2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)最新文献

Semi-automated estimation of the local flood depth on SAR images 基于SAR图像的局部洪水深度半自动估计

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) Pub Date : 2017-10-12 DOI: 10.1109/RTSI.2017.8065898

Abdelhakim Benoudjit, R. Guida

{"title":"Semi-automated estimation of the local flood depth on SAR images","authors":"Abdelhakim Benoudjit, R. Guida","doi":"10.1109/RTSI.2017.8065898","DOIUrl":"https://doi.org/10.1109/RTSI.2017.8065898","url":null,"abstract":"In the context of a flooding, a clear cloud-free SAR (Synthetic Aperture Radar) image proves mainly useful to retrieve flood features that can provide an extensive understanding of the disaster. Among these features, extremely important is the water depth on which this paper will focus by looking for a semi-automated algorithm for its estimation in the neighborhood of a given building from a pair of SAR images. In this study, two SAR images acquired during dry and flooded conditions are necessary, as well as a DSM (Digital Surface Model) to give an a priori knowledge of the height of the building and its footprint. The whole process is divided into two main parts: First, an extraction of the building's double-bounce contribution using Genetic Algorithms, then the computation of the inundated building's height, to eventually evaluate the water level locally in the neighborhood of this building. Thanks to the semi-automation of the double-reflection line retrieval, the execution time of the whole process was reduced from a few minutes (time to manually delineate the double-bounce line) to a few seconds, while keeping an error in the estimated flood depth in the order of a few decimeters (35cm on average).","PeriodicalId":173474,"journal":{"name":"2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130520479","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: 1

Comparison of ship detectability between TerraSAR-X and Sentinel-1 TerraSAR-X与Sentinel-1舰船可探测性比较

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) Pub Date : 2017-10-12 DOI: 10.1109/RTSI.2017.8065913

D. Velotto, B. Tings, Carlos Bentes

{"title":"Comparison of ship detectability between TerraSAR-X and Sentinel-1","authors":"D. Velotto, B. Tings, Carlos Bentes","doi":"10.1109/RTSI.2017.8065913","DOIUrl":"https://doi.org/10.1109/RTSI.2017.8065913","url":null,"abstract":"In this paper, the detectability of ship signatures in Synthetic Aperture Radar (SAR) imagery acquired by the TerraSAR-X/TanDEM-X and Sentinel-1 is compared. The comparison takes into account different sensors acquisition parameters and environmental conditions on a large variety of ship size and types. In the first step, ocean targets are detected using the Near Real Time (NRT)-optimized Constant-False-Alarm-Rate (CFAR) algorithm. The optimizations include the ocean/land and false targets discrimination. In the second step, all detected targets are automatically matched in space and time with the recorded Automatic Identification System (AIS) messages. A manual cross-check is performed at the end of the assignments to have a clean SAR ship signature database. Additionally, the local wind field is retrieved from the SAR backscatter of the ocean surface surrounding the detected ships, by applying the Geophysical Model Functions (GMF) inversion XMOD2 for X-band data and CMOD5 for C-band data. Similarly, the local sea state conditions are calculated by the XWAVE and CWAVE empirical model functions. The final detectability model takes into account all SAR-based information, i.e. wind speed and sea state, as well as relevant SAR parameters, e.g. incidence angle. The overall probability of detection are derived for three ship size categories, i.e. small, medium and large, adopting an L2-regularized Logistic Regression classifier trained on detected and nondetected ship samples.","PeriodicalId":173474,"journal":{"name":"2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134560810","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

Intelligent scheduling for in-car notifications 智能调度车内通知

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) Pub Date : 2017-10-11 DOI: 10.1109/RTSI.2017.8065957

Jonathan Wright, Q. Stafford-Fraser, M. Mahmoud, P. Robinson, Eduardo Dias, L. Skrypchuk

引用次数: 3

An IoT-aware system for elderly monitoring 用于老年人监控的物联网感知系统

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) Pub Date : 2017-09-01 DOI: 10.1109/RTSI.2017.8065906

L. Mainetti, V. Mighali, L. Patrono, Piercosimo Rametta, M. Stefanizzi

引用次数: 14

An assistive mobile system supporting blind and visual impaired people when are outdoor 一种辅助移动系统，当盲人和视障人士在户外时提供支持

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) Pub Date : 2017-09-01 DOI: 10.1109/RTSI.2017.8065886

T. V. Mataro, F. Masulli, S. Rovetta, Alberto Cabri, C. Traverso, E. Capris, S. Torretta

引用次数: 9

The role of materials and products characterization in the additive manufacturing industry 材料和产品表征在增材制造行业中的作用

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) Pub Date : 2017-09-01 DOI: 10.1109/RTSI.2017.8065965

D. Caputo, P. Aprea, N. Gargiulo, B. Liguori

引用次数: 4

How new laser development can help laser shock peening penetration to widen industrial applications? 新的激光发展如何帮助激光冲击强化渗透扩大工业应用?

