Variation and change in thetrapandstrutvowels of RP: a real time comparison of five acoustic data sets

2007 ◽  
Vol 37 (3) ◽  
pp. 293-320 ◽  
Author(s):  
Anne H. Fabricius
Keyword(s):  
Real Time ◽  
Short Vowel ◽  
Data Sets ◽  
Single Measurement ◽  
Methodological Innovation ◽  
Acoustic Data ◽  
A Value ◽  
Short Vowels ◽  
Geometric Relationship ◽  
Angle Calculation

The present study examines evidence for change in real time within the short vowel subsystem of the RP accent of English over the course of the twentieth century. It compares plots of average formant positions for the short vowels, stemming from several data corpora. It furthermore describes a change over time in the juxtaposition of thetrapandstrutvowels as captured in the calculated angle and distance between the two, usingtrapas a fixed point. This representation of a relationship in a single measurement by means of angle calculation is a methodological innovation for the sociophonetic enterprise. A value specifying the geometric relationship between two vowel positions is precise and replicable, as well as abstract enough to be comparable across data sets. Differences between ‘phonetic’ and ‘sociolinguistic’ stances on the interpretation of acoustic vowel data in formant plots and the issue of suitable vowel normalisation procedures for sociophonetics will also be discussed.

Real-time Approximation of Photometric Polygonal Lights

2020 ◽  
Vol 3 (1) ◽  
pp. 1-18
Author(s):  
Christian Luksch ◽  
Lukas Prost ◽  
Michael Wimmer
Keyword(s):  
Real Time ◽  
Specular Reflection ◽  
Near Field ◽  
Measurement Data ◽  
Data Sets ◽  
Photometric Measurement ◽  
Integration Technique ◽  
Time Approximation ◽  
Light Emitter ◽  
Selection Of

We present a real-time rendering technique for photometric polygonal lights. Our method uses a numerical integration technique based on a triangulation to calculate noise-free diffuse shading. We include a dynamic point in the triangulation that provides a continuous near-field illumination resembling the shape of the light emitter and its characteristics. We evaluate the accuracy of our approach with a diverse selection of photometric measurement data sets in a comprehensive benchmark framework. Furthermore, we provide an extension for specular reflection on surfaces with arbitrary roughness that facilitates the use of existing real-time shading techniques. Our technique is easy to integrate into real-time rendering systems and extends the range of possible applications with photometric area lights.

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

Facial Nerve EMG: Low-Tech Monitoring with a Stopwatch

2021 ◽  
Author(s):  
Julian Prell ◽  
Christian Scheller ◽  
Sebastian Simmermacher ◽  
Christian Strauss ◽  
Stefan Rampp
Keyword(s):  
Facial Nerve ◽  
Real Time ◽  
Random Order ◽  
Automated Analysis ◽  
Data Sets ◽  
Time Analysis ◽  
Frequency Pattern ◽  
Real Time Analysis ◽  
Offline Analysis ◽  
Automated Algorithms

Abstract Objective The quantity of A-trains, a high-frequency pattern of free-running facial nerve electromyography, is correlated with the risk for postoperative high-grade facial nerve paresis. This correlation has been confirmed by automated analysis with dedicated algorithms and by visual offline analysis but not by audiovisual real-time analysis. Methods An investigator was presented with 29 complete data sets measured during actual surgeries in real time and without breaks in a random order. Data were presented either strictly via loudspeaker (audio) or simultaneously by loudspeaker and computer screen (audiovisual). Visible and/or audible A-train activity was then quantified by the investigator with the computerized equivalent of a stopwatch. The same data were also analyzed with quantification of A-trains by automated algorithms. Results Automated (auto) traintime (TT), known to be a small, yet highly representative fraction of overall A-train activity, ranged from 0.01 to 10.86 s (median: 0.58 s). In contrast, audio-TT ranged from 0 to 1,357.44 s (median: 29.69 s), and audiovisual-TT ranged from 0 to 786.57 s (median: 46.19 s). All three modalities were correlated to each other in a highly significant way. Likewise, all three modalities correlated significantly with the extent of postoperative facial paresis. As a rule of thumb, patients with visible/audible A-train activity < 1 minute presented with a more favorable clinical outcome than patients with > 1 minute of A-train activity. Conclusion Detection and even quantification of A-trains is technically possible not only with intraoperative automated real-time calculation or postoperative visual offline analysis, but also with very basic monitoring equipment and real-time good quality audiovisual analysis. However, the investigator found audiovisual real-time-analysis to be very demanding; thus tools for automated quantification can be very helpful in this respect.

