scholarly journals Use of a trained denoising autoencoder to estimate the noise level in the ECG

2021 ◽  
Vol 7 (2) ◽  
pp. 562-565
Author(s):  
Fars Samann ◽  
Thomas Schanze
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Standard Deviation ◽  
Noise Level ◽  
Deep Neural Networks ◽  
Statistical Properties ◽  
Training Dataset ◽  
Effective Algorithm ◽  
Denoising Autoencoder ◽  
Noise Level Estimation

Abstract Noise level estimation plays an important role in many applications of signal and image processing, like denoising, compression and detection. Recently, deep neural networks have also been increasingly used for this purpose. In this paper, we develop an effective algorithm of noise level estimation of ECG segments based on trained denoising autoencoder (DAE) with a statistical thresholding method. An important observation is that a well-trained DAE model provides a clean representation of the corrupted training dataset. Two identical cascaded trained DAE models are considered to estimate the statistical properties, e.g., mean and standard deviation, from the trained DAE outputs after applying noise free aligned and jittered training dataset respectively. Two statistical thresholds are calculated from these statistical properties to classify whether the ECG segment is noise-free or jittered or noisy segment. The accuracy of the proposed method is quite promising in classifying and estimating unknow noise level.

Pest detection and classification to reduce pesticide use in fruit crops based on deep neural networks and image processing

2021 ◽  
Author(s):  
Agustina Suarez ◽  
Romina Soledad Molina ◽  
Giovanni Ramponi ◽  
Ricardo Petrino ◽  
Luciana Bollati ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Deep Neural Networks ◽  
Pesticide Use ◽  
Fruit Crops ◽  
Pest Detection

scholarly journals Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

2020 ◽  
Vol 10 (6) ◽  
pp. 2104
Author(s):  
Michał Tomaszewski ◽  
Paweł Michalski ◽  
Jakub Osuchowski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Detection Efficiency ◽  
Training Data ◽  
Training Dataset ◽  
Training Set ◽  
Convolutional Network

This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.

Automated Recognition of Arrhythmia Using Deep Neural Networks for 12-Lead Electrocardiograms with Fractional Time–Frequency Domain Extension

2020 ◽  
Vol 10 (11) ◽  
pp. 2764-2767
Author(s):  
Chuanbin Ge ◽  
Di Liu ◽  
Juan Liu ◽  
Bingshuai Liu ◽  
Yi Xin
Keyword(s):  
Neural Networks ◽  
Frequency Domain ◽  
Deep Neural Networks ◽  
Fractional Fourier Transform ◽  
Training Dataset ◽  
Clinical Tool ◽  
Time Frequency ◽  
Ecg Signals ◽  
Physiological Signal ◽  
Ecg Data

Arrhythmia is a group of conditions in which the heartbeat is irregular. There are many types of arrhythmia. Some can be life-threatening. Electrocardiogram (ECG) is an effective clinical tool used to diagnosis arrhythmia. Automatic recognition of different arrhythmia types in ECG signals has become an important and challenging issue. In this article, we proposed an algorithm to detect arrhythmia in 12-lead ECG signals and classify signals into 9 categories. Two 19-layer deep neural networks combining convolutional neural network and gated recurrent unit were proposed to realize this work. The first one was trained directly with the raw 12-lead ECG data while the other one was trained with an 18-"lead" ECG data, where the six extra leads containing morphology information in fractional time–frequency domain were generated utilizing fractional Fourier transform (FRFT). Overall detection results were obtained by fusing the output of these two networks and the final classification results on the testing dataset reports our proposed algorithm obtained a F1 score of 0.855. Furthermore, with our proposed algorithm, a better F1 score 0.81 was attained using training dataset provided by the China Physiological Signal Challenge held in 2018.

scholarly journals A HYBRID MODEL USING THE PRETRAINED BERT AND DEEP NEURAL NETWORKS WITH RICH FEATURE FOR EXTRACTIVE TEXT SUMMARIZATION

2021 ◽  
Vol 37 (2) ◽  
pp. 123-143
Author(s):  
Tuan Minh Luu ◽  
Huong Thanh Le ◽  
Tan Minh Hoang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Text Summarization ◽  
Training Dataset ◽  
Extractive Summarization ◽  
Input Text ◽  
Summarization System ◽  
Fully Connected