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) Pub Date : 2017-09-01 DOI: 10.1109/RTSI.2017.8065959

D. Rostohar, Jörg Koerner, R. Boedefeld, A. Lucianetti, T. Mocek

引用次数: 3

Predicting clinical outcomes in patients with traumatic bleeding: A secondary analysis of the CRASH-2 dataset 预测外伤性出血患者的临床结果:对CRASH-2数据集的二次分析

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) Pub Date : 2017-09-01 DOI: 10.1109/RTSI.2017.8065901

Gianluca Roveda, Moses A. Koledoye, Enea Parimbelli, J. Holmes

{"title":"Predicting clinical outcomes in patients with traumatic bleeding: A secondary analysis of the CRASH-2 dataset","authors":"Gianluca Roveda, Moses A. Koledoye, Enea Parimbelli, J. Holmes","doi":"10.1109/RTSI.2017.8065901","DOIUrl":"https://doi.org/10.1109/RTSI.2017.8065901","url":null,"abstract":"Severe bleeding is one of the main causes of death in hospitals for patients with trauma. Early treatment using tranexamic acid, timely transfer to the intensive care unit and prompt surgical interventions are key factors determining short-term survival and clinical outcomes. The aim of this research is to apply machine learning methods to predict clinical outcomes for patients with severe bleeding from trauma, in order to inform clinical decision making in the hospital setting. The presented study consists in a secondary analysis of the CRASH-2 (Clinical Randomisation of an Antifibrinolytic in Significant Haemorrhage) study data. This dataset contains 20,207 patient entry and outcome data. Machine learning methods have been used to create prognostic models for the prediction of outcomes such as death, significant head injury, need for a surgical operation and admission into the ICU. Results show that patients admitted in the ICU have a higher mortality rate as compared to other patients, highlighting the need for a more detailed analysis of the causes of death in the ICU. Another meaningful result is that a significant head injury can be predicted from a patient's hospital entry data, which may have a significant impact on early treatment decisions and, eventually, improve outcomes.","PeriodicalId":173474,"journal":{"name":"2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114430269","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

Deep learning for virtual metrology: Modeling with optical emission spectroscopy data 虚拟计量的深度学习:光学发射光谱数据建模

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) Pub Date : 2017-09-01 DOI: 10.1109/RTSI.2017.8065905

M. Terzi, Chiara Masiero, A. Beghi, Marco Maggipinto, Gian Antonio Susto

{"title":"Deep learning for virtual metrology: Modeling with optical emission spectroscopy data","authors":"M. Terzi, Chiara Masiero, A. Beghi, Marco Maggipinto, Gian Antonio Susto","doi":"10.1109/RTSI.2017.8065905","DOIUrl":"https://doi.org/10.1109/RTSI.2017.8065905","url":null,"abstract":"Virtual Metrology is one of the most prominent Advanced Process Control applications in Semiconductor Manufacturing. The goal of Virtual Metrology is to provide estimations of quantities that are important for production and to assess process quality, but are costly or impossible to be measured. Virtual Metrology solutions are based on Machine Learning approaches. The bottleneck of developing Virtual Metrology solutions is generally the feature extraction phase that can be time-consuming, and can deeply affect the estimation performance. In particular, in presence of data with additional dimensions, such as time, feature extraction is typically performed by means of heuristic approaches that may pick features with poor predictive capabilities. In this work, we propose the usage of modern Deep Learning approaches to bypass manual feature extraction and to provide high-performance automatic Virtual Metrology modules. The proposed methodology is tested on a real industrial dataset related to Etching. The dataset at hand contains Optical Emission Spectroscopy data and it is paradigmatic of the feature extraction problem under examination.","PeriodicalId":173474,"journal":{"name":"2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129525891","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: 11

Visual evaluation of health warning cues in anti smoking PSAs images 反吸烟公益广告图像中健康警示线索的视觉评价

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) Pub Date : 2017-09-01 DOI: 10.1109/RTSI.2017.8065948

D. Rossi, E. Modica, A. Maglione, I. Venuti, Ambra Brizi, F. Babiloni, G. Cartocci

{"title":"Visual evaluation of health warning cues in anti smoking PSAs images","authors":"D. Rossi, E. Modica, A. Maglione, I. Venuti, Ambra Brizi, F. Babiloni, G. Cartocci","doi":"10.1109/RTSI.2017.8065948","DOIUrl":"https://doi.org/10.1109/RTSI.2017.8065948","url":null,"abstract":"Visual stimuli are a significant part of our daily experience, it is an open issue which one are able to catch our attention or interest. In the present study, we aimed to better understand how an anti-smoking Public Service Announcement (PSA) can gain this focus of attention by the mean of an eye tracker device during the vision of a series of PSAs. We propose here a non-dimensional index of Visual Interest (VI) that can describe the visual pattern of 40 subject (divided based on their age), highlighting if an Area Of Interest (AOI) is salient or not in the stimuli proposed. We selected 3 PSAs images that contain a health warning related to smoking habits (such as a tracheostomy, or guy in the act of coughing). The results of the analysis showed a significant enhancement of VI in the AOI with a strong health warning message (tracheostomy), irrespective of the dimension of the AOI taken into account (p < 0.01); in particular, this hold true for the adult sample population. The VI index proposed here, therefore represents a good estimator of the importance of an AOI inside an image.","PeriodicalId":173474,"journal":{"name":"2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124617593","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: 5