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Article ; Online: Real-time litchi detection in complex orchard environments

Zeyu Jiao / Kai Huang / Qun Wang / Zhenyu Zhong / Yingjie Cai

Artificial Intelligence in Agriculture, Vol 11, Iss , Pp 13-

A portable, low-energy edge computing approach for enhanced automated harvesting

2024 Volume 22

Abstract: Litchi, a succulent and perishable fruit, presents a narrow annual harvest window of under two weeks. The advent of smart agriculture has driven the adoption of visually-guided, automated litchi harvesting techniques. However, conventional approaches ... ...

Abstract	Litchi, a succulent and perishable fruit, presents a narrow annual harvest window of under two weeks. The advent of smart agriculture has driven the adoption of visually-guided, automated litchi harvesting techniques. However, conventional approaches typically rely on laboratory-based, high-performance computing equipment, which presents challenges in terms of size, energy consumption, and practical application within litchi orchards. To address these limitations, we propose a real-time litchi detection methodology for complex environments, utilizing portable, low-energy edge computing devices. Initially, the litchi orchard imagery is collected to enhance data generalization. Subsequently, a convolutional neural network (CNN)-based single-stage detector, YOLOx, is constructed to accurately pinpoint litchi fruit locations within the images. To facilitate deployment on portable, low-energy edge devices, we employed channel pruning and layer pruning algorithms to compress the trained model, reducing its size and parameters. Additionally, the knowledge distillation technique is harnessed to fine-tune the network. Experimental findings demonstrated that our proposed method achieved a 97.1% compression rate, yielding a compact litchi detection model of a mere 6.9 MB, while maintaining 94.9% average precision and 97.2% average recall. Processing 99 frames per second (FPS), the method exhibited a 1.8-fold increase in speed compared to the unprocessed model. Consequently, our approach can be readily integrated into portable, low-computational automatic harvesting equipment, ensuring real-time, precise litchi detection within orchard settings.
Keywords	Litchi detection ; Automated harvesting ; Edge computing ; Neural networks ; Model compression ; Agriculture ; S
Subject code	006
Language	English
Publishing date	2024-03-01T00:00:00Z
Publisher	KeAi Communications Co., Ltd.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Effect of a family-involvement combined aerobic and resistance exercise protocol on cancer-related fatigue in patients with breast cancer during postoperative chemotherapy

Chuhan Huang / Yingjie Cai / Yufei Guo / Jingjing Jia / Tieying Shi

BMJ Open, Vol 13, Iss

study protocol for a quasi-randomised controlled trial

2023 Volume 3

Abstract: Introduction Cancer-related fatigue (CRF) is one of the most common and debilitating side effects experienced by patients with breast cancer (BC) during postoperative chemotherapy. Family-involvement combined aerobic and resistance exercise has been ... ...

Abstract	Introduction Cancer-related fatigue (CRF) is one of the most common and debilitating side effects experienced by patients with breast cancer (BC) during postoperative chemotherapy. Family-involvement combined aerobic and resistance exercise has been introduced as a promising non-pharmacological intervention for CRF symptom relief and improving patients’ muscle strength, exercise completion, family intimacy and adaptability and quality of life. However, evidence for the practice of home participation in combined aerobic and resistance exercise for the management of CRF in patients with BC is lacking.Methods and analysis We present a protocol for a quasi-randomised controlled trial involving an 8-week intervention. Seventy patients with BC will be recruited from a tertiary care centre in China. Participants from the first oncology department will be assigned to the family-involvement combined aerobic and resistance exercise group (n=28), while participants from the second oncology department will be assigned to the control group that will receive standard exercise guidance (n=28). The primary outcome will be the Piper Fatigue Scale-Revised (R-PFS) score. The secondary outcomes will include muscle strength, exercise completion, family intimacy and adaptability and quality of life, which will be evaluated by the stand-up and sit-down chair test, grip test, exercise completion rate, Family Adaptability and Cohesion Scale, Second Edition-Chinese Version (FACESⅡ-CV) and Functional Assessment of Cancer Therapy -Breast (FACT-B) scale. Analysis of covariance will be applied for comparisons between groups, and paired t-tests will be used for comparison of data before and after exercise within a group.Ethics and dissemination This study has been approved by the Ethics Committee of the First Affiliated Hospital of Dalian Medical University (PJ-KS-KY-2021-288). The results of this study will be published via peer-reviewed publications and presentations at conferences.Trail registration number ChiCTR2200055793.
Keywords	Medicine ; R
Subject code	796
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: COVID-19 contact tracking based on person reidentification and geospatial data

