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Detection and Classification of Mental Stress Using In-Ear Plethysmography and a Vision Transformer

IEEE SENSORS JOURNAL 2025年第2期25卷 4015-4027页

作者： Barki, Hika Nkenyereye, Lionel Chung, Wan-Young Pukyong Natl Univ Dept AI Convergence Busan 48513 South Korea Pukyong Natl Univ Dept Elect Engn Busan 48513 South Korea

This study addresses the critical need for effective mental stress monitoring, linked to severe health issues like depression and heart disease. We introduce a robust method using in-ear photoplethysmogram (PPG) signals for detecting and classifying stress levels. The objective of this study is to develop a precise stress monitoring technique using advanced signal processing and deep learning. Raw PPG data were collected from 15 subjects undergoing stress-inducing activities in a controlled setting. The data underwent preprocessing and were transformed into image-like time-frequency representations. We employed vision transformer (ViT) models for classification, which were fine-tuned and compared against other state-of-the-art deep learning models. The ViT classifier significantly outperformed existing models, achieving an average accuracy of 97.78% and an F1-score of 97.79%. While the dataset is relatively small, these results suggest a promising direction for stress monitoring by illustrating the potential of combining in-ear PPG signals with ViT models. The study indicates the efficacy of this novel approach for accurate mental stress diagnosis, which could have significant implications for mental health applications. Future work will focus on validating these findings with a larger sample size and exploring the integration of this technology into wearable devices for real-world stress monitoring.

关键词： Human factors Sensors Accuracy Monitoring Hardware Biomedical monitoring Heart rate variability Transformers real-time systems deep learning Classification filtering mental stress photoplethysmogram (PPG) time-frequency analysis vision transformer (ViT)

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Age-based clustering of seagrass blades using AI models

Age-based clustering of seagrass blades using AI models

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Conference on real-time image processing and deep learning

作者： Sevinc, Omer Mehrubeoglu, Mehrube Cammarata, Kirk Huang, Chi McLauchlan, Lifford Texas A&M Univ Dept Engn Corpus Christi TX 78412 USA Texas A&M Univ Dept Life Sci Corpus Christi TX 78412 USA Texas A&M Univ Dept Elect Engn & Comp Sci Kingsville TX USA

ISBN: (纸本)9781510673878;9781510673861

Seagrass ecosystems play a vital role in maintaining marine biodiversity and ecological balance, making their monitoring and management essential. This study proposes a novel approach for clustering of seagrass images into three distinct age categories: young, medium, and old, using deep learning and unsupervised machine learning techniques. VGG-16 convolutional neural networks (CNN) are employed for feature extraction from the seagrass images, followed by K-means clustering to categorize the image samples into the specified age groups. The implemented methodology begins with the collection and annotation of a diverse seagrass image dataset, including samples from various locations and conditions. images are first pre-processed to ensure consistent size and quality. To enable real-time capabilities, an optimized VGG-16 CNN is then fine-tuned on the annotated dataset to learn discriminative features that capture age-related characteristics of the seagrass leaves within the constraints of real-time image processing. After feature extraction, the K-means clustering algorithm is applied to group the images into young, medium, and old categories based on the learned features. The clustering results are evaluated using quantitative metrics such as the silhouette score and Davies-Bouldin index, demonstrating the effectiveness of the proposed method in capturing age-related patterns in seagrass imagery. This research contributes to the field of seagrass monitoring by providing an automated and real-time approach to classifying seagrass images into age categories which can facilitate more accurate assessments of seagrass health and growth dynamics. A real-time capability would equip decision-makers with a valuable tool for immediate responses and support the sustainable management of seagrass ecosystems in various marine environments.

