bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AI
Authors: Alkanhel Reem Ibrahim , Saleh Hager , Elaraby Ahmed , Alharbi Saleh , Elmannai Hela , Alaklabi Saad , Alsamhi Saeed Hamood , Mostafa Sherif
Published: December 2024

For people with diabetes, controlling blood glucose level (BGL) is a significant issue since the disease affects how the body metabolizes food, which makes careful insulin regulation necessary. Patients have to manually check their blood sugar levels, which can be laborious and inaccurate. Many variables affect BGL changes, making accurate prediction challenging. To anticipate BGL many steps ahead, we propose a novel hybrid deep learning model framework based on Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs), which can be integrated into the Internet of Things (IoT)-enabled diabetes management systems, improving prediction accuracy and timeliness by allowing real-time data processing on edge devices. While the GRU layer records temporal relationships and sequence information, the CNN layer analyzes the incoming data to extract significant features. Using a publicly accessible type 1 diabetes dataset, the hybrid model’s performance is compared to that of the standalone Long Short-Term Memory (LSTM), CNN, and GRU models. The findings show that the hybrid CNN-GRU model performs better than the single models, indicating its potential to significantly improve real-time BGL forecasting in IoT-based diabetes management systems.
Development of a Multiple Temperature Sensors Device for the Characterization, Control and Monitoring of Microbiological Incubators
Authors: Salinas Domján Carolina , Valente Mauro A. , Romero Marcelo R.
Published: December 2024

An increasing number of projects require the precise knowledge and control of thermal conditions within the study system and their temporal evolution. This is particularly critical for equipment such as laboratory ovens and microbiological incubators, which are essential in various fields of chemistry and microbiology areas. These devices allow and facilitate the execution of experimental work in controlled environments, leading to reproducible experiments. This work presents a methodology for assembling and calibrating a highly accessible and low-cost data logger equipped with multiple temperature sensors. The final calibrated dispositive is straightforward to construct and allows the simultaneous and independent temperature measurement from multiple positions within the same system, which is then applied to the study, characterization, control, and monitoring of the internal thermal behavior of a laboratory oven dedicated to microbiological agents’ cultivation. This approach ensures, through a robust methodology, a precise characterization by quantitative methods that allows objective decision making in the management and control of the temperature inside the system. Additionally, the device is suitable for extension and application in diverse research environments by modifying the sensor calibration to achieve a desired temperature range or number of measurement units, representing a potential work tool for laboratory systems.
Radiation Detection—CD/DVD, Glass, and Emerging Materials for Radon Exposure Assessment
Authors: Rathebe Phoka C. , Kholopo Mota
Published: December 2024

This review aimed to explore advances in radon detection methods, emphasizing cost-effectiveness and accessible techniques such as CDs, DVDs, and glass-based detectors. In this review, we compared traditional methods like alpha track detectors and continuous radon monitors with emerging innovations that leverage polycarbonate material and IoT-integrated systems. Our evaluation of the synthesis suggests that CDs and DVDs provide scalable solutions for long-term radon monitoring, while glass-based detectors like CR-39 offer high sensitivity for epidemiological studies. The integration of IoT and AI technologies further enhances real-time radon monitoring, paving the way for precise, scalable, and affordable radon mitigation strategies. This work highlights the importance of low-cost, innovative approaches in reducing radon-related lung cancer risks and informs future research on optimizing the technologies for diverse environments.
A Novel Detection Scheme for Motor Bearing Structure Defects in a High-Speed Train Using Stator Current
Authors: Sun Qi , Zhu Juan , Chen Chunjun
Published: December 2024

Railway traction motor bearings (RTMB) are critical components in high-speed trains (HST) that are particularly susceptible to failure due to the high stress and rotational frequency they experience. To address the challenge of high false-positive rates in existing monitoring systems, this paper introduces a novel sensorless monitoring scheme that leverages stator current to detect fault-related characteristics, eliminating the need for additional sensors. This approach employs a hybrid signal preprocessing algorithm that integrates adaptive notch filtering (ANF) with envelope spectrum analysis (ESA) to effectively sparse the stator current and extract relevant fault features. A deep belief network (DBN) is utilized for the classification of the health status of the RTMB. To validate the scheme’s feasibility and effectiveness, we conducted experiments on a 1:1 scale high-speed railway traction motor, demonstrating that mechanical defects in RTMB can be reliably indicated by changes in stator current. Based on the analysis of experimental results, it was concluded that the fault detection accuracy of RTMB based on stator current is at least 17.3% higher than that of the fault diagnosis methods based on vibration in diagnosing whether the system has a fault. Among them, the method proposed in this paper is the best in diagnosing the presence and type of faults, with an accuracy that is at least 8.9% higher than other methods. This study not only presents a new method for RTMB monitoring but also contributes to the field by offering a more accurate and efficient alternative to current practices.
Sensing in Inland Waters to Promote Safe Navigation: A Case Study in the Aveiro’s Lagoon
Authors: Carvalho Diogo Miguel , Dias João Miguel , de Abreu Jorge Ferraz
Published: December 2024