scholarly journals Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 859
Author(s):  
Abdulaziz O. AlQabbany ◽  
Aqil M. Azmi
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Real Time ◽  
Data Streams ◽  
Learning Algorithm ◽  
Concept Drift ◽  
The United States ◽  
Careful Consideration ◽  
Data Sets ◽  
Stream Data

We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data’s underlying distribution, a significant issue, especially when learning from data streams. It requires learners to be adaptive to dynamic changes. Random forest is an ensemble approach that is widely used in classical non-streaming settings of machine learning applications. At the same time, the Adaptive Random Forest (ARF) is a stream learning algorithm that showed promising results in terms of its accuracy and ability to deal with various types of drift. The incoming instances’ continuity allows for their binomial distribution to be approximated to a Poisson(1) distribution. In this study, we propose a mechanism to increase such streaming algorithms’ efficiency by focusing on resampling. Our measure, resampling effectiveness (ρ), fuses the two most essential aspects in online learning; accuracy and execution time. We use six different synthetic data sets, each having a different type of drift, to empirically select the parameter λ of the Poisson distribution that yields the best value for ρ. By comparing the standard ARF with its tuned variations, we show that ARF performance can be enhanced by tackling this important aspect. Finally, we present three case studies from different contexts to test our proposed enhancement method and demonstrate its effectiveness in processing large data sets: (a) Amazon customer reviews (written in English), (b) hotel reviews (in Arabic), and (c) real-time aspect-based sentiment analysis of COVID-19-related tweets in the United States during April 2020. Results indicate that our proposed method of enhancement exhibited considerable improvement in most of the situations.

Fast and accurate detection of surface defect based on improved YOLOv4

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiawei Lian ◽  
Junhong He ◽  
Yun Niu ◽  
Tianze Wang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Surface Defect ◽  
Steel Ingot ◽  
Industrial Applications ◽  
Data Sets ◽  
Data Set ◽  
Processing Technologies ◽  
Content Type ◽  
Public Data

Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

scholarly journals A Distributed Content-Based Video Retrieval System for Large Data-sets

2021 ◽  
Author(s):  
ElMehdi SAOUDI ◽  
Said Jai Andaloussi
Keyword(s):  
Real Time ◽  
Retrieval System ◽  
Video Retrieval ◽  
Multimedia Databases ◽  
Video Data ◽  
Multimedia Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Data Set ◽  
Content Based Video Retrieval

Abstract With the rapid growth of the volume of video data and the development of multimedia technologies, it has become necessary to have the ability to accurately and quickly browse and search through information stored in large multimedia databases. For this purpose, content-based video retrieval ( CBVR ) has become an active area of research over the last decade. In this paper, We propose a content-based video retrieval system providing similar videos from a large multimedia data-set based on a query video. The approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key-frames for rapid browsing and efficient video indexing. We have implemented the proposed approach on both, single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments are performed using various benchmark action and activity recognition data-sets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to state-of-the-art methods.

scholarly journals Geographic Information and Geo-visualisation in support of Disaster Resilience

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Edward Kurwakumire ◽  
Paul Muchechetere ◽  
Shelter Kuzhazha ◽  
Guy Blachard Ikokou
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Disaster Preparedness ◽  
Geographic Information ◽  
Public Access ◽  
Data Sets ◽  
Disaster Resilience ◽  
Data Systems ◽  
Information Infrastructures ◽  
Timely Access

<p><strong>Abstract.</strong> Society continues to become more spatially enabled as spatial data becomes increasingly available and accessible. This is partly due to democratisation of data achieved through open access of framework data sets. On the other hand, mobile devices such as smartphones have become more accessible, giving the public access to applications that use spatial data. This has tremendously increased the consumption of spatial data at the level of the general public. Spatial data has a history in planning and decision making as detailed in literature on promises and benefits of geographic information. We extend these promises to sustainability and disaster resilience. It is our belief that geographic information (GI) and geographic information infrastructures (GIIs) contribute positively towards the achievement of sustainability in cities and nations and in disaster resilience. This study carries out a review of geo-visualisation and GI applications in order to determine their suitability and impact in disaster resilience. Real-time GI are significant for cities to ensure sustainability and to increase disaster preparedness. Geographic information infrastructures need to be integrated with BIG data systems to ensure that local government agencies have timely access to real time geographic information so that decisions on sustainability and disaster resilience can be effectively done.</p>

scholarly journals [Phonemic Transitions of Arabic Words in The Malay Language] Al-Tahwalat al-Sautiah lil Alfaz al-Arabiah fi al-Lughah al-Malawiya.