Deep neural networks have been applied successfully to extractive text summarization tasks with the accompany of large training datasets. However, when the training dataset is not large enough, these models reveal certain limitations that affect the quality of the system’s summary. In this paper, we propose an extractive summarization system basing on a Convolutional Neural Network and a Fully Connected network for sentence selection. The pretrained BERT multilingual model is used to generate embeddings vectors from the input text. These vectors are combined with TF-IDF values to produce the input of the text summarization system. Redundant sentences from the output summary are eliminated by the Maximal Marginal Relevance method. Our system is evaluated with both English and Vietnamese languages using CNN and Baomoi datasets, respectively. Experimental results show that our system achieves better results comparing to existing works using the same dataset. It confirms that our approach can be effectively applied to summarize both English and Vietnamese languages.

scholarly journals COSMO: Computing with Stochastic Numbers in Memory

2022 ◽  
Vol 18 (2) ◽  
pp. 1-25
Author(s):  
Saransh Gupta ◽  
Mohsen Imani ◽  
Joonseop Sim ◽  
Andrew Huang ◽  
Fan Wu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Energy Efficient ◽  
Deep Neural Networks ◽  
Parallel Architecture ◽  
Low Energy ◽  
Stochastic Computing ◽  
Wide Range ◽  
Low Energy Consumption ◽  
Sc Addition

Stochastic computing (SC) reduces the complexity of computation by representing numbers with long streams of independent bits. However, increasing performance in SC comes with either an increase in area or a loss in accuracy. Processing in memory (PIM) computes data in-place while having high memory density and supporting bit-parallel operations with low energy consumption. In this article, we propose COSMO, an architecture for co mputing with s tochastic numbers in me mo ry, which enables SC in memory. The proposed architecture is general and can be used for a wide range of applications. It is a highly dense and parallel architecture that supports most SC encodings and operations in memory. It maximizes the performance and energy efficiency of SC by introducing several innovations: (i) in-memory parallel stochastic number generation, (ii) efficient implication-based logic in memory, (iii) novel memory bit line segmenting, (iv) a new memory-compatible SC addition operation, and (v) enabling flexible block allocation. To show the generality and efficiency of our stochastic architecture, we implement image processing, deep neural networks (DNNs), and hyperdimensional (HD) computing on the proposed hardware. Our evaluations show that running DNN inference on COSMO is 141× faster and 80× more energy efficient as compared to GPU.

Patch based fast noise level estimation using DCT and standard deviation

2018 ◽  
Vol 22 (S6) ◽  
pp. 14495-14504
Author(s):  
S. B. Mohan ◽  
T. A. Raghavendiran ◽  
R. Rajavel
Keyword(s):  
Standard Deviation ◽  
Noise Level ◽  
Noise Level Estimation

scholarly journals Interactive Artistic Multi-style Transfer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiaohui Wang ◽  
Yiran Lyu ◽  
Junfeng Huang ◽  
Ziying Wang ◽  
Jingyan Qin
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Deep Neural Networks ◽  
Subjective Evaluation ◽  
User Interaction ◽  
Evaluation Framework ◽  
Transfer System ◽  
Style Transfer ◽  
Artistic Style ◽  
Artistic Styles

AbstractArtistic style transfer is to render an image in the style of another image, which is a challenge problem in both image processing and arts. Deep neural networks are adopted to artistic style transfer and achieve remarkable success, such as AdaIN (adaptive instance normalization), WCT (whitening and coloring transforms), MST (multimodal style transfer), and SEMST (structure-emphasized multimodal style transfer). These algorithms modify the content image as a whole using only one style and one algorithm, which is easy to cause the foreground and background to be blurred together. In this paper, an iterative artistic multi-style transfer system is built to edit the image with multiple styles by flexible user interaction. First, a subjective evaluation experiment with art professionals is conducted to build an open evaluation framework for style transfer, including the universal evaluation questions and personalized answers for ten typical artistic styles. Then, we propose the interactive artistic multi-style transfer system, in which an interactive image crop tool is designed to cut a content image into several parts. For each part, users select a style image and an algorithm from AdaIN, WCT, MST, and SEMST by referring to the characteristics of styles and algorithms summarized by the evaluation experiments. To obtain richer results, the system provides a semantic-based parameter adjustment mode and the function of preserving colors of content image. Finally, case studies show the effectiveness and flexibility of the system.