Maritime navigation safety relies on preventing accidents, such as collisions and groundings. However, several factors can exacerbate these risks, including inexistent or inadequate buoyage systems and nautical charts with outdated bathymetry. The International Hydrographic Organization (IHO) highlights high costs and traditional methods as obstacles to updating bathymetric information, impacting both safety and socio-economic factors. Navigation in inland and coastal waters is particularly complex due to the presence of shallow intertidal zones that are not signaled, where navigation depends on tidal height, vessel draw, and local knowledge. To address this, recreational vessels can use electronic maritime sensors to share critical data with nearby vessels. This article introduces a low-cost maritime data sharing system using IoT technologies for both inland (e.g., Ria de Aveiro) and coastal waters. The system enables the collection and sharing of meteorological and oceanographic data, including depth, tide height, wind direction, and speed. Using a case study in the Ria de Aveiro lagoon, known for its navigational difficulties, the system was developed with a Contextual Design approach focusing on sailors’ needs. It allows for the real-time sharing of data, helping vessels to anticipate maneuvers for safer navigation. The results demonstrate the system’s potential to improve maritime safety in both inland and coastal areas.
Context-Aware Integrated Navigation System Based on Deep Learning for Seamless Localization
Authors: Hwang Byungsun , Lee Seongwoo , Kim Kyounghun , Kim Soohyun , Seon Joonho , Kim Jinwook , Kim Jeongho , Sun Youngghyu , Kim Jinyoung
Published: December 2024

An integrated navigation system is a promising solution to improve positioning performance by complementing estimated positioning in each sensor, such as a global positioning system (GPS), an inertial measurement unit (IMU), and an odometer sensor. However, under GPS-disabled environments, such as urban canyons or tunnels where the GPS signals are difficult to receive, the positioning performance of the integrated navigation system decreases. Therefore, deep learning-based integrated navigation systems have been proposed to ensure seamless localization under various positioning conditions. Nevertheless, the conventional deep learning-based systems are applied with a lack of consideration of context features on surface condition, wheel slip, and movement pattern, which are factors causing positioning performance. In this paper, a context-aware integrated navigation system (CAINS) is proposed to ensure seamless localization, especially under GPS-disabled conditions. In the proposed CAINS, two deep learning layers are designed with context-aware and state estimation layers. The context-aware layer extracts vehicle context features from IMU data, while the state estimation layer predicts the GPS position increments by modeling the relationship between context features, velocity, attitude, and position increments. From simulation results, it is confirmed that the positioning accuracy can be significantly improved based on the proposed CAINS when compared with conventional navigation systems.
Composite Improved Algorithm Based on Jellyfish, Particle Swarm and Genetics for UAV Path Planning in Complex Urban Terrain
Authors: Wang Qi , Yi Wenjun
Published: December 2024

Path planning technology is of great consequence in the field of unmanned aerial vehicles (UAVs). In order to enhance the safety, path smoothness, and shortest path acquisition of UAVs undertaking tasks in complex urban multi-obstacle environments, this paper proposes an innovative composite improvement algorithm that integrates the advantages of the jellyfish search algorithm and the particle swarm algorithm. The algorithm effectively overcomes the shortcomings of a single algorithm, including parameter setting issues, slow convergence rates, and a tendency to become trapped in local optima. Additionally, it enhances the path smoothness, which improves the path optimisation. This enhances the capacity of UAVs to optimise their paths in environments characterised by multiple obstacles. To evaluate the practical effectiveness of the algorithm, a three-dimensional complex city model was constructed for the purposes of the study, and an adaptation function was designed for the purpose of evaluation. The experimental evaluation of 23 benchmark functions, the simulation test of the 3D city model, and 100 repetitive experiments demonstrate that the composite improved algorithm has a considerable advantage over the other comparative algorithms regarding performance. It exhibits fast convergence, high accuracy, and both global and local search capabilities, which enable the effective planning of a UAV flight path and the maintenance of good stability. In comparison to traditional algorithms, the composite improved algorithm demonstrably reduces the flight time and the number of obstacle avoidance manoeuvres required by the UAV. It provides robust technical support for the path planning of the UAV in complex urban environments and facilitates the advancement and implementation of related technologies.
SURABHI: Self-Training Using Rectified Annotations-Based Hard Instances for Eidetic Cattle Recognition
Authors: Ramesh Manu , Reibman Amy R.
Published: December 2024