关键词： deep learning machine learning K-means clustering VGG-16 CNN seagrass age classification transfer learning ecosystem monitoring

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Adaptive specular reflection removal in light field microscopy using multi-polarization hybrid illumination and deep learning

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OPTICS AND LASERS IN ENGINEERING 2025年 186卷

作者： Shi, Wenqi Quan, Hongda Kong, Lingbao Fudan Univ Shanghai Engn Res Ctr Ultraprecis Opt Mfg Sch Informat Sci & Technol Shanghai 200433 Peoples R China

Specular highlights pose a significant challenge in light field microscopy (LFM), leading to information loss and inaccurate observations, especially on reflective surfaces. Existing methods for specular highlight removal often suffer from high computational complexity, limited applicability and extended processing times. To address these limitations, this paper introduces an adaptive hybrid illumination scheme that combines multiple polarized light sources with a deep learning-based control system to dynamically modulate illumination and eliminate specular highlights. This method effectively removes highlight reflections and provides uniform illumination without complex optical setups or mechanical components. By leveraging various polarization angles and utilizing precise electronic control through a neural network, the system dynamically adjusts lighting in real-time, achieving uniform illumination and superior image quality. Experimental results show that the proposed method effectively eliminates specular highlights, significantly improves 3D reconstruction accuracy and reduces processing time to <0.4 s (at least twice as fast as traditional approaches). This system offers a promising solution for applications requiring high-speed, high-precision imaging, such as biological analysis, industrial inspection, and materials research, providing an efficient and effective alternative for specular reflection removal in LFM.

关键词： Specular reflection removal Multi-polarization Adaptive hybrid modulated illumination deep learning Light field microscopy Three-dimensional reconstruction

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Non-contact vibration sensor using deep learning and image processing

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MEASUREMENT 2021年 183卷

作者： Guo, Jinquan Wu, Xinran Liu, Jiantao Wei, Tieping Yang, Xiaoxiang Yang, Xinyi He, Bingwei Zhang, Weihao Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China Xiamen Port Holding Grp Co Ltd Xiamen 361000 Fujian Peoples R China Fujian Univ Technol Sch Mech & Automat Engn Fuzhou 350118 Peoples R China Quanzhou Normal Univ Quanzhou 362000 Fujian Peoples R China Xiamen Inst Environm Sci Xiamen 361000 Fujian Peoples R China

This paper proposes a non-contact vibration measurement method based on deep learning and image processing. The deep learning method is used to realize the automatic and efficient selection of effective pixels and the optical flow method is used to extract vibration signals to realize non-contact and targetless visual vibration measurement. In this study, a carbon plate board and aluminum C-beam structure were measured and verified under artificial and non-human excitation in a laboratory environment. Additionally, bridge and cable structures in an outdoor environment were selected as measurement targets to verify the reliability of the proposed method. This paper compares the experimental results of Canny and Sobel edge detection algorithms and deep learning methods to verify the efficiency of deep learning. The results demonstrate that our method is robust, even under real-world unfavorable conditions, meaning it can serve as a novel measurement method in the field of vibration measurement.

关键词： Vibration measurement Optical flow deep learning Convolutional neural network Photogrammetry Computer vision Non-contact measurement

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Coupling deep and handcrafted features to assess smile genuineness

Coupling deep and handcrafted features to assess smile genui...

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Conference on real-time image processing and deep learning

作者： Pawlus, Benedykt Smolka, Bogdan Kawulok, Jolanta Kawulok, Michal Fac Automat Control Elect & Comp Sci Gliwice Poland

ISBN: (纸本)9781510673878;9781510673861

Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed underpinned with handcrafted features, as well as those that rely on deep learning to elaborate the useful features. As both of these approaches have certain benefits and limitations, in this work we propose to combine the features learned by a long short-term memory network with the features handcrafted to capture the dynamics of facial action units. The results of our experiments indicate that the proposed solution is more effective than the baseline techniques and it allows for assessing the smile genuineness from video sequences in real-time.

