- 标题
- 摘要
- 关键词
- 实验方案
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Convolution neural network-based time-domain equalizer for DFT-Spread OFDM VLC system
摘要： This paper presents a novel time-domain equalizer for visible light communication (VLC) system using machine learning (ML) method. In this work, we employ discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM) as modem scheme and convolution neural network (CNN) as kernel processing unit of equalizer. After estimating channel state information (CSI) from training sequence, the proposed equalizer recovers transmitted symbols according to the estimated CSI. Numerical simulations indicate that the equalizer can significantly enhance bit error rate (BER) performance. For example, when signal-to-noise ratio (SNR) is 20dB and 16/32/64-quadrature amplitude modulation (QAM) is exploited, original BER is about 0.5 while the BER after recovery achieves 10?5, which is much lower than forward error correction (FEC) limit 3.8×10?3. This work promotes the application of ML in VLC domain. To the best of our knowledge, this is the first time a CNN-based equalizer has been explored.
关键词： Machine learning (ML),Discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM),Visible light communication (VLC),Convolution neural network (CNN)
更新于2025-09-10 09:29:36
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Automatic classification of label-free cells from small cell lung cancer and poorly differentiated lung adenocarcinoma with 2D light scattering static cytometry and machine learning
摘要： Small cell lung cancer (SCLC) needs to be classified from poorly differentiated lung adenocarcinoma (PDLAC) for appropriate treatment of lung cancer patients. Currently, the classification is achieved by experienced clinicians, radiologists and pathologists based on subjective and qualitative analysis of imaging, cytological and immunohistochemical (IHC) features. Label-free classification of lung cancer cell lines is developed here by using two-dimensional (2D) light scattering static cytometric technique. Measurements of scattered light at forward scattering (FSC) and side scattering (SSC) by using conventional cytometry show that SCLC cells are overlapped with PDLAC cells. However, our 2D light scattering static cytometer reveals remarkable differences between the 2D light scattering patterns of SCLC cell lines (H209 and H69) and PDLAC cell line (SK-LU-1). By adopting support vector machine (SVM) classifier with leave-one-out cross-validation (LOO-CV), SCLC and PDLAC cells are automatically classified with an accuracy of 99.87%. Our label-free 2D light scattering static cytometer may serve as a new, accurate, and easy-to-use method for the automatic classification of SCLC and PDLAC cells.
关键词： static cytometry,2D light scattering,label-free,lung cancer,machine learning
更新于2025-09-10 09:29:36
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Machine-Learning Designs of Anisotropic Digital Coding Metasurfaces
摘要： Digital coding representations of meta-atoms make it possible to realize intelligent designs of metasurfaces by means of machine learning algorithms. Here, a machine-learning method to design anisotropic digital coding metasurfaces is proposed, and meta-atoms may require any absolute phase values at di?erent positions and under di?erent polarizations. A deep-learning neural network to predict the vast and complex system is proposed, in which only 70 000 training coding patterns are used to train the network. Another 10 000 randomly chosen coding patterns are employed to validate the neural network, showing an accuracy of 90.05% of phase responses with 2° error in the 360° phase. Using the learned network, the correct coding pattern among 18 billion of billions of choices for the required phase can be readily found in a second, ?nishing automatic design of anisotropic meta-atoms. Three functional 1-bit anisotropic coding metasurfaces are intelligently achieved by the learned network. It is convenient to realize dual-beam scattering with left-handed circular polarization (LHCP) for one beam while right-handed circular polarization (RHCP) for the others, dual-beam scattering with circular polarization for one beam while linear polarization (LP) for the others, and triple-beam scattering with LHCP and RHCP for two beams while LP for the third one.
