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  1. Article ; Online: Multi-modal deep learning networks for RGB-D pavement waste detection and recognition.

    Li, Yangke / Zhang, Xinman

    Waste management (New York, N.Y.)

    2024  Volume 177, Page(s) 125–134

    Abstract: To create a clean living environment, governments around the world have hired a large number of workers to clean up waste on pavements, which is inefficient for waste management. To better alleviate this problem, relevant scholars have proposed several ... ...

    Abstract To create a clean living environment, governments around the world have hired a large number of workers to clean up waste on pavements, which is inefficient for waste management. To better alleviate this problem, relevant scholars have proposed several deep learning methods based on RGB images to achieve waste detection and recognition. Considering the limitations of color images, we propose an efficient multi-modal learning solution for pavement waste detection and recognition. Specifically, we construct a high-quality outdoor pavement waste dataset called OPWaste, which is more in line with real needs. Compared to other waste datasets, OPWaste dataset not only has the advantages of rich background and high diversity, but also provides color and depth images. Meanwhile, we explore six different multi-modal fusion methods and propose a novel multi-modal multi-scale network (MM-Net) for RGB-D waste detection and recognition. MM-Net introduces a novel multi-scale refinement module (MRM) and multi-scale interaction module (MIM). MRM can effectively refine critical features using attention mechanisms. MIM can gradually realize information interaction between hierarchical features. In addition, we select several representative methods and perform comparative experiments. Experimental results show that MM-Net based on the image addition fusion method outperforms other deep learning models and reaches 97.3% and 84.4% on mAP
    MeSH term(s) Humans ; Deep Learning ; Recycling ; Waste Management
    Language English
    Publishing date 2024-02-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2001471-5
    ISSN 1879-2456 ; 0956-053X
    ISSN (online) 1879-2456
    ISSN 0956-053X
    DOI 10.1016/j.wasman.2024.01.047
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Robust Iris-Localization Algorithm in Non-Cooperative Environments Based on the Improved YOLO v4 Model.

    Xiong, Qi / Zhang, Xinman / Wang, Xingzhu / Qiao, Naosheng / Shen, Jun

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 24

    Abstract: Iris localization in non-cooperative environments is challenging and essential for accurate iris recognition. Motivated by the traditional iris-localization algorithm and the robustness of the YOLO model, we propose a novel iris-localization algorithm. ... ...

    Abstract Iris localization in non-cooperative environments is challenging and essential for accurate iris recognition. Motivated by the traditional iris-localization algorithm and the robustness of the YOLO model, we propose a novel iris-localization algorithm. First, we design a novel iris detector with a modified you only look once v4 (YOLO v4) model. We can approximate the position of the pupil center. Then, we use a modified integro-differential operator to precisely locate the iris inner and outer boundaries. Experiment results show that iris-detection accuracy can reach 99.83% with this modified YOLO v4 model, which is higher than that of a traditional YOLO v4 model. The accuracy in locating the inner and outer boundary of the iris without glasses can reach 97.72% at a short distance and 98.32% at a long distance. The locating accuracy with glasses can obtained at 93.91% and 84%, respectively. It is much higher than the traditional Daugman's algorithm. Extensive experiments conducted on multiple datasets demonstrate the effectiveness and robustness of our method for iris localization in non-cooperative environments.
    Language English
    Publishing date 2022-12-16
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22249913
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: SAVSDN: A Scene-Aware Video Spark Detection Network for Aero Engine Intelligent Test.

    Kou, Jie / Zhang, Xinman / Huang, Yuxuan / Zhang, Cong

    Sensors (Basel, Switzerland)

    2021  Volume 21, Issue 13

    Abstract: Due to carbon deposits, lean flames, or damaged metal parts, sparks can occur in aero engine chambers. At present, the detection of such sparks deeply depends on laborious manual work. Considering that interference has the same features as sparks, almost ...

