Article
Engineering, Electrical & Electronic
Manish Narwaria
Summary: The landscape of signal processing and education is significantly affected by the emergence of machine learning (ML) and particularly deep learning (DL). DL is capable of modeling complex and unknown relationships between signals and tasks, and has been successful in recognizing useful information in various applications. It learns a mapping function from labeled data, and is able to correctly recognize and classify relevant information in test signals.
IEEE SIGNAL PROCESSING MAGAZINE
(2023)
Article
Mathematics
Luke T. Woods, Zeeshan A. Rana
Summary: We use encoder-only transformers and human pose estimation keypoint data to model American Sign Language (ASL). By using an enhanced version of the WLASL dataset and a novel normalisation technique, we demonstrate the impact of model architecture on accurately classifying sets of ASL signs. We achieve high accuracy on reduced vocabulary datasets, setting a new benchmark for this task.
Review
Chemistry, Analytical
Ovishake Sen, Anna M. Sheehan, Pranay R. R. Raman, Kabir S. Khara, Adam Khalifa, Baibhab Chatterjee
Summary: Brain-Computer Interfaces (BCIs) have gained popularity for their potential applications in various fields. This review paper analyzes existing research on handwriting and speech recognition from neural signals, providing a valuable resource for future researchers in this area.
Review
Chemistry, Analytical
Xin Roy Lim, Chin Poo Lee, Kian Ming Lim, Thian Song Ong, Ali Alqahtani, Mohammed Ali
Summary: Autonomous vehicles heavily rely on accurate traffic sign recognition in order to drive safely and efficiently. However, the variability of traffic signs, complex background scenes, and changes in illumination pose challenges to the development of reliable recognition systems. This paper provides a comprehensive overview of the latest advancements in traffic sign recognition, including preprocessing techniques, feature extraction methods, classification techniques, datasets, and performance evaluation. It also discusses the limitations and future research prospects in this field.
Article
Biotechnology & Applied Microbiology
Salil Apte, Mathieu Falbriard, Frederic Meyer, Gregoire P. Millet, Vincent Gremeaux, Kamiar Aminian
Summary: Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed three machine-learning models to estimate peak horizontal power for level, uphill, and downhill running using gait spatiotemporal parameters, accelerometer, and gyroscope signals extracted from foot-worn IMUs. The prediction was compared to reference horizontal power obtained during running on a treadmill with an embedded force plate.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Manish Narwaria, Aditya Tatu
Summary: Machine learning methods are widely used in multimedia signal processing, but they often ignore the uncertainty in ground-truth data, leading to overemphasizing single-target values. To address this issue, an uncertainty aware loss function is proposed to explicitly consider data uncertainty.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Hardware & Architecture
Ali H. Alrubayi, M. A. Ahmed, A. A. Zaidan, A. S. Albahri, B. B. Zaidan, O. S. Albahri, A. H. Alamoodi, Mamoun Alazab
Summary: This study presents a pattern recognition model for static gestures in Malaysian Sign Language (MSL) using machine learning techniques, involving two phases of data acquisition and processing to construct an accurate dataset and applying ten different machine learning techniques for gesture recognition.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Chemistry, Analytical
Cameron J. Huggins, Rebecca Clarke, Daniel Abasolo, Erreka Gil-Rey, Jonathan H. Tobias, Kevin Deere, Sarah J. Allison
Summary: This study aims to evaluate the accuracy of machine learning models in binary and ternary classification tasks for postmenopausal women, and to more accurately characterize weight-bearing activities important for skeletal health using accelerometer data.
Article
Nanoscience & Nanotechnology
Hao Luo, Jingyi Du, Peng Yang, Yuxiang Shi, Zhaoqi Liu, Dehong Yang, Li Zheng, Xiangyu Chen, Zhong Lin Wang
Summary: In this study, a voice and gesture signal translator (VGST) is designed, which can translate natural actions into electrical signals and achieve efficient communication in human-machine interface. By spraying silk protein on the copper of the device, the VGST can achieve improved output and a wide frequency response. The VGST can be used as a high-fidelity platform to effectively recover recorded music and can also be combined with machine learning algorithms to realize the function of speech recognition with a high accuracy rate.