2017 ◽  
Vol 14 (1) ◽  
pp. 90-111
Author(s):  
Tasnim Binti Mohd Annuar
Keyword(s):  
Short Vowel ◽  
Language Family ◽  
Short Vowels ◽  
Research Findings

This study aims to identify the phonic adaptations of Arabic loanwords in the Malay language which do not affect the syllabic system of those loanwords. The importance of the study is that it helps to produce a combined lexicon between Malay and Arabic which depends on the phonic adaptations to suit the Malay phonic systems. The method used in this study is contrastive method as the Arabic and Malay are not of one language family. Among the research findings is that the phonic adaptations that do not lead to the change of syllabic system of the loanwords include the replacement of consonant with consonant, and the replacement of short vowel with short vowel, and the replacement of semivowel with short vowel, and the replacement of the long vowel with diphthong, and the dissimilation  between the short vowels. Keywords : Arabic, Malay, Contrastive method, phones, Malay phones.        تسعى هذه الدراسة إلى الكشف عن التحولات الصوتيةالطارئة على الألفاظ العربية المقترضة في اللغة الملايوية وهي لا تؤثر في النظام المقطعي لتلك الألفاظ. وتظهر أهمية الدراسة في أنها تساعد في صناعة المعجم المشترك بين العربية والملايوية، حيث يعتمد الأمر على التولات الصوتية الطارئة لمناسبة النظام الصوتي في الملايوية. والمنهج المتبع في هذه الدراسة هو المنهج التقابلي، لأن العربية والملايوية ليستا من فصيلة واحدة. ومما توصلت إليه الدراسة أن التحولات الصوتية التي لا تؤدي إلى تغيير النظام المقطعي للالفاظ المقترضة تشمل إبدال الصامت بالصامت، وإبدال الحركة القصيرة بالحركة القصيرة، وإبدال شبه الحركة بالحركة القيصرة، وإبدال الحركة الطويلة بالحركة المزدوجة، والمخالفة بين الحركات. الكلمات المفتاحية: اللغة العبية، اللغة الملايوية، المنهج التقابلي، الأصوات، أصوات اللغة الملايوية.  

A Real-Time Log Analyzer Based on MongoDB

2014 ◽  
Vol 571-572 ◽  
pp. 497-501 ◽  
Author(s):  
Qi Lv ◽  
Wei Xie
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Performance Comparison ◽  
Log Analysis ◽  
Data Sets ◽  
Time Data ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Implementation Approach ◽  
And Performance

Real-time log analysis on large scale data is important for applications. Specifically, real-time refers to UI latency within 100ms. Therefore, techniques which efficiently support real-time analysis over large log data sets are desired. MongoDB provides well query performance, aggregation frameworks, and distributed architecture which is suitable for real-time data query and massive log analysis. In this paper, a novel implementation approach for an event driven file log analyzer is presented, and performance comparison of query, scan and aggregation operations over MongoDB, HBase and MySQL is analyzed. Our experimental results show that HBase performs best balanced in all operations, while MongoDB provides less than 10ms query speed in some operations which is most suitable for real-time applications.

A Network Implementation of Real-Time Dynamic Simulation With Interactive Animated Graphics

1988 ◽  
Author(s):  
M. W. Dubetz ◽  
J. G. Kuhl ◽  
E. J. Haug
Keyword(s):  
Real Time ◽  
Dynamic Simulation ◽  
High Speed ◽  
Dynamic Performance ◽  
Data Communication ◽  
Computing System ◽  
Data Sets ◽  
Parallel Processor ◽  
Network Computing ◽  
Time Dynamic

Abstract This paper presents a network based implementation of real-time dynamic simulation methods. An interactive animated graphics environment is presented that permits the engineer to view high quality animated graphics rendering of dynamic performance, to interact with the simulation, and to study the effects of design variations, while the simulation is being carried out. An industry standard network computing system is employed to interface the parallel processor that carries out the dynamic simulation and a high speed graphics processor that creates and displays animated graphics. Multi-windowing and graphics processing methods that are employed to provide visualization and operator control of the simulation are presented. A vehicle dynamics application is used to illustrate the methods developed and to analyze communication bandwidth requirements for implementation with a compute server that is remote from the graphics workstation. It is shown that, while massive data sets are generated on the parallel processor during realtime dynamic simulation and extensive graphics data are generated on the workstation during rendering and display, data communication requirements between the compute server and the workstation are well within the capability of existing networks.

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