Boxing Zhang / Huan Lei / Yingjie Cai / Zhenyu Zhong / Zeyu Jiao

Journal of King Saud University: Computer and Information Sciences, Vol 35, Iss 5, Pp 101558- (2023)

2023

Abstract: Efficient contact tracing is a crucial step in preventing the spread of COVID-19. However, the current methods rely heavily on manual investigation and truthful reporting by high-risk individuals. Mobile applications and Bluetooth-based contact tracing ... ...

Abstract	Efficient contact tracing is a crucial step in preventing the spread of COVID-19. However, the current methods rely heavily on manual investigation and truthful reporting by high-risk individuals. Mobile applications and Bluetooth-based contact tracing methods have also been adopted, but privacy concerns and reliance on personal data have limited their effectiveness. To address these challenges, in this paper, a geospatial big data method that combines person reidentification and geospatial information for contact tracing is proposed. The proposed real-time person reidentification model can identify individuals across multiple surveillance cameras, and the surveillance data is fused with geographic information and mapped onto a 3D geospatial model to track movement trajectories. After real-world verification, the proposed method achieves a first accuracy rate of 91.56%, a first-five accuracy rate of 97.70%, and a mean average precision of 78.03% with an inference speed of 13 ms per image. Importantly, the proposed method does not rely on personal information, mobile phones, or wearable devices, avoiding the limitations of existing contact tracing schemes and providing significant implications for public health in the post-COVID-19 era.
Keywords	COVID-19 ; Contact tracking ; Person reidentification ; Geospatial data ; Public health ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	306
Language	English
Publishing date	2023-05-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Medical Image Segmentation Algorithm for Three-Dimensional Multimodal Using Deep Reinforcement Learning and Big Data Analytics

Weiwei Gao / Xiaofeng Li / Yanwei Wang / Yingjie Cai

Frontiers in Public Health, Vol

2022 Volume 10

Abstract: To avoid the problems of relative overlap and low signal-to-noise ratio (SNR) of segmented three-dimensional (3D) multimodal medical images, which limit the effect of medical image diagnosis, a 3D multimodal medical image segmentation algorithm using ... ...

Abstract	To avoid the problems of relative overlap and low signal-to-noise ratio (SNR) of segmented three-dimensional (3D) multimodal medical images, which limit the effect of medical image diagnosis, a 3D multimodal medical image segmentation algorithm using reinforcement learning and big data analytics is proposed. Bayesian maximum a posteriori estimation method and improved wavelet threshold function are used to design wavelet shrinkage algorithm to remove high-frequency signal component noise in wavelet domain. The low-frequency signal component is processed by bilateral filtering and the inverse wavelet transform is used to denoise the 3D multimodal medical image. An end-to-end DRD U-Net model based on deep reinforcement learning is constructed. The feature extraction capacity of denoised image segmentation is increased by changing the convolution layer in the traditional reinforcement learning model to the residual module and introducing the multiscale context feature extraction module. The 3D multimodal medical image segmentation is done using the reward and punishment mechanism in the deep learning reinforcement algorithm. In order to verify the effectiveness of 3D multimodal medical image segmentation algorithm, the LIDC-IDRI data set, the SCR data set, and the DeepLesion data set are selected as the experimental data set of this article. The results demonstrate that the algorithm's segmentation effect is effective. When the number of iterations is increased to 250, the structural similarity reaches 98%, the SNR is always maintained between 55 and 60 dB, the training loss is modest, relative overlap and accuracy all exceed 95%, and the overall segmentation performance is superior. Readers will understand how deep reinforcement learning and big data analytics test the effectiveness of 3D multimodal medical image segmentation algorithm.
Keywords	deep reinforcement learning ; three-dimensional multimodal ; wavelet shrinkage ; medical image segmentation ; high-frequency signal component ; Public aspects of medicine ; RA1-1270
Subject code	006
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Interaction between trouble sleeping and depression on hypertension in the NHANES 2005–2018