    Abstract Due to carbon deposits, lean flames, or damaged metal parts, sparks can occur in aero engine chambers. At present, the detection of such sparks deeply depends on laborious manual work. Considering that interference has the same features as sparks, almost all existing object detectors cannot replace humans in carrying out high-precision spark detection. In this paper, we propose a scene-aware spark detection network, consisting of an information fusion-based cascading video codec-image object detector structure, which we name SAVSDN. Unlike video object detectors utilizing candidate boxes from adjacent frames to assist in the current prediction, we find that efforts should be made to extract the spatio-temporal features of adjacent frames to reduce over-detection. Visualization experiments show that SAVSDN can learn the difference in spatio-temporal features between sparks and interference. To solve the problem of a lack of aero engine anomalous spark data, we introduce a method to generate simulated spark images based on the Gaussian function. In addition, we publish the first simulated aero engine spark data set, which we name SAES. In our experiments, SAVSDN far outperformed state-of-the-art detection models for spark detection in terms of five metrics.
    MeSH term(s) Calcium ; Humans
    Chemical Substances Calcium (SY7Q814VUP)
    Language English
    Publishing date 2021-06-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s21134453
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Magnetic Ring Multi-Defect Stereo Detection System Based on Multi-Camera Vision Technology.

    Zhang, Xinman / Gong, Weiyong / Xu, Xuebin

    Sensors (Basel, Switzerland)

    2020  Volume 20, Issue 2

    Abstract: Magnetic rings are the most widely used magnetic material product in industry. The existing manual defect detection method for magnetic rings has high cost, low efficiency and low precision. To address this issue, a magnetic ring multi-defect stereo ... ...

    Abstract Magnetic rings are the most widely used magnetic material product in industry. The existing manual defect detection method for magnetic rings has high cost, low efficiency and low precision. To address this issue, a magnetic ring multi-defect stereo detection system based on multi-camera vision technology is developed to complete the automatic inspection of magnetic rings. The system can detect surface defects and measure ring height simultaneously. Two image processing algorithms are proposed, namely, the image edge removal algorithm (IERA) and magnetic ring location algorithm (MRLA), separately. On the basis of these two algorithms, connected domain filtering methods for crack, fiber and large-area defects are established to complete defect inspection. This system achieves a recognition rate of 100% for defects such as crack, adhesion, hanger adhesion and pitting. Furthermore, the recognition rate for fiber and foreign matter defects attains 92.5% and 91.5%, respectively. The detection speed exceeds 120 magnetic rings per minutes, and the precision is within 0.05 mm. Both precision and speed meet the requirements of real-time quality inspection in actual production.
    Language English
    Publishing date 2020-01-10
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s20020392
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition.

    Jing, Kunlei / Zhang, Xinman / Song, Guokun

    Sensors (Basel, Switzerland)

    2020  Volume 20, Issue 15

    Abstract: Palmprint recognition has been widely studied for security applications. However, there is a lack of in-depth investigations on robust palmprint recognition. Regression analysis being intuitively interpretable on robustness design inspires us to propose ... ...

    Abstract Palmprint recognition has been widely studied for security applications. However, there is a lack of in-depth investigations on robust palmprint recognition. Regression analysis being intuitively interpretable on robustness design inspires us to propose a correntropy-induced discriminative nonnegative sparse coding method for robust palmprint recognition. Specifically, we combine the correntropy metric and
    MeSH term(s) Algorithms ; Biometric Identification ; Databases, Factual ; Entropy ; Hand ; Humans ; Regression Analysis
    Language English
    Publishing date 2020-07-30
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s20154250
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Cyclization or bridging: which occurs faster is the key to the self-assembly mechanism of Pd

    Zhang, Xinman / Takahashi, Satoshi / Aratsu, Keisuke / Kikuchi, Isamu / Sato, Hirofumi / Hiraoka, Shuichi

    Physical chemistry chemical physics : PCCP

    2022  Volume 24, Issue 5, Page(s) 2997–3006

    Abstract: The self-assembly processes of ... ...

