scholarly journals Key Points’ Location in Infrared Images of the Human Body Based on Mscf-ResNet

2021 ◽  
Vol 14 (1) ◽  
pp. 15
Author(s):  
Shengguo Ge ◽  
Siti Nurulain Mohd Rum
Keyword(s):  
Human Body ◽  
Infrared Image ◽  
Research Direction ◽  
Brightness Distribution ◽  
Future Research ◽  
Infrared Images ◽  
Positioning Accuracy ◽  
Main Research ◽  
Multi Scale ◽  
Key Points

The human body generates infrared radiation through the thermal movement of molecules. Based on this phenomenon, infrared images of the human body are often used for monitoring and tracