Article
Multidisciplinary Sciences
Mirza S. Sarwar, Ryusuke Ishizaki, Kieran Morton, Claire Preston, Tan Nguyen, Xu Fan, Bertille Dupont, Leanna Hogarth, Takahide Yoshiike, Ruixin Qiu, Yiting Wu, Shahriar Mirabbasi, John D. W. Madden
Summary: Soft sensors capable of differentiating shear and normal force can enhance machines' control in physical interactions with humans. The capacitive sensor, composed of patterned elastomer, distinguishes between normal force and shear using signal summation and differences. With low crosstalk and high sensitivity, this sensor shows potential for application in humanoid robotics.
SCIENTIFIC REPORTS
(2023)
Article
Chemistry, Analytical
Daekwang Jung, Kyumin Kang, Hyunjin Jung, Duhwan Seong, Soojung An, Jiyong Yoon, Wooseok Kim, Mikyung Shin, Hyoung Baac, Sangmin Won, Changhwan Shin, Donghee Son
Summary: The article introduces a highly sensitive multi-channel pressure sensor array fabricated through a thermal evaporation process, with a broad pressure range and excellent performance, enabling precise control of the sensor's performance. The sensor adopts a rigid-island structure, demonstrating excellent sensitivity and stability under external mechanical stimuli.
Article
Computer Science, Interdisciplinary Applications
Jing Zhu, Shiqing Wei, Changlin Yang, Xiannian Xie, Yizhou Li, Xiaowei Li, Bin Hu
Summary: This research proposes a content-based multiple evidence fusion method for mild depression detection using Electroencephalography and eye movement data. The experimental results show that the proposed method outperforms other fusion methods and single modality results, achieving high accuracy and sensitivity.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Chemistry, Multidisciplinary
Yun Deng, Xiaogang Guo, Yongshui Lin, Zhixin Huang, Ying Li
Summary: Wearable and stretchable sensors play a crucial role in monitoring human behavior and health, but traditional sensors have limitations in their mechanical properties. To address this, a dual-phase metamaterial (chiral-horseshoes) inspired by biological structures is designed and fabricated. Experimental studies show that the designed microstructures can replicate the mechanical properties of natural animals. A flexible strain sensor with a high gauge factor is also fabricated, indicating its potential application in electronic skin. Additionally, the dual-phase metamaterial can be combined with artificial intelligence algorithms to create a flexible stretchable display, reducing image distortion during stretching.
Article
Chemistry, Multidisciplinary
Yun Deng, Xiaogang Guo, Yongshui Lin, Zhixin Huang, Ying Li
Summary: In this study, a dual-phase metamaterial with wide and programmable mechanical properties was designed. It can replicate the mechanical properties of natural animal skin. A flexible strain sensor was fabricated, which can accurately monitor human behavior signals and potentially be used for flexible displays in combination with artificial intelligence algorithms.
Article
Chemistry, Multidisciplinary
Wang Xiang, Yan Xie, Yechao Han, Zhihe Long, Wanglinhan Zhang, Tianyan Zhong, Shan Liang, Lili Xing, Xinyu Xue, Yang Zhan
Summary: This study presents a self-powered wearable brain-machine-interface system that utilizes pulse detection and brain stimulation to cease action. The system converts mechanical energy from human daily activities into electricity using a piezoelectric generator, measures pulse using a neck pulse biosensor, and implements behavioral intervention through brain stimulation.
Article
Engineering, Electrical & Electronic
Xin Liu, Jiazhe Hu
Summary: This article discusses the impact of multi-feature information fusion on dance movement recognition technology, employing structural risk mitigation principles and image thresholding methods for analysis. The experimental results demonstrate that this technique is well-studied and effective in recognizing dance movements.
JOURNAL OF SENSORS
(2021)
Article
Computer Science, Interdisciplinary Applications
Fu-sheng Zhang, Dong-yuan Ge, Jun Song, Wen-jiang Xiang
Summary: This research focuses on improving the understanding and perception of the outdoor scene by mobile robots through multi-sensor information fusion technology. The proposed algorithms and models successfully integrate laser and visual information, and achieve three-dimensional scene understanding through convolutional neural networks. The average recognition rate of this technology is as high as 89.36% and the image segmentation time is less than 180 ms.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2022)
Article
Robotics
Taeyeong Kim, Jaehun Kim, Insang You, Joosung Oh, Sung-Phil Kim, Unyong Jeong
Summary: The study proposes an artificial dynamic sensory system based on position-encoded spike spectrum, which achieves high-resolution spatiotemporal tactile perception and real-time recognition of complex dynamic motions.
