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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Wenjuan Shu Fanping Zeng Zhen Ling Junyi Liu Tingting Lu Guozhu Chen School of Computer Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences. School of Computer Science and Technology University of Science and Technology of China Hefei China School of Computer Science and Engineering Southeast University Nanjing Jiangsu China

ISBN: (纸本)9781665406697

Resources of cloud workloads can be automatically allocated according to the requirements of the application. In the long-term running process, resource requirements change dynamically. Insufficient allocation may lead to the decline of service quality, and excessive allocation will lead to the waste of resources. Therefore, it is crucial to accurately predict resource demand. This paper aims to improve resource utilization in the data center by predicting the resources required for each application. Resource demand forecasting understands and manages future resource needs by mining current and past resource usage patterns. Because we need to analyze time series data with long-term dependence and noise, it is challenging to predict future resource *** designed and implemented an attention-based GRU model. The attention mechanism was added to the GRU model to quickly filter out valuable information from large amounts of data. We used the Azure and Alibaba cluster trace to train our neural network, and used three evaluation indicators RMSE, MAPE and R2 to evaluate our proposed method. The experimental results show that our prediction method has 4.5% improvements in RMSE evaluation criteria and 9.5% improvements in MAPE evaluation criteria compared with single GRU model (without attention mechanism) used. That is, the prediction model with the attention mechanism can improve the accuracy of resource prediction. At the same time, we also studied the influence of the window size on the experimental results, finding that the prediction results are more accurate as the window size increases.

关键词： Data centers Cloud computing Time series analysis neural networks Demand forecasting Predictive models Data models

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Synthetic Data for the Analysis of Archival Documents: Handwriting Determination

Synthetic Data for the Analysis of Archival Documents: Handw...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Christian Bartz Laurenz Seidel Duy-Hung Nguyen Joseph Bethge Haojin Yang Christoph Meinel Hasso Plattner Institute University of Potsdam Potsdam Germany

ISBN: (数字)9781728191089

ISBN: (纸本)9781728191096

Archives contain a wealth of information and are invaluable for historical research. Thanks to digitization, many archives are preserved in a digital format, making it easier to share and access documents from an archive. Handwriting and handwritten notes are commonly found in archives and contain a lot of information that can not be extracted by analyzing documents with optical Character Recognition (OCR) for printed text. In this paper, we present an approach for determining whether a scan of a document contains handwriting. As a preprocessing step, this approach can help to identify documents that need further analysis with a full recognition pipeline. Our method consists of a deep neural network that classifies whether a document contains handwriting. Our method is designed in such a way that we overcome the most significant challenge when working with archival data, which is the scarcity of annotated training data. To overcome this problem, we introduce a data generation method to successfully train our proposed deep neural network. Our experiments show that our model, trained on synthetic data, can achieve promising results on a real-world dataset from an art-historical archive.

关键词： Training neural networks Training data Tools Data models optical character recognition software Task analysis

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Automated Grading for Handwritten Answer Sheets using Convolutional neural networks 2

Automated Grading for Handwritten Answer Sheets using Convol...

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2nd International Conference on New Trends in computing Sciences (ICTCS)

作者： Shaikh, Eman Mohiuddin, Iman Manzoor, Ayisha Latif, Ghazanfar Mohammad, Nazeeruddin Prince Mohammad Bin Fahd Univ Dept Comp Engn Al Khobar Saudi Arabia Prince Mohammad Bin Fahd Univ Dept Comp Sci Al Khobar Saudi Arabia

ISBN: (纸本)9781728128825

optical Character Recognition (OCR) is an extensive research field in image processing and pattern recognition. Traditional character recognition methods cannot distinguish a character or a word from a scanned image. This paper proposes a system, which is to develop a method that uses a personal computer, a portable scanner and an application program that would automatically correct the handwritten answer sheets. For handwritten character recognition, the scanned images are fed through a machine learning classifier known as the Convolutional neural network (CNN). Two CNN models were proposed and trained on 250 images that were collected from students at Prince Mohammad Bin Fahd University. The proposed system will finally output the final score of the student by comparing each classified answer with the correct answer. The experimental results exhibited that the proposed system performed a high testing accuracy of 92.86%. The system can be used by the instructors in several educational institutions to automatically grade the handwritten answer sheets of students effectively.

关键词： Handwritten Numerals Recognition Convolutional neural network Handwritten Character Recognition Scanned document Segmentation

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Abnormal Event Detection Method in Surveillance Video Based on Temporal CNN and Sparse optical Flow 19

Abnormal Event Detection Method in Surveillance Video Based ...

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5th International Conference on computing and Data Engineering (ICCDE)

作者： Xia, Hongxia Li, Ting Liu, Wenxuan Zhong, Xian Yuan, JingLing Wuhan Univ Technol Coll Comp Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781450361248

Abnormal event detection in surveillance video is very important for intelligent analysis of video. Most anomalous event detection methods rely on complex feature extraction to represent motion and appearance. Convolutional neural network (CNN) is a powerful and efficient tool that can fully meet the needs of feature extraction. In this paper, tracking CNN features over time can effectively detect local anomalies. We detect local anomalies by combining temporal CNN models with optical flow. Based on the traditional optical flow method, the foreground mask is used to improve the efficiency of optical flow calculation and the robustness of the optical flow. The method was tested and evaluated on real-life surveillance video and benchmark datasets to verify its effectiveness.

