scholarly journals Sparse Representation for Different Animal Vertebra Classification along the Fixation Trajectory of Pedicle Screw

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
YangYang Liu ◽  
ZhiQiang Wang ◽  
Kang Wang ◽  
ZhiYu Qian ◽  
Yang Gao ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Pedicle Screw ◽  
Intellectual Property Rights ◽  
Near Infrared ◽  
Superior Performance ◽  
Classification Methods ◽  
Medical Instruments ◽  
Ideal Method ◽  
Different Tissues ◽  
Accuracy Problem

Pedicle screw (PS) implantation is an ideal method for the treatment of severe multilevel vertebral instability. The key problem is the accuracy of PS fixation. In this paper, the spectrum of different tissues along the fixation trajectory of PS is studied to tackle the accuracy problem. Fresh porcine vertebrae, bovine vertebrae, and ovine vertebrae were measured by using the near-infrared spectrum (NIRs) device to obtain the reflected spectrum from these vertebrae. Along the fixation trajectory of PS, the classification method based on the sparse representation-based classifier (SRC) was applied to different vertebral tissues (cortical bones and cancellous bones). Considering the large amount of spectral data, sparse preserving projection (SPP) was applied to improve the performance of SRC. The proposed method based on the SPP method for dimensionality reduction and the SRC method for tissue recognition was first used in vertebrae classification and showed superior performance compared with other classification methods, such as SVM and 1NN. The results gained from this project are vital significant to the development of hi-tech medical instruments with independent intellectual property rights.

scholarly journals Investigation of optical reflectance from different animal vertebra along the fixation trajectory of pedicle screw in frequency domain

2019 ◽  
Vol 13 (01) ◽  
pp. 2050003
Author(s):  
Yangyang Liu ◽  
Huan Zhang ◽  
Ying Tong ◽  
Zhiyu Qian ◽  
Weitao Li
Keyword(s):  
Frequency Domain ◽  
Pedicle Screw ◽  
Near Infrared ◽  
Average Energy ◽  
Frequency Domain Method ◽  
Application Research ◽  
Medical Instruments ◽  
Important Direction ◽  
The Time Domain ◽  
Operative Monitoring

Accurate placement of pedicle screw (PS) is crucial in spinal surgery. Developing new real-time intra-operative monitoring and navigation methods is an important direction of clinical application research. In this paper, we studied the spectrum along the fixation trajectory of PS in frequency domain to tackle the accuracy problem. Fresh porcine vertebrae, bovine vertebrae and ovine vertebrae were measured with the near-infrared spectrum (NIR) device to obtain the reflected spectrum from the vertebrae. Along the fixation trajectory of PS, average energy from different groups was calculated and used for identifying different tissues and compared to achieve the optimal recognition factor. Compared with the time domain approach, the frequency domain method could divide the spectra measured at different tissue points into different groups more stably and accurately, which could serve as a new method to assist the PS insertion. The results gained from this study are significant to the development of hi-tech medical instruments with independent intellectual property rights.

scholarly journals Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

2021 ◽  
Author(s):  
Chenguang Li ◽  
Hongjun Yang ◽  
Long Cheng
Keyword(s):  
Classification Accuracy ◽  
Near Infrared ◽  
Support Vector ◽  
Classification Methods ◽  
Experimental Paradigm ◽  
Functional Near Infrared Spectroscopy ◽  
Machine Method ◽  
Physiological Signal ◽  
Computer Interfaces ◽  
Mode Decomposition

AbstractAs a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

scholarly journals Roles of Low Temperature Sputtered Indium Tin Oxide for Solar Photovoltaic Technology

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7758
Author(s):  
Susana Fernández ◽  
José Pablo González ◽  
Javier Grandal ◽  
Alejandro F. Braña ◽  
María Belén Gómez-Mancebo ◽  
...  
Keyword(s):  
Tin Oxide ◽  
Indium Tin Oxide ◽  
Near Infrared ◽  
Crystalline Silicon ◽  
Cost Effective ◽  
Superior Performance ◽  
Solar Photovoltaic ◽  
Full Spectrum ◽  
P Type ◽  
The Impact

