2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)最新文献_第3页

Issue Based OCR Error Prediction in Video Streams 基于问题的视频流OCR误差预测

2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2020-09-23 DOI: 10.23919/spa50552.2020.9241245

Dirk Siegmund, Luís Rüger Sacco, Arjan Kuijper

{"title":"Issue Based OCR Error Prediction in Video Streams","authors":"Dirk Siegmund, Luís Rüger Sacco, Arjan Kuijper","doi":"10.23919/spa50552.2020.9241245","DOIUrl":"https://doi.org/10.23919/spa50552.2020.9241245","url":null,"abstract":"This paper increases the reliability of Optical Character Recognition (OCR) systems in natural scene by proposing a novel Image Quality Assessment (IQA) system. We propose to increase reliability based on the principle that OCR accuracy is a function of the quality of the input image. Detected text boxes are analyzed regarding their OCR score and different quality issues, such as blur, light and reflection effects. The novelty of our approach is to model IQA as a classification task, where one class represents high quality elements and each of the other classes represent a specific quality issue. We demonstrate how this methodology allows the training of IQA systems for complex quality metrics, even when no data labeled with the desired metric is available. Furthermore, a single IQA system outputs the quality score as well as the quality issues for a given image. We built on publicly available databases to generate 60k text boxes for each class and obtain 97,1% classification accuracy on a test set of 24k images. We conclude that the learnt quality metric is a valid indicator of common OCR errors by evaluating on the ICDAR 2003 Robust Word Recognition dataset.","PeriodicalId":157578,"journal":{"name":"2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124540816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SPA 2020 Index SPA 2020指数

2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2020-09-23 DOI: 10.23919/spa50552.2020.9241260

引用次数: 0

TIRUB: A Safety and Energy-Aware Scheduling Algorithm TIRUB:一种安全和能源感知调度算法

2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2020-09-23 DOI: 10.23919/spa50552.2020.9241280

Javier Hoffmann, M. Brandalero, M. Hübner

引用次数: 0

A New Method of Noncausal Identification of Time-varying Systems 时变系统的一种非因果辨识新方法

2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2020-09-23 DOI: 10.23919/spa50552.2020.9241263

M. Niedźwiecki, M. Ciołek, Artur Gańcza, Piotr Kaczmarek

引用次数: 0

Pneumonia detection in X-ray chest images based on convolutional neural networks and data augmentation methods 基于卷积神经网络和数据增强方法的x线胸片肺炎检测

2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2020-09-23 DOI: 10.23919/spa50552.2020.9241305

Jakub Garstka, M. Strzelecki

{"title":"Pneumonia detection in X-ray chest images based on convolutional neural networks and data augmentation methods","authors":"Jakub Garstka, M. Strzelecki","doi":"10.23919/spa50552.2020.9241305","DOIUrl":"https://doi.org/10.23919/spa50552.2020.9241305","url":null,"abstract":"Artificial intelligence is gaining in importance in our everyday lives. Convolutional neural networks (CNN) are a very promising and perspective technology in the area of medical images processing, where it could contribute to diagnostics becoming easier and more reliable. Accurate diagnosis is an important factor in the selection of proper and effective treatment. In this paper, a self-constructed convolutional neural network trained on a relatively small dataset for classification of lung X-ray images is presented. This CNN enables classification into one of three categories: healthy, those with bacterial pneumonia, and those with viral pneumonia. Such classification, that considers pneumonia distinction, is rather uncommon among scientific publications. Also, a comparative analysis of the degree of impact of data augmentation on the model’s performance and prevention of overfitting was performed. The obtained accuracy of the categorical classification has reached the level of 85% while the sensitivity was equal 0.95. Such results are promising for further work and improvement.","PeriodicalId":157578,"journal":{"name":"2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114401888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Surrogate Data for Deep Learning Architectures in Rehabilitative Edge Systems 修复边缘系统中深度学习架构的代理数据

2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2020-09-23 DOI: 10.23919/spa50552.2020.9241275

