A building block attitude detection algorithm based on robot self-error-correction

2021 ◽  
Author(s):  
ZeYuan Cai ◽  
ZhiQuan Feng ◽  
LiRan Zhou ◽  
XiaoHui Yang
Keyword(s):  
Error Correction ◽  
Building Block ◽  
Detection Algorithm ◽  
Attitude Detection

scholarly journals A Motion Detection Algorithm Using Local Phase Information

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Aurel A. Lazar ◽  
Nikul H. Ukani ◽  
Yiyin Zhou
Keyword(s):  
Motion Detection ◽  
Building Block ◽  
Moving Objects ◽  
Reconstruction Algorithm ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Local Phase ◽  
Local Motion ◽  
Phase Information ◽  
Temporal Derivative

Previous research demonstrated thatglobalphase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using onlylocalphase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector; it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm.

scholarly journals SoftMax based User Attitude Detection Algorithm for Sentimental Analysis

2018 ◽  
Vol 125 ◽  
pp. 313-320 ◽  
Author(s):  
Bhavish Khanna N ◽  
Sharon Moses J ◽  
Nirmala M
Keyword(s):  
Detection Algorithm ◽  
Attitude Detection

Forward Error Correction (FEC) Building Block

2002 ◽  
Author(s):  
M. Luby ◽  
L. Vicisano ◽  
J. Gemmell ◽  
L. Rizzo ◽  
M. Handley ◽  
...  
Keyword(s):  
Error Correction ◽  
Building Block ◽  
Forward Error Correction ◽  
Forward Error

Group Customer Special Line Correction Algorithm Design and Application Based on PTN Technology

2014 ◽  
Vol 989-994 ◽  
pp. 2004-2007
Author(s):  
Heng Jun Zhu ◽  
Deng Feng Li
Keyword(s):  
Error Correction ◽  
Technology Development ◽  
Transmission Rate ◽  
Signal To Noise Ratio ◽  
Algorithm Design ◽  
Development Trend ◽  
Detection Algorithm ◽  
Iteration Algorithm ◽  
Correction Algorithm ◽  
Technology Application

The group customer line service has become one of the key businesses for communication operators, and the line PTN technology development currency, the PTN technology application, and the development trend are researched. According to the PTN technology and client group line error correction algorithm, the multi granularity hash correction algorithm is used for data video aware, and when the PTN data is changed fast, the fuzzy block effect happened. The customer line service performance is bad. An improved group customer line correction algorithm is proposed based on PTN technology. The hidden Markov model is used for packet loss rate prediction, and the multiple steps are selected in random, and the data stream iteration algorithm is designed. The tamper detection algorithm is obtained. PTN technology group customer line correction is realized. Simulation results show that the new method can reduce error transmission rate of the PTN group customer line, the customer loss and delay of the data transmission can be controlled, and the peak signal to noise ratio is improved, the error correction performance is better, and it can be effectively applied to communications operator service.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

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