We propose a self-training scheme, SURABHI, that trains deep-learning keypoint detection models on machine-annotated instances, together with the methodology to generate those instances. SURABHI aims to improve the keypoint detection accuracy not by altering the structure of a deep-learning-based keypoint detector model but by generating highly effective training instances. The machine-annotated instances used in SURABHI are hard instances—instances that require a rectifier to correct the keypoints misplaced by the keypoint detection model. We engineer this scheme for the task of predicting keypoints of cattle from the top, in conjunction with our Eidetic Cattle Recognition System, which is dependent on accurate prediction of keypoints for predicting the correct cow ID. We show that the final cow ID prediction accuracy on previously unseen cows also improves significantly after applying SURABHI to a deep-learning detection model with high capacity, especially when available training data are minimal. SURABHI helps us achieve a top-6 cow recognition accuracy of 91.89% on a dataset of cow videos. Using SURABHI on this dataset also improves the number of cow instances with correct identification by 22% over the baseline result from fully supervised training.
Core Payload of the Space Gravitational Wave Observatory: Inertial Sensor and Its Critical Technologies
Authors: Wang Shaoxin , Liu Dongxu , Zhan Xuan , Dong Peng , Shen Jia , Wang Juan , Gao Ruihong , Guo Weichuan , Xu Peng , Qi Keqi , Luo Ziren
Published: December 2024

Since Einstein’s prediction regarding the existence of gravitational waves was directly verified by the ground-based detector Advanced LIGO, research on gravitational wave detection has garnered increasing attention. To overcome limitations imposed by ground vibrations and interference at arm’s length, a space-based gravitational wave detection initiative was proposed, which focuses on analyzing a large number of waves within the frequency range below 1 Hz. Due to the weak signal intensity, the TMs must move along their geodesic orbit with a residual acceleration less than 10−15 m/s2/Hz1/2. Consequently, the core payload-inertial sensor was designed to shield against stray force noise while maintaining the high-precision motion of the test mass through a drag-free control system, providing an ultra-stable inertial reference for laser interferometry. To meet these requirements, the inertial sensor integrates a series of unit settings and innovative designs, involving numerous subsystems and technologies. This article provides a comprehensive overview of these critical technologies used in the development of inertial sensors for space gravitational wave detection and discusses future trends and potential applications for these sensors.
A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection
Authors: Li Yizhe , Xie Yidong , He Hu
Published: December 2024

Aluminum is extensively utilized in the aerospace, aviation, automotive, and other industries. The presence of surface defects on aluminum has a significant impact on product quality. However, traditional detection methods fail to meet the efficiency and accuracy requirements of industrial practices. In this study, we propose an innovative aluminum surface defect detection method based on an optimized two-stage Faster R-CNN network. A 2D camera serves as the image sensor, capturing high-resolution images in real time. Optimized lighting and focus ensure that defect features are clearly visible. After preprocessing, the images are fed into a deep learning network incorporated with a multi-scale feature pyramid structure, which effectively enhances defect recognition accuracy by integrating high-level semantic information with location details. Additionally, we introduced an optimized Convolutional Block Attention Module (CBAM) to further enhance network efficiency. Furthermore, we employed the genetic K-means algorithm to optimize prior region selection, and a lightweight Ghost model to reduce network complexity by 14.3%, demonstrating the superior performance of the Ghost model in terms of loss function optimization during training and validation as well as in terms of detection accuracy, speed, and stability. The network was trained on a dataset of 3200 images captured by the image sensor, split in an 8:1:1 ratio for training, validation, and testing, respectively. The experimental results show a mean Average Precision (mAP) of 94.25%, with individual Average Precision (AP) values exceeding 80%, meeting industrial standards for defect detection.
Adjoint-Assisted Shape Optimization of Microlenses for CMOS Image Sensors
Authors: Arfin Rishad , Niegemann Jens , McGuire Dylan , Bakr Mohamed H.
Published: December 2024

Recently, there have been significant developments in the designs of CMOS image sensors to achieve high-resolution sensing capabilities. One of the fundamental factors determining the sensor’s ability to capture high-resolution images is its efficiency in focusing the visible light onto the photosensitive region of the submicron scale. In most CMOS imaging technologies, this is typically achieved through microlenses. Light collection under diverse conditions can be significantly improved through the efficient design of microlenses. While the optimization of microlenses appears to be imperative, achieving efficient designs of microlenses for high-density pixels under various conditions remains a significant challenge. Therefore, a systematic optimization approach is required to accelerate the development of efficient microlenses with enhanced optical performance. In this paper, we present an approach to optimize the shape of CMOS microlenses through adjoint sensitivity analysis (ASA). A novel figure of merit (FOM) is developed and incorporated into the optimization process to enhance the light collection. The gradient of the FOM is computed iteratively using two field simulations only. The functionality and robustness of the optimization framework are thoroughly evaluated. Furthermore, the performance of the optimized CMOS microlenses is compared to that of the conventional microlenses. The adjoint-assisted optimization framework presented here can be further used to develop efficient optical devices that perform optical manipulation such as concentrating, bending, or dispersing light in compact imaging systems.
Semantic Scene Completion in Autonomous Driving: A Two-Stream Multi-Vehicle Collaboration Approach
Authors: Li Junxuan , Zhang Yuanfang , Han Jiayi , Han Peng , Luo Kaiqing
Published: December 2024

