Results of the automated analysis of 328 bladder specimens using the Leyden television analysis system (LEYTAS)

1983 ◽  
Vol 1 (2) ◽  
pp. 77-81 ◽  
Author(s):  
H. J. Tanke ◽  
J. A. M. Brussee ◽  
A. M. J. van Driel-Kulker ◽  
M. J. van der Burg ◽  
C. F. H. M. Schelvis-Knepfle ◽  
...  
Keyword(s):  
Automated Analysis ◽  
Analysis System

scholarly journals A comparison of the Mouse Song Analyzer and DeepSqueak ultrasonic vocalization analysis systems in C57BL/6J, FVB.129, and FVB mice

2021 ◽  
Author(s):  
Matthew S Binder ◽  
Zachary P Pranske ◽  
Joaquin N Lugo
Keyword(s):  
Fundamental Frequency ◽  
Vocal Communication ◽  
Automated Analysis ◽  
Ultrasonic Vocalization ◽  
Ultrasonic Vocalizations ◽  
Automated System ◽  
Two Systems ◽  
Similar Quantity ◽  
Analysis System

Vocal communication is an essential behavior in mammals and is relevant to human neurodevelopmental conditions. Mice produce communicative vocalizations, known as ultrasonic vocalizations (USVs), that can be recorded with various programs. The Mouse Song Analyzer is an automated analysis system, while DeepSqueak is a semi-automated system. We used data from C57BL/6J, FVB.129, and FVB mice to compare whether the DeepSqueak and Mouse Song Analyzer systems measure a similar total number, duration, and fundamental frequency of USVs. We found that the two systems detected a similar quantity of USVs for FVB.129 mice (r= .90, p< .001), but displayed lower correlations for C57BL/6J (r= .76, p< .001) and FVB mice (r= .60, p< .001). We also found that DeepSqueak detected significantly more USVs for C57BL/6J mice than the Mouse Song Analyzer. The two systems detected a similar duration of USVs for C57BL/6J (r= .82, p< .001), but lower correlations for FVB.129 (r= .13, p< .001) and FVB mice (r= .51, p< .01) were found, with DeepSqueak detecting significantly more USVs per each strain. We found lower than acceptable correlations for fundamental frequency in C57BL/6J (r= .54, p< .01), FVB.129 (r= .76, p< .001), and FVB mice (r= .07, p= .76), with the Mouse Song Analyzer detecting a significantly higher fundamental frequency for FVB.129 mice. These findings demonstrate that the strain of mouse used significantly affects the number, duration, and fundamental frequency of USVs that are detected between programs. Overall, we found that DeepSqueak is more accurate than the Mouse Song Analyzer.

Automated Multichannel Signal Classification Systems for Ultrasonic Nondestrucitve Evaluation

2006 ◽  
Vol 321-323 ◽  
pp. 1266-1269 ◽  
Author(s):  
J. Kim ◽  
P. Ramuhalli ◽  
L. Udpa ◽  
S. Udpa
Keyword(s):  
Neural Network ◽  
Spatial Correlation ◽  
Principal Component ◽  
Automated Analysis ◽  
Classification Systems ◽  
Inspection Systems ◽  
Weld Inspection ◽  
Correlation Information ◽  
Trained Neural Network ◽  
Analysis System

A key requirement in most ultrasonic weld inspection systems is the ability for rapid automated analysis to identify the type of flaw. Incorporation of spatial correlation information from adjacent A-scans can improve performance of the analysis system. This paper describes two neural network based classification techniques that use correlation of adjacent A-scans. The first method relies on differences in individual A-scans to classify signals using a trained neural network, with a post-processing mechanism to incorporate spatial correlation information. The second technique transforms a group of spatially localized signals using a 2-dimensional transform, and principal component analysis is applied to the transform coefficients to generate a reduced dimensional feature vectors for classification. Results of applying the proposed techniques to data obtained from weld inspection are presented, and the performances of the two approaches are compared.

scholarly journals Age-related features of the organization of the microcirculatory bed of the bulbar conjunctiva

