scholarly journals Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

2018 ◽  
Vol 8 (8) ◽  
pp. 1258 ◽  
Author(s):  
Shuming Jiao ◽  
Zhi Jin ◽  
Chenliang Chang ◽  
Changyuan Zhou ◽  
Wenbin Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Frequency ◽  
Critical Issue ◽  
Compression Process ◽  
Compression Scheme ◽  
Quality Degradation ◽  
Digital Format ◽  
Enormous Amount ◽  
Frequency Features

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed “JPEG + deep learning” hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression.

scholarly journals Neutrosophy for physiological data compression: in particular by neural nets using deep learning

2020 ◽  
pp. 74-80
Author(s):  
Philippe Schweizer ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Boundary Surface ◽  
Human Perception ◽  
Neural Nets ◽  
Physiological Data ◽  
Compression Process ◽  
The Neural Network ◽  
Algorithmic Solution ◽  
Algorithmic Techniques

We would like to show the small distance in neutropsophy applications in sciences and humanities, has both finally consider as a terminal user a human. The pace of data production continues to grow, leading to increased needs for efficient storage and transmission. Indeed, the consumption of this information is preferably made on mobile terminals using connections invoiced to the user and having only reduced storage capacities. Deep learning neural networks have recently exceeded the compression rates of algorithmic techniques for text. We believe that they can also significantly challenge classical methods for both audio and visual data (images and videos). To obtain the best physiological compression, i.e. the highest compression ratio because it comes closest to the specificity of human perception, we propose using a neutrosophical representation of the information for the entire compression-decompression cycle. Such a representation consists for each elementary information to add to it a simple neutrosophical number which informs the neural network about its characteristics relative to compression during this treatment. Such a neutrosophical number is in fact a triplet (t,i,f) representing here the belonging of the element to the three constituent components of information in compression; 1° t = the true significant part to be preserved, 2° i = the inderterminated redundant part or noise to be eliminated in compression and 3° f = the false artifacts being produced in the compression process (to be compensated). The complexity of human perception and the subtle niches of its defects that one seeks to exploit requires a detailed and complex mapping that a neural network can produce better than any other algorithmic solution, and networks with deep learning have proven their ability to produce a detailed boundary surface in classifiers.

scholarly journals Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development

2018 ◽  
Author(s):  
Yuta Tokuoka ◽  
Takahiro G Yamada ◽  
Noriko F Hiroi ◽  
Tetsuya J Kobayashi ◽  
Kazuo Yamagata ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Critical Issue ◽  
Segmentation Algorithm ◽  
Embryonic Cells ◽  
Mouse Development ◽  
Instance Segmentation

AbstractIn embryology, image processing methods such as segmentation are applied to acquiring quantitative criteria from time-series three-dimensional microscopic images. When used to segment cells or intracellular organelles, several current deep learning techniques outperform traditional image processing algorithms. However, segmentation algorithms still have unsolved problems, especially in bioimage processing. The most critical issue is that the existing deep learning-based algorithms for bioimages can perform only semantic segmentation, which distinguishes whether a pixel is within an object (for example, nucleus) or not. In this study, we implemented a novel segmentation algorithm, based on deep learning, which segments each nucleus and adds different labels to the detected objects. This segmentation algorithm is called instance segmentation. Our instance segmentation algorithm, implemented as a neural network, which we named QCA Net, substantially outperformed 3D U-Net, which is the best semantic segmentation algorithm that uses deep learning. Using QCA Net, we quantified the nuclear number, volume, surface area, and center of gravity coordinates during the development of mouse embryos. In particular, QCA Net distinguished nuclei of embryonic cells from those of polar bodies formed in meiosis. We consider that QCA Net can greatly contribute to bioimage segmentation in embryology by generating quantitative criteria from segmented images.