关键词： anisotropic elements,multiple controls,digital coding metasurfaces,machine-learning
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Automatic Insar Phase Modeling and Quality Assessment Using Machine Learning and Hypothesis Testing
摘要： PS-InSAR time series yield large volumes of data points, observed during many epochs. While traditional processing algorithms use a single parameterization for the behavior of all points, in reality this behavior will differ significantly between points and over time. It is a challenge to find the optimal parameterization for this behavior, and to assess the quality of the measurements per point and per epoch. Here we propose a post-processing method to improve the model estimation of PS-InSAR phase time series. The method combines machine learning (ML) algorithms and hypothesis testing (HT) into the ML/HT method efficiently leading to significant improvements in data interpretation, parameterization, as well as the quality of the estimated parameters. Moreover we show that we can find structure in the data regardless of spatial location and temporal complexity. In contrast to conventional assumptions that nearby points behave in the same way, with unchanged characteristics over time, a method is developed that takes individual behavior into account. Demonstrating that we can move from spatial and temporal analysis tools to semantic-based analysis.
关键词： stochastics,machine learning,InSAR,hypothesis testing
更新于2025-09-10 09:29:36
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Spotlight on Bioimaging and Signal Processing [In the Spotlight]
摘要： The Bio-Imaging and Signal Processing Technical Committee (BISP-TC) of the IEEE Signal Processing Society (SPS) promotes activities in the broad technical areas of computerized image and signal processing with a clear focus on applications in biology and medicine. Specific topics of interest include image reconstruction, compressed sensing, superresolution, image restoration, registration and segmentation, pattern recognition, object detection, localization, tracking, quantification and classification, machine learning, multimodal image and signal fusion, analytics, visualization, and statistical modeling. Application areas covered by the TC include biomedical imaging from nano to macroscale, encompassing all modalities of molecular imaging and microscopy, anatomical imaging, and functional imaging, as well as genomic signal processing, computational biology, and bioinformatics, with the ultimate overarching aim of enabling precision medicine.
关键词： Biomedical Imaging,Bioimaging,Machine Learning,Precision Medicine,Signal Processing
更新于2025-09-10 09:29:36
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[Lecture Notes in Electrical Engineering] Advanced Multimedia and Ubiquitous Engineering Volume 518 (MUE/FutureTech 2018) || Superpixel Based ImageCut Using Object Detection
摘要： The edge preserving image segmentation required by online shopping malls or the design ?eld is clearly limited to pixel based image machine learning, making it dif?cult for the industry to accept the results of the latest machine learning techniques. Existing studies of image segmentation have shown that using any size square as a study unit without targeting meaningful pixels provides a simple method of learning, but produces a high error rate in image segmentation and also there is no way to calibrate the resulting images. Therefore, this paper proposes image segmentation techniques through superpixel based machine learning to develop technologies for automatically identifying and separating objects from images. In addition, the main reasons for superpixel based imagecut using object detection is to reduce the amount of data processed, thereby effectively delivering higher computational rates and larger image processing.
关键词： Removal image background,Superpixel,Image segmentation,Machine learning,Object detection
更新于2025-09-10 09:29:36
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Noise reduction and retrieval by modified lidar inversion method combines joint retrieval method and machine learning
摘要： To address the problem in which the signal-to-noise ratio of a raw atmospheric lidar signal decreases rapidly as the range increases, which has a tremendous effect on the accuracy and the effective range of lidar retrieval, many de-noising algorithms have been proposed. Among these methods, those based on the ensemble Kalman Filter (EnKF) exhibit good performance. EnKF-based methods can simultaneously denoise lidar signals and yield accurate retrieval results. However, due to poor forecasting in the EnKF step, biases exist in the results of these methods. In this study, a modified lidar inversion method was proposed for horizontal aerosol characteristic retrieval, which combines the joint retrieval method and Gaussian processing machine learning. This method compensates for the poor forecasting in the EnKF step in the joint retrieval method through the Gaussian processing machine learning algorithm, which can reduce the biases in the retrieval results. The modified lidar inversion method was applied to both simulated and real lidar signals, and the results show that the modified lidar inversion method is effective and practical in aerosol extinction characteristics’ analysis.
