Sensors最新文献

{"title":"CLEAR: Multimodal Human Activity Recognition via Contrastive Learning Based Feature Extraction Refinement.","authors":"Mingming Cao, Jie Wan, Xiang Gu","doi":"10.3390/s25030896","DOIUrl":"10.3390/s25030896","url":null,"abstract":"<p><p>Human activity recognition (HAR) has become a crucial research area for many applications, such as Healthcare, surveillance, etc. With the development of artificial intelligence (AI) and Internet of Things (IoT), sensor-based HAR has gained increasing attention and presents great advantages to existing work. Relying solely on existing labeled data may not adequately address the challenge of ensuring the model's generalization ability to new data. The 'CLEAR' method is designed to improve the accuracy of multimodal human activity recognition. This approach employs data augmentation, multimodal feature fusion, and contrastive learning techniques. These strategies are utilized to refine and extract highly discriminative features from various data sources, thereby significantly enhancing the model's capacity to identify and classify diverse human activities accurately. CLEAR achieves high generalization performance on unknown datasets using only training data. Furthermore, CLEAR can be directly applied to various target domains without retraining or fine-tuning. Specifically, CLEAR consists of two parts. First, it employs data augmentation techniques in both the time and frequency domains to enrich the training data. Second, it optimizes feature extraction using attention-based multimodal fusion techniques and employs supervised contrastive learning to improve feature discriminability. We achieved accuracy rates of 81.09%, 90.45%, and 82.75% on three public datasets USC-HAD, DSADS, and PAMAP2, respectively. Additionally, when the training data are reduced from 100% to 20%, the model's accuracy on the three datasets decreases by only about 5%, demonstrating that our model possesses strong generalization capabilities. Additionally, when the training data are reduced from 100% to 20%, the model's accuracy on the three datasets decreases by only about 5%, demonstrating that our model possesses strong generalization capabilities.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11820580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detailed Modeling of Surface-Plasmon Resonance Spectrometer Response for Accurate Correction.","authors":"Ricardo David Araguillin-López, Angel Dickerson Méndez-Cevallos, César Costa-Vera","doi":"10.3390/s25030894","DOIUrl":"10.3390/s25030894","url":null,"abstract":"<p><p>This work identifies and models the inline devices in an experimental surface-plasmon resonance spectroscopy setup to determine the system's transfer function. This allows for the comparison of theoretical and experimental responses and the analysis of the dynamics of the components of an analyte placed on the sensor at the nanometer scale. The transfer functions of individual components, including the light source, polarizers, spectrometer, optical fibers, and the SPR sensor, were determined experimentally and theoretically. The theoretical model employed Planck's law for the light source, manufacturer specifications for some components, and experimental characterization for others, such as the polarizers and optical fibers. The SPR sensor was modeled using characteristic matrix theory, incorporating the optical constants of the prism, gold film, chromium adhesive layer, and analyte. The combined transfer functions created a comprehensive model of the entire experimental system. This model successfully reproduced the experimental SPR spectrum with a similarity greater than 95%. The system's operational range was also extended, constrained by the signal-to-noise ratio at the spectrum's edges. The detailed model allows for the accurate correction of the measured spectra, which will be essential for the further analysis of nanosuspensions and molecules dissolved in liquids.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11819726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Mobile Robot Path Planning Based on MSIAR-GWO Algorithm.","authors":"Danfeng Chen, Junlang Liu, Tengyun Li, Jun He, Yong Chen, Wenbo Zhu","doi":"10.3390/s25030892","DOIUrl":"10.3390/s25030892","url":null,"abstract":"<p><p>Path planning is of great research significance as it is key to affecting the efficiency and safety of mobile robot autonomous navigation task execution. The traditional gray wolf optimization algorithm is widely used in the field of path planning due to its simple structure, few parameters, and easy implementation, but the algorithm still suffers from the disadvantages of slow convergence, ease of falling into the local optimum, and difficulty in effectively balancing exploration and exploitation in practical applications. For this reason, this paper proposes a multi-strategy improved gray wolf optimization algorithm (MSIAR-GWO) based on reinforcement learning. First, a nonlinear convergence factor is introduced, and intelligent parameter configuration is performed based on reinforcement learning to solve the problem of high randomness and over-reliance on empirical values in the parameter selection process to more effectively coordinate the balance between local and global search capabilities. Secondly, an adaptive position-update strategy based on detour foraging and dynamic weights is introduced to adjust the weights according to changes in the adaptability of the leadership roles, increasing the guiding role of the dominant individual and accelerating the overall convergence speed of the algorithm. Furthermore, an artificial rabbit optimization algorithm bypass foraging strategy, by adding Brownian motion and Levy flight perturbation, improves the convergence accuracy and global optimization-seeking ability of the algorithm when dealing with complex problems. Finally, the elimination and relocation strategy based on stochastic center-of-gravity dynamic reverse learning is introduced for the inferior individuals in the population, which effectively maintains the diversity of the population and improves the convergence speed of the algorithm while avoiding falling into the local optimal solution effectively. In order to verify the effectiveness of the MSIAR-GWO algorithm, it is compared with a variety of commonly used swarm intelligence optimization algorithms in benchmark test functions and raster maps of different complexities in comparison experiments, and the results show that the MSIAR-GWO shows excellent stability, higher solution accuracy, and faster convergence speed in the majority of the benchmark-test-function solving. In the path planning experiments, the MSIAR-GWO algorithm is able to plan shorter and smoother paths, which further proves that the algorithm has excellent optimization-seeking ability and robustness.