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Retraction: Implementation Method CUSUM To Determine The Accident Prone Areas in Web Based (J. Phys.: Conf. Ser. 1114 012093)

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Rozzi Kesuma Dinata Dahlan Abdullah H Hartono Cut Ita Erliana Berliana Kusuma Riasti Akbar Iskandar Ifit Novita Sari Nurmawati Ricardo Freedom Nanuru Lusi Dwi Putri Rosida Tiurma Manurung S. Sriadhi A A Saleh T Astari Muammar Khaddafi Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics Universitas Sebelas Maret Surakarta. Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Departement of: Social Science Education Program Post Graduate Program Universitas Kanjuruhan Malang Malang Indonesia Faculty of industry Technique University of 45 Surabaya Indonesia Universitas Kristen Indonesia Maluku Ambon Indonesia Department of Civil Engineering Universitas Lancang Kuning Pekanbaru Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Elementary School Teacher Education STKIP Citra Bangsa Aceh Utara Aceh Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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Feature extraction of the Indonesian phonemes using discrete wavelet and wavelet packet transform

Feature extraction of the Indonesian phonemes using discrete...

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International Conference on Information Technology and Electrical engineering (ICITEE)

作者： Risanuri Hidayat Domy Kristomo Immer Togarma Dept. of Electrical Engineering and Information Technology Gadjah Mada University Yogyakarta Indonesia Study Program of Computer Engineering STMIK AKAKOM of Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9781509041404

The development of the pattern recognition techniques in voice recognition has rapidly increased. Many methods are used to create a system that is reliable and easily accessible so that the recognition accuracy is high. To find the pattern recognition system that is robust and reliable, proper feature extraction methods are needed. In this case, the proper method of the feature extraction is an efficient and effective method when it is used in recognizing specific features of data. This paper presents an effective and efficient method of the extracting features for speech processing. The focus of this work is to explore the feature extraction methods which are the most efficient and effective in recognizing Indonesian phonemes. The Feature extraction methods used and compared in the study are the Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT). The wavelet transform was done on the 2 nd level until 4 th level of decomposition. The comparison of the performance of both feature extraction methods are presented at the end of this section, with a statistical comparison, to draw a conclusion that the DWT methods have a better performance in terms of effectiveness and efficiency compared with the WPT method. The results of the study show that the effectiveness ratio is 60% versus 40% and the efficiency ratio is 57% versus 43%.

关键词： Feature extraction Discrete wavelet transforms Speech recognition Euclidean distance Wavelet packets

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Design of Decision Support System Determination of Inventory Inventory Using Single Exponential Smoothing Forecasting Method

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Muhammad Iqbal Dahlan Abdullah H Hartono Cut Ita Erliana S Sriadhi Nurmawati M Adhi Prasnowo Abdurrozzaq Hasibuan Akbar Iskandar Kundharu Saddhono Abdussakir Hastuti Musa Muhammad Ikhsan Setiawan Youmeni Sagala Rumiris Siahaan Eka Mayastika Sinaga Anggraini Syahputri Feby Milanie Henry Aspan Muammar Khaddafi Department of Computer System Universitas Pembangunan Panca Budi Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Department of Industrial Engineering Universitas Maarif Hasyim Latif Sidoarjo Indonesia Faculty of Engineering Universitas Islam Sumatera UtaraMedan-Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Department of Mathematics Universitas Muhammadiyah Parepare Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia STIE Bina Karya Tebing Tinggi Indonesia Universitas Pembangunan Panca Budi Medan Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia

The development of information and communication technology at this time has touched all sides of life. Almost no side of human life is not touched by this information technology, which is more synonymous with the world of computerization. No exception also with the computerization that is popular in the corporate environment and government agencies. If in ancient times everything was still done manually. However, at this time all leaders and management have realized the importance of this information technology products that can facilitate them in carrying out the day-to-day corporate functions. Decision Making System of goods purchasing is one of the determinants of the accumulation of goods. If the decision is taken right then stockpiling of goods can be avoided. In this thesis the authors designed a web-based system that helps Management in making the decision of the purchase amount of goods. the system was developed by using Microsoft Visual Studio 2010 and using SQL Server database processing applications. The end result of system design is expected to help minimize the accumulation of goods.