ACS APPLIED MATERIALS & INTERFACES
(2023)
Article
Computer Science, Information Systems
Muhammed Zahid Ozturk, Chenshu Wu, Beibei Wang, K. J. Ray Liu
Summary: This article presents GaitCube, a high-accuracy gait recognition system using a single commodity millimeter-wave radio. By introducing a novel feature representation called gait data cube and utilizing a pipeline of signal processing, GaitCube can automatically detect and segment human walking and effectively extract gait data. Experimental results show that GaitCube achieves high accuracy in different locations and times, enabling practical and ubiquitous gait-based identification.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Review
Chemistry, Analytical
Ahmad Chaddad, Yihang Wu, Reem Kateb, Ahmed Bouridane
Summary: In this study, a comprehensive analysis of numerous articles related to EEG signal processing was conducted. The entire process of EEG signal processing was surveyed, including acquisition, pretreatment, feature extraction, classification, and application. Various methods and techniques used for EEG signal processing were discussed and compared, along with the identification of current limitations and analysis of future development trends. Suggestions for future research in the field of EEG signal processing were offered.
Review
Engineering, Electrical & Electronic
Lian Yue, Lu Zongxing, Dong Hui, Jia Chao, Liu Ziqiang, Liu Zhoujie
Summary: Researchers are exploring ways to enable machines to interpret human body language for more intelligent and efficient human-machine interaction. Foot gesture recognition has emerged as a popular technology with its simplicity, speed, and accuracy in capturing various gestures and conveying body information.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Information Systems
Stanislaw Saganowski
Summary: This paper discusses the potential of bringing emotion recognition technology from the lab to everyday life, reviews the latest advances in the field, and highlights the challenges that need to be addressed in the future.
Review
Engineering, Electrical & Electronic
Henrique Dantas, Taylor C. Hansen, David J. Warren, V. John Mathews
Summary: This article reviews technologies and algorithms for decoding volitional movement intent using bioelectrical signals recorded from the human body, addressing deficiencies in current state-of-the-art methods and proposing three approaches to mitigate them. Experimental results demonstrate the effectiveness of these approaches.
IEEE SIGNAL PROCESSING MAGAZINE
(2021)
Article
Chemistry, Analytical
Itaf Omar Joudeh, Ana-Maria Cretu, Stephane Bouchard, Synthia Guimond
Summary: This article focuses on predicting arousal and valence values using diverse data sources. The goal is to adapt VR environments for users with mental health disorders and avoid discouragement. By improving preprocessing and adding novel feature selection and decision fusion processes, the authors achieve better results compared to previous approaches. The study highlights the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments.
Article
Computer Science, Artificial Intelligence
Izaro Goienetxea, Inigo Mendialdua, Igor Rodriguez, Basilio Sierra
Summary: Binarization techniques decompose multi-class problems into easier binary sub-problems, with One versus One (OVO) being a popular technique. An extension called PSEUDOVO has been developed to improve the performance of DYNOVO. Empirical studies show promising results for the PSEUDOVO extension.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Chemistry, Analytical
Ibon Merino, Jon Azpiazu, Anthony Remazeilles, Basilio Sierra
Summary: In this study, transferring pretrained weights from 2D networks to their corresponding 3D versions improved the performance of 3D deep learning methods. EfficientNetB0 achieved the highest accuracy in industrial object recognition using extrusion, which is comparable to state-of-the art methods.
Article
Chemistry, Analytical
Arantzazu Florez, Elena Murga, Itziar Ortiz de Zarate, Arrate Jaureguibeitia, Arkaitz Artetxe, Basilio Sierra
Summary: The use of biosensors in the food industry is increasingly necessary for real-time measurement of different analytes in food. By modeling the kinetic reaction and adjusting an exponential decay model to the biosensor response, a novel mathematical approach is proposed to estimate the measurement output in advance, reducing the required measurement time by about 40% while maintaining low error rates to meet industry accuracy standards.
Article
Computer Science, Information Systems
Jose Luis Outon, Ibon Merino, Ivan Villaverde, Aitor Ibarguren, Hector Herrero, Paul Daelman, Basilio Sierra
Summary: The SHERLOCK project aims to integrate an autonomous industrial mobile manipulator (AIMM) to perform cooperative tasks between a robot and a human. By combining autonomous navigation and 3D perception technology, AIMM achieved pose estimation and manipulation of industrial objects in a real environment with a success rate of 83.33%.
Article
Automation & Control Systems
Alberto Diez-Olivan, Patxi Ortego, Javier Del Ser, Itziar Landa-Torres, Diego Galar, David Camacho, Basilio Sierra
Summary: Industrial prognosis involves predicting failures of industrial assets based on data collected by IoT sensors, but concept drift can impact the data over time. To address this, contextual and operational changes must be detected and managed to trigger rapid model adaptation mechanisms. The proposed adaptive learning approach using a dendritic cell algorithm and neural network model demonstrated superior performance compared to other drift detectors and classification models in experimental results on a real-world industrial problem.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Chemistry, Analytical
Aitor Ibarguren, Paul Daelman
Summary: This paper introduces a path-driven mobile co-manipulation architecture and algorithm for handling collaborative part transportation, allowing the creation of collaborative lanes for conveying large components.