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11821003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time Series.","authors":"Colin O Quinn, Ronald H Brown, George F Corliss, Richard J Povinelli","doi":"10.3390/s25030895","DOIUrl":"10.3390/s25030895","url":null,"abstract":"<p><p>Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency data and correlated variables, followed by an update phase that redistributes low-frequency observations across high-frequency periods. This process repeats, refining estimates with each iteration cycle. The ISD algorithm's key contribution is its ability to disaggregate multiple, nonuniformly spaced time series with overlapping intervals into a single daily representation. In two case studies using natural gas data, ISD successfully disaggregates billing cycle and grouped residential customer data into daily time series, achieving a 1.4-4.3% WMAPE improvement for billing cycle data and a 4.6-10.4% improvement for residential data over existing methods.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11819941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-Dimensional Visualization Using Proportional Photon Estimation Under Photon-Starved Conditions.","authors":"Jin-Ung Ha, Hyun-Woo Kim, Myungjin Cho, Min-Chul Lee","doi":"10.3390/s25030893","DOIUrl":"10.3390/s25030893","url":null,"abstract":"<p><p>In this paper, we propose a new method for three-dimensional (3D) visualization that proportionally estimates the number of photons in the background and the object under photon-starved conditions. Photon-counting integral imaging is one of the techniques for 3D image visualization under photon-starved conditions. However, conventional photon-counting integral imaging has the problem that a random noise is generated in the background of the image by estimating the same number of photons in entire areas of images. On the other hand, our proposed method reduces the random noise by estimating the proportional number of photons in the background and the object. In addition, the spatial overlaps have been applied to the space where photons overlap to obtain the enhanced 3D images. To demonstrate the feasibility of our proposed method, we conducted optical experiments and calculated the performance metrics such as normalized cross-correlation, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). For SSIM of 3D visualization results by our proposed method and conventional method, our proposed method achieves about 3.42 times higher SSIM than conventional method. Therefore, our proposed method can obtain better 3D visualization of objects than conventional photon-counting integral imaging methods under photon-starved conditions.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11819839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in IR Thermal Imaging and Sensing for Medical Applications.","authors":"Antoni Z Nowakowski, Mariusz Kaczmarek","doi":"10.3390/s25030891","DOIUrl":"10.3390/s25030891","url":null,"abstract":"<p><p>The state of the art in IR thermal imaging methods for applications in medical diagnostics is discussed. A review of advances in IR thermal imaging technology in the years 1960-2024 is presented. Recently used artificial intelligence (AI) methods in the analysis of thermal images are the main interest. IR thermography is discussed in view of novel applications of machine learning methods for improved diagnostic analysis and medical treatment. The AI approach aims to improve image quality by denoising thermal images, using applications of AI super-resolution algorithms, removing artifacts, object detection, face and characteristic features localization, complex matching of diagnostic symptoms, etc.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11820461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-User MIMO Downlink Precoding with Dynamic User Selection for Limited Feedback.","authors":"Mikhail Bakulin, Taoufik Ben Rejeb, Vitaly Kreyndelin, Denis Pankratov, Aleksei Smirnov","doi":"10.3390/s25030866","DOIUrl":"10.3390/s25030866","url":null,"abstract":"<p><p>In modern (5G) and future Multi-User (MU) wireless communication systems Beyond 5G (B5G) using Multiple-Input Multiple-Output (MIMO) technology, base stations with a large number of antennas communicate with many mobile stations. This technology is becoming especially relevant in modern multi-user wireless sensor networks in various application scenarios. The problem of organizing an MU mode on the downlink has arisen, which can be solved by precoding at the Base Station (BS) without using additional channel frequency-time resources. In order to utilize an efficient precoding algorithm at the base station, full Channel State Information (CSI) is needed for each mobile station. Transmitting this information for massive MIMO systems normally requires the allocation of high-speed channel resources for the feedback. With limited feedback, reduced information (partial CSI) is used, for example, the codeword from the codebook that is closest to the estimated channel vector (or matrix). Incomplete (or inaccurate) CSI causes interference from the signals, transmitted to neighboring mobile stations, that ultimately results in a decrease in the number of active users served. In this paper, we propose a new downlink precoding approach for MU-MIMO systems that also uses codebooks to reduce the information transmitted over a feedback channel. A key aspect of the proposed approach, in contrast to the existing ones, is the transmission of new, uncorrelated information in each cycle, which allows for accumulating CSI with higher accuracy without increasing the feedback overhead. The proposed approach is most effective in systems with dynamic user selection. In such systems, increasing the accuracy of CSI leads to an increase in the number of active users served, which after a few cycles, can reach a maximum value determined by the number of transmit antennas at the BS side. This approach appears to be promising for addressing the challenges associated with current and future massive MIMO systems, as evidenced by our statistical simulation results. Various methods for extracting and transmitting such uncorrelated information over a feedback channel are considered. In many known publications, the precoder, codebooks, CSI estimation methods and other aspects of CSI transmission over a feedback channel are separately optimized, but a comprehensive approach to jointly solving these problems has not yet been developed. In our paper, we propose to fill this gap by combining a new approach of precoding and CSI estimation with CSI accumulation and transmission over a feedback channel.