Article
Multidisciplinary Sciences
Xun Zhao, Yihao Zhou, Jing Xu, Guorui Chen, Yunsheng Fang, Trinny Tat, Xiao Xiao, Yang Song, Song Li, Jun Chen
Summary: The study shows that the magnetoelastic effect can exist not only in traditional rigid metals, but also in 1D soft fibers with a stronger magnetomechanical coupling. By inventing a textile magnetoelastic generator, the conversion of biomechanical energy to electrical energy is achieved.
NATURE COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Junhyung Kim, Suhan Kim, Yong-Lae Park
Summary: This study proposes a design of soft sensor arrays that can operate with a reduced number of wires without degrading performance, and demonstrates practical applications in fingertip tactile sensing and foot pressure sensing.
NPJ FLEXIBLE ELECTRONICS
(2022)
Article
Chemistry, Analytical
Suparat Yeamkuan, Kosin Chamnongthai
Summary: This study introduces a method for determining 3D points of intention using multimodal fusion of hand pointing and eye gaze, and experimental results demonstrate the accuracy of the method in measuring 3D points of intention at different distances.
Article
Multidisciplinary Sciences
Yue Li, Yuan Wei, Yabao Yang, Lu Zheng, Lei Luo, Jiuwei Gao, Hanjun Jiang, Juncai Song, Manzhang Xu, Xuewen Wang, Wei Huang
Summary: Flexible and wearable pressure sensors are crucial for accurate and real-time tracking of physiological signals. This study presents a novel type of sensor that overcomes the saturation and achieves ultra-high sensitivity and wide detection range through the soft-strain effect. The sensor demonstrates excellent stability and capability for monitoring arterial pulse waves.
Article
Computer Science, Information Systems
Huiru Shao, Jing Li, Jia Zhang, Hui Yu, Jiande Sun
Summary: This article proposes an eye-based recognition method for identity recognition on mobile devices, which combines static periocular features extracted by deep neural network and dynamic saccadic velocity features to achieve a recognition accuracy of up to 97.99%.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2021)
Review
Optics
Fang Zhong, Wei Hu, Peining Zhu, Han Wang, Chao Ma, Nan Lin, Zuyong Wang
Summary: This article reviews the development of piezoresistive sensors and discusses their potential applications in electronic skin.
OPTO-ELECTRONIC ADVANCES
(2022)
Article
Computer Science, Theory & Methods
Lidia Fotia, Flavia Delicato, Giancarlo Fortino
Summary: The Internet of Things (IoT) enables smart objects to provide smart services inserted into information networks for human beings. The introduction of edge computing in IoT reduces decision-making latency, saves bandwidth resources, and expands cloud services at the network's edge. However, decentralized trust management poses challenges for edge-based IoT systems. Trust management is crucial for reliable mining and data fusion, improved user privacy and data security, and context-aware service provisioning.
ACM COMPUTING SURVEYS
(2023)
Review
Computer Science, Information Systems
Claudia Greco, Giancarlo Fortino, Bruno Crispo, Kim-Kwang Raymond Choo
Summary: This paper provides a comprehensive review of literature on penetration testing of IoT devices and systems. It identifies existing and potential IoT penetration testing applications and proposed approaches, and highlights recent advances in AI-enabled penetration testing methods at the network edge.
ENTERPRISE INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xiuwen Fu, Pasquale Pace, Gianluca Aloi, Antonio Guerrieri, Wenfeng Li, Giancarlo Fortino
Summary: In this study, a interdependent network model for cyber-manufacturing systems (CMS) is developed based on the perspective of physical-service networking. The proposed realistic cascading failure model takes into account the load distribution characteristics of the physical network and the service network. The experiments confirm that attacks on the physical network are more likely to trigger cascading failures and cause more damage, and interdependency failures are the main cause of performance degradation in the service network during cascading failures, while isolation failures are the main cause of performance degradation in the physical network during cascading failures.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Alessandro Sabato, Shweta Dabetwar, Nitin Nagesh Kulkarni, Giancarlo Fortino
Summary: Engineering structures and infrastructure are still being used beyond their design lifetime. Noncontact methods, such as photogrammetry and infrared thermography, provide accurate and continuous spatial information to assess the condition of these structures. The incorporation of artificial intelligence algorithms expedites and improves the assessment process. This article summarizes the recent efforts in utilizing AI-aided noncontact sensing techniques, particularly image-based methods, for structural health monitoring (SHM) and discusses future directions to advance AI-aided image-based SHM techniques for engineering structures.