    Abstract The self-assembly processes of Pd
    Language English
    Publishing date 2022-02-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 1476244-4
    ISSN 1463-9084 ; 1463-9076
    ISSN (online) 1463-9084
    ISSN 1463-9076
    DOI 10.1039/d1cp04448f
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Aspirin use and head and neck cancer survival and recurrence.

    Zhang, Xinman / Ilyas, Omar S / Getz, Kayla R / Rozek, Laura S / Taylor, Jeremy M G / Chinn, Steven B / Wolf, Gregory T / Mondul, Alison M

    Cancer causes & control : CCC

    2023  Volume 35, Issue 4, Page(s) 605–609

    Abstract: Background: Head and neck cancer (HNC) has low 5-year survival, and evidence-based recommendations for tertiary prevention are lacking. Aspirin improves outcomes for cancers at other sites, but its role in HNC tertiary prevention remains understudied.!## ...

    Abstract Background: Head and neck cancer (HNC) has low 5-year survival, and evidence-based recommendations for tertiary prevention are lacking. Aspirin improves outcomes for cancers at other sites, but its role in HNC tertiary prevention remains understudied.
    Methods: HNC patients were recruited in the University of Michigan Head and Neck Cancer Specialized Program of Research Excellence (SPORE) from 2003 to 2014. Aspirin data were collected through medical record review; outcomes (overall mortality, HNC-specific mortality, and recurrence) were collected through medical record review, Social Security Death Index, or LexisNexis. Cox proportional hazards models were used to evaluate the associations between aspirin use at diagnosis (yes/no) and HNC outcomes.
    Results: We observed no statistically significant associations between aspirin and cancer outcome in our HNC patient cohort (n = 1161) (HNC-specific mortality: HR = 0.91, 95% CI = 0.68-1.21; recurrence: HR = 0.94, 95% CI = 0.73-1.19). In analyses stratified by anatomic site, HPV status, and disease stage, we observed no association in any strata examined with the possible exception of a lower risk of recurrence in oropharynx patients (HR = 0.60, 95% CI 0.35-1.04).
    Conclusions: Our findings do not support a protective association between aspirin use and cancer-specific death or recurrence in HNC patients, with the possible exception of a lower risk of recurrence in oropharynx patients.
    MeSH term(s) Humans ; Aspirin/therapeutic use ; Head and Neck Neoplasms/drug therapy ; Proportional Hazards Models
    Chemical Substances Aspirin (R16CO5Y76E)
    Language English
    Publishing date 2023-11-17
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1064022-8
    ISSN 1573-7225 ; 0957-5243
    ISSN (online) 1573-7225
    ISSN 0957-5243
    DOI 10.1007/s10552-023-01815-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3375: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  8. Article ; Online: An Improved Recognition Approach for Noisy Multispectral Palmprint by Robust L2 Sparse Representation with a Tensor-Based Extreme Learning Machine.

    Cheng, Dongxu / Zhang, Xinman / Xu, Xuebin

    Sensors (Basel, Switzerland)

    2019  Volume 19, Issue 2

    Abstract: For the past decades, recognition technologies of multispectral palmprint have attracted more and more attention due to their abundant spatial and spectral characteristics compared with the single spectral case. Enlightened by this, an innovative robust ... ...

    Abstract For the past decades, recognition technologies of multispectral palmprint have attracted more and more attention due to their abundant spatial and spectral characteristics compared with the single spectral case. Enlightened by this, an innovative robust L2 sparse representation with tensor-based extreme learning machine (RL2SR-TELM) algorithm is put forward by using an adaptive image level fusion strategy to accomplish the multispectral palmprint recognition. Firstly, we construct a robust L2 sparse representation (RL2SR) optimization model to calculate the linear representation coefficients. To suppress the affection caused by noise contamination, we introduce a logistic function into RL2SR model to evaluate the representation residual. Secondly, we propose a novel weighted sparse and collaborative concentration index (WSCCI) to calculate the fusion weight adaptively. Finally, we put forward a TELM approach to carry out the classification task. It can deal with the high dimension data directly and reserve the image spatial information well. Extensive experiments are implemented on the benchmark multispectral palmprint database provided by PolyU. The experiment results validate that our RL2SR-TELM algorithm overmatches a number of state-of-the-art multispectral palmprint recognition algorithms both when the images are noise-free and contaminated by different noises.
    Language English
    Publishing date 2019-01-09
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s19020235
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Hypercomplex extreme learning machine with its application in multispectral palmprint recognition.