Article
Automation & Control Systems
David Montero, Naiara Aginako, Basilio Sierra, Marcos Nieto
Summary: The demand for real-time face clustering algorithms has increased in recent years, especially for security and surveillance purposes. However, current methods are not suitable for real-time applications and online methods are less accurate. To address these limitations, researchers propose an online gaussian mixture-based clustering method (OGMC) that reduces dependency on data order and size and can handle complex data distributions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Review
Chemistry, Analytical
Jose Maria Martinez-Otzeta, Itsaso Rodriguez-Moreno, Inigo Mendialdua, Basilio Sierra
Summary: Random Sample Consensus (RANSAC) is a robust estimation method for models contaminated by outliers. It starts with sample selection, evaluates the adequacy of the estimation, and repeats the process until a stopping criterion is met. RANSAC is widely used in robotics, particularly for finding geometric shapes in point clouds or estimating camera view transformations.
Article
Computer Science, Artificial Intelligence
Arantzazu Florez, Itsaso Rodriguez-Moreno, Arkaitz Artetxe, Igor Garcia Olaizola, Basilio Sierra
Summary: Detecting changes in data streams is a crucial problem in Industry 4.0. Traditional machine learning algorithms are often static and lack the ability to generalize to new concepts, resulting in a deterioration of predictive performance when there is a change in data distribution. Drift detecting methods offer a solution to identify concept drift in data. This paper introduces CatSight, a new approach for detecting sudden or abrupt drift in industrial processes, which combines Common Spatial Patterns with traditional machine learning algorithms and demonstrates its effectiveness through evaluation on a real use case.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Engineering, Electrical & Electronic
Mikel Labayen, Xabier Mendialdua, Naiara Aginako, Basilio Sierra
Summary: The automation of railway operations is growing, but there are no accepted certification rules for computer vision and AI-enhanced perception technologies. To meet the needs for trusted AI solutions, a semi-automatic system based on virtual scenarios is being developed to evaluate performance under different visibility conditions.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Information Systems
Meritxell Gomez-Omella, Basilio Sierra, Susana Ferreiro
Summary: The Internet of Things (IoT) technologies play a crucial role in Industry 4.0, digitizing industries and services for enhanced productivity. This research proposes metrics to measure Data Quality (DQ) in streaming time series and implements techniques and tools for monitoring and improving information quality.
Article
Computer Science, Information Systems
David Velasquez, Enrique Perez, Xabier Oregui, Arkaitz Artetxe, Jorge Manteca, Jordi Escayola Mansilla, Mauricio Toro, Mikel Maiza, Basilio Sierra
Summary: This research proposes a hybrid machine learning model for real-time fault and anomaly detection in industrial systems. The results show that the model performs well in improving anomaly detection.
Article
Computer Science, Information Systems
Itsaso Rodriguez-Moreno, Jose Maria Martinez-Otzeta, Izaro Goienetxea, Igor Rodriguez, Basilio Sierra
Summary: This paper introduces an approach using CSP algorithm to classify videos based on skeleton joints signals, and ultimately achieve action recognition through image classification techniques. The results of the tests indicate promising outcomes on two data sets.
Article
Computer Science, Information Systems
Jon Martin, Ander Ansuategi, Inaki Maurtua, Aitor Gutierrez, David Obregon, Oskar Casquero, Marga Marcos
Summary: In order to meet the increasing demands of a rising population, greenhouses must produce more in a more efficient and sustainable way. Innovative mobile robotic solutions with flexible navigation and manipulation strategies can help monitor fields in real-time, performing early pest detection and selective treatment tasks autonomously. The Robotframework architecture is a generic ROS-based framework that simplifies the development of new robotic applications by easily integrating different modules and skills, demonstrating its benefits in enhancing pest detection and pesticide spraying in greenhouses.
Article
Computer Science, Theory & Methods
Vignesh Sampath, Inaki Maurtua, Juan Jose Aguilar Martin, Aitor Gutierrez
Summary: This article discusses the importance of image and data acquisition, preprocessing, and pattern recognition in computer vision application development. Particularly, the occurrence of imbalance issues in complex real-world problems is inevitable. Research shows that techniques based on GANs are able to address these imbalances effectively and boost the performance of computer vision algorithms.
JOURNAL OF BIG DATA
(2021)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