Vibration-based damage detection for bridges by deep convolutional denoising autoencoder

2020 ◽  
pp. 147592172094283 ◽  
Author(s):  
Zhiqiang Shang ◽  
Limin Sun ◽  
Ye Xia ◽  
Wei Zhang
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Deep Neural Networks ◽  
Beam Model ◽  
Structural Damage Detection ◽  
Denoising Autoencoder ◽  
Vibration Data

One of the main challenges for structural damage detection using monitoring data is to acquire features that are sensitive to damages but insensitive to noise (e.g. sensor measurement noise) as well as environmental and operational effects (e.g. temperature effect). Inspired by the capabilities of deep learning methods in representation learning, various deep neural networks have been developed to obtain effective damage features from raw vibration data. However, most of the available deep neural networks are supervised, resulting in practical difficulties owing to the lack of damage labels. This article proposes a damage detection strategy based on an unsupervised deep neural network, referred to as deep convolutional denoising autoencoder, which accepts multi-dimensional cross-correlation functions as input. The strategy aims to extract damage features from field measurements of undamaged structures under the influence of noise and temperature uncertainties. In the proposed strategy, cross-correlation functions of vibration data are first calculated as basic features; then deep convolutional denoising autoencoder is developed to reconstruct cross-correlation functions from their noise-corrupted versions to extract desired features; exponentially weighted moving average control charts are finally established for these features to identify minor structural damages. The strategy is evaluated through a numerical simply supported beam model and an experimental continuous beam model. The mechanism of deep convolutional denoising autoencoder to extract damage features is interpreted by visualizing feature maps of convolutional layers in the encoder. It is found that these layers perform rough estimations of modal properties and preserve the damage information as the general trend of these properties in multiple extra frequency bands. The results show that the proposed strategy is competent for structural damage detection under the exposed environment and worth further exploring its capabilities in applications of real bridges.

scholarly journals Bias in Deep Neural Networks in Land Use Characterization for International Development

2021 ◽  
Vol 13 (15) ◽  
pp. 2908
Author(s):  
Do-Hyung Kim ◽  
Guzmán López ◽  
Diego Kiedanski ◽  
Iyke Maduako ◽  
Braulio Ríos ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Land Use ◽  
International Development ◽  
Deep Neural Networks ◽  
Satellite Image ◽  
Fine Tuning ◽  
Training Dataset ◽  
Model Accuracy ◽  
The Neural Network

Understanding the biases in Deep Neural Networks (DNN) based algorithms is gaining paramount importance due to its increased applications on many real-world problems. A known problem of DNN penalizing the underrepresented population could undermine the efficacy of development projects dependent on data produced using DNN-based models. In spite of this, the problems of biases in DNN for Land Use and Land Cover Classification (LULCC) have not been a subject of many studies. In this study, we explore ways to quantify biases in DNN for land use with an example of identifying school buildings in Colombia from satellite imagery. We implement a DNN-based model by fine-tuning an existing, pre-trained model for school building identification. The model achieved overall 84% accuracy. Then, we used socioeconomic covariates to analyze possible biases in the learned representation. The retrained deep neural network was used to extract visual features (embeddings) from satellite image tiles. The embeddings were clustered into four subtypes of schools, and the accuracy of the neural network model was assessed for each cluster. The distributions of various socioeconomic covariates by clusters were analyzed to identify the links between the model accuracy and the aforementioned covariates. Our results indicate that the model accuracy is lowest (57%) where the characteristics of the landscape are predominantly related to poverty and remoteness, which confirms our original assumption on the heterogeneous performances of Artificial Intelligence (AI) algorithms and their biases. Based on our findings, we identify possible sources of bias and present suggestions on how to prepare a balanced training dataset that would result in less biased AI algorithms. The framework used in our study to better understand biases in DNN models would be useful when Machine Learning (ML) techniques are adopted in lieu of ground-based data collection for international development programs. Because such programs aim to solve issues of social inequality, MLs are only applicable when they are transparent and accountable.

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