    Lu, Longbin / Zhang, Xinman / Xu, Xuebin

    PloS one

    2019  Volume 14, Issue 4, Page(s) e0209083

    Abstract: An extreme learning machine (ELM) is a novel training method for single-hidden layer feedforward neural networks (SLFNs) in which the hidden nodes are randomly assigned and fixed without iterative tuning. ELMs have earned widespread global interest due ... ...

    Abstract An extreme learning machine (ELM) is a novel training method for single-hidden layer feedforward neural networks (SLFNs) in which the hidden nodes are randomly assigned and fixed without iterative tuning. ELMs have earned widespread global interest due to their fast learning speed, satisfactory generalization ability and ease of implementation. In this paper, we extend this theory to hypercomplex space and attempt to simultaneously consider multisource information using a hypercomplex representation. To illustrate the performance of the proposed hypercomplex extreme learning machine (HELM), we have applied this scheme to the task of multispectral palmprint recognition. Images from different spectral bands are utilized to construct the hypercomplex space. Extensive experiments conducted on the PolyU and CASIA multispectral databases demonstrate that the HELM scheme can achieve competitive results. The source code together with datasets involved in this paper can be available for free download at https://figshare.com/s/01aef7d48840afab9d6d.
    MeSH term(s) Adult ; Algorithms ; Dermatoglyphics ; Female ; Humans ; Machine Learning ; Male ; Middle Aged ; Neural Networks, Computer ; Pattern Recognition, Automated/methods ; Young Adult
    Language English
    Publishing date 2019-04-15
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0209083
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: A Parallel Algorithm Framework for Feature Extraction of EEG Signals on MPI.

    Xiong, Qi / Zhang, Xinman / Wang, Wen-Feng / Gu, Yuhong

    Computational and mathematical methods in medicine

    2020  Volume 2020, Page(s) 9812019

    Abstract: In this paper, we present a parallel framework based on MPI for a large dataset to extract power spectrum features of EEG signals so as to improve the speed of brain signal processing. At present, the Welch method has been wildly used to estimate the ... ...

    Abstract In this paper, we present a parallel framework based on MPI for a large dataset to extract power spectrum features of EEG signals so as to improve the speed of brain signal processing. At present, the Welch method has been wildly used to estimate the power spectrum. However, the traditional Welch method takes a lot of time especially for the large dataset. In view of this, we added the MPI into the traditional Welch method and developed it into a reusable master-slave parallel framework. As long as the EEG data of any format are converted into the text file of a specified format, the power spectrum features can be extracted quickly by this parallel framework. In the proposed parallel framework, the EEG signals recorded by a channel are divided into
    MeSH term(s) Algorithms ; Big Data ; Brain/physiology ; Brain-Computer Interfaces/statistics & numerical data ; Databases, Factual/statistics & numerical data ; Electroencephalography/instrumentation ; Electroencephalography/statistics & numerical data ; Fourier Analysis ; Humans ; Pattern Recognition, Automated/statistics & numerical data ; Programming Languages ; Signal Processing, Computer-Assisted
    Language English
    Publishing date 2020-05-27
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2252430-7
    ISSN 1748-6718 ; 1748-670X ; 1027-3662
    ISSN (online) 1748-6718
    ISSN 1748-670X ; 1027-3662
    DOI 10.1155/2020/9812019
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6373: Show issues Location:
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