Yingjie Cai / Manshuang Chen / Weixia Zhai / Chunhui Wang

BMC Public Health, Vol 22, Iss 1, Pp 1-

2022 Volume 11

Abstract: Abstract Background Hypertension, trouble sleeping and depression, as three major public health problems, were closely related. This study evaluated the independent association of trouble sleeping and depression with hypertension and interaction effect ... ...

Abstract	Abstract Background Hypertension, trouble sleeping and depression, as three major public health problems, were closely related. This study evaluated the independent association of trouble sleeping and depression with hypertension and interaction effect between trouble sleeping and depression on hypertension in Americans. Method The data of this cross-sectional study was from the 2005–2018 National Health and Nutritional Examination Survey (NHANES) with hypertension, depression, trouble sleeping and confounding factor information. Multivariate logistic regression model and subgroup analyses of depression severity were conducted to assess the relationship between trouble sleeping and depression on hypertension. Relative excess risk due to interaction (RERI), attributable proportion of interaction (AP) and synergy index (S) were utilized to assess the additive interaction. Results A total of 30,434 participants (weighted n = 185,309,883) were examined with 16,304 (49.37%) known hypertensive subjects. Compared with participants without trouble sleeping, those with trouble sleeping had a higher risk of hypertension [OR = 1.359 (95% CI: 1.229–1.503)]. We also found the significant association of depression with an increased risk of hypertension [OR = 1.276 (95% CI: 1.114–1.462)], compared with those without depression. Moreover, there was a significant interaction between trouble sleeping and depression on hypertension risk [RERI = 0.528 (95% CI: 0.182–0.873), AP = 0.302 (95% CI: 0.140–0.465), S = 3.413 (95% CI: 1.301–8.951)]. Conclusion There was a synergistic interaction between trouble sleeping and depression on hypertension, especially the significant synergistic effect between moderate depression and trouble sleeping on hypertension. The results suggested that improving the psychological status and trouble sleeping of patients may be beneficial to the prevention and treatment of hypertension.
Keywords	Hypertension ; Trouble sleeping ; Depression ; Interaction ; Public aspects of medicine ; RA1-1270
Subject code	610 ; 150
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Segmentation for Multimodal Brain Tumor Images Using Dual-Tree Complex Wavelet Transform and Deep Reinforcement Learning

Gang Liu / Xiaofeng Li / Yingjie Cai

Computational Intelligence and Neuroscience, Vol

2022 Volume 2022

Abstract: Image segmentation is an effective tool for computer-aided medical treatment, to retain the detailed features and edges of the segmented image and improve the segmentation accuracy. Therefore, a segmentation algorithm using deep reinforcement learning ( ... ...

Abstract	Image segmentation is an effective tool for computer-aided medical treatment, to retain the detailed features and edges of the segmented image and improve the segmentation accuracy. Therefore, a segmentation algorithm using deep reinforcement learning (DRL) and dual-tree complex wavelet transform (DTCWT) for multimodal brain tumor images is proposed. First, the bivariate concept in DTCWT is used to determine whether the image noise points belong to the real or imaginary region, and the noise probability is checked after calculation; second, the wavelet coefficients corresponding to the region where the noise is located are selected to transform the noise into normal pixel points by bivariate; then, the conditional probability of occurrence of marker points in the edge and center regions of the image is calculated with the target points, and the initial segmentation of the image is achieved by the known wavelet coefficients; finally, the segmentation framework is constructed using DRL, and the network is trained by loss function to optimize the segmentation results and achieve accurate image segmentation. The experiment was evaluated on BraTS2018 dataset, CQ500 dataset, and a hospital brain tumor dataset. The results show that the algorithm in this paper can effectively remove multimodal brain tumor image noise, and the segmented image has good retention of detail features and edges, and the segmented image has high similarity with the original image. The highest information loss index of the segmentation results is only 0.18, the image boundary error is only about 0.3, and F-value is high, which indicates that the proposed algorithm is accurate and can operate efficiently, and has practical applicability.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571
Subject code	006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Ensuring Computers Understand Manual Operations in Production