关键词： abnormal event detection temporal CNN optical-flow feature extraction robustness

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An Augmented Treble Stream Deep neural network for Video Analysis

An Augmented Treble Stream Deep Neural Network for Video Ana...

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International Conference on Information Visualisation (IV)

作者： Chaolong Zhang Yuanping Xu Zhijie Xu Mei Gong Benjun Guo Dengguo Yao School of Computing & Engineering University of Huddersfield Huddersfield UK School of Software Engineering Chengdu University of Information Technology Chengdu China

ISBN: (数字)9781728191348

ISBN: (纸本)9781728191355

Video analysis for human action recognition is one of the most important research areas in pattern recognition and computer vision due to its wide applications. Deep learning-based approaches have been proven more effective than conventional feature engineering-based models. However, the performance is still unreliable when facing real-world application scenarios. Inspired by the Convolutional neural network (CNN) and Recurrent Long-Short Term Model (LSTM), this paper presents an augmented treble-stream deep neural network architecture that supports direct extraction of spatial-temporal features from video streams and their corresponding dense optical flows. This innovative approach assists effective detection of complex video event features that are annotated by rich event "appearance" and motion features. Substantially improved recognition accuracy is recorded during the experiments that are carried and benchmarked over public video event datasets, for example, UCF 101 and HMDB 51. Analytical evaluation approves the validity and effectiveness of the treble-stream neural network design.

关键词： Visualization Computational modeling neural networks Streaming media Feature extraction Pattern recognition optical flow

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Traffic Matrix Prediction for optical networks

Traffic Matrix Prediction for Optical Networks

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18th SBMO International Conference / 12th SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC)

作者： Mesquita, Leonardo A. Jacobina Assis, Karcius Day R. Univ Fed Bahia Elect Engn Dept Salvador BA Brazil

ISBN: (纸本)9781728130996

The rapidly increasing data demand from current Internet services, such as, cloud computing and high-quality video streaming is raising the pressure on network operators to provide reliable high-speed connections while keeping costs low. Without having to rely on the bandwidth expansion of optical wave-guides or modulation level efficiency, one of the ways to increase spectral efficiency of a network is by the optimal utilization of already existing resources. Since the resource management can be improved when the future traffic is known beforehand, in this work, we investigate the ability of recurrent neural networks (RNN) with long short-term memory (LSTM) to realize traffic matrix prediction based on previous traffic history of the computer network. Real traffic data has been used to train the RNN, from an anonymized dataset containing the traffic history of the Abilene and GEANT optical networks from the years 2004-2005 and test the viability of LSTM RNN models for proper traffic prediction. The proposed models achieved a mean square error of 0.0026 for the Abilene and 0.00058 for the GEANT network.

关键词： Traffic Matrix Prediction optical networks

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Experimental Quantum Generative Adversarial networks for Image Generation

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Physical Review Applied 2021年第2期16卷 024051-024051页

作者： He-Liang Huang Yuxuan Du Ming Gong Youwei Zhao Yulin Wu Chaoyue Wang Shaowei Li Futian Liang Jin Lin Yu Xu Rui Yang Tongliang Liu Min-Hsiu Hsieh Hui Deng Hao Rong Cheng-Zhi Peng Chao-Yang Lu Yu-Ao Chen Dacheng Tao Xiaobo Zhu Jian-Wei Pan Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics University of Science and Technology of China Hefei 230026 China Shanghai Branch CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Shanghai Research Center for Quantum Sciences Shanghai 201315 China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou Henan 450000 China School of Computer Science Faculty of Engineering University of Sydney Australia Hon Hai Research Institute Taipei 114 Taiwan

Quantum machine learning is expected to be one of the first practical applications of near-term quantum devices. Pioneer theoretical works suggest that quantum generative adversarial networks (GANs) may exhibit a potential exponential advantage over classical GANs, thus attracting widespread attention. However, it remains elusive whether quantum GANs implemented on near-term quantum devices can actually solve real-world learning tasks. Here, we devise a flexible quantum GAN scheme to narrow this knowledge gap. In principle, this scheme has the ability to complete image generation with high-dimensional features and could harness quantum superposition to train multiple examples in parallel. We experimentally achieve the learning and generating of real-world handwritten digit images on a superconducting quantum processor. Moreover, we utilize a gray-scale bar dataset to exhibit competitive performance between quantum GANs and the classical GANs based on multilayer perceptron and convolutional neural network architectures, respectively, benchmarked by the Fréchet distance score. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices and opens up an avenue for exploring quantum advantages in various GAN-related learning tasks.