Vehicle-to-vehicle communication enables capturing sensor information from diverse perspectives, greatly aiding in semantic scene completion in autonomous driving. However, the misalignment of features between ego vehicle and cooperative vehicles leads to ambiguity problems, affecting accuracy and semantic information. In this paper, we propose a Two-Stream Multi-Vehicle collaboration approach (TSMV), which divides the features of collaborative vehicles into two streams and regresses interactively. To overcome the problems caused by feature misalignment, the Neighborhood Self-Cross Attention Transformer (NSCAT) module is designed to enable the ego vehicle to query the most similar local features from collaborative vehicles through cross-attention, rather than assuming spatial-temporal synchronization. A 3D occupancy map is finally generated from the features of collaborative vehicle aggregation. Experimental results on both V2VSSC and SemanticOPV2V datasets demonstrate TSMV outpace state-of-the-art collaborative semantic scene completion techniques.
Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture Classification
Authors: Liu Zhuofu , Shu Zihao , Cascioli Vincenzo , McCarthy Peter W.
Published: December 2024

Sedentary behaviors, including poor postures, are significantly detrimental to health, particularly for individuals losing motion ability. This study presents a posture detection system utilizing four force-sensitive resistors (FSRs) and two triaxial accelerometers selected after rigorous assessment for consistency and linearity. We compared various machine learning algorithms based on classification accuracy and computational efficiency. The k-nearest neighbor (KNN) algorithm demonstrated superior performance over Decision Tree, Discriminant Analysis, Naive Bayes, and Support Vector Machine (SVM). Further analysis of KNN hyperparameters revealed that the city block metric with K = 3 yielded optimal classification results. Triaxial accelerometers exhibited higher accuracy in both training (99.4%) and testing (99.0%) phases compared to FSRs (96.6% and 95.4%, respectively), with slightly reduced processing times (0.83 s vs. 0.85 s for training; 0.51 s vs. 0.54 s for testing). These findings suggest that, apart from being cost-effective and compact, triaxial accelerometers are more effective than FSRs for posture detection.
Impact of Power and Time in Hepatic Microwave Ablation: Effect of Different Energy Delivery Schemes
Authors: Trujillo Macarena , Najafabadi Mahtab Ebad , Romero Antonio , Prakash Punit , Cornelis Francois H.
Published: December 2024

Microwave ablation often involves the use of continuous energy-delivery protocols with a fixed power and time. To achieve larger ablation zones, a range of protocols and power levels have been studied in experimental studies. The objective of the present study was to develop and experimentally evaluate the performance of a coupled computational electromagnetic–bioheat transfer model of 2.45 GHz microwave ablation under a variety of continuous and pulsed power delivery schemes. The main aim was to obtain an in-depth knowledge of the influence of energy delivery settings on ablation zone profiles and thermal damage in the peri-ablation zone. In addition to the theoretical model, we evaluated the power delivery schemes using ex vivo experiments and compared them to previously published data from in vivo experiments. The results showed slight differences in terms of the ablation zone size for different power delivery schemes under ex vivo conditions, with the applied energy level being the most important factor that determines ablation zone size; however, under in vivo conditions, applying a high-power pulse prior to and following a longer constant power application (BOOKEND 95 W protocol) presented the most favorable ablation zones. Moreover, the modeling and experimental studies identified threshold applied power and ablation times beyond which increases did not yield substantive increases in ablation zone extents.
Evaluation of a Non-Enzymatic Electrochemical Sensor Based on Co(OH)2-Functionalized Carbon Nanotubes for Glucose Detection
Authors: Bolaños-Mendez Diego , Fernández Lenys , Uribe Rafael , Cunalata-Castro Alisson , González Gema , Rojas Isamara , Chico-Proano Andrés , Debut Alexis , Celi Luis Alberto , Espinoza-Montero Patricio
Published: December 2024

This work reports on the assessment of a non-hydrolytic electrochemical sensor for glucose sensing that is developed using functionalized carbon nanotubes (fCNTs)/Co(OH)2. The morphology of the nanocomposite was investigated by scanning electron microscopy, which revealed that the CNTs interacted with Co(OH)2. This content formed a nanocomposite that improved the electrochemical characterizations of the electrode, including the electrochemical active surface area and capacitance, thus improving sensitivity to glucose. In the electrochemical characterization by cyclic voltammetry and chronoamperometry, the increase in catalytic activity by Co(OH)2 improved the stability and reproducibility of the glucose sensor without the use of enzymes, and its concentration range was between 50 and 700 μmol L−1. The sensor exhibited good linearity towards glucose with LOD value of 43.200 µmol L−1, which proved that the Co(OH)2-fCNTs composite is judicious for constructing cost effective and feasible sensor for glucose detection.
Quantum Positioning Scheme Based on Microwave–Optical Entanglement
Authors: Miao Qiang , Wu Dewei
Published: December 2024

