- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Features Extraction for Cuffless Blood Pressure Estimation by Autoencoder from Photoplethysmography
摘要： Several studies have been proposed to estimate blood pressure (BP) with cuffless devices using only a Photoplethysmograph (PPG) sensor on the basis of the physiological knowledge that the PPG changes depend on the state of the cardiovascular system. In these studies, machine learning algorithms were used to extract various features from the wave height and the elapsed time from the rising point of the pulse wave to feature points have been used to estimate the BP. However, the accuracy is still not adequate to be used as medical equipment because their features cannot express fully information of the pulse waveform which changes according to the BP. And, no other effective knowledge about the pulse waveform for estimating BP has been found yet. Therefore, in this study, we focus on the autoencoder which can extract complex features and can add new features of the pulse waveform for estimating the BP. By using autoencoder, we extracted 100 features from the coupling signal of the pulse wave and from its first-order differentiation and second-order differentiation. The result of examination with 1363 test subjects show that the correlation coefficients and the standard deviation of the difference between the measured BP and the estimated BP got improved from R = 0.67, SD = 13.97 without autoencoder to R = 0.78, SD = 11.86 with autoencoder.
关键词： blood pressure estimation,autoencoder,cuffless devices,neural network,Photoplethysmography
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Human Gait Recognition with Micro-Doppler Radar and Deep Autoencoder
摘要： The micro-Doppler signals from moving objects contain useful information about their motions. This paper introduces a novel approach for human gait recognition based on backscattered signals from a micro-Doppler radar. Three different signal techniques are utilized for the extraction of micro-Doppler features via time-frequency and time-scale representations. To classify the human motions into various types, this paper presents a deep autoencoder with the use of local patches extracted along the spectrogram and scalogram. The network configuration and the learning parameters of the deep autoencoder, which are considered as hyperparameters, are optimized by a Bayesian optimization algorithm. Experimental results produced by the proposed technique on real radar data show a significant improvement compared to several existing approaches.
关键词： Short-time Fourier Transform,micro-Doppler radar,deep autoencoder,S-method,wavelet transform,Bayesian optimization
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Skip-Connected Deep Convolutional Autoencoder for Restoration of Document Images
摘要： The denoising and deblurring of images are the two essential restoration tasks in the document image processing task. As the preprocessing stages of the processing pipeline, the quality of denoising and deblurring heavily influences the result of subsequent tasks, such as character detection and recognition. In this paper, we propose a novel neural method for restoring document images. We named our network Skip-Connected Deep Convolutional Autoencoder (SCDCA), which is composed of multiple layers of convolution followed by a batch normalization layer and the leaky rectified linear unit (LeakyReLU) activation function. Inspired by the idea of residual learning, we use two types of skip connections in the network. One is identity mapping between convolution layers and the other is used to connect the input and output. Through these connections, the network learns the residual between the noisy and clean images instead of learning an ordinary transformation function. We empirically evaluate our algorithm on an open and challenging document images dataset. We also assess our restoring results using the optical character recognition (OCR) test. Experimental results have demonstrated the effectiveness and efficiency of our proposed algorithm by comparing with several state-of-the-art methods.
关键词： residual learning,convolutional autoencoder,denoising,deblurring,skip connections,deep learning,document image restoration
更新于2025-09-09 09:28:46
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Transfer Learning from Synthetic to Real Images Using Variational Autoencoders for Precise Position Detection
摘要： Capturing and labeling camera images in the real world is an expensive task, whereas synthesizing labeled images in a simulation environment is easy for collecting large-scale image data. However, learning from only synthetic images may not achieve the desired performance in the real world due to a gap between synthetic and real images. We propose a method that transfers learned detection of an object position from a simulation environment to the real world. This method uses only a significantly limited dataset of real images while leveraging a large dataset of synthetic images using variational autoencoders. Additionally, the proposed method consistently performed well in different lighting conditions, in the presence of other distractor objects, and on different backgrounds. Experimental results showed that it achieved accuracy of 1.5mm to 3.5mm on average. Furthermore, we showed how the method can be used in a real-world scenario like a “pick-and-place” robotic task.
关键词： variational autoencoder,transfer learning,position detection,deep learning,computer simulation
更新于2025-09-09 09:28:46
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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 - Deep Learning Hyperspectral Image Classification using Multiple Class-Based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations
摘要： Herein, we present a system for hyperspectral image segmentation that utilizes multiple class–based denoising autoencoders which are efficiently trained. Moreover, we present a novel hyperspectral data augmentation method for labelled HSI data using linear mixtures of pixels from each class, which helps the system with edge pixels which are almost always mixed pixels. Finally, we utilize a deep neural network and morphological hole-filling to provide robust image classification. Results run on the Salinas dataset verify the high performance of the proposed algorithm.
关键词： remote sensing,Deep learning,denoising autoencoder
更新于2025-09-09 09:28:46