scholarly journals All-Optical Computing Based on Convolutional Neural Networks

2020 ◽  
Author(s):  
Kun Liao ◽  
Ye Chen ◽  
Zhongcheng Yu ◽  
Xiaoyong Hu ◽  
Xingyuan Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Convolutional Neural Networks ◽  
Rapid Development ◽  
Computing Time ◽  
Optical Computing ◽  
Amplitude Control ◽  
Promising Alternative ◽  
Ultralow Energy ◽  
All Optical

Abstract The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-energy-consumption computing. Existing computing instruments are pre-dominantly electronic processors. The scaling of computing speed is limited not only by data transfer between memory and processing units, but also by RC delay associated with integrated circuits. Using photons as information carriers is a promising alternative. Here, we report a strategy to realize ultrafast and ultralow-energy-consumption all-optical computing based on convolutional neural networks, leveraging entirely linear optical interactions. The device is constructed from cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments to enable complete phase and amplitude control in each waveguide branch. The generic device concept can be used for equation solving, multifunctional logic operation, Fourier transformation, series expanding and encoding, as well as many other mathematical operations. Multiple computing functions were experimentally demonstrated to validate all-optical computing performances. The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit. Our approach can be further expanded to fulfill other complex computing tasks based on non-von Neumann architectures and thus paves a new way for on-chip all-optical computing.

scholarly journals All-optical computing based on convolutional neural networks

2021 ◽  
Vol 4 (11) ◽  
pp. 200060-200060
Author(s):  
Kun Liao ◽  
◽  
Ye Chen ◽  
Zhongcheng Yu ◽  
Xiaoyong Hu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Optical Computing ◽  
All Optical

scholarly journals Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1263
Author(s):  
Zhaojun Wang ◽  
Jiangning Wang ◽  
Congtian Lin ◽  
Yan Han ◽  
Zhaosheng Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Digital Technology ◽  
Rapid Development ◽  
Image Data ◽  
Image Database ◽  
Test Accuracy ◽  
Practical Application ◽  
Accuracy Rate ◽  
Deep Convolutional Neural Networks

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

scholarly journals Deep learning networks reflect cytoarchitectonic features used in brain mapping

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kai Kiwitz ◽  
Christian Schiffer ◽  
Hannah Spitzer ◽  
Timo Dickscheid ◽  
Katrin Amunts
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Brain Mapping ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Promising Alternative ◽  
Cortical Areas ◽  
Cytoarchitectonic Mapping ◽  
Profile Approach

AbstractThe distribution of neurons in the cortex (cytoarchitecture) differs between cortical areas and constitutes the basis for structural maps of the human brain. Deep learning approaches provide a promising alternative to overcome throughput limitations of currently used cytoarchitectonic mapping methods, but typically lack insight as to what extent they follow cytoarchitectonic principles. We therefore investigated in how far the internal structure of deep convolutional neural networks trained for cytoarchitectonic brain mapping reflect traditional cytoarchitectonic features, and compared them to features of the current grey level index (GLI) profile approach. The networks consisted of a 10-block deep convolutional architecture trained to segment the primary and secondary visual cortex. Filter activations of the networks served to analyse resemblances to traditional cytoarchitectonic features and comparisons to the GLI profile approach. Our analysis revealed resemblances to cellular, laminar- as well as cortical area related cytoarchitectonic features. The networks learned filter activations that reflect the distinct cytoarchitecture of the segmented cortical areas with special regard to their laminar organization and compared well to statistical criteria of the GLI profile approach. These results confirm an incorporation of relevant cytoarchitectonic features in the deep convolutional neural networks and mark them as a valid support for high-throughput cytoarchitectonic mapping workflows.

scholarly journals Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhenguo Nie ◽  
Haoliang Jiang ◽  
Levent Burak Kara
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Topology Optimization ◽  
Structural Analysis ◽  
Stress Field ◽  
Convolutional Neural Networks ◽  
Von Mises Stress ◽  
Channel Network ◽  
Promising Alternative ◽  
And Topology

Abstract The demand for fast and accurate structural analysis is becoming increasingly more prevalent with the advance of generative design and topology optimization technologies. As one step toward accelerating structural analysis, this work explores a deep learning-based approach for predicting the stress fields in 2D linear elastic cantilevered structures subjected to external static loads at its free end using convolutional neural networks (CNNs). Two different architectures are implemented that take as input the structure geometry, external loads, and displacement boundary conditions, and output the predicted von Mises stress field. The first is a single input channel network called SCSNet as the baseline architecture, and the second is the multichannel input network called StressNet. Accuracy analysis shows that StressNet results in significantly lower prediction errors than SCSNet on three loss functions, with a mean relative error of 2.04% for testing. These results suggest that deep learning models may offer a promising alternative to classical methods in structural design and topology optimization. Code and dataset are available.2

scholarly journals Experimental Discussion on Fire Image Recognition Based on Deep Learning

2021 ◽  
Vol 2066 (1) ◽  
pp. 012071
Author(s):  
Yongyi Cui ◽  
Fang Qu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Rapid Development ◽  
Fire Detection ◽  
Learning Technology ◽  
Fire Recognition

Abstract Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.

scholarly journals Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks

2021 ◽  
Vol 11 (21) ◽  
pp. 9844
Author(s):  
Xinzhe Yuan ◽  
Dustin Tanksley ◽  
Liujun Li ◽  
Haibin Zhang ◽  
Genda Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Damage Assessment ◽  
Prediction Accuracy ◽  
Computing Time ◽  
Earthquake Damage ◽  
Learning Approaches ◽  
Image Encoding ◽  
Ground Motion Records