Towards Real-Time Moment Tensor Inversions in a Data Rich Micro-Seismic Environment using Deep Learning

2021 ◽  
Author(s):  
Nima Nooshiri ◽  
Christopher J. Bean ◽  
Francesco Grigoli ◽  
Torsten Dahm
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
High Frequency ◽  
Moment Tensor ◽  
Source Parameters ◽  
Data Set ◽  
Real Time Information ◽  
Time Information ◽  
Current Inversion

<p>Despite advanced seismological methods, source characterization for micro-seismic events remains challenging since current inversion and modelling of high-frequency waveforms are complex and time consuming. For a real-time application like induced-seismicity monitoring, these methods are slow for true real-time information because they require repeated evaluation of the often computationally expensive forward operation. Moreover, because of the low amplitude and high-frequency content of the recorded micro-seismic signals, routine inversion procedure can become unstable and manual parameter tuning is often required. Therefore, real-time and automatic source inversion procedures are difficult and not standard. A more promising alternative to the current inversion methods for rapid source parameter inversion is to use a deep-learning neural network model that is calibrated on a data set of past and/or possible future observations. Such data-driven model, once trained, offers the potential for rapid real-time information on seismic sources in a monitoring context.</p><p>In this study, we investigate how a supervised deep-learning model trained on a data set of synthetic seismograms can be used to rapidly invert for source parameters. The inversion is represented in compact form by a convolutional neural network which yields seismic moment tensor. In other words, a neural-network algorithm is trained to encapsulate the information about the relationship between observations and underlying point-source models. The learning-based model allows rapid inversion once seismic waveforms are available. Moreover, we find that the method is robust with respect to perturbations such as observational noise and missing data. In this study, we seek to demonstrate that this approach is viable for micro-seismicity real-time estimation of source parameters. As a demonstration test, we plan to apply the new approach to data collected at the geothermal field system in the Hengill area, Iceland, within the framework of the COSEISMIQ project funded through the EU GEOTHERMICA programme.</p>

scholarly journals A Long Short-Term Memory neural network for the detection of epileptiform spikes and high frequency oscillations

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
A. V. Medvedev ◽  
G. I. Agoureeva ◽  
A. M. Murro
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Frequency ◽  
Short Term Memory ◽  
Global Network ◽  
Short Term ◽  
Term Memory ◽  
Network Training ◽  
High Frequency Oscillations ◽  
Long Short Term Memory

AbstractOver the last two decades, the evidence has been growing that in addition to epileptic spikes high frequency oscillations (HFOs) are important biomarkers of epileptogenic tissue. New methods of artificial intelligence such as deep learning neural networks can provide additional tools for automated analysis of EEG. Here we present a Long Short-Term Memory neural network for detection of spikes, ripples and ripples-on-spikes (RonS). We used intracranial EEG (iEEG) from two independent datasets. First dataset (7 patients) was used for network training and testing. The second dataset (5 patients) was used for cross-institutional validation. 1000 events of each class (spike, RonS, ripple and baseline) were selected from the candidates initially found using a novel threshold method. Network training was performed using random selections of 50–500 events (per class) from all patients from the 1st dataset. This ‘global’ network was then tested on other events for each patient from both datasets. The network was able to detect events with a good generalisability namely, with total accuracy and specificity for each class exceeding 90% in all cases, and sensitivity less than 86% in only two cases (82.5% for spikes in one patient and 81.9% for ripples in another patient). The deep learning networks can significantly accelerate the analysis of iEEG data and increase their diagnostic value which may improve surgical outcome in patients with localization-related intractable epilepsy.

scholarly journals An Unconstrained Face Recognition Method Based on Siamese Networks

2021 ◽  
Author(s):  
Song CunLi ◽  
Shouyong Ji
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Network Model ◽  
High Frequency ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Low Frequency ◽  
Image Features ◽  
Frequency Features ◽  
Frequency Feature

Abstract It is aimed at the low accuracy and low efficiency of face recognition under unlimited conditions.In this paper, a Siamese neural Network model SN-LF (Siamese Network based on LBP and Frequency Feature perception) is designed based on the Local Binary Pattern (LBP) and the Frequency sensing model.Based on Siamese neural networks, the network adopts circular LBP algorithm and frequency feature perception to realize face recognition under unrestricted conditions.The LBP algorithm can eliminate the influence of light on the image and provide directional input to the network model at the same time.Frequency feature sensing divides the image features into low frequency features and high frequency features. The low frequency features are compressed in the Siamese neural network to increase the recognition efficiency of the network. At the same time, information is exchanged with the high frequency features, so that the target noise data can be eliminated while the feature data is retained.In this way, the recognition rate of the network is maintained, and the computing speed of the network is improved.Simulation experiments are carried out on standard face dataset CASIA-Webface and Yale-B, and compared with other network models. The experimental results show that the proposed SN-LF network structure can improve the recognition accuracy of the algorithm, and achieve a good recognition accuracy.