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11820205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Study on Vibration Attenuation Characteristics of Ballastless Track Structures in Urban Rail Transit.","authors":"Wuji Guo, Zhiping Zeng, Mengxuan Ye, Fushan Liu, Weidong Wang, Cheng Chang, Qiuyi Li, Ping Li","doi":"10.3390/s25030868","DOIUrl":"10.3390/s25030868","url":null,"abstract":"<p><p>With the rapid development of urban rail transit, the intensity and impact range of train-induced vibrations are increasing. Investigating the transmission characteristics and attenuation patterns of these vibrations in track structures aids in understanding train-induced environmental vibrations. This study conducted rail impact experiments on a long sleeper integrated slab of a straight section of a subway tunnel. The hammer struck the rail at various positions, and acceleration sensors recorded the responses of the rail, slab, and tunnel. In order to determine the impact force, the vertical wheel-rail force and the vibration response of track structures were measured. Then, the Lance-LC1304B force hammer was selected for the experiment, and the hammer impact force reached 30 kN, the magnitude of which reached the measured wheel-rail force size for the line. Based on the results of the impact tests, the vibration attenuation characteristics of the track structure were analyzed. Accordingly, reference values for the truncation time and truncation distance in the vehicle-track coupled dynamics model's moving window were provided. By comparing the results of the hammering experiment with the train-induced vibration results, the main excitation frequencies during train operation were determined. These findings provide valuable insights for the development of rail transit systems.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11820510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Unseen: A Survey of Multi-Sensor Fusion and the Role of Explainable AI (XAI) in Autonomous Vehicles.","authors":"De Jong Yeong, Krishna Panduru, Joseph Walsh","doi":"10.3390/s25030856","DOIUrl":"10.3390/s25030856","url":null,"abstract":"<p><p>Autonomous vehicles (AVs) rely heavily on multi-sensor fusion to perceive their environment and make critical, real-time decisions by integrating data from various sensors such as radar, cameras, Lidar, and GPS. However, the complexity of these systems often leads to a lack of transparency, posing challenges in terms of safety, accountability, and public trust. This review investigates the intersection of multi-sensor fusion and explainable artificial intelligence (XAI), aiming to address the challenges of implementing accurate and interpretable AV systems. We systematically review cutting-edge multi-sensor fusion techniques, along with various explainability approaches, in the context of AV systems. While multi-sensor fusion technologies have achieved significant advancement in improving AV perception, the lack of transparency and explainability in autonomous decision-making remains a primary challenge. Our findings underscore the necessity of a balanced approach to integrating XAI and multi-sensor fusion in autonomous driving applications, acknowledging the trade-offs between real-time performance and explainability. The key challenges identified span a range of technical, social, ethical, and regulatory aspects. We conclude by underscoring the importance of developing techniques that ensure real-time explainability, specifically in high-stakes applications, to stakeholders without compromising safety and accuracy, as well as outlining future research directions aimed at bridging the gap between high-performance multi-sensor fusion and trustworthy explainability in autonomous driving systems.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11819880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Portable Magnetoelectric Gaussmeter Based on Torque Effect.","authors":"Jingen Wu, Jiacheng Qiao, Xianfeng Liang, Yongjun Du, Jieqiang Gao, Yiwei Xu, Jinghong Guo, Min Lu, Ming Zhang, Zhongqiang Hu","doi":"10.3390/s25030855","DOIUrl":"10.3390/s25030855","url":null,"abstract":"<p><p>A giant magnetoelectric coefficient has been discovered in laminated magnetoelectric composites incorporating piezoelectric and magnetostrictive layers, which reveals a high sensitivity in AC magnetic field detection under a DC bias field. However, the DC-biased magnetoelectric composites are not capable of detecting DC magnetic fields due to the interference with the DC signal to be measured. Here, we demonstrate a portable magnetoelectric gaussmeter based on torque effect that can detect both DC and AC magnetic fields. The proposed gaussmeter is equipped with a magnetoelectric sensor, a charge amplification module, a signal processing circuit, a power module, a data processing program, a display module, etc. The proposed gaussmeter indicates such performance indexes as an intensity range of 0~10 Oe, frequency range of DC~500 Hz, AC detection limit of 0.01 Oe, DC detection limit of 0.08 Oe, and frequency resolution of 1 Hz. Being powered by a power adapter (or a battery) of 5V 2A, the whole device system is pocket-size, low-cost, and highly portable, demonstrating its potential for magnetic field detection as a distributed sensor.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11821033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}