IS&T | Library

Abstract

This study proposes a human dynamic behavior recognition method based on joint point extraction and deep learning algorithms. Human skeletal information is collected using a Kinect camera to obtain three-dimensional (3D) joint coordinates, completing the extraction of skeletal joint data. Based on the characteristics of bones in 3D space, processing is performed using 3D skeletal joint point cloud data. An improved PointNet++ method is employed to process the point cloud data: a dual-scale feature extraction strategy is adopted to enhance the network’s multiscale feature capture capability, and the farthest point sampling algorithm is optimized to better preserve behavioral details. Finally, a graph convolutional network approach incorporating a channel attention mechanism and graph topology optimization is used to achieve human dynamic behavior recognition based on 3D skeletons. Experimental results show that the method achieves a maximum F₁ score of 0.98, a misrecognition rate below 0.4%, and a coefficient of variation of 0, demonstrating high recognition accuracy. However, the performance of this research method may be limited in complex scenes with severe occlusion or non-standard perspectives. Future work will focus on exploring multimodal data fusion and real-time optimization to further enhance its robustness and practicality in open environments.

Digital Library: JIST

FastTrack Article

Design of a Wearable Pulse Sensor based on Flexible Piezoelectric Film

4 0

P(VDF–TrFE) piezoelectric film
pulse detection
circular-array sensor
signal conditioning circuit
wearable device

Pu Xuan Ma, Xu Sheng Wang, Tao Han, TianQi Liu, Ke Ran Ren, CongJun Cao

Pages 1 - 18, © Society for Imaging Science and Technology 2026

DOI

10.2352/J.ImagingSci.Technol.2026.70.3.030415

Abstract

This paper designs a wearable pulse sensor based on the flexible poly(vinylidene fluoride–trifluoroethylene) (P(VDF–TrFE)) piezoelectric film to address issues such as discomfort, inconvenience, and low accuracy in traditional pulse sensors. The sensor aims to achieve continuous detection of human pulse signals, providing robust support for the prevention and treatment of cardiovascular diseases. First, flexible P(VDF–TrFE) piezoelectric films were prepared as the sensor substrate by using the tape-casting method. Conductive electrodes were printed on the film’s surface via screen-printing technology, and square- and circular-array sensors were developed by incorporating a mesh shielding layer design for comparative performance evaluation in pulse signal acquisition. Second, to address the low-frequency and weak nature of pulse signals, which are prone to various noise interferences, a precise signal conditioning circuit with amplification and filtering functionalities was designed to acquire high-fidelity and low-noise pulse wave signals. Experimental results demonstrate that the prepared P(VDF–TrFE) film exhibits excellent dielectric, piezoelectric, and ferroelectric properties with a maximum d₃₃ value of −25 pC ⋅ N⁻¹, enhancing the sensor’s ability to rapidly and accurately capture low-frequency pulse signals. The designed flexible pulse sensor conforms well to human skin, meeting wearable and comfort requirements. Among the tested designs, the circular-array sensor detected continuous pulse wave signals with the most physiological characteristic points, exhibiting higher sensitivity and clarity than the square sensor and demonstrating superior detection performance. Additionally, the designed signal conditioning circuit effectively mitigated 50 Hz power frequency and high-frequency noise interferences, successfully amplifying the average peak voltage from 0.069 V to 5.467 V. It displayed a clear and stable pulse waveform while retaining the primary features of the pulse signal, achieving high sensitivity, stability, and accuracy in signal acquisition while suppressing noise. Therefore, the wearable pulse sensor, based on the flexible piezoelectric film, designed in our work effectively detects and captures human pulse wave signals, offering significant potential for applications in medical health monitoring and wearable device research.

Digital Library: JIST

FastTrack Article

Performance of 3D-Printed Lightweight Bionic Energy-Absorbing Structures

3 0

3D printing
bionic loofah structure
main characteristic structure
lightweight
energy absorption

Linlin Liu, Silong Liu, Shaoxue Sun, Ruobing Han, Junjie Jia

Pages 1 - 9, © Society for Imaging Science and Technology 2026

DOI

10.2352/J.ImagingSci.Technol.2026.70.3.030410

Abstract

This study develops a lightweight bionic energy-absorbing structure (loofah sponge bionic structure [LSBS]), inspired by the highly porous loofah sponge, suitable for additive manufacturing. The loofah sponge is partitioned into four functional regions and characterized by regional compression tests, based on which eleven main characteristic structures are extracted and integrated into a parametric 3D model. Finite element simulations in ANSYS Workbench 15.0, combined with structural specific strength and structural specific stiffness indices, are used to evaluate lightweight performance under static and compressive loading. The LSBS specimens are fabricated by DLP (UV-curable resin [UVCR]) and FDM (PLA) and tested in quasi-static compression. The PLA-LSBS exhibits markedly higher energy absorption than UVCR-LSBS, attaining 4.39 J⋅g⁻¹ mass-specific energy absorption and 5.48 J⋅cm⁻³ volume-specific energy absorption, with a 135.10% higher peak load and only 0.83 g extra mass. These results verify the effectiveness of the extracted loofah-inspired features and demonstrate a feasible pathway for designing lightweight, high-energy-absorbing structures via 3D printing.