Microwaves exhibit superior performance in free-space transmission compared to optical waves, primarily due to their ability to penetrate fog and experience lower losses in the Earth’s atmosphere. Based on microwave–optical entanglement prepared by nano-cavity electro-opto-mechanic converters, we propose a scheme of a quantum positioning system using the distance-based positioning method. Principles of microwave–optical entanglement preparation and our QPS scheme are introduced in detail. The entanglement feature, system stability and positioning feature of the scheme are analyzed after simulations. Furthermore, we delve into the impact of key parameters, such as transmissivity and photon conversion efficiency, on positioning. Notably, the entanglement degrees for both microwave–optic entanglement at the transmitter and optic–optic entanglement at the receiver surpass one, affirming the efficiency of the scheme in preparing and maintaining entanglement. When transmissivity in beam-splitter models of both ground stations equals 0.5, our scheme achieves a minimal positioning error of 6.4×10−7m2 under ideal conditions. Additionally, we map out traces of a plane through continuous positioning using our scheme. These results demonstrate the theoretical efficiency and robustness of our proposed approach.
Recent Progress in Nanomaterial-Based Fluorescence Assays for the Detection of Food-Borne Pathogens
Authors: Song Shiyu , Han Lu , Chen Min , Pan Leiqing , Tu Kang
Published: December 2024

Food safety is of great concern, and food-borne bacterial infections and diseases are a major crisis for health. Therefore, it is necessary to develop rapid detection techniques for the prevention and recognition of food safety hazards caused by food-borne pathogens. In recent years, the fluorescence assay has become a widely utilized detection method due to its good signal amplification effect, high detection sensitivity, high stability, and short detection time. In this review, the different kinds of fluorescence materials were concentrated, including quantum dots (QDs), carbon dots (CDs), metal–organic frameworks (MOFs), and upconversion nanoparticles (UCNPs). The optical properties and applications of different kinds of fluorescent materials were analyzed and compared. Furthermore, according to the biosensing components, different fluorescence biosensors are reviewed, including label-free based fluorescence probes, aptamer-based biosensors, and antibody-based biosensors. Finally, we focused our attention on the discussion of fluorescent detection techniques combined with other techniques and their applications. The review presents future trends in fluorescence sensors, providing new sights for the detection of food-borne pathogens.
Kalman Filter-Based Fusion of LiDAR and Camera Data in Bird’s Eye View for Multi-Object Tracking in Autonomous Vehicles
Authors: Alfeqy Loay , Abdelmunim Hossam E. Hassan , Maged Shady A. , Emad Diaa
Published: December 2024

Accurate multi-object tracking (MOT) is essential for autonomous vehicles, enabling them to perceive and interact with dynamic environments effectively. Single-modality 3D MOT algorithms often face limitations due to sensor constraints, resulting in unreliable tracking. Recent multi-modal approaches have improved performance but rely heavily on complex, deep-learning-based fusion techniques. In this work, we present CLF-BEVSORT, a camera-LiDAR fusion model operating in the bird’s eye view (BEV) space using the SORT tracking framework. The proposed method introduces a novel association strategy that incorporates structural similarity into the cost function, enabling effective data fusion between 2D camera detections and 3D LiDAR detections for robust track recovery during short occlusions by leveraging LiDAR depth. Evaluated on the KITTI dataset, CLF-BEVSORT achieves state-of-the-art performance with a HOTA score of 77.26% for the Car class, surpassing StrongFusionMOT and DeepFusionMOT by 2.13%, with high precision (85.13%) and recall (80.45%). For the Pedestrian class, it achieves a HOTA score of 46.03%, outperforming Be-Track and StrongFusionMOT by (6.16%). Additionally, CLF-BEVSORT reduces identity switches (IDSW) by over 45% for cars compared to baselines AB3DMOT and BEVSORT, demonstrating robust, consistent tracking and setting a new benchmark for 3DMOT in autonomous driving.
Modeling and Simulation of Eddy Current Dissipation Magnetic Acceleration Noise of Space Inertial Sensors
Authors: Li Pengxuan , Sun Zhiyin , Gao Wei , Cao Bingzhang , Li Yunzhao , Li Liyi , Wang Lei
Published: December 2024

The magnetic acceleration noise (MAN) that stems from the eddy current dissipation of a test mass (TM) serves as an important source of noise for space inertial sensors. Given the problem that the eddy current dissipation magnetic acceleration noise (ECDMAN) of a cubic TM defies analytical solutions, an analytical model of ECDMAN for a spherical TM, which has the same volume as the cubic TM, is systematically derived on the basis of the principles of electromagnetism and the fluctuation-dissipation theorem, and this model can be used as an approximate analytical model for the evaluation of this noise term. Based on the approximate analytical model, with the TM of the LISA Pathfinder (LPF) as the research object, this paper obtains a modification coefficient using the approach of combining the analytical method with the finite element method (FEM), and establishes a semi-analytical model of ECDMAN for the cubic TM. Using the parameters of the LPF’s TM, the calculation error of the semi-analytical model is reduced by about 4.64% compared with the approximate analytical model. Finally, a generalized modeling approach for the semi-analytical model of ECDMAN is put forward, which is applicable to TMs with different parameters and can realize the real-time and rapid evaluation of ECDMAN during in-orbit experiments.
Preliminary Characterization of a Novel Aerosol Jet-Printed Strain Sensor for Feasibility Assessment in a Variable Stiffness Arterial Simulator Application
Authors: Filippi Federico , Fiori Giorgia , Genovesi Annalisa , Barletta Massimiliano , Lancini Matteo , Serpelloni Mauro , Scorza Andrea , Sciuto Salvatore Andrea
Published: December 2024