Zeyu Jiao / Guozhu Jia / Yingjie Cai

Applied Sciences, Vol 10, Iss 3, p

Deep-Learning-Based Action Recognition in Industrial Workflows

2020 Volume 966

Abstract: In this study, we consider fully automated action recognition based on deep learning in the industrial environment. In contrast to most existing methods, which rely on professional knowledge to construct complex hand-crafted features, or only use basic ... ...

Abstract	In this study, we consider fully automated action recognition based on deep learning in the industrial environment. In contrast to most existing methods, which rely on professional knowledge to construct complex hand-crafted features, or only use basic deep-learning methods, such as convolutional neural networks (CNNs), to extract information from images in the production process, we exploit a novel and effective method, which integrates multiple deep-learning networks including CNNs, spatial transformer networks (STNs), and graph convolutional networks (GCNs) to process video data in industrial workflows. The proposed method extracts both spatial and temporal information from video data. The spatial information is extracted by estimating the human pose of each frame, and the skeleton image of the human body in each frame is obtained. Furthermore, multi-frame skeleton images are processed by GCN to obtain temporal information, meaning the action recognition results are predicted automatically. By training on a large human action dataset, Kinetics, we apply the proposed method to the real-world industrial environment and achieve superior performance compared with the existing methods.
Keywords	deep learning ; action recognition ; convolutional neural network ; spatial transformer network ; graph convolutional network ; industrial workflows ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2020-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Simultaneous Gesture Classification and Force Estimation Strategy Based on Wearable A-Mode Ultrasound and Cascade Model.

Zengyu, Qing / Lu, Zongxing / Zhoujie, Liu / Yingjie, Cai / Shaoxiong, Cai / Baizheng, He / Ligang, Yao

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

2022 Volume 30, Page(s) 2301–2311

Abstract: The existing Human-Machine Interfaces (HMI) based on gesture recognition using surface electromyography (sEMG) have made significant progress. However, the sEMG has inherent limitations as well as the gesture classification and force estimation have not ... ...

Abstract	The existing Human-Machine Interfaces (HMI) based on gesture recognition using surface electromyography (sEMG) have made significant progress. However, the sEMG has inherent limitations as well as the gesture classification and force estimation have not been effectively combined. There are limitations in applications such as prosthetic control and clinical rehabilitation, etc. In this paper, a grasping gesture and force recognition strategy based on wearable A-mode ultrasound and two-stage cascade model is proposed, which can simultaneously estimate the force while classifying the grasping gesture. This paper experiments five grasping gestures and four force levels (5-50%MVC). The results demonstrate that the performance of the proposed model is significantly better than that of the traditional model both in classification and regression (p < 0.001). Additionally, the two-stage cascade regression model (TSCRM) used the Gaussian Process regression model (GPR) with the mean and standard deviation (MSD) feature obtains excellent results, with normalized root-mean-square error (nRMSE) and correlation coefficient (CC) of 0.10490.0374 and 0.94610.0354, respectively. Besides, the latency of the model meets the requirement of real-time recognition (T < 15ms). Therefore, the research outcomes prove the feasibility of the proposed recognition strategy and provide a reference for the field of prosthetic control, etc.
MeSH term(s)	Algorithms ; Electromyography/methods ; Gestures ; Hand ; Hand Strength ; Humans ; Wearable Electronic Devices
Language	English
Publishing date	2022-08-22
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1166307-8
ISSN	1558-0210 ; 1063-6528 ; 1534-4320
ISSN (online)	1558-0210
ISSN	1063-6528 ; 1534-4320
DOI	10.1109/TNSRE.2022.3196926
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Sustainable fashion

Lina Lin / Tiancheng Jiang / Lexin Xiao / Md. Nahid Pervez / Xiaobo Cai / Vincenzo Naddeo / Yingjie Cai

Scientific Reports, Vol 12, Iss 1, Pp 1-

eco-friendly dyeing of wool fiber with novel mixtures of biodegradable natural dyes

2022 Volume 16

Abstract: Abstract Natural materials, especially natural colorants, have achieved global prominence and might be regarded as an environmentally beneficial alternative to hazardous synthetic dyes. The color limitation of natural dyes hinders their application in ... ...