2020 ◽  
pp. 57-61
Author(s):  
V. M. Chertok ◽  
V. A. Nevzorova ◽  
A. K. Savchenko ◽  
O. V. Miroshnichenko ◽  
A. V. Laryushkina
Keyword(s):  
Age Groups ◽  
Average Diameter ◽  
Automated Analysis ◽  
The Body ◽  
Bulbar Conjunctiva ◽  
Specific Density ◽  
Microcirculatory Bed ◽  
Age Related ◽  
Analysis System ◽  
Age Related Changes

Objective: Analysis of age-related changes of microcirculatory bed of bulbar conjunctiva.Methods: 46 individuals of both sexes, divided into 5 age groups according to WHO recommendations, were examined. Biomicroscopy of bulbar conjunctiva was performed using a non-mydriatic retinal camera TOPCON TRC-NW8F (Japan); the obtained images were processed with a mea‑ suring device of automated analysis system ImageScope (Leica, Germany).Results: The average diameter of arterioles, arterioleto-venule ratio (AVR) and specific density of capillaries were the largest, and the diameter of venules was the smallest among the subjects aged 18–44 years. The most sensitive indicators of the state of microcirculatory bed were AVR and the specific density of capillaries, the values of which in the group of 45–59-year-olds were 10–11% lower than in people aged 18–24 and 25–44 years. Differences in other indicators between people aged 18-24 and 45–59 years were not significant. Between the groups of 60–74 and 75–86-year-old participants of the study, pronounced differences (about 18%) were found only in the specific density of capillaries: compared with 18–24 and 45–59-year-olds, this indicator decreased by almost 1.5 times, AVR – only by a third, and changes in the average diameter of arterioles and venules did not exceed 9–12%. Elderly people more often demonstrated arteriolar spasm, their uneven caliber, avascular fields and other disorders of the structure of the micro‑ circulatory bed.Conclusions: As the body ages, in the microcirculatory bed of the bulbar conjunctiva, the number of atypical vascular formations increases, the diameter of the arterioles decreases, the AVR and the specific density of capillaries decrease, the diameter of the venules increases. 

scholarly journals Wavelet Statistical Feature based Malware Class Recognition and Classification using Supervised Learning Classifier

2017 ◽  
Vol 10 (2) ◽  
pp. 400-406 ◽  
Author(s):  
Aziz Makandar ◽  
Anita Patrot
Keyword(s):  
Feature Extraction ◽  
Automated Analysis ◽  
Input Image ◽  
Access Time ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Learning Classifier ◽  
Feature Based ◽  
Three Stages ◽  
Analysis System

Malware is a malicious instructions which may harm to the unauthorized private access through internet. The types of malware are incresing day to day life, it is a challenging task for the antivius vendors to predict and caught on access time. This paper aims to design an automated analysis system for malware classes based on the features extracted by Discrete Wavelet Transformation (DWT) and then by applying four level decomposition of malware. The proposed system works in three stages, pre-processing, feature extraction and classification. In preprocessing, input image is normalized in to 256x256 by applying wavelet we are denoising the image which helps to enhance the image. In feature extraction, DWT is used to decompose image into four level. For classification the support vector machine (SVM) classifiers are used to discriminate the malware classes with statistical features extracted from level 4 decomposition of DWT such as Daubechies (db4), Coiflet (coif5) and Bi-orthogonal (bior 2.8). Among these wavelet features the db4 features effectively classify the malware class type with high accuracy 91.05% and 92.53% respectively on both dataset. The analysis of proposed method conducted on two dataset and the results are promising.

Automated analysis system for various solvents by capillary GC coupled with a personal computer.

1991 ◽  
Vol 40 (6) ◽  
pp. 277-282
Author(s):  
Eiji SAITOH ◽  
Hajime TOKUDA ◽  
Kiyoshi MATSUMOTO
Keyword(s):  
Personal Computer ◽  
Automated Analysis ◽  
Capillary Gc ◽  
Analysis System
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