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Customizing bioinformatics graduate programs for diversified student backgrounds

Customizing bioinformatics graduate programs for diversified...

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Frontiers in Education (FIE) Conference

作者： Huanmei Wu Oindrila Raha Jian Zhang Bioinformatics Program SOIC Indianapolis IN USA School of Computer Engineering Shenzhen Polytechnic Shenzhen Guangdong P.R. China

ISBN: (纸本)9781509017911

For graduate programs, usually, the curricula are developed based on the generic learning outcomes of the matching undergraduate programs. However, most graduate programs have diversified students from various backgrounds with undergraduate degrees from an assortment of majors. Even for graduate students coming from the same undergraduate majors, they might come from different countries with differing undergraduate learning outcomes. It is even more challenging for graduate programs where there are no corresponding undergraduate programs. The graduate program curriculum should be customized based on the backgrounds of special student groups. This paper will describe the redesign of our professional Bioinformatics MS program for incoming domestic and international students with diverse backgrounds. The MS students in Bioinformatics program may have a previous degree in biotechnology, computer science, biology, computer engineering, electrical engineering, biomedical engineering, or other science and engineering fields. Thus, it is of great significance to categorize the program specific competencies and student learning outcomes from their previous study. It will help to customize the student specific plan of study in the Bioinformatics MS program so that they can complete the graduate study with the job-ready skills. For example, detailed studies have been carried out for the Bioinformatics related programs in India, where a substantial population of our Bioinformatics MS students comes from. In India, Bioinformatics is a growing subject and has emerged as an independent program from biomedical engineering and biotechnology. The Bioinformatics in India is taught at in all different degree levels: Bachelors, Masters, PhD, and certificates. Graduate students from India who are familiar with the Indian education system have helped to compare and contrast the various Bioinformatics related degree programs, including both Bioinformatics and Biotechnology degree programs

关键词： Bioinformatics Genomics Mathematics Computational biology Biotechnology Databases

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Retraction: Design of Decision Support System Determination of Inventory Inventory Using Single Exponential Smoothing Forecasting Method (J. Phys.: Conf. Ser. 1114 012082)

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Journal of Physics: Conference Series 2018年第1期1114卷

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Supporting differentiated resilience classes in multilayer networks

Supporting differentiated resilience classes in multilayer n...

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International Workshop on Design of Reliable Communication Networks (DRCN)

作者： Abdulaziz Alashaikh David Tipper Teresa Gomes Graduate Telecommunications and Networking Program University of Pittsburgh Pittsburgh PA Department of Electrical and Computer Engineering INESC Coimbra Coimbra Portugal

The concept of providing differentiated classes of resilient services over communications networks has received a lot of attention in the literature. A number of proposals tried to address the problem by provisioning different classes of services with different protection schemes. However, most of these works are applied to a single layer and lack cross layer coordination in multilayer scenarios. In addition, there is an increasing need for supporting services with very high resilience requirements over future communications networks. This, however, has to be done in a cost efficient manner and without over-engineering the network. In this paper, we introduce a novel approach that simplifies the whole design and achieves both high resilience and differentiation. Our approach, utilizes the spine concept idea of embedding a subnetwork at the physical layer with comparatively high availability link and node values [1]. This lays a foundation for differentiation between multiple classes of flows. Then, cross layer mapping and spine-aware routing are performed in a way that transfers this differentiation capability to the upper layer network and flows. We provide two joint routing-mapping optimization formulations and evaluate their performance in a multilayer scenario.

关键词： Routing Communication networks Nonhomogeneous media Resilience Reliability engineering Cross layer design

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Author Correction: A CCG expansion in ABCD3 causes oculopharyngodistal myopathy in individuals of European ancestry

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Nature communications 2024年第1期15卷 8955页