Abstract	Abstract Natural materials, especially natural colorants, have achieved global prominence and might be regarded as an environmentally beneficial alternative to hazardous synthetic dyes. The color limitation of natural dyes hinders their application in textiles. The present work aims to prepare more color shades of wool yarns via dyeing with ternary natural dye mixtures without adding mordants. In this study, a sustainable dyeing approach for wool yarn was evaluated with three natural dyes, madder red (MR), gardenia blue (GB), and gardenia yellow (GY), by following an industrial dyeing procedure in the absence of a mordant. In the beginning, a preliminary assessment of dye stabilities was carried out, and it was found that the three natural dyes were sensitive to temperature and acid (degradation tendency). Then, the dyeing behavior was systematically evaluated, including a single natural dye, a binary natural dye mixture, and a ternary natural dye mixture. The results of wool yarn dyeing with a single natural dye show that the dye exhaustion percentage (E%) of MR, GY, and GB was in the ranges of 78.7–94.1%, 13.4–44.1%, and 54.8–68.5%, respectively. The dyeing results of wool yarns dyed with binary and ternary natural dye mixtures (a color triangle framework of dyed wool yarn) were characterized by colorimetric values (L, a, b, C, h, and K/S), and are presented to enlighten various colorful shades. Finally, color uniformity and colorfastness tests confirmed the vital contribution of natural dyes toward wool yarn coloration. Particularly, colorfastness to washing confirmed the stability of natural dyes with reference to the lower amount of dyes released into the effluent, which is beneficial for the environment. Overall, this study provides a good background for enhancing the industrialization trend of natural dyes by modulating their dyeing scheme.
Keywords	Medicine ; R ; Science ; Q
Subject code	660
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A presurgical voxel-wise predictive model for cerebellar mutism syndrome in children with posterior fossa tumors

Wei Yang / Yiming Li / Zesheng Ying / Yingjie Cai / Xiaojiao Peng / HaiLang Sun / Jiashu Chen / Kaiyi Zhu / Geli Hu / Yun Peng / Ming Ge

NeuroImage: Clinical, Vol 37, Iss , Pp 103291- (2023)

2023

Abstract: Background: This study aimed to investigate cerebellar mutism syndrome (CMS)-related voxels and build a voxel-wise predictive model for CMS. Methods: From July 2013 to January 2022, 188 pediatric patients diagnosed with posterior fossa tumor were ... ...

Abstract	Background: This study aimed to investigate cerebellar mutism syndrome (CMS)-related voxels and build a voxel-wise predictive model for CMS. Methods: From July 2013 to January 2022, 188 pediatric patients diagnosed with posterior fossa tumor were included in this study, including 38 from a prospective cohort recruited between 2020 and January 2022, and the remaining from a retrospective cohort recruited in July 2013-Aug 2020. The retrospective cohort was divided into the training and validation sets; the prospective cohort served as a prospective validation set. Voxel-based lesion symptoms were assessed to identify voxels related to CMS, and a predictive model was constructed and tested in the validation and prospective validation sets. Results: No significant differences were detected among these three data sets in CMS rate, gender, age, tumor size, tumor consistency, presence of hydrocephalus and paraventricular edema. Voxels related to CMS were mainly located in bilateral superior and inferior cerebellar peduncles and the superior part of the cerebellum. The areas under the curves for the model in the training, validation and prospective validation sets were 0.889, 0.784 and 0.791, respectively. Conclusions: Superior and inferior cerebellar peduncles and the superior part of the cerebellum were related to CMS, especially the right side, and voxel-based lesion-symptom analysis could provide valuable predictive information before surgery.
Keywords	Lesion-symptom mapping ; Cerebellar mutism syndrome ; Predictive model ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurology. Diseases of the nervous system ; RC346-429
Subject code	616
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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