scholarly journals Shape Feature Extraction Techniques for Fruits: A Review

2021 ◽  
pp. 2425-2430
Author(s):  
Israa Mohammed Hassoon
Keyword(s):  
Feature Extraction ◽  
Food Industry ◽  
Automated System ◽  
Shape Feature ◽  
Extraction Techniques ◽  
Quality Estimation ◽  
Advantages And Disadvantages ◽  
Quality Of Food ◽  
Shape Feature Extraction

          Fruits sorting, recognizing, and classifying are essential post-harvest operations, as they contribute to the quality of food industry, thereby increasing the exported quantity of food. Today, an automated system for fruit classification and recognition is very important, especially when exporting to markets where quality of fruit must be high. In this study, the advantages and disadvantages of the various shape-based feature extraction algorithms and technologies that are used in sorting, classifying, and grading of fruits, as well as fruits quality estimation, are discussed in order to provide a good understanding of the use of shape-based feature extraction techniques.

scholarly journals Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 114
Author(s):  
Tiziano Zarra ◽  
Mark Gino K. Galang ◽  
Florencio C. Ballesteros ◽  
Vincenzo Belgiorno ◽  
Vincenzo Naddeo
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Performance Optimization ◽  
Industrial Plant ◽  
Mathematical Treatment ◽  
Extraction Techniques ◽  
Peak Period ◽  
Linear Discriminant ◽  
Artificial Neural

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

The structure and application of an automated system for monitoring passenger traffic

2021 ◽  
Vol 14 (3) ◽  
pp. 4-11
Author(s):  
Evgeniy Anikeev
Keyword(s):  
Data Collection ◽  
General Structure ◽  
Automated System ◽  
Passenger Transport ◽  
Service Levels ◽  
Passenger Traffic ◽  
Advantages And Disadvantages ◽  
Transport Services ◽  
Transport Control

Various methods of collecting data on passenger traffic, their advantages and disadvantages are considered. It is shown that in order to improve the quality of transport services, it is necessary to regularly collect and refine data on passenger traffic. The goals and methods of obtaining information about passenger traffic in the system of municipal passenger transport are indicated. All currently existing methods are divided into three categories: data collection using technical means, data collection with the help of censors and volunteers, and interpretation of fare payments. All the methods presented in the article were compared in terms of labor intensity, costs and accuracy of the results obtained. The advantages and disadvantages of each method are considered. The general structure of an automated system for collecting data on passenger traffic is presented. The necessity of creating a centralized system for collecting and processing data associated with all passenger transport control systems has been substantiated. The tasks solved by this system at all levels of transport services for passengers are shown. Each of the tasks is assigned to one of three service levels: pre-transport, transport and post-transport. It is shown that only solving problems at all levels can ensure high-quality operation of the municipal passenger transport system.

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