Wearable strain sensors are widespread in many fields, including the biomedical field where they are used for their stretchability and ability to be applied to non-regular surfaces. The study of the propagation speed of the pressure wave generated by the heartbeat within vessels, i.e., the Pulse Wave Velocity (PWV), is of significant relevance in this field to assess arterial stiffness, a parameter commonly used for the early diagnosis of cardiovascular diseases. In this context, arterial simulators are useful tools to study the relationship between the PWV and other hemodynamic quantities in vitro. This study aims to characterize novel strain sensors to assess their suitability within an arterial simulator capable of varying the stiffness of an arterial surrogate by varying the transmural pressure. Six sensors deposited on arterial surrogates by Aerosol Jet Printing technology were subjected to deformation through a load frame. The results show that the sensors were able to distinguish strains of 0.1%, the maximum strain was around 6–8%, and the fatigue strength depended strongly on the strain rate.
Does Heart Rate Variability Predict Impairment of Operational Performance in Divers?
Authors: Freiberger John , Derrick Bruce , Chon Ki H. , Hossain Md Billal , Posada-Quintero Hugo F. , Cooter Mary , Moon Richard
Published: December 2024

We examined data from Naval Sea Systems Command grant project N0463A-12-C-001, “Hypercapnia: cognitive effects and monitoring”, with the objective of validating or repudiating heart rate variability (HRV) as a warning sign of cognitive impairment from diving gas narcosis or oxygen toxicity. We compared HRV feature scores to their temporally corresponding cognitive outcomes under normal and narcotizing conditions to identify specific HRV features associated with cognitive changes. N0463A-12-C-001 was conducted between 17 September 2013 and 29 January 2016 and employed NASA’s multi-attribute task battery (MATB-II) flight simulator to examine the independent effects of CO2, N2, and O2 partial pressure on diver performance at simulated depths up to 61 msw (200 fsw). We assessed the association of 23 distinct HRV features scores from 432 of the study’s analyzable exposure stages in relation to MATB-II’s four performance subclasses (motor, memory, attention, strategy) while controlling for exercise and CO2, N2, and O2 gas partial pressure. Performance decrements were associated with normalized high-frequency HRVfeatures (HFnu, p = 0.0016) and the number of pairs of successive R-R intervals that differed by more than 50 ms (NN50count1, p = 0.04). Secondary analysis with stratification restricted to non-exercise stages showed that several HRV parameters, including root mean square of the successive difference (RMSSD, p = 0.0015), width of Poincaré plot (p = 0.0017), NN50count1 (p = 0.0019), and standard deviation of normal-to-normal R peaks (p = 0.0082), were associated with performance impairment. The RMSSD association retained statistical significance after Bonferroni correction for multiple tests. HRV features collected from divers tested under narcotizing conditions of breathing gas partial pressure and exercise were associated with performance impairment.
HDCTfusion: Hybrid Dual-Branch Network Based on CNN and Transformer for Infrared and Visible Image Fusion
Authors: Wang Wenqing , Li Lingzhou , Yang Yifei , Liu Han , Guo Runyuan
Published: December 2024

The purpose of infrared and visible image fusion is to combine the advantages of both and generate a fused image that contains target information and has rich details and contrast. However, existing fusion algorithms often overlook the importance of incorporating both local and global feature extraction, leading to missing key information in the fused image. To address these challenges, this paper proposes a dual-branch fusion network combining convolutional neural network (CNN) and Transformer, which enhances the feature extraction capability and motivates the fused image to contain more information. Firstly, a local feature extraction module with CNN as the core is constructed. Specifically, the residual gradient module is used to enhance the ability of the network to extract texture information. Also, jump links and coordinate attention are used in order to relate shallow features to deeper ones. In addition, a global feature extraction module based on Transformer is constructed. Through the powerful ability of Transformer, the global context information of the image can be captured and the global features are fully extracted. The effectiveness of the proposed method in this paper is verified on different experimental datasets, and it is better than most of the current advanced fusion algorithms.
Self-Powered p-NiO/n-ZnO Heterojunction Ultraviolet Photodetector Based on Honeycomb Nano-Mesh Structure
Authors: Wang Nan , Liu Yang , Li Mingyang , Zhao Jieting , Zhang Xiaoqi , Jiang Dayong
Published: December 2024

Ultraviolet (UV) photodetectors (PDs) are characterized by wide wavelength selectivity and strong anti-interference capability. The focus of research is not only limited to the adjustment of the structure composition, but it also delves deeper into its working mechanism and performance optimization. In this study, a heterojunction self-powered photodetector with a unique honeycomb structure was successfully constructed by combining the advantages of two semiconductor materials, zinc oxide (ZnO) and nickel oxide (NiO), using magnetron sputtering and hydrothermal synthesis. The detector has high responsivity, high detectivity and favorable spectral selectivity under UV irradiation. The nearly 10-fold increase in responsivity and detectivity of the detector with the introduction of the honeycomb structure under zero-bias conditions is attributed to the macroporous structure of the ZnO honeycomb nano-mesh, which increases the surface active sites and facilitates the enhancement of light trapping. This study provides significant value to the field of UV detection by improving detector performance through structural optimization.
An Unbiased Feature Estimation Network for Few-Shot Fine-Grained Image Classification
Authors: Wang Jiale , Lu Jin , Yang Junpo , Wang Meijia , Zhang Weichuan
Published: December 2024

