scholarly journals Experimental Assessment of Feature Extraction Techniques Applied to the Identification of Properties of Common Objects, Using a Radar System

2021 ◽  
Vol 11 (15) ◽  
pp. 6745
Author(s):  
José Francisco Díez-Pastor ◽  
Pedro Latorre-Carmona ◽  
José Luis Garrido-Labrador ◽  
José Miguel Ramírez-Sanz ◽  
Juan J. Rodríguez
Keyword(s):  
Feature Extraction ◽  
User Interfaces ◽  
Traffic Control ◽  
High Speed ◽  
Classification Performance ◽  
Tangible User Interfaces ◽  
Extraction Techniques ◽  
Research Systems ◽  
Orientation Movement ◽  
New Applications

Radar technology has evolved considerably in the last few decades. There are many areas where radar systems are applied, including air traffic control in airports, ocean surveillance, and research systems, to cite a few. Other types of sensors have recently appeared, which allow tracking sub-millimeter motion with high speed and accuracy rates. These millimeter-wave radars are giving rise to myriad new applications, from the recognition of the material close objects are made, to the recognition of hand gestures. They have also been recently used to identify how a person interacts with digital devices through the physical environment (Tangible User Interfaces, TUIs). In this case, the radar is used to detect the orientation, movement, or distance from the objects to the user’s hands or the digital device. This paper presents a thoughtful comparative analysis of different feature extraction techniques and classification strategies applied on a series of datasets that cover problems such as the identification of materials, element counting, or determining the orientation and distance of objects to the sensor. The results outperform previous works using these datasets, especially when the accuracy was lowest, showing the benefits feature extraction techniques have on classification performance.

Hair Detection in Mushroom Based on Image Processing

2015 ◽  
Vol 713-715 ◽  
pp. 402-405
Author(s):  
Zhan Si Deng ◽  
Tong Qiang Li
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
High Speed ◽  
Detection System ◽  
Target Identification ◽  
Accurate Method ◽  
Inspection System ◽  
Extraction Techniques ◽  
On Line

Nowadays,artificial recognition is widely used in the mushroom inspection system, however, it depends on subjective judgment of inspectors.Therefore,the testing personnel's experience, technology and other factors will affect the objectivity and accuracy of test results.Commodity inspection system need a high-speed, objective and accurate method for the on-line hair detection in the mushroom.On the basis of summary of domestic and foreign research, this paper studies the target identification and feature extraction techniques based on computer vision, conducts a feasibility study for the real-time hair detection system.

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

scholarly journals A Survey on Classification algorithms of Brain Images in Alzheimer’s disease based on Feature Extraction techniques

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ruhul Amin Hazarika ◽  
Arnab Kumar Maji ◽  
Samarendra Nath Sur ◽  
Babu Sena Paul ◽  
Debdatta Kandar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Classification Algorithms ◽  
Extraction Techniques ◽  
Brain Images

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.

scholarly journals High-Speed Dynamic Projection Mapping onto Human Arm with Realistic Skin Deformation

2021 ◽  
Vol 11 (9) ◽  
pp. 3753
Author(s):  
Hao-Lun Peng ◽  
Yoshihiro Watanabe
Keyword(s):  
User Interfaces ◽  
High Speed ◽  
Target Surface ◽  
Human Vision ◽  
Surface Model ◽  
Projection Mapping ◽  
Skin Deformation ◽  
Human Arm ◽  
Conventional Methods ◽  
Joint Tracking

Dynamic projection mapping for a moving object according to its position and shape is fundamental for augmented reality to resemble changes on a target surface. For instance, augmenting the human arm surface via dynamic projection mapping can enhance applications in fashion, user interfaces, prototyping, education, medical assistance, and other fields. For such applications, however, conventional methods neglect skin deformation and have a high latency between motion and projection, causing noticeable misalignment between the target arm surface and projected images. These problems degrade the user experience and limit the development of more applications. We propose a system for high-speed dynamic projection mapping onto a rapidly moving human arm with realistic skin deformation. With the developed system, the user does not perceive any misalignment between the arm surface and projected images. First, we combine a state-of-the-art parametric deformable surface model with efficient regression-based accuracy compensation to represent skin deformation. Through compensation, we modify the texture coordinates to achieve fast and accurate image generation for projection mapping based on joint tracking. Second, we develop a high-speed system that provides a latency between motion and projection below 10 ms, which is generally imperceptible by human vision. Compared with conventional methods, the proposed system provides more realistic experiences and increases the applicability of dynamic projection mapping.

Exploring the Impact of Interaction Modality on Students’ Learning Performance

2021 ◽  
pp. 073563312110272
Author(s):  
Neila Chettaoui ◽  
Ayman Atia ◽  
Med Salim Bouhlel
Keyword(s):  
User Interfaces ◽  
Teaching Method ◽  
Learning Performance ◽  
Controlled Study ◽  
Embodied Interaction ◽  
Multimodal Learning ◽  
Tangible User Interfaces ◽  
Embodied Learning ◽  
Significant Difference ◽  
The Impact

Embodied learning pedagogy highlights the interconnections between the brain, body, and the concrete environment. As a teaching method, it provides means of engaging the physical body in multimodal learning experiences to develop the students’ cognitive process. Based on this perspective, several research studies introduced different interaction modalities to support the implementation of an embodied learning environment. One such case is the use of tangible user interfaces and motion-based technologies. This paper evaluates the impacts of motion-based, tangible-based, and multimodal interaction merging between tangible interfaces and motion-based technology on improving students’ learning performance. A controlled study was performed at a primary school with 36 participants (aged 7 to 9), to evaluate the educational potential of embodied interaction modalities compared to tablet-based learning. The results highlighted a significant difference in the learning gains between all groups, as determined by one-way ANOVA [F (3,32) = 6.32, p = .017], in favor of the multimodal learning interface. Findings revealed that a multimodal learning interface supporting richer embodied interaction that took advantage of affording the power of body movements and manipulation of physical objects might improve students’ understanding of abstract concepts in educational contexts.

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