关键词： Imaging & optical processing Machine learning Quantum algorithms Quantum computation Quantum information with solid state qubits Artificial neural networks Superconducting qubits

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Multidirectional wavelet-based fringe analysis for surface diagnosis by optical profilometry 2

Multidirectional wavelet-based fringe analysis for surface d...

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2nd International Conference on Intelligent computing in Data Sciences (ICDS)

作者： Bailich, Mohammed Bahich, Mustapha Amar, Said Bellach, Benaissa Rahmoun, Mohammed Mohamed First Univ EIT Dept ENSAO Oujda Morocco Moulay Ismail Univ Fac Sci Phys Dept Meknes Morocco Hassan II Univ Fac Sci Bern Msik Phys Dept Casablanca Morocco

Scientists, engineers, and manufacturers have a critical need for better techniques of diagnosis and quality control. The optical metrology uses the practical light and computer sciences, in order to perform simulation, design, computing and inspection for many scientific and industrial applications, such as optics, mechanics, aeronautics, electronics and many others. The fringe pattern analysis is a method to perform some operations on the optical images, in order to get an interferometric phase map and then to extract some useful information from it. In this paper, an exciting improvements of local fringe demodulation algorithm are presented, which are based on a new multidirectional wavelet. The numerical and experimental works show interesting gains compared to the other standard algorithms. Our approach executes as fast as the popular phase retrieval methods, but with substantially improved accuracy in demodulating noisy fringes. All of this comes without any pre-processing by filtering models. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： optical imaging Computer vision Image processing Multidirectional wavelets Phase recovery Fringe projection

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Mitigating the Adverse Effect of Compton Scatter on the Positioning of Gamma Interactions in Large Monolithic PET Detectors

Mitigating the Adverse Effect of Compton Scatter on the Posi...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Milan Decuyper Mariele Stockhoff Stefaan Vandenberghe Roel Van Holen Medical Image and Signal Processing Ghent University Ghent Belgium

In a typical monolithic PET detector setup, scintillation light is captured by an array of photodetectors from which the first interaction position is estimated. This is necessary to draw an accurate line of response. However, a majority of gamma rays undergo one or more Compton interactions before photoelectric interaction. For these events, it is more difficult to recover the first interaction position. In this study we use optical simulation data and neural networks to understand and mitigate the degrading effect of Compton scatter on positioning accuracy. A neural network was trained to predict the 3D first interaction position. Additionally, a network was trained to classify events into three classes: events scattered over a 2D distance smaller than 1 mm (class 0), between 1 mm and 5 mm (class 1) and further than 5 mm (class 2). Finally, a pipeline was designed where events are first classified with the scatter detection network and subsequently discarded (class 2) or positioned with separate networks trained for class 0 and 1. With one neural network trained for all events, an average 3D positioning error of 1.5 mm and FWHM of 0.49 mm is achieved. The scatter detection network achieves an overall accuracy of 65%. Through the combination of scatter detection and separate positioning neural networks for class 0 and 1, the average 3D positioning error reduces with 0.29 mm. Hence, we show that an improvement of about 20% can be achieved through the inclusion of Compton scatter detection. The ultimate goal is to apply the presented methodology to experimental data.

关键词： Three-dimensional displays neural networks Pipelines Detectors optical computing optical fiber networks Data models

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Detection of ships in inland river using high-resolution optical satellite imagery based on mixture of deformable part models

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JOURNAL OF PARALLEL AND DISTRIBUTED computing 2019年 132卷 1-7页

作者： Song, Pengfei Qi, Lei Qian, Xueming Lu, Xiaoqiang Chinese Acad Sci Key Lab Spectral Imaging Technol CAS Xian Inst Opt & Precis Mech Xian 710119 Shaanxi Peoples R China Xi An Jiao Tong Univ Xian 710049 Shaanxi Peoples R China CCCC Railway Consultants Grp Co Ltd Beijing 100088 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Ship detection using optical satellite imagery is of great significance in many applications such as traffic surveillance, pollution monitoring, etc. So far, a lot of ship detection methods have been developed for images covering open sea, offshore area and harbors. Compared to the ship detection in sea and offshore area, it is more difficult to detect ships in inland river due to several challenges. First of all, many ships in inland river are clustered together and hard to be separated from each other. Secondly, ships lying alongside the pier are very likely to be recognized as part of the pier. Thirdly, ships in inland river is usually smaller than those in the sea. A hierarchical method is proposed to detect the ships in inland river in this paper. The Regions of Interest (ROIs) are firstly extracted based on water land segmentation using multi-spectral information. Then two kinds of ship candidates are extracted based on the panchromatic band. The isolated ships are detected by analyzing the shape of connected components and the clustered ships are detected by using mixtures multi-scale Deformable Part models (DPM) and Histogram of Oriented Gradient (HOG). At last, a Back Propagation neural network (BPNN) is trained to classify the ship candidates using the multi-spectral bands. The experiments using Quickbird satellite images show that our approach is effective in ship detection and performs particularly well in separating the ships clustered together and staying alongside the pier. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Inland river Ship detection optical satellite imagery Deformable part model

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