关键词： Facial expression recognition smile genuineness deep learning handcrafted features facial action units

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A Review of CNN Applications in Smart Agriculture Using Multimodal Data

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SENSORS 2025年第2期25卷 472页

作者： El Sakka, Mohammad Ivanovici, Mihai Chaari, Lotfi Mothe, Josiane CNRS UMR5505 IRIT Inst Rech Informat Toulouse F-31400 Toulouse France Univ Toulouse F-31400 Toulouse France Transilvania Univ Brasov Dept Elect & Comp Brasov 500036 Romania Toulouse INP F-31000 Toulouse France Univ Toulouse INSPE F-31400 Toulouse France

This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency. Key approaches analyzed involve image classification, image segmentation, regression, and object detection methods that use diverse data types ranging from RGB and multispectral images to radar and thermal data. By processing UAV and satellite data with CNNs, real-time and large-scale crop monitoring can be achieved, supporting advanced farm management. A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. Future directions point toward integrating IoT and cloud platforms for real-time data processing and leveraging large language models for regulatory insights. Potential research advancements emphasize improving increased data accessibility and hybrid modeling to meet the agricultural demands of climate variability and food security, positioning CNNs as pivotal tools in sustainable agricultural practices. A related repository that contains the reviewed articles along with their publication links is made available.

关键词： convolutional neural network smart agriculture weed detection crop disease detection crop classification yield prediction water management remote sensing machine learning

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real-time Data Collection and Trajectory Scheduling Using a DRL-Lagrangian Framework in Multiple UAVs Collaborative Communication Systems

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REMOTE SENSING 2024年第23期16卷

作者： Wang, Shanshan Luo, Zhiyong Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

UAV-assisted communication facilitates efficient data collection from IoT nodes by exploiting UAVs' flexible deployment and wide coverage capabilities. In this paper, we consider a scenario in which UAVs equipped with high-precision sensors collect sensing data from ground terminals (GTs) in real-time over a wide geographic area and transmit the collected data to a ground base station (BS). Our research aims to jointly optimize the trajectory scheduling and the allocation of collection time slots for multiple UAVs, to maximize the system's data collection rates and fairness while minimizing energy consumption within the task deadline. Due to UAVs' limited sensing distance and battery energy, ensuring timely data processing in target areas presents a challenge. To address this issue, we propose a novel constraint optimization-based deep reinforcement learning-Lagrangian UAV real-time data collection management (CDRLL-RDCM) framework utilizing centralized training and distributed execution. In this framework, a CNN-GRU network units extract spatial and temporal features of the environmental information. We then introduce the PPO-Lagrangian algorithm to iteratively update the policy network and Lagrange multipliers at different time scales, enabling the learning of more effective collaborative policies for real-time UAV decision-making. Extensive simulations show that our proposed framework significantly improves the efficiency of multi-UAV collaboration and substantially reduces data staleness.

关键词： UAV deep reinforcement learning real-time data collection trajectory scheduling

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Underwater image enhancement using lightweight vision transformer

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MULtimeDIA TOOLS AND APPLICATIONS 2024年第31期83卷 75603-75625页

作者： Daud, Muneeba Afzal, Hammad Mahmood, Khawir Natl Univ Sci & Technol Islamabad Pakistan

deep learning-based models have recently shown a strong potential in Underwater image Enhancement (UIE) that are satisfying and have the right colors and details, but these methods significantly increase the parameters and complexity of the image processing models and therefore cannot be deployed directly to the edge devices. Vision Transformers (ViT) based architectures have recently produced amazing results in many vision tasks such as image classification, super-resolution, and image restoration. In this study, we introduced a lightweight Context-Aware Vision Transformer (CAViT), based on the Mean Head tokenization strategy and uses a self-attention mechanism in a single branch module that is effective at simulating long-distance dependencies and global features. To further improve the image quality we proposed an efficient variant of our model which derived results by applying White Balancing and Gamma Correction methods. We evaluated our model on two standard datasets, i.e., Large-Scale Underwater image (LSUI) and Underwater image Enhancement Benchmark Dataset (UIEB), which subsequently contributed towards more generalized results. Overall findings indicate that our real-time UIE model outperforms other deep learning based models by reducing the model complexity and improving the image quality (i.e., 0.6 dB PSNR improvement while using only 0.3% parameters and 0.4% float operations).

