speed.
Article
Engineering, Electrical & Electronic
Xin Wang, Xingxing Jiang, Qiuyu Song, Jie Liu, Jianfeng Guo, Zhongkui Zhu
Summary: This study proposes a variational mode extraction (VME) method for extracting specific modes from complicated signals. By exploring the convergence property of VME, strategies for identifying ICF and determining the balance parameter are designed, and a bandwidth estimation strategy is constructed. The effectiveness of the proposed method for bearings fault diagnosis is verified and compared with other methods.