Digital Library: JIST

FastTrack Article

Experiments and Analysis of Spreading Behavior of Purified Water and Anhydrous Ethanol Droplets Impacting the Surfaces of Ceramic Spheres

9 0

droplet
impact
ceramic sphere
spreading behavior
oscillation

Junjie Xiao, Zhuang Zhang, Qingjie Shi, Weili Wen, Yuansheng Qi

Pages 1 - 11, © Society for Imaging Science and Technology 2026

DOI

10.2352/J.ImagingSci.Technol.2026.70.3.030413

Work Presented at Archiving 2026

Abstract

The dynamic spreading characteristics of purified water and anhydrous ethanol droplets impacting the surface of ceramic spheres were investigated experimentally using a high-speed digital camera operating at 3,000 frames per second. The spreading height factor of the droplets was quantitatively calculated by adding gratings behind the images in AutoCAD. The results are as follows: the droplets generated by the drip flow method are stable in size, are regular in shape, have easily controllable speed, and are ideal for spreading experiments; purified water droplets exhibit pronounced oscillations after impacting the smooth surface of ceramic spheres, whereas anhydrous ethanol droplets rapidly reach a stable state after a single spreading. This behavior difference is attributed to the lower surface tension of anhydrous ethanol droplets compared to purified water, resulting in a weaker contraction force. The time for purified water droplets to reach their minimum spreading height increases with increase in impact velocity. The minimum spreading height decreases to a constant value with increase in the impact velocity of droplets. In the retraction stage, the spreading height factor of the purified water droplet first decreases and then increases with increase in the impact velocity of droplets. The minimum spreading height of anhydrous ethanol droplets impacting the smooth surface of ceramic spheres decreases with increase in droplet impact velocity. However, it no longer decreases with increase in impact velocity when the spreading height factor reaches 0.06. As the impact velocity of purified water droplets increases, the droplets spread further across the horizontal center plane of the sphere and continue downward without retraction. Its critical impact velocity decreases with increase in diameter ratio between the droplet and the ceramic sphere.

Digital Library: JIST

FastTrack Article

32 11

Hyperspectral and Multispectral Fusion Method based on Quadratic Optimization Model

spectral fusion
deep learning
attention mechanism
matrix decomposition
secondary optimization

Zican Sang, Long Ma

Pages 1 - 24, © Society for Imaging Science and Technology 2026

DOI

10.2352/J.ImagingSci.Technol.2026.70.5.050401

Abstract

Hyperspectral super-resolution fusion technology aims to fuse hyperspectral images with multispectral images in the same scene for the super-resolution reconstruction of hyperspectral data. Current deep learning methods are usually trained by data augmentation or constructing complex encoding–decoding networks, often neglecting the physical characteristics of hyperspectral data involving width, height, and three-dimensional channel information. Traditional methods continue to play a role in super-resolution reconstruction although there are deficiencies in the fusion result. For this reason, this paper proposes a Quadratic Optimization Model (QOM) that combines deep learning and traditional mathematical methods. The model first utilizes a three-module neural network for initial fusion; designs corresponding modules for the recovery of dimensional, spatial, and spectral information; and introduces spatial and channel attention mechanisms to enhance feature extraction capability. Subsequently, the preliminary fusion results are optimized by secondary super-resolution through the traditional matrix decomposition method to further improve fusion quality. The experimental results demonstrate that the QOM achieves excellent performance on all seven datasets, exhibiting strong fusion quality while maintaining favorable computational complexity (TFLOPS: 8.6024, Params: 2.9307). Noise experiments verify its high robustness.

Digital Library: JIST

FastTrack Article

Influence of Structural Parameters of Corrugated Octagonal Tray on Compressive Strength

17 0

corrugated octagonal tray
compressive strength
hypotenuse length
aspect ratio

Fangfang Lu, Weisheng Song, Yang Xue, Yan Zhang, Tieqing Su, Mengguang Yan

Pages 1 - 7, © Society for Imaging Science and Technology 2026

DOI

10.2352/J.ImagingSci.Technol.2026.70.3.030414

Abstract

The work studies the impact of five structural parameters on the compressive strength of corrugated octagonal trays: aspect ratio, handle position, handle width, hypotenuse length, and hypotenuse angle. It aims at enhancing the design of fruit and vegetable paper tray packaging. Initial single-factor experiments established the influence of these parameters on compressive strength. Subsequent orthogonal testing and comprehensive analysis optimized the tray design. Notably, compressive strength peaks at a hypotenuse angle of 145∘ and a hypotenuse length of 60 mm. The strength also peaks at an aspect ratio of approximately 1.4:1. The optimal performance is achieved with a hypotenuse length of 65 mm, handle width of 25 mm, handle position at HL, a hypotenuse angle of 145∘, and an aspect ratio of 1.2:1. These findings underscore the importance of these parameters in tray design for maximum compressive strength.

