Pretest exposure, changes in pattern complexity, and choice.

1968 ◽  
Vol 66 (1) ◽  
pp. 139-143 ◽  
Author(s):  
Richard B. May
Keyword(s):  
Pattern Complexity ◽  
Pretest Exposure

A Feature-Segmentation Model of Short-Term Visual Memory

Perception ◽  
10.1068/p3320 ◽  
2002 ◽  
Vol 31 (5) ◽  
pp. 579-589 ◽  
Author(s):  
Koji Sakai ◽  
Toshio Inui
Keyword(s):  
Visual Memory ◽  
Random Variable ◽  
Short Term ◽  
Pattern Complexity ◽  
Convex Part ◽  
Good Account ◽  
Feature Segmentation ◽  
Short Term Visual Memory ◽  
The Relationship ◽  
Response Feature

A feature-segmentation model of short-term visual memory (STVM) for contours is proposed. Memory of the first stimulus is maintained until the second stimulus is observed. Three processes interact to determine the relationship between stimulus and response: feature encoding, memory, and decision. Basic assumptions of the model are twofold: (i) the STVM system divides a contour into convex parts at regions of concavity; and (ii) the value of each convex part represented in STVM is an independent Gaussian random variable. Simulation showed that the five-parameter fits give a good account of the effects of the four experimental variables. The model provides evidence that: (i) contours are successfully encoded within 0.5 s exposure, regardless of pattern complexity; (ii) memory noise increases as a linear function of retention interval; (iii) the capacity of STVM, defined by pattern complexity (the degree that a pattern can be handled for several seconds with little loss), is about 4 convex parts; and (iv) the confusability contributing to the decision process is a primary factor in deteriorating recognition of complex figures. It is concluded that visually presented patterns can be retained in STVM with considerable precision for prolonged periods of time, though some loss of precision is inevitable.

scholarly journals Nanopore Sequencing of a Forensic STR Multiplex Reveals Loci Suitable for Single-Contributor STR Profiling

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 381 ◽  
Author(s):  
Olivier Tytgat ◽  
Yannick Gansemans ◽  
Jana Weymaere ◽  
Kaat Rubben ◽  
Dieter Deforce ◽  
...  
Keyword(s):  
Tandem Repeats ◽  
Massively Parallel Sequencing ◽  
Flow Cell ◽  
Nanopore Sequencing ◽  
Flow Cells ◽  
Pattern Complexity ◽  
Str Loci ◽  
Oxford Nanopore ◽  
Oxford Nanopore Technologies ◽  
Short Tandem

Nanopore sequencing for forensic short tandem repeats (STR) genotyping comes with the advantages associated with massively parallel sequencing (MPS) without the need for a high up-front device cost, but genotyping is inaccurate, partially due to the occurrence of homopolymers in STR loci. The goal of this study was to apply the latest progress in nanopore sequencing by Oxford Nanopore Technologies in the field of STR genotyping. The experiments were performed using the state of the art R9.4 flow cell and the most recent R10 flow cell, which was specifically designed to improve consensus accuracy of homopolymers. Two single-contributor samples and one mixture sample were genotyped using Illumina sequencing, Nanopore R9.4 sequencing, and Nanopore R10 sequencing. The accuracy of genotyping was comparable for both types of flow cells, although the R10 flow cell provided improved data quality for loci characterized by the presence of homopolymers. We identify locus-dependent characteristics hindering accurate STR genotyping, providing insights for the design of a panel of STR loci suited for nanopore sequencing. Repeat number, the number of different reference alleles for the locus, repeat pattern complexity, flanking region complexity, and the presence of homopolymers are identified as unfavorable locus characteristics. For single-contributor samples and for a limited set of the commonly used STR loci, nanopore sequencing could be applied. However, the technology is not mature enough yet for implementation in routine forensic workflows.

scholarly journals Enhancing Accuracy in a Touch Operation Biometric System: A Case on the Android Pattern Lock Scheme

