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Eye tracking: empirical foundations for a minimal reporting guideline

Behavior research methods 2023年第1期55卷 364-416页

作者： Kenneth Holmqvist Saga Lee Örbom Ignace T C Hooge Diederick C Niehorster Robert G Alexander Richard Andersson Jeroen S Benjamins Pieter Blignaut Anne-Marie Brouwer Lewis L Chuang Kirsten A Dalrymple Denis Drieghe Matt J Dunn Ulrich Ettinger Susann Fiedler Tom Foulsham Jos N van der Geest Dan Witzner Hansen Samuel B Hutton Enkelejda Kasneci Alan Kingstone Paul C Knox Ellen M Kok Helena Lee Joy Yeonjoo Lee Jukka M Leppänen Stephen Macknik Päivi Majaranta Susana Martinez-Conde Antje Nuthmann Marcus Nyström Jacob L Orquin Jorge Otero-Millan Soon Young Park Stanislav Popelka Frank Proudlock Frank Renkewitz Austin Roorda Michael Schulte-Mecklenbeck Bonita Sharif Frederick Shic Mark Shovman Mervyn G Thomas Ward Venrooij Raimondas Zemblys Roy S Hessels Department of Psychology Nicolaus Copernicus University Torun Poland. kenneth.holmqvist@ur.de. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. Experimental Psychology Helmholtz Institute Utrecht University Utrecht The Netherlands. Lund University Humanities Lab and Department of Psychology Lund University Lund Sweden. Department of Ophthalmology SUNY Downstate Health Sciences University Brooklyn NY USA. Tobii Pro AB Danderyd Sweden. Social Health and Organizational Psychology Utrecht University Utrecht The Netherlands. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. TNO Soesterberg The Netherlands. Department of Ergonomics Leibniz Institute for Working Environments and Human Factors Dortmund Germany. Institute of Informatics LMU Munich Munich Germany. Institute of Child Development University of Minnesota Minneapolis USA. School of Psychology University of Southampton Southampton UK. School of Optometry and Vision Sciences Cardiff University Cardiff UK. Department of Psychology University of Bonn Bonn Germany. Vienna University of Economics and Business Vienna Austria. Department of Psychology University of Essex Essex UK. Department of Neuroscience Erasmus MC Rotterdam The Netherlands. Machine Learning Group Department of Computer Science IT University of Copenhagen Copenhagen Denmark. SR Research Ltd Ottawa Canada. Human-Computer Interaction University of Tübingen Tübingen Germany. University of British Columbia Columbia Canada. Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool UK. Department of Education and Pedagogy Division Education Faculty of Social and Behavioral Sciences Ut

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

关键词： Data quality Eye movements Eye tracking Replicability Reporting guidelines Reporting practices Reporting standards Reproducibility

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Electron Acceleration in Carbon Nanotubes

arXiv

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arXiv 2025年

作者： Bonţoiu, Cristian Bonatto, Alexandre Apsimon, Öznur Bandiera, Laura Cavoto, Gianluca Drebot, Illya Gatti, Giancarlo Giner-Navarro, Jorge Lei, Bifeng Martín-Luna, Pablo Rago, Ilaria Pérez, Juan Rodríguez Nunes, Bruno Silveira Sytov, Alexei Valagiannopoulos, Costas Welsch, Carsten P. Xia, Guoxing Resta-López, Javier Department of Physics University of Liverpool Oxford Street LiverpoolL69 7ZE United Kingdom Graduate Program in Information Technology and Healthcare Management the Beam Physics Group Federal University of Health Sciences of Porto Alegre RS Porto Alegre90050-170 Brazil Department of Physics and Astronomy University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Cockroft Institute Keckwick Lane Daresbury WarringtonWA4 4AD United Kingdom INFN Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Dipartimento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 2 Roma00185 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Rome00185 Italy INFN Sezione di Milano Via G. Celoria 16 Milano20133 Italy Centro de Láseres Pulsados Building M5 Science Park Calle Adaja 8 Salamanca Villamayor37185 Spain Universidad de Valencia Paterna46071 Spain Universidad de Valencia CSIC Paterna46980 Spain Nuclear and Energy Research Institute IPEN–CNEN SP São Paulo Brazil National Technical University of Athens School of Electrical and Computer Engineering Zografou157 73 Greece Departamento de Física Aplicada y Electromagnetismo Universidad de Valencia Burjassot46100 Spain