scholarly journals Multiscale Convolutional Neural Networks with Attention for Plant Species Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xianfeng Wang ◽  
Chuanlei Zhang ◽  
Shanwen Zhang
Keyword(s):  
Neural Network ◽  
Plant Species ◽  
High Frequency ◽  
Species Recognition ◽  
Low Frequency ◽  
Plant Leaf ◽  
Network Training ◽  
Feature Based ◽  
The Rich ◽  
Frequency Features

Plant species recognition is a critical step in protecting plant diversity. Leaf-based plant species recognition research is important and challenging due to the large within-class difference and between-class similarity of leaves and the rich inconsistent leaves with different sizes, colors, shapes, textures, and venations. Most existing plant leaf recognition methods typically normalize all leaf images to the same size and then recognize them at one scale, which results in unsatisfactory performances. A novel multiscale convolutional neural network with attention (AMSCNN) model is constructed for plant species recognition. In AMSCNN, multiscale convolution is used to learn the low-frequency and high-frequency features of the input images, and an attention mechanism is utilized to capture rich contextual relationships for better feature extraction and improving network training. Extensive experiments on the plant leaf dataset demonstrate the remarkable performance of AMSCNN compared with the hand-crafted feature-based methods and deep-neural network-based methods. The maximum accuracy attained along with AMSCNN is 95.28%.

scholarly journals Recognition of Handwritten Digits using Convolutional Neural Network and Linear Binary Pattern

2019 ◽  
Vol 9 (1) ◽  
pp. 4368-4372
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Handwritten Documents ◽  
Python Language ◽  
Digital Format ◽  
Proposed Model ◽  
Human Effort

Over the past few years there has been a tremendous developments observed in the field of computer technology and artificial intelligence, especially the use of machine learning concepts in Research and Industries. The human effort can be further more reduced in recognition, learning, predicting and many other areas using machine learning and deep learning. Any information which has been handwritten documents consisting of digits in digital form like images, recognizing such digits is a challenging task. The proposed system can recognize any handwritten digits in the document which has been converted into digital format. The proposed model includes Convolutional Neural Network (CNN), a deep learning approach with Linear Binary Pattern (LBP) used for feature extraction. In order to classify more effectively we also have used Support Vector Machine to recognize mere similar digits like 1 and 7, 5 and 6 and many others. The proposed system CNN and LBP is implemented on python language; also the system is tested with different images of handwritten digits taken from MNIST dataset. By using proposed model we could able to achieve 98.74% accuracy in predicting the digits in image format.

scholarly journals Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Qian Zhang ◽  
Yuan Ma ◽  
Guoli Li ◽  
Jinhui Ma ◽  
Jinjin Ding
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Domain Decomposition ◽  
Frequency Domain ◽  
High Frequency ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Frequency Components ◽  
Frequency Domain Decomposition

In this paper, we focus on the accuracy improvement of short-term load forecasting, which is useful in the reasonable planning and stable operation of the system in advance. For this purpose, a short-term load forecasting model based on frequency domain decomposition and deep learning is proposed. The original load data are decomposed into four parts as the daily and weekly periodic components and the low- and high-frequency components. Long short-term memory (LSTM) neural network is applied in the forecasting for the daily periodic, weekly periodic, and low-frequency components. The combination of isolation forest (iForest) and Mallat with the LSTM method is constructed in forecasting the high-frequency part. Finally, the four parts of the forecasting results are added together. The actual load data of a Chinese city are researched. Compared with the forecasting results of empirical mode decomposition- (EMD-) LSTM, LSTM, and recurrent neural network (RNN) methods, the proposed method can effectively improve the accuracy and reduce the degree of dispersion of forecasting and actual values.

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