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Allan Ng’ang’a ◽  
Paula M. W. Musuva
Keyword(s):  
Repeated Measures ◽  
Smart Devices ◽  
Biometric System ◽  
Data Set ◽  
Pattern Complexity ◽  
User Training ◽  
Average Accuracy ◽  
Within Subjects ◽  
Dynamic Time ◽  
Future Work

The main objective of this research study is to enhance the functionality of an Android pattern lock application by determining whether the time elements of a touch operation, in particular time on dot (TOD) and time between dot (TBD), can be accurately used as a biometric identifier. The three hypotheses that were tested through this study were the following–H1: there is a correlation between the number of touch stroke features used and the accuracy of the touch operation biometric system; H2: there is a correlation between pattern complexity and accuracy of the touch operation biometric system; H3: there is a correlation between user training and accuracy of the touch operation biometric system. Convenience sampling and a within-subjects design involving repeated measures were incorporated when testing an overall sample size of 12 subjects drawn from a university population who gave a total of 2,096 feature extracted data. Analysis was done using the Dynamic Time Warping (DTW) Algorithm. Through this study, it was shown that the extraction of one-touch stroke biometric feature coupled with user training was able to yield high average accuracy levels of up to 82%. This helps build a case for the introduction of biometrics into smart devices with average processing capabilities as they would be able to handle a biometric system without it compromising on the overall system performance. For future work, it is recommended that more work be done by applying other classification algorithms to the existing data set and comparing their results with those obtained with DTW.

Pitch and Duration in Recognition of Music-Like Structures

1974 ◽  
Vol 38 (2) ◽  
pp. 419-428 ◽  
Author(s):  
Philip J. Chamberlain
Keyword(s):  
Recognition Performance ◽  
Memory Task ◽  
Two Dimensions ◽  
Pattern Complexity ◽  
Pitch Information ◽  
Musical Patterns ◽  
Pattern Information ◽  
The Relationship ◽  
Average Information ◽  
Different Levels

To determine whether patterning of pitch or duration contributes most to the recognition of melodic structures, tone sequences resembling musical patterns or melodies were used in a recognition memory task. Nine categories of pattern complexity were produced by using three different levels of average information per tone in each of the two dimensions. These categories of tone sequences were presented to groups of Ss and their recognition performance measured. Only patterning of pitch was a significant factor in recognition. Performance was better with larger values of average information per tone, but behavior at maximum values of pitch information suggested that the relationship between performance and pattern information is nonmonotonic.

scholarly journals Software Toolchain for Large-Scale RE-NFA Construction on FPGA

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Yi-Hua E. Yang ◽  
Viktor K. Prasanna
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Regular Expression ◽  
Finite Automata ◽  
Fixed Number ◽  
Regular Expressions ◽  
Pattern Complexity ◽  
Regular Expression Matching ◽  
Area Increase ◽  
Prototype Software

We present a software toolchain for constructing large-scaleregular expression matching(REM) on FPGA. The software automates the conversion of regular expressions into compact and high-performance nondeterministic finite automata (RE-NFA). Each RE-NFA is described as an RTL regular expression matching engine (REME) in VHDL for FPGA implementation. Assuming a fixed number of fan-out transitions per state, ann-statem-bytes-per-cycle RE-NFA can be constructed inO(n×m)time andO(n×m)memory by our software. A large number of RE-NFAs are placed onto a two-dimensionalstaged pipeline, allowing scalability to thousands of RE-NFAs with linear area increase and little clock rate penalty due to scaling. On a PC with a 2 GHz Athlon64 processor and 2 GB memory, our prototype software constructs hundreds of RE-NFAs used by Snort in less than 10 seconds. We also designed a benchmark generator which can produce RE-NFAs with configurable pattern complexity parameters, including state count, state fan-in, loop-back and feed-forward distances. Several regular expressions with various complexities are used to test the performance of our RE-NFA construction software.

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