Laser wakefield acceleration (LWFA) may achieve TeV/m gradients using high-density solid-state plasmas as accelerating media. However, the application of bulk solid materials requires attosecond laser pulses, such as X-ray lasers, to drive wakefields at these high densities. Additionally, the short wakefield wavelengths associated with solid-state plasmas greatly limit the accelerating length. An alternative approach employs 2D carbon-based nanomaterials, like graphene or carbon nanotubes (CNTs), configured into structured targets. These nanostructures are designed with voids or low-density regions to effectively reduce the overall plasma density. This reduction enables the use of longer-wavelength lasers and also extends the plasma wavelength and the acceleration length. In this study, we present, to our knowledge, the first numerical demonstration of electron acceleration via self-injection into a wakefield bubble driven by an infrared laser pulse in structured CNT targets, similar to the behavior observed in gaseous plasmas for LWFA in the nonlinear (or bubble) regime. Using the PIConGPU code, bundles of CNTs are modeled in a 3D geometry as 25 nm-thick carbon tubes with an initial density of 1022 cm−3. The carbon plasma is ionized by a three-cycle, 800 nm wavelength laser pulse with a peak intensity of 1021 W cm−2, achieving an effective plasma density of 1020 cm−3. The same laser also drives the wakefield bubble, responsible for the electron self-injection and acceleration. Simulation results indicate that fs-long electron bunches with hundreds of pC charge can be self-injected and accelerated at gradients exceeding 1 TeV/m. Both charge and accelerating gradient figures are unprecedented when compared with LWFA in gaseous plasma. © 2025, CC BY-NC-ND.

关键词： X ray lasers

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SPA-SSD: Exploit Heterogeneity and Parallelism of 3D SLC-TLC Hybrid SSD to Improve Write Performance

SPA-SSD: Exploit Heterogeneity and Parallelism of 3D SLC-TLC...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Wenhui Zhang Qiang Cao Hong Jiang Jie Yao Yuanyuan Dong Puyuan Yang Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering University of Texas at Arlington Arlington Texas School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Alibaba Group. Hangzhou China

To address the write performance problem suffered by MLC/TLC flash, researchers have proposed hybrid SSD that aims to combine the strengths of SLC flash, used as the write-buffer zone for its superior write performance, and MLC/TLC flash, as the capacity zone for its high storage density. While leveraging SLC as a physical write-buffer zone is proven effective in traditional 2D hybrid SSDs, how to effectively incorporate SLC into a 3D-stacked TLC to form a hybrid SSD has not been studied to the best of our knowledge. Yet this is a timely and important performance issue for 3D-stacked TLC given its one-shot programming scheme that results in much worse write performance than the programming scheme in 2D TLC where pages are associated with different bits of a cell and programmed in sequence separately. We believe that naively adopting the two-physical-zone approach to 3D hybrid SSD will miss a great opportunity for performance optimization because it ignores the inherent four-level parallelism (channel/chip/die/plane) of the flash chip array. To this end, we propose in this paper an SLC and Parallelism Aware hybrid SSD (SPA-SSD) to take full advantages of SLC's superior write performance, the internal multi-level parallelism of SSD, and the high storage density of 3D-stacked TLC flash. Two novel techniques enable SPA-SSD to be highly effective: (1) Type-Parallelism Joint Page Allocation (TPJ-PA), which allocates pages for write transactions according to not only available SLC pages but also parallelism to maximize resource utilization within the hybrid SSD, and (2) Queue-length and Parallelism Constrained Data Migration (QPC-DM), which triggers data migration without degrading user write performance by analyzing the device queue length and available flash resources. To evaluate performance of SPA-SSD, a hybrid SSD simulator, called HybridSim, is developed based on MQSim. Experimental results on HybridSim show that TPJ-PA improves write throughput by 60%, while QPC-DM

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Concurrency control protocol for parallel B-tree structures that improves the efficiency of request transfers and SMOs within a node

Concurrency control protocol for parallel B-tree structures ...