Different functionalities of materials based on indium tin oxide and fabricated at soft conditions were investigated with the goal of being used in a next generation of solar photovoltaic devices. These thin films were fabricated in a commercial UNIVEX 450B magnetron sputtering. The first studied functionality consisted of an effective n-type doped layer in an n-p heterojunction based on p-type crystalline silicon. At this point, the impact of the ITO film thickness (varied from 45 to 140 nm) and the substrate temperature (varied from room temperature to 250 °C) on the heterojunction parameters was evaluated separately. To avoid possible damages in the heterojunction interface, the applied ITO power was purposely set as low as 25 W; and to minimize the energy consumption, no heat treatment process was used. The second functionality consisted of indium-saving transparent conductive multicomponent materials for full spectrum applications. This was carried out by the doping of the ITO matrix with transition metals, as titanium and zinc. This action can reduce the production cost without sacrificing the optoelectronic film properties. The morphology, chemical, structural nature and optoelectronic properties were evaluated as function of the doping concentrations. The results revealed low manufactured and suitable films used successfully as conventional emitter, and near-infrared extended transparent conductive materials with superior performance that conventional ones, useful for full spectrum applications. Both can open interesting choices for cost-effective photovoltaic technologies.

scholarly journals Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Qingshan She ◽  
Kang Chen ◽  
Yuliang Ma ◽  
Thinh Nguyen ◽  
Yingchun Zhang
Keyword(s):  
Sparse Representation ◽  
Extreme Learning Machine ◽  
Motor Imagery ◽  
Vital Role ◽  
Superior Performance ◽  
Common Spatial Pattern ◽  
Significant Information ◽  
Feature Representations ◽  
Novel Approach ◽  
Learning Machine

Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant information which leads to a less efficient classification. In this paper, we propose a novel approach called FDDL-ELM, which combines the discriminative power of extreme learning machine (ELM) with the reconstruction capability of sparse representation. Firstly, the common spatial pattern (CSP) algorithm is adopted to perform spatial filtering on raw EEG data to enhance the task-related neural activity. Secondly, the Fisher discrimination criterion is employed to learn a structured dictionary and obtain sparse coding coefficients from the filtered data, and these discriminative coefficients are then used to acquire the reconstructed feature representations. Finally, a nonlinear classifier ELM is used to identify these features in different MI tasks. The proposed method is evaluated on 2-class Datasets IVa and IIIa of BCI Competition III and 4-class Dataset IIa of BCI Competition IV. Experimental results show that our method achieved superior performance than the other existing algorithms and yielded the accuracies of 80.68%, 87.54%, and 63.76% across all subjects in the above-mentioned three datasets, respectively.

Supervised Manifold Learning for NIR Modeling of Cigarette Brand Identification

2012 ◽  
Vol 457-458 ◽  
pp. 1258-1263
Author(s):  
Ying He ◽  
Xiang Qian Ding ◽  
Lin Tao Ma
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Near Infrared ◽  
Model Performance ◽  
Classification Model ◽  
Dimensional Structure ◽  
Classification Methods ◽  
Brand Identification ◽  
Spectra Analysis ◽  
Cigarette Brand

For NIR data has the character of high dimension, nonlinear, and high noise, we often confront the problem of dimensionality reduction when building the classification model on Near-Infrared spectra data. Traditional classification methods and linear dimensionality reduction techniques are difficult to improve the model performance. In this paper, a novel nonlinear modeling for NIR spectra analysis was proposed by combining S-Isomap and KNN. S-Isomap is a supervised manifold learning method which can effectively find out the intrinsic low dimensional structure and extract important information from the raw data. Compared with KLLE, KPCA, and other classification methods such as SVM or LDA, the results show that S-Isomap-KNN method performs the best on the modeling of cigarette brand identification. The method is also a good technique for visualization.

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