IEEE SENSORS JOURNAL
(2023)
Article
Automation & Control Systems
Ke Wang, Zicong Chen, Mingjia Zhu, Siu-Ming Yiu, Chien-Ming Chen, Mohammad Mehedi Hassan, Stefano Izzo, Giancario Fortino
Summary: Artificial intelligence-driven automation is becoming the technical trend in the new automation era. Convolutional neural network (CNN) technology has been widely used in industrial automation for defect detection and machine vision-driven automation for robot-human tracking. However, the high dependence on neural networks can lead to potential failures in defect detection system.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Peng Xu, Ke Wang, Mohammad Mehedi Hassan, Chien-Ming Chen, Weiguo Lin, Md Rafiul Hassan, Giancarlo Fortino
Summary: This paper employs a One-Shot Neural Architecture Search (NAS) to generate derivative models with different scales and studies the relationship between network sizes and model robustness. The experimental results show that reducing model parameters can increase model robustness under maximum adversarial attacks, while increasing model parameters can enhance model robustness under minimum adversarial attacks. This analysis helps to understand the adversarial robustness of models with different scales for edge AI transportation systems.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Kai Lin, Jian Gao, Yihui Li, Claudio Savaglio, Giancarlo Fortino
Summary: This paper investigates the quality and real-time assurance problem of collaborative decision-making in large-scale intelligent transportation systems during multi-task parallel execution. It develops a collaborative decision architecture with cognitive networking and proposes an AI-driven cognitive networking collaborative decision-making algorithm.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Chemistry, Analytical
Alaa Menshawi, Mohammad Mehedi Hassan, Nasser Allheeib, Giancarlo Fortino
Summary: A generic framework has been developed for heart problem diagnosis using a hybrid of machine learning and deep learning techniques. The framework utilizes a novel voting technique based on the prediction probabilities of multiple models to eliminate bias. Experimental results show that the framework outperforms single machine learning models, classical stacking techniques, and traditional voting techniques, achieving an accuracy of 95.6%.
Review
Chemistry, Analytical
Amira Bourechak, Ouarda Zedadra, Mohamed Nadjib Kouahla, Antonio Guerrieri, Hamid Seridi, Giancarlo Fortino
Summary: Given its advantages, edge computing has emerged as key support for intelligent applications and 5G/6G IoT networks. However, there are concerns about its capabilities to handle the computational complexity of machine learning techniques for big IoT data analytics. This paper aims to explore the confluence of AI and edge computing in various application domains to leverage existing research and identify new perspectives.
Review
Computer Science, Artificial Intelligence
Vincenzo Barbuto, Claudio Savaglio, Min Chen, Giancarlo Fortino
Summary: The Edge Intelligence (EI) paradigm is a promising solution to the limitations of cloud computing in the development and provision of next-generation Internet of Things (IoT) services. This paper provides a systematic analysis of the state-of-the-art manuscripts on EI, exploring the past, present, and future directions of the EI paradigm and its relationships with IoT and cloud computing.