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作者： Yoshihara, Tomohiro Kobayashi, Dai Yokota, Haruo Research and Development Group Hitachi Ltd. Yokohama-shi244–0817 Japan Department of Computer Science Graduate School of Information Science and Engineering Tokyo Institute of Technology Tokyo152–8552 Japan Department of Computer Science School of Computing Tokyo Institute of Technology Tokyo152–8552 Japan

Many concurrency control protocols for B-trees use latch-coupling because its execution is efficient on a single machine. Some studies have indicated that latch-coupling may involve a performance bottleneck when using multicore processors in a shared-everything environment, but no studies have considered the possible performance bottleneck caused by sending messages between processing elements (PEs) in shared-nothing environments. We propose two new concurrency control protocols, "LCFB" and "LCFB-link", which require no latch-coupling in optimistic processes. The LCFB-link also innovates B-link approach within each PE to reduce the cost of modifications in the PE, as a solution to the difficulty of consistency management for the side pointers in a parallel B-tree. The B-link algorithm is well known as a protocol without latch-coupling, but Blink has the difficulty of guaranteeing the consistency of the side pointers in a parallel B-tree. Experimental results in various environments indicated that the system throughput of the proposed protocols was always superior to those of the conventional protocols, particularly in large-scale configurations, and using LCFB-link was effective for higher update ratios. In addition, to mitigate access skew, data should migrate between PEs. We have demonstrated that our protocols always improve the system throughput and are effective as concurrency controls for data migration. Copyright © 2018 The Institute of Electronics, information and Communication Engineers

关键词： Concurrency control

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GIScience in the Era of Artificial Intelligence: A research Agenda Towards Autonomous GIS

arXiv

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arXiv 2025年

作者： Li, Zhenlong Ning, Huan Gao, Song Janowicz, Krzysztof Li, Wenwen Arundel, Samantha T. Yang, Chaowei Bhaduri, Budhendra Wang, Shaowen Zhu, A. Xing Gahegan, Mark Shekhar, Shashi Ye, Xinyue McKenzie, Grant Cervone, Guido Hodgson, Michael E. Geoinformation and Big Data Research Lab Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of Wisconsin – Madison WI United States STKO Lab Department of Geography and Regional Research University of Vienna Vienna Austria Spatial Analysis Research Center School of Geographical Sciences and Urban Planning Arizona State University AZ United States Center of Excellence for Geospatial Information Science U.S. Geological Survey VA United States NSF Spatiotemporal Innovation Center Department of Geography & Geoinformation Science George Mason University VA United States TN United States CyberGIS Center for Advanced Digital and Spatial Studies Department of Geography and Geographic Information Science University of Illinois Urbana-Champaign IL United States School of Computer Science University of Auckland New Zealand Department of Computer Science & Engineering University of Minnesota MN United States Department of Landscape Architecture & Urban Planning Center for Geospatial Sciences Applications & Technology Texas A&M University TX United States Platial Analysis Lab Department of Geography McGill University Quebec Canada Institute for Computational and Data Sciences Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of South Carolina SC United States

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five autonomous levels, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modeling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future. © 2025, CC BY.

关键词： Image retrieval

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Unsupervised Keyword Extraction Method Based on Chinese Patent Clustering

Unsupervised Keyword Extraction Method Based on Chinese Pate...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Yuxin Xie Xuegang Hu Yuhong Zhang Shi Li Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Intelligent Connected Vehicle Research Institute (JAC Technical Center) Anhui Jianghuai Automobile Group Corp.Ltd Hefei China

Recently, patent data analysis has attracted a lot of attention, and patent keyword extraction is a hot problem. Most existing methods for patent keyword extraction are based on the frequency of words without semantic information. In this paper, we propose an Unsupervised Keyword Extraction Method (UKEM) based on Chinese patent clustering. More specifically, we use a Skip-gram model to train word embeddings based on a Chinese patent corpus. Then each patent is represented as a vector called patent vector. These patent vectors are clustered to obtain several cluster centroids. Next, the distance between each word vector in patent abstract and cluster centroid is computed to indicate the semantic importance of this word. The experimental results on several Chinese patent datasets show that the performance of our proposed method is better than several competitive methods.