关键词： Tokenization Feature extraction image enhancement Vision transformers

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Illuminate the night: lightweight fusion and enhancement model for extreme low-light burst images

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JOURNAL OF real-time image processing 2024年第6期21卷 1-13页

作者： Avsar, Hasan Sarigul, Mehmet Karacan, Levent Iskenderun Tech Univ Comp Engn Dept Hatay Turkiye Cukurova Univ Dept Comp Engn Adana Turkiye Gaziantep Univ Dept Comp Engn Gaziantep Turkiye

Taking photographs under low ambient light can be challenging due to the inability of camera sensors to gather sufficient light, resulting in dark images with increased noise and reduced image quality. Standard photography techniques and traditional enhancement methods often fail to provide satisfactory solutions for images captured under extremely low ambient light conditions. To address this problem, data-driven methods have been proposed to model complex non-linear relationships between extremely dark and long-exposure images. Recently, burst photography has become interested in improving single-image low-light image enhancement to provide more information about the scene. In this study, we propose a novel unified fusion and enhancement model inspired by recent advancements in learning-based burst image processing methods. Our model processes a burst set of raw input images across multiple scales to fuse complementary information and predict possible enhancements over the fused information, thereby producing images with longer exposure. Additionally, we introduce a new data augmentation technique, the amplification ratio scaling multiplier, for training to further improve generalization. Experimental results demonstrate that our model achieves state-of-the-art performance in the perceptual metric LPIPS while maintaining highly competitive distortion metrics PSNR and SSIM compared to existing low-light burst image enhancement techniques.

关键词： Extremely low light Extremely dark image enhancement deep learning Burst photography

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Review of machine learning in robotic grasping control in space application

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ACTA ASTRONAUTICA 2024年 220卷 37-61页

作者： Jahanshahi, Hadi Zhu, Zheng H. York Univ Dept Mech Engn 4700 Keele St Toronto ON M3J 1P3 Canada

This article presents a comprehensive survey of the integration of machine learning techniques into robotic grasping, with a special emphasis on the challenges and advancements for space applications. The incorporation of artificial intelligence, particularly through deep learning, reinforcement learning, transfer learning, convolutional neural networks and recurrent neural networks, has significantly revolutionized robotic grasping. These advancements facilitate autonomous, efficient, and sophisticated manipulation in the challenging environment of outer space, transitioning from traditional mechanical grippers to sophisticated systems powered by advanced algorithms. This transition highlights the critical integration of sensory perception, grasp planning, and execution mechanisms, enhancing robots' capabilities to perceive, interact with, and manipulate objects with unprecedented precision and adaptability. The article meticulously outlines significant advancements achieved through the deployment of convolutional neural networks for visual information processing, RNNs for sequential decision-making, RL for autonomous strategy refinement, and transfer learning for leveraging pre-learned knowledge in novel tasks. These technologies address the unique challenges of space environments, such as varied textures, occlusions, microgravity conditions, and the sim-to-real gap, by enhancing sample efficiency, improving sim-to-real transfer capabilities, and integrating multimodal data for better object localization and pose estimation. Furthermore, the review explores the specific challenges faced in space robotic grasping, including handling varied textures and occlusions, adapting to unpredictable conditions, achieving real-time processing, and ensuring safety and reliability. It proposes future research directions focused on overcoming these hurdles, such as enhanced generalization through multimodal learning, robust sim-to-real transfer techniques, and the development of

关键词： Machine learning Space robotics Robotic grasping deep learning Reinforcement learning Transfer learning Convolutional neural networks Recurrent neural networks Sim-to-real transfer Multimodal learning

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