关键词： lidar,Gaussian processing machine learning,ensemble Kalman Filter,signal-to-noise ratio,aerosol extinction characteristics
更新于2025-09-10 09:29:36
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Effective Raman spectra identification with tree-based methods
摘要： Treatment of spectral information is an essential tool for the examination of various cultural heritage materials. Raman spectroscopy has become an everyday practice for compound identification due to its non-intrusive nature, but often it can be a complex operation. Spectral identification and analysis on artists’ materials is being done with the aid of already existing spectral databases and spectrum matching algorithms. We demonstrate that with a machine learning method called Extremely Randomised Trees, we can learn a model in a supervised learning fashion, able to accurately match an entire-spectrum range into its respective mineral. Our approach was tested and was found to outperform the state-of-the-art methods on the corrected RRUFF dataset, while maintaining low computational complexity and inherently supporting parallelisation.
关键词： Randomised trees,Random forest,Mineral identification,Raman spectroscopy,Machine learning,Classification,Raman spectra identification
更新于2025-09-10 09:29:36
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Computer vision system and near-infrared spectroscopy for identification and classification of chicken with wooden breast, and physicochemical and technological characterization
摘要： Wooden Breast (WB) anomaly on poultry meat causes changes in appearance, reduction of technological and nutritional quality, and consumer acceptance. The objective of this study was to identify and classify chicken with WB using a Computer Vision System (CVS) and spectral information from the Near Infrared (NIR) region by linear and nonlinear algorithms. Moreover, it was characterized the physicochemical and technological parameters, which supported a decision tree modelling. Pectoralis major muscle (n = 80) were collected from a poultry slaughterhouse, spectral information was obtained by NIR and CVS, and WB of chicken was characterized. Combining image analyses with a Support Vector Machine (SVM) classification model, 91.8% of chicken breasts were correctly classified as WB or Normal (N). NIR spectral information showed 97.5% of accuracy. WB showed significant increases in moisture and lipid contents and value of a*, decreases of protein and ash contents, and water holding capacity. The shear force of raw WB was 49.51% hardness, and after cooking was 31.79% softer than N breast. CVS and NIR spectroscopy can be applied as rapid and non-destructive methods for identifying and classifying WB in slaughterhouses.
关键词： Machine learning,Broilers,Image processing,Algorithms
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Ostrava, Czech Republic (2018.9.17-2018.9.20)] 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Evaluation of facial attractiveness for purposes of plastic surgery using machine-learning methods and image analysis
摘要： Many current studies conclude that facial attractiveness perception is data-based and irrespective of the perceiver. However, analyses of facial geometric image data and its visual impact always exceeded power of classical statistical methods. In this study, we have applied machine-learning methods to identify geometric features of a face associated with an increase of facial attractiveness after undergoing rhinoplasty. Furthermore, we explored how accurate classification of faces into sets of facial emotions and their facial manifestations is, since categorization of human faces into emotions manifestation should take into consideration the fact that total face impression is also dependent on expressed facial emotion. Both profile and portrait facial image data were collected for each patient (n = 42), processed, landmarked and analysed using R language. Multivariate linear regression was performed to select predictors increasing facial attractiveness after undergoing rhinoplasty. The sets of used facial emotions originate from Ekman-Friesen FACS scale, but was improved substantially. Bayesian naive classifiers, decision trees (CART) and neural networks were learned to allow assigning a new face image data into one of facial emotions. Enlargements of both a nasolabial and nasofrontal angle within rhinoplasty were determined as significant predictors increasing facial attractiveness (p < 0.05). Neural networks manifested the highest predictive accuracy of a new face classification into facial emotions. Geometrical shape of a mouth, then eyebrows and finally eyes affect in descending order final classified emotion, as was identified using decision trees. We performed machine-learning analyses to point out which facial geometric features, based on large data evidence, affect facial attractiveness the most, and therefore should preferentially be treated within plastic surgeries.
关键词： Bayes naive classifier,facial emotions,decision trees,rhinoplasty,facial attractiveness,neural networks,machine learning
更新于2025-09-10 09:29:36