关键词： Patents Semantics Data mining Knowledge engineering Neural networks Big Data computer science

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VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

arXiv

引用

arXiv 2020年

作者： Sekuboyina, Anjany Kumar Husseini, Malek E. Bayat, Amirhossein Löffler, Maximilian Liebl, Hans Li, Hongwei Tetteh, Giles Kukačka, Jan Payer, Christian Štern, Darko Urschler, Martin Chen, Maodong Cheng, Dalong Lessmann, Nikolas Hu, Yujin Wang, Tianfu Yang, Dong Xu, Daguang Ambellan, Felix Amiranashvili, Tamaz Ehlke, Moritz Lamecker, Hans Lehnert, Sebastian Lirio, Marilia de Olaguer, Nicolás Pérez Ramm, Heiko Sahu, Manish Tack, Alexander Zachow, Stefan Jiang, Tao Ma, Xinjun Angerman, Christoph Wang, Xin Brown, Kevin Kirszenberg, Alexandre Puybareau, Élodie Chen, Di Bai, Yiwei Rapazzo, Brandon H. Yeah, Timyoas Zhang, Amber Xu, Shangliang Hou, Feng He, Zhiqiang Zeng, Chan Xiangshang, Zheng Liming, Xu Netherton, Tucker J. Mumme, Raymond P. Court, Laurence E. Huang, Zixun He, Chenhang Wang, Li-Wen Ling, Sai Ho Huynh, Lê Duy Boutry, Nicolas Jakubicek, Roman Chmelik, Jiri Mulay, Supriti Sivaprakasam, Mohanasankar Paetzold, Johannes C. Shit, Suprosanna Ezhov, Ivan Wiestler, Benedikt Glocker, Ben Valentinitsch, Alexander Rempfler, Markus Menze, Björn H. Kirschke, Jan S. Department of Informatics Technical University of Munich Germany Munich School of BioEngineering Technical University of Munich Germany Department of Neuroradiology Klinikum Rechts der Isar Germany Department for Quantitative Biomedicine University of Zurich Switzerland Friedrich Miescher Institute for Biomedical Engineering Switzerland Institute of Biological and Medical Imaging Helmholtz Zentrum München Germany Department of Computing Imperial College London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Austria Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria School of Computer Science The University of Auckland New Zealand Computer Vision Group iFLYTEK Research South China China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Shenzhen Research Institute of Big Data China School of Biomedical Engineering Health Science Center Shenzhen University China NVIDIA Corporation United States Zuse Institute Berlin Germany 1000shapes GmbH Berlin Germany Damo Academy Alibaba Group China Department of Mathematics University of Innsbruck Austria Department of Electronic Engineering Fudan University China New York University United States France Deep Reasoning AI Inc United States Technical University of Munich Germany East China Normal University China Chinese Academy of Sciences China Lenovo Group China Ping An Technologies China College of Computer Science and Technology Zhejiang University China Real Doctor AI Research Centre Zhejiang University China The University of Texas MD Anderson Cancer Center United States Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Department of Computing The Hong Kong Polytechnic University China The School of Biomedical Engineering University of Technology Sydney Australia Department of Biomedical Engineering Brno University of Technology Czech Republic India

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://***/nqjyw/, https://***/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://***/anjany/verse. © 2020, CC BY-NC-ND.

关键词： computerized tomography

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Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