Contemporary deep learning approaches for post-earthquake damage assessments based on 2D convolutional neural networks (CNNs) require encoding of ground motion records to transform their inherent 1D time series to 2D images, thus requiring high computing time and resources. This study develops a 1D CNN model to avoid the costly 2D image encoding. The 1D CNN model is compared with a 2D CNN model with wavelet transform encoding and a feedforward neural network (FNN) model to evaluate prediction performance and computational efficiency. A case study of a benchmark reinforced concrete (r/c) building indicated that the 1D CNN model achieved a prediction accuracy of 81.0%, which was very close to the 81.6% prediction accuracy of the 2D CNN model and much higher than the 70.8% prediction accuracy of the FNN model. At the same time, the 1D CNN model reduced computing time by more than 90% and reduced resources used by more than 69%, as compared to the 2D CNN model. Therefore, the developed 1D CNN model is recommended for rapid and accurate resultant damage assessment after earthquakes.

scholarly journals Accurate Pupil Center Detection in Off-the-Shelf Eye Tracking Systems Using Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6847
Author(s):  
Andoni Larumbe-Bergera ◽  
Gonzalo Garde ◽  
Sonia Porta ◽  
Rafael Cabeza ◽  
Arantxa Villanueva
Keyword(s):  
Neural Networks ◽  
Eye Tracking ◽  
Convolutional Neural Networks ◽  
Computing Time ◽  
Training Data ◽  
Estimation Errors ◽  
Pupil Center ◽  
Research Areas ◽  
Facial Landmark ◽  
Tracking Technology

Remote eye tracking technology has suffered an increasing growth in recent years due to its applicability in many research areas. In this paper, a video-oculography method based on convolutional neural networks (CNNs) for pupil center detection over webcam images is proposed. As the first contribution of this work and in order to train the model, a pupil center manual labeling procedure of a facial landmark dataset has been performed. The model has been tested over both real and synthetic databases and outperforms state-of-the-art methods, achieving pupil center estimation errors below the size of a constricted pupil in more than 95% of the images, while reducing computing time by a 8 factor. Results show the importance of use high quality training data and well-known architectures to achieve an outstanding performance.

scholarly journals Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 146 ◽  
Author(s):  
Xinhua Liu ◽  
Yao Zou ◽  
Hailan Kuang ◽  
Xiaolin Ma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Age Estimation ◽  
Data Augmentation ◽  
Rapid Development ◽  
Estimation Methods ◽  
Face Images

Face images contain many important biological characteristics. The research directions of face images mainly include face age estimation, gender judgment, and facial expression recognition. Taking face age estimation as an example, the estimation of face age images through algorithms can be widely used in the fields of biometrics, intelligent monitoring, human-computer interaction, and personalized services. With the rapid development of computer technology, the processing speed of electronic devices has greatly increased, and the storage capacity has been greatly increased, allowing deep learning to dominate the field of artificial intelligence. Traditional age estimation methods first design features manually, then extract features, and perform age estimation. Convolutional neural networks (CNN) in deep learning have incomparable advantages in processing image features. Practice has proven that the accuracy of using convolutional neural networks to estimate the age of face images is far superior to traditional methods. However, as neural networks are designed to be deeper, and networks are becoming larger and more complex, this makes it difficult to deploy models on mobile terminals. Based on a lightweight convolutional neural network, an improved ShuffleNetV2 network based on the mixed attention mechanism (MA-SFV2: Mixed Attention-ShuffleNetV2) is proposed in this paper by transforming the output layer, merging classification and regression age estimation methods, and highlighting important features by preprocessing images and data augmentation methods. The influence of noise vectors such as the environmental information unrelated to faces in the image is reduced, so that the final age estimation accuracy can be comparable to the state-of-the-art.

scholarly journals Design of task-specific optical systems using broadband diffractive neural networks

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Yi Luo ◽  
Deniz Mengu ◽  
Nezih T. Yardimci ◽  
Yair Rivenson ◽  
Muhammed Veli ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Network Architecture ◽  
Optical Network ◽  
Optical Computing ◽  
Optical Systems ◽  
Fashion Products ◽  
All Optical ◽  
Optical Neural Network

AbstractDeep learning has been transformative in many fields, motivating the emergence of various optical computing architectures. Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks. Diffraction-based all-optical object recognition systems, designed through this framework and fabricated by 3D printing, have been reported to recognize hand-written digits and fashion products, demonstrating all-optical inference and generalization to sub-classes of data. These previous diffractive approaches employed monochromatic coherent light as the illumination source. Here, we report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep learning. We experimentally validated the success of this broadband diffractive neural network architecture by designing, fabricating and testing seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize (1) a series of tuneable, single-passband and dual-passband spectral filters and (2) spatially controlled wavelength de-multiplexing. Merging the native or engineered dispersion of various material systems with a deep-learning-based design strategy, broadband diffractive neural networks help us engineer the light–matter interaction in 3D, diverging from intuitive and analytical design methods to create task-specific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning.

Forecasting Energy Consumption in Residential Department Using Convolutional Neural Networks

2021 ◽  
pp. 18-30
Author(s):  
Julio Barzola-Monteses ◽  
Marcos Guerrero ◽  
Franklin Parrales-Bravo ◽  
Mayken Espinoza-Andaluz
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Convolutional Neural Networks
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