Few-shot fine-grained image classification (FSFGIC) aims to classify subspecies with similar appearances under conditions of very limited data. In this paper, we observe an interesting phenomenon: different types of image data augmentation techniques have varying effects on the performance of FSFGIC methods. This indicates that there may be biases in the features extracted from the input images. The bias of the acquired feature may cause deviation in the calculation of similarity, which is particularly detrimental to FSFGIC tasks characterized by low inter-class variation and high intra-class variation, thus affecting the classification accuracy. To address the problems mentioned, we propose an unbiased feature estimation network. The designed network has the capability to significantly optimize the quality of the obtained feature representations and effectively reduce the feature bias from input images. Furthermore, our proposed architecture can be easily integrated into any contextual training mechanism. Extensive experiments on the FSFGIC tasks demonstrate the effectiveness of the proposed algorithm, showing a notable improvement in classification accuracy.
Evaluation of Different Sensor Systems for Classifying the Behavior of Dairy Cows on Pasture
Authors: Pichlbauer Barbara , Chapa Gonzalez Jose Maria , Bobal Martin , Guse Christian , Iwersen Michael , Drillich Marc
Published: December 2024

Monitoring animal behavior using sensor technologies requires prior testing under varying conditions because behaviors can differ significantly, such as between grazing and confined cows. This study aimed to validate several sensor systems for classifying rumination and lying behaviors in cows on pasture under different environmental conditions, compare the sensors’ performance at different time resolutions, and evaluate a correction algorithm for rumination data. Ten Simmental dairy cows were monitored on pasture, each simultaneously equipped with an ear-tag accelerometer (ET), two different leg-mounted accelerometers (LMs), and a noseband sensor (NB). Indirect visual observations using drone-recorded video footage served as the gold standard for validation. The concordance correlation coefficient (CCC) for rumination time was very high for both the ET and NB (0.91–0.96) at a 10 min time resolution. Applying the correction algorithm to 1 min data improved the CCC for the NB from 0.68 to 0.89. For lying time, the CCC was moderate for the ET (0.55) but nearly perfect for both LMs (0.99). In conclusion, both sensors evaluated for classifying rumination are suitable for cows on pasture. We recommend using a correction algorithm for 1 min NB data. For the measurement of lying time, the LMs significantly outperformed the ET.
COSMIC-2 RFI Prediction Model Based on CNN-BiLSTM-Attention for Interference Detection and Location
Authors: Song Cheng-Long , Jin Rui-Min , Han Chao , Wang Dan-Dan , Guo Ya-Ping , Cui Xiang , Wang Xiao-Ni , Bai Pei-Rui , Zhen Wei-Min
Published: December 2024

As the application of the Global Navigation Satellite System (GNSS) continues to expand, its stability and safety issues are receiving more and more attention, especially the interference problem. Interference reduces the signal reception quality of ground terminals and may even lead to the paralysis of GNSS function in severe cases. In recent years, Low Earth Orbit (LEO) satellites have been highly emphasized for their unique advantages in GNSS interference detection, and related commercial and academic activities have increased rapidly. In this context, based on the signal-to-noise ratio (SNR) and radio-frequency interference (RFI) measurements data from COSMIC-2 satellites, this paper explores a method of predicting RFI measurements using SNR correlation variations in different GNSS signal channels for application to the detection and localization of civil terrestrial GNSS interference signals. Research shows that the SNR in different GNSS signal channels shows a correlated change under the influence of RFI. To this end, a CNN-BiLSTM-Attention model combining a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and attention mechanism is proposed in this paper, and the model takes the multi-channel SNR time series of the GNSS as the input and outputs the maximum measured value of RFI in the multi-channels. The experimental results show that compared with the traditional band-pass filtering inter-correlation method and other deep learning models, the model in this paper has a root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R2) of 1.0185, 1.8567, and 0.9693, respectively, in RFI prediction, which demonstrates a higher RFI detection accuracy and a wide range of rough localization capabilities, showing significant competitiveness. Since the correlation changes in the SNR can be processed to decouple the signal strength, this model is also suitable for future GNSS-RO missions (such as COSMIC-1, CHAMP, GRACE, and Spire) for which no RFI measurements have yet been made.
A Study of the Optimal Logic Combinations of RO-Based PUFs on FPGAs to Maximize Identifiability
Authors: Aparicio-Téllez Raúl , Garcia-Bosque Miguel , Díez-Señorans Guillermo , Aznar Francisco , Celma Santiago
Published: December 2024