Digital Library: JIST

FastTrack Article

A High-Fidelity Data Acquisition Framework for Training Visual Language Models for Desktop Environments

13 0

visual language models
data acquisition
behavioral cloning
imitation learning

Lei Zhu, Chenhao Duan, Xiaoyan Xue, Yuan Zhang, Chongtao Sun, Xiao Xing, Yanping Du

DOI

10.2352/J.ImagingSci.Technol.2026.70.3.030416

Abstract

Large-scale visual language models (VLMs) show great potential in desktop automation, but their performance is highly dependent on extensive, high-quality imitation learning datasets. Current data acquisition methods generally face core challenges such as low synchronization accuracy, high storage costs, and the resulting exacerbation of covariate drift. To address these issues, this paper proposes and implements a high-fidelity, storage-efficient visual-behavioral data acquisition and training framework called sict. The framework achieves more than 99% storage space saving while guaranteeing nanosecond data synchronization accuracy through a multiprocess asynchronous architecture that leverages high-precision monotonic clocks and variable frame rate video coding techniques. The study constructs a hierarchical desktop operation benchmark dataset based on this framework and fine-tunes the Qwen2.5-VL-7B model. Experimental results show that the 7B model trained by the sict framework outperforms a zero-sample model ten times larger by a wide margin, demonstrating that the fidelity of data collection is a key factor determining the model’s maximum capability. This work provides an efficient and feasible solution for training highly powerful desktop intelligences.

Digital Library: JIST

FastTrack Article

An Enhanced K-means Clustering based Method for Cloud Manufacturing Service Matching of Doctor Blade in Paper Gravure Printing Machine

25 1

gravure printing machine
doctor blade
manufacturing services
matching method
K-means

Yuanyang Wang, Shanhui Liu, Keliang Wei, Yangzhen Cao, Hongen Yang, Yueting Yan

DOI

10.2352/J.ImagingSci.Technol.2026.70.3.030409

Abstract

A service matching method for the cloud manufacturing of paper gravure printing machine doctor blades based on improved K-means clustering is proposed. This approach is aimed at the problem of poor accuracy of both service clustering and supply and demand matching in cloud-based doctor blade manufacturing for paper gravure printing machines. First, based on the improved K-means clustering algorithm, doctor blade cloud manufacturing services are clustered to form a set of services with high similarity within groups and low similarity between groups. Second, the extension theory is used to establish a correlation function to select the doctor blade cloud manufacturing service set with the highest correlation degree with processing demand to form a candidate service set. Finally, the analytic hierarchy process and grey relational analysis are used to select the best cloud manufacturing service based on the subjective demand preference of users to achieve the matching purpose. The experimental results demonstrate that the accuracy of this method in solving the manufacturing service problem of gravure printing machine doctor blades can exceed 90% in approximately 30 min.

Digital Library: JIST

FastTrack Article

YOLOv8-PD: An Optimized YOLOv8n-Based Model for Household Appliance Packaging Damage Detection

8 0

damage detection
attention mechanism
LSKA
MPCA

Lei Zhu, Yuan Li, Yuan Zhang, Yanping Du, Pengge Zhang

DOI

10.2352/J.ImagingSci.Technol.2026.70.3.030407

MonoHybrid: Self-Supervised Monocular Depth Estimation with Hybrid Network

Abstract

With the rapid development of logistics automation and the digital transformation of the home appliance industry, damage to heavy appliance packaging cartons during storage and transportation has become increasingly frequent, adversely affecting product image and delivery quality. Common surface defects such as scratches, holes, and wet stains can easily lead to disputes and economic losses. Therefore, a highly efficient, automated, and terminal-deployable intelligent detection algorithm is urgently required to achieve accurate identification and recording of packaging damages. To address the limitations of YOLOv8n in carton surface damage detection—specifically, its constrained accuracy and the frequent occurrence of missed and false detections—the authors propose an improved object detection algorithm, YOLOv8-PD (Packaging Damage). The proposed model enhances detection performance while maintaining high efficiency through three key optimizations: introducing a large-kernel receptive field attention module (SPPF_LSKA) in the backbone to improve global context modeling; adopting the Wise-IoU loss function to refine bounding box regression accuracy; and incorporating a multi-path coordinate attention (MPCA) mechanism to strengthen key region perception. Experiments conducted on a self-constructed dataset containing three categories—scratches, holes, and wet stains—demonstrate that YOLOv8-PD achieves improvements of 1.4%, 0.9%, and 1.4% in mAP@0.5, Precision, and Recall, respectively, compared with the baseline YOLOv8n. These results validate the proposed method’s superior accuracy and real-time performance in industrial application scenarios.

Digital Library: JIST

FastTrack Article

Regular Article

27 0

monocular depth estimation
hybrid network
self-supervision

Wei Jiang, Bingfei Nan, Guofa Wang

DOI

10.2352/J.ImagingSci.Technol.2026.70.4.040503