BIG DATA AND COGNITIVE COMPUTING
(2023)
Editorial Material
Computer Science, Artificial Intelligence
David B. Kaber, Andreas Nuernberger, Giancarlo Fortino, David Mendonca
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Aitizaz Ali, Muhammad Fermi Pasha, Antonio Guerrieri, Antonella Guzzo, Xiaobing Sun, Aamir Saeed, Amir Hussain, Giancarlo Fortino
Summary: This paper proposes a hybrid deep learning model for Industrial Internet of Medical Things (IIoMT) that addresses security challenges using homomorphic encryption (HE) and blockchain technology, providing higher privacy and security. By deploying a pre-trained model on edge devices and utilizing a consortium blockchain for data sharing and updating, the model can effectively classify and train local models while delivering higher efficiency and low latency.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Cybernetics
Zhihan Lv, Chen Cheng, Antonio Guerrieri, Giancarlo Fortino
Summary: More data are generated through mobile network technology, giving birth to the cyber-physical social intelligent ecosystem (C & P-SIE). This survey studies the development of physical social intelligence, discussing its applications in various domains such as intelligent transportation, healthcare, public service, economy, and social networking. It also explores the future prospects of behavior modeling in C & P-SIE under information security, data-driven techniques, and cooperative artificial intelligence technologies. This research provides a theoretical foundation and new opportunities for the digital and intelligent development of smart cities and social systems.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Giancarlo Fortino, Lidia Fotia, Fabrizio Messina, Domenico Rosaci, Giuseppe M. L. Sarne
Summary: This article introduces a multi-agent SIoT architecture that incorporates a reputation system based on clustering of smart objects, providing reliability for transactions in SIoT scenarios. By enabling feedback between smart objects, and communication between edge servers and the cloud, reputation values are updated, enhancing the trustworthiness of objects.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Information Systems
Syed Tauhidun Nabi, Md. Rashidul Islam, Md. Golam Rabiul Alam, Mohammad Mehedi Hassan, Salman A. AlQahtani, Gianluca Aloi, Giancarlo Fortino
Summary: This research utilizes 6.2 million real network time series LTE data traffic and other associated parameters to build a traffic forecasting model using multivariate feature inputs and deep learning algorithms, which can forecast traffic at a granular eNodeB-level and provide eNodeB-wise forecasted PRB utilization.
Article
Computer Science, Artificial Intelligence
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Zhimin Shao, Weibei Dou, Yu Pan
Summary: This paper proposes a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to integrate multimodal information at both the BBA level and multimodal level, aiming to enhance accuracy, robustness, and reliability. The DDEF approach utilizes the Dirichlet framework and BBA methods for effective uncertainty estimation and employs the Dempster-Shafer Theory for dual-level fusion. The experimental results show that the proposed DDEF outperforms existing methods in synthetic digit classification and real-world medical prognosis after BCI treatment.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Jiaxin Qi, Jiawei Zhang, Chonghui Zhang, Fan Liu, Weiping Ding
Summary: In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Bin Yu, Ruihui Xu, Mingjie Cai, Weiping Ding
Summary: This paper introduces a clustering method based on non-Euclidean metric and multi-granularity staged clustering to address the challenges posed by complex spatial structure data to traditional clustering methods. The method improves the similarity measure and employs an attenuation-diffusion pattern for local to global clustering, achieving good clustering results.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jian Zhu, Pengbo Hu, Bingqian Li, Yi Zhou
Summary: The acquisition of multi-view hash representation for heterogeneous data is highly important for multimedia retrieval. Current approaches suffer from limited retrieval precision due to insufficient integration of multi-view features and failure to effectively utilize metric information from diverse samples. In this paper, we propose an innovative method called Fast Metric Multi-View Hashing (FMMVH), which demonstrates the superiority of gate-based fusion over traditional methods. We also introduce a novel deep metric loss function to leverage metric information from dissimilar samples. By optimizing and employing model compression techniques, our FMMVH method significantly outperforms existing state-of-the-art methods on benchmark datasets, with up to 7.47% improvement in mean Average Precision (mAP).
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Fayaz Ali Dharejo, Iyyakutti Iyappan Ganapathi, Muhammad Zawish, Basit Alawode, Moath Alathbah, Naoufel Werghi, Sajid Javed
Summary: The resource-limited nature of underwater vision equipment affects underwater robotics and ocean engineering tasks. Super-resolution methods, particularly using Vision Transformers (ViTs), have emerged to enhance low-resolution underwater images. However, ViTs face challenges in handling severe degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) overcome these challenges by preserving long-range dependencies through evolving channel capacity. This study proposes a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale super-resolution for underwater images.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Weiwei Jiang, Haoyu Han, Yang Zhang, Jianbin Mu
Summary: This study incorporates federated learning and split learning paradigms with satellite-terrestrial integrated networks and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. The proposed solution is demonstrated to be effective through a case study of electricity theft detection based on a real-world dataset.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Najah Abuali, Mohammad Bilal Khan, Farman Ullah, Mohammad Hayajneh, Hikmat Ullah, Shahid Mumtaz
Summary: The demand for innovative solutions in biomedical systems for precise diagnosis and management of critical diseases is increasing. A promising technology, non-invasive and intelligent Internet of Medical Things (IoMT) system, emerges to assess patients with reduced health risks. This research introduces a comprehensive framework for early diagnosis of respiratory abnormalities through RF sensing and SDR technology. The results highlight the superior performance of deep learning frameworks in classifying respiratory anomalies.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu
Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.
INFORMATION FUSION
(2024)