One of the challenges that wireless sensor networks (WSNs) need to address is achieving security and privacy while keeping low power consumption at sensor nodes. Physically unclonable functions (PUFs) offer a challenge–response functionality that leverages the inherent variations in the manufacturing process of a device, making them an optimal solution for sensor node authentication in WSNs. Thus, identifiability is the fundamental property of any PUF. Consequently, it is necessary to design structures that optimize the PUF in terms of identifiability. This work studies different architectures of oscillators to analyze which ones exhibit the best properties to construct a RO-based PUF. For this purpose, Generalized Galois Ring Oscillators (GenGAROs) are used. A GenGARO is a novel type of oscillator formed by a combination of up to two input logical operations connected in cascade, where one input is the output of the previous operation and the other is the feedback signal. GenGAROs include some previously proposed oscillators as well as many new oscillator designs. Thus, the architecture of GenGAROs is analyzed to implement a GenGARO-PUF on an Artix-FPGA. With this purpose, an exhaustive study of logical operation combinations that optimize PUF performance in terms of identifiability has been conducted. From this, it has been observed that certain logic gates in specific positions within the oscillator contribute to constructing a PUF with good properties, and by applying certain constraints, any oscillator generated with these constraints can be used to construct a PUF with an equal error rate on the order of or below 10−11 using 100-bit responses. As a result, a design methodology for FPGA-based RO-PUFs has been developed, enabling the generation of multiple PUF primitives with high identifiability that other designers could exploit to implement RO-based PUFs with good properties.
Preparation and the Photoelectric Properties of ZnO-SiO2 Films with a Sol–Gel Method Combined with Spin-Coating
Authors: Zhou Ziyang , Yang Weiye , Peng Hongyan , Zhao Shihua
Published: December 2024

This study explores the fabrication of ZnO-SiO2 composite films on silicon substrates via a sol–gel method combined with spin-coating, followed by annealing at various temperatures. The research aims to enhance the UV emission and photoelectric properties of the films. XRD showed that the prepared ZnO sample has a hexagonal structure. SEM images revealed the formation of ZnO nanorods within a dense SiO2 substrate, ranging from 10 μm to 30 μm in length. Photoluminescent analysis showed that the film exhibited strong UV emission centered at 360 nm. The response time measurements indicated that the optimal photoresponse time was approximately 2.9 s. These results suggest the potential of ZnO-SiO2 films as efficient UV-emitting materials with high photoconductivity and a stably reproducible response under visible light, thereby laying the foundation for their application in advanced optoelectronic devices.
Fusing Events and Frames with Coordinate Attention Gated Recurrent Unit for Monocular Depth Estimation
Authors: Duan Huimei , Guo Chenggang , Ou Yuan
Published: December 2024

Monocular depth estimation is a central problem in computer vision and robot vision, aiming at obtaining the depth information of a scene from a single image. In some extreme environments such as dynamics or drastic lighting changes, monocular depth estimation methods based on conventional cameras often perform poorly. Event cameras are able to capture brightness changes asynchronously but are not able to acquire color and absolute brightness information. Thus, it is an ideal choice to make full use of the complementary advantages of event cameras and conventional cameras. However, how to effectively fuse event data and frames to improve the accuracy and robustness of monocular depth estimation remains an urgent problem. To overcome these challenges, a novel Coordinate Attention Gated Recurrent Unit (CAGRU) is proposed in this paper. Unlike the conventional ConvGRUs, our CAGRU abandons the conventional practice of using convolutional layers for all the gates and innovatively designs the coordinate attention as an attention gate and combines it with the convolutional gate. Coordinate attention explicitly models inter-channel dependencies and coordinate information in space. The coordinate attention gate in conjunction with the convolutional gate enable the network to model feature information spatially, temporally, and internally across channels. Based on this, the CAGRU can enhance the information density of the sparse events in the spatial domain in the recursive process of temporal information, thereby achieving more effective feature screening and fusion. It can effectively integrate feature information from event cameras and standard cameras, further improving the accuracy and robustness of monocular depth estimation. The experimental results show that the method proposed in this paper achieves significant performance improvements on different public datasets.
Depth-Based Intervention Detection in the Neonatal Intensive Care Unit Using Vision Transformers
Authors: Hajj-Ali Zein , Dosso Yasmina Souley , Greenwood Kim , Harrold JoAnn , Green James R.
Published: December 2024

Depth cameras can provide an effective, noncontact, and privacy-preserving means to monitor patients in the Neonatal Intensive Care Unit (NICU). Clinical interventions and routine care events can disrupt video-based patient monitoring. Automatically detecting these periods can decrease the time required for hand-annotating recordings, which is needed for system development. Moreover, the automatic detection can be used in the future for real-time or retrospective intervention event classification. An intervention detection method based solely on depth data was developed using a vision transformer (ViT) model utilizing real-world data from patients in the NICU. Multiple design parameters were investigated, including encoding of depth data and perspective transform to account for nonoptimal camera placement. The best-performing model utilized ∼85 M trainable parameters, leveraged both perspective transform and HHA (Horizontal disparity, Height above ground, and Angle with gravity) encoding, and achieved a sensitivity of 85.6%, a precision of 89.8%, and an F1-Score of 87.6%.