T. Lee, H. W. Chan, K. Leo, E. Chew, Ling Zhao, S. Sanei

{"title":"Surrogate Data for Deep Learning Architectures in Rehabilitative Edge Systems","authors":"T. Lee, H. W. Chan, K. Leo, E. Chew, Ling Zhao, S. Sanei","doi":"10.23919/spa50552.2020.9241275","DOIUrl":"https://doi.org/10.23919/spa50552.2020.9241275","url":null,"abstract":"The age of Big Data came about with the profitable mining of large amounts of data stored on the Cloud data servers accessible through the Internet. Much of these data were provided by users of extensive social networks. The availability of these data for training, coupled with advances in processing power have led to the surge in deep learning applications.While the Cloud provides wide scale data storage and analytic facilities, there is a move to perform data analyses closer to where data is gathered. Low resource but powerful processors have led the move toward these systems at the “edge“ rather than remotely.However, certain environments which can benefit from edge systems do not produce large volumes of data, such as in clinical applications. We augment such data using surrogate time series and compare various neural network architectures which would allow data analysis in rehabilitative edge systems.We show that a neural network using temporal data provides excellent classification results while using a fraction of the resources in an earlier work. Our novel framework shows how deep learning tools can be trained in a data scarce environment and then deployed in resource constrained edge systems.","PeriodicalId":157578,"journal":{"name":"2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131491599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Switched-capacitor finite impulse response rotator filter 开关电容有限脉冲响应旋转滤波器

2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2020-09-23 DOI: 10.23919/spa50552.2020.9241247

P. Pawlowski, R. Dlugosz, A. Dabrowski

引用次数: 1

SMART4ALL Open Calls, the right funding instrument to boost technology and business development in South, East and Central Europe SMART4ALL公开呼叫，正确的融资工具，以促进技术和业务发展在南欧，东欧和中欧

2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2020-09-23 DOI: 10.23919/spa50552.2020.9241258

Antonio Montalvo

{"title":"SMART4ALL Open Calls, the right funding instrument to boost technology and business development in South, East and Central Europe","authors":"Antonio Montalvo","doi":"10.23919/spa50552.2020.9241258","DOIUrl":"https://doi.org/10.23919/spa50552.2020.9241258","url":null,"abstract":"SMART4ALL is a four-year Innovation Action project funded under Horizon 2020 framework under call DT-ICT-01-2019: Smart Anything Everywhere – Area 2: Customized low energy computing powering CPS and the IoT. The target of the project is to establish a unique Pan European network of Digital Innovation Hubs that will not only support innovation and reveal business opportunities across South, Eastern and Central Europe, but will build capacity via the development of self-sustained, cross-border pathfinder application experiments. The project will provide a total funding of 2,2 Mio Euros via 9 open calls and it will support 88 cross-border pathfinder application experiments from European consortia. Each experiment will get funding up to €80,000 and it will be supported with novel coaching services from world lead experts in ethics, technology, funding and business development. This presentation will describe in detail the Open Calls, including their value proposition, the processes for eligibility (who can apply), evaluation and selection, the financial support and how to submit an application.","PeriodicalId":157578,"journal":{"name":"2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132392508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geospatial Coverage and Signal Quality Measurements of Terrestrial DAB+ Network in Northern Poland 波兰北部地面DAB+网络的地理空间覆盖和信号质量测量

2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2020-09-23 DOI: 10.23919/spa50552.2020.9241292

Przemysław Falkowski-Gilski, M. Kulawiak, M. Kulawiak

引用次数: 0

A Survey on Machine Learning Approaches to ECG Processing 心电处理中的机器学习方法综述

2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2020-09-23 DOI: 10.23919/spa50552.2020.9241283

Javier Hoffmann, Safdar Mahmood, Priscile Suawa Fogou, N. George, Solaiman Raha, Sabur Safi, K. Schmailzl, M. Brandalero, M. Hübner

{"title":"A Survey on Machine Learning Approaches to ECG Processing","authors":"Javier Hoffmann, Safdar Mahmood, Priscile Suawa Fogou, N. George, Solaiman Raha, Sabur Safi, K. Schmailzl, M. Brandalero, M. Hübner","doi":"10.23919/spa50552.2020.9241283","DOIUrl":"https://doi.org/10.23919/spa50552.2020.9241283","url":null,"abstract":"Electrocardiogram (ECG) signals convey a substantial amount of information that can be used for detecting and predicting the occurrence of several diseases and conditions. Approaches to ECG analysis were traditionally based on Signal Processing (SP), but several recent work have managed to substantially increase the quality of the analyses by using Machine Learning (ML) techniques. Still, while ML offers the potential to extract a substantially more information and predict diseases with better accuracy, it is also intrinsically more computationally expensive. Given the importance of this field and recent advances, we present a survey on ML approaches to ECG processing, focusing on particular diseases and conditions that can be detected and the different algorithms used for that. Moreover, we also discuss recent implementations of such algorithms on low-power wearable devices. We identify an opportunity for the development of novel embedded architectures that could enable the continuous monitoring of ECG signals and identify emerging technologies that could help in paving the way towards that.","PeriodicalId":157578,"journal":{"name":"2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"12 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134562475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9