arXiv

引用

arXiv 2022年

作者： Sudre, Carole H. Van Wijnen, Kimberlin Dubost, Florian Adams, Hieab Atkinson, David Barkhof, Frederik Birhanu, Mahlet A. Bron, Esther E. Camarasa, Robin Chaturvedi, Nish Chen, Yuan Chen, Zihao Chen, Shuai Dou, Qi Evans, Tavia Ezhov, Ivan Gao, Haojun Sanguesa, Marta Girones Gispert, Juan Domingo Anson, Beatriz Gomez Hughes, Alun D. Ikram, M. Arfan Ingala, Silvia Jaeger, H. Rolf Kofler, Florian Kuijf, Hugo J. Kutnar, Denis Li, Bo Lorenzini, Luigi Menze, Bjoern Molinuevo, Jose Luis Pan, Yiwei Puybareau, Elodie Rehwald, Rafael Su, Ruisheng Shi, Pengcheng Tillin, Therese Tochon, Guillaume Urien, Hélène van der Velden, Bas H.M. van der Velpen, Isabelle F. Wiestler, Benedikt Wolters, Frank J. Yilmaz, Pinar de Groot, Marius Vernooij, Meike W. de Bruijne, Marleen MRC Unit for Lifelong Health and Ageing at UCL University College London London United Kingdom Centre for Medical Image Computing University College London London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Clinical Genetics and Radiology Erasmus MC Rotterdam Netherlands Centre for Medical Imaging University College London London United Kingdom Department of Radiology and Nuclear Medicine Amsterdam University Medical Centre Amsterdam Netherlands Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Radiology University of Massachusetts Medical School Worcester United States School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Chinese University of Hong Kong China Department of Informatics Technische Universitat Munchen Munich Germany Department of Radiology Zhejiang University Hangzhou China Image Sciences Institute University Medical Center Utrecht Utrecht Netherlands Pasqual Maragall Foundation Barcelona Spain Department of Radiology Hospital San Pau i santa Creu Barcelona Spain Department of Epidemiology Erasmus MC Rotterdam Netherlands Institute of Neurology University College London London United Kingdom Department of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen Shenzhen China LRDE EPITA Paris France ISEP-Institut Supérieur d’Électronique de Paris Issy-les-Moulineaux France Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Departme

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level. © 2022, CC BY-SA.

关键词： Brain

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Design of a Compact Meander-Based Antenna for Search and Rescue using PDMS

Design of a Compact Meander-Based Antenna for Search and Res...

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IEEE Conference on Antenna Measurements & Applications (CAMA)

作者： Rais Ahmad Sheikh Ping Jack Soh Abdalla Abdurrahman Ali Azremi Abdullah Al-Hadi Mohamad Kamal A. Rahim Hidayath Mirza Toufiq Md Hossain Advanced Communication Engineering (ACE) CoE School of Computer and Communication Engineering Universiti Malaysia Perlis Arau Perlis Malaysia Advanced Microwave and RF Research Group School of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia College of Engineering Jazan University Jazan Kingdom of Saudi Arabia School of Engineering and Information Technology University of New South Wales Northcott Drive Canberra ACT-2610 Australia

This work presents the design of a compact meander-based antenna for search and rescue application. The proposed antenna is designed on a polydimethylsiloxane (PDMS) substrate, which is robust, flexible, waterproof and suitable for use in harsh environments. Due to the need for operation at a relatively low frequency of 406 MHz, antennas for the search and rescue application require an extended electrical length, thus typically resulting in large sizes. This is alleviated by implementing the meander-based structure on the antenna to ensure size compactness. Besides that, to minimize the effects of the human body when worn, the antenna is integrated with a full ground plane on its reverse side to shield the body from electromagnetic coupling. Results indicated that the antenna operated with a center frequency of 406 MHz and bandwidth of 30.88 MHz (7.61 %). A unidirectional radiation is produced and the final size is 190 × 60 × 5 mm (0.257 × 0.081 × 0.0068) λo.

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A faster v-order string comparison algorithm 22

A faster v-order string comparison algorithm

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22nd Prague Stringology Conference, PSC 2018

作者： Alatabbi, Ali Daykin, Jacqueline W. Mhaskar, Neerja Rahman, M. Sohel Smyth, William F. Department of Informatics King's College London London United Kingdom Department of Computer Science Aberystwyth University Department of Information Science Stellenbosch University South Africa Algorithms Research Group Department of Computing and Software McMaster University Canada Department of Computer Science and Engineering Bangladesh University of Engineering and Science Bangladesh School of Engineering and Information Technology Murdoch University WA Australia

ISBN: (纸本)9788001064849

V -order is a total order on strings that determines an instance of Unique Maximal Factorization Families (UMFFs) [7-10], a generalization of Lyndon words [12]. V -order has also recently been proposed as an alternative to lexicographic order (lexorder) in the computation of suffix arrays and in the suffix-sorting induced by the Burrows-Wheeler Transform (BWT) [11]. The central problem of efficient V -ordering of strings was considered in [2-4, 9, 10], culminating in a remarkably simple, linear time, constant space comparison algorithm [1]. In this paper we improve on this result to achieve significant speed-up in almost all cases of interest. © Proceedings of the Prague Stringology Conference, PSC 2018. All rights reserved.

关键词： Combinatorial mathematics

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