作者： Andrea Cortese Sarah J Beecroft Stefano Facchini Riccardo Curro Macarena Cabrera-Serrano Igor Stevanovski Sanjog R Chintalaphani Hasindu Gamaarachchi Ben Weisburd Chiara Folland Gavin Monahan Carolin K Scriba Lein Dofash Mridul Johari Bianca R Grosz Melina Ellis Liam G Fearnley Rick Tankard Justin Read Ashirwad Merve Natalia Dominik Elisa Vegezzi Ricardo P Schnekenberg Gorka Fernandez-Eulate Marion Masingue Diane Giovannini Martin B Delatycki Elsdon Storey Mac Gardner David J Amor Garth Nicholson Steve Vucic Robert D Henderson Thomas Robertson Jason Dyke Vicki Fabian Frank Mastaglia Mark R Davis Marina Kennerson Ros Quinlivan Simon Hammans Arianna Tucci Melanie Bahlo Catriona A McLean Nigel G Laing Tanya Stojkovic Henry Houlden Michael G Hanna Ira W Deveson Paul J Lockhart Phillipa J Lamont Michael C Fahey Enrico Bugiardini Gianina Ravenscroft Department of Neuromuscular Diseases UCL Queen Square Institute of Neurology London UK. andrea.cortese@ucl.ac.uk. Department of Brain and Behavioral Sciences University of Pavia Pavia Italy. andrea.cortese@ucl.ac.uk. Pawsey Supercomputing Research Centre Kensington WA Australia. Department of Neuromuscular Diseases UCL Queen Square Institute of Neurology London UK. Department of Brain and Behavioral Sciences University of Pavia Pavia Italy. Harry Perkins Institute of Medical Research Nedlands WA Australia. Department of Neurology and Instituto de Biomedicina de Sevilla Hospital Universitario Virgen del Rocío/Universidad de Sevilla/CSIC Sevilla 41013 Spain. Genomics and Inherited Disease Program Garvan Institute of Medical Research Sydney NSW Australia. Centre for Population Genomics Garvan Institute of Medical Research and Murdoch Children's Research Institute Sydney NSW Australia. School of Computer Science and Engineering University of New South Wales Sydney NSW Australia. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. Centre for Medical Research University of Western Australia Nedlands WA Australia. Northcott Neuroscience Laboratory ANZAC Research Institute Sydney NSW 2139 Australia. Faculty of Medicine and Health University of Sydney Sydney NSW 2006 Australia. Population Health and Immunity Division The Walter and Eliza Hall Institute of Medical Research 1 G Royal Parade Parkville VIC 3052 Australia. Department of Medical Biology The University of Melbourne 1G Royal Parade Parkville VIC3052 Australia. Department of Mathematics and Statistics Curtin University Perth WA Australia. Bruce Lefroy Centre Murdoch Children's Research Institute Parkville VIC Australia. Department of Paediatrics University of Melbourne Royal Children's Hospital Parkville VIC Australia. Department of Neuropathology National Hospital for Neurology and Neurosurgery London United Kingdom. IRCCS Mondino Foundation

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An Automated Approach for Identifying Geometric Distortions in Gamma Cameras

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MEDICAL PHYSICS 2016年第6期43卷 3773-3774页

作者： Mann, S. Nelson, J. Samei, E. Clinical Imaging Physics Group and Carl E. Ravin Advanced Imaging Laboratories Department of Radiology Duke University Medical Center Durham North Carolina 27705 and Medical Physics Graduate Program Duke University Durham North Carolina 27705 Clinical Imaging Physics Group and Carl E. Ravin Advanced Imaging Laboratories Department of Radiology Duke University Medical Center Durham North Carolina 27705 and Departments of Physics Electrical and Computer Engineering and Biomedical Engineering and Medical Physics Graduate Program Duke University Durham North Carolina 27705

Purpose: To develop a clinically-deployable, automated process for detecting artifacts in routine nuclear medicine (NM) quality assurance (QA) bar phantom images. Methods: An artifact detection algorithm was created to analyze bar phantom images as part of an ongoing QA program. A low noise, high resolution reference image was acquired from an x-ray of the bar phantom with a Philips Digital Diagnost system utilizing image stitching. NM bar images, acquired for 5 million counts over a 512×512 matrix, were registered to the template image by maximizing mutual information (MI). The MI index was used as an initial test for artifacts; low values indicate an overall presence of distortions regardless of their spatial location. Images with low MI scores were further analyzed for bar linearity, periodicity, alignment, and compression to locate differences with respect to the template. Findings from each test were spatially correlated and locations failing multiple tests were flagged as potential artifacts requiring additional visual analysis. The algorithm was initially deployed for GE Discovery 670 and Infinia Hawkeye gamma cameras. Results: The algorithm successfully identified clinically relevant artifacts from both systems previously unnoticed by technologists performing the QA. Average MI indices for artifact-free images are 0.55. Images with MI indices < 0.50 have shown 100% sensitivity and specificity for artifact detection when compared with a thorough visual analysis. Correlation of geometric tests confirms the ability to spatially locate the most likely image regions containing an artifact regardless of initial phantom orientation. Conclusion: The algorithm shows the potential to detect gamma camera artifacts that may be missed by routine technologist inspections. Detection and subsequent correction of artifacts ensures maximum image quality and may help to identify failing hardware before it impacts clinical workflow. Going forward, the algorithm is being deploye

关键词： Medical X-ray imaging Medical image artifacts Cameras Image analysis Nuclear medicine imaging Spatial analysis Quality assurance Medical image noise

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A simplified, lossless re-analysis of PAPER-64

arXiv

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arXiv 2019年

作者： Kolopanis, Matthew Jacobs, Daniel C. Cheng, Carina Parsons, Aaron R. Kohn, Saul A. Pober, Jonathan C. Aguirre, James E. Ali, Zaki S. Bernardi, Gianni Bradley, Richard F. Carilli, Chris L. DeBoer, David R. Dexter, Matthew R. Dillon, Joshua S. Kerrigan, Joshua Klima, Pat Liu, Adrian MacMahon, David H.E. Moore, David F. Thyagarajan, Nithyanandan Nunhokee, Chuneeta D. Walbrugh, William P. Walker, Andre School of Earth and Space Exploration Arizona State U. TempeAZ United States Astronomy Dept. U. California BerkeleyCA United States Radio Astronomy Lab. U. California BerkeleyCA United States Dept. of Physics and Astronomy U. Penn. PhiladelphiaPA United States Dept. of Physics Brown University Providence RI United States INAF-Istituto di Radioastronomia via Gobetti 101 Bologna40129 Italy Department of Physics and Electronics Rhodes University PO Box 94 Grahamstown6140 South Africa South African Radio Astronomy Observatory Black River Park 2 Fir Street Observatory Cape Town7925 South Africa Dept. of Electrical and Computer Engineering U. Virginia CharlottesvilleVA United States National Radio Astronomy Obs. CharlottesvilleVA United States Dept. of Astronomy U. Virginia CharlottesvilleVA United States National Radio Astronomy Observatory SocorroNM United States Cavendish Lab. Cambridge United Kingdom Department of Physics and McGill Space Institute McGill University MontrealQC Canada CIFAR Azrieli Global Scholar Gravity & the Extreme Universe Program Canadian Institute for Advanced Research 661 University Ave. Suite 505 TorontoONM5G 1M1 Canada

We present limits on the 21cm power spectrum from the Epoch of Reionization (EoR) using data from the 64 antenna configuration of the Donald C. Backer Precision Array for Probing the Epoch of Reionization (PAPER) analyzed through a power spectrum pipeline independent from previous PAPER analyses. Previously reported results from PAPER have been found to contain significant signal loss (Cheng et al. 2018). Several lossy steps from previous PAPER pipelines have not been included in this analysis, namely: delay-based foreground filtering, optimal fringe-rate filtering, and empirical covariance-based estimators. Steps which remain in common with previous analyses include redundant calibration and local sidereal time (LST) binning. The power spectra reported here are effectively the result of applying a linear Fourier transform analysis to the calibrated, LST binned data. This analysis also uses more data than previous publications, including the complete available redshift range of z ∼ 7.5 to 11. In previous PAPER analyses, many power spectrum measurements were found to be detections of noncosmological power at levels of significance ranging from two to hundreds of times the theoretical noise. Here, excess power is examined using redundancy between baselines and power spectrum jackknives. The upper limits we find on the 21cm power spectrum from reionization are (1500 mK)2, (1900 mK)2, (280 mK)2, (200 mK)2, (380 mK)2, (300 mK)2 at redshifts z = 10.87, 9.93, 8.68, 8.37, 8.13, and 7.48, respectively. For reasons described in (Cheng et al. 2018), these limits supersede all previous PAPER results (Ali et al. 2018). Copyright © 2019, The Authors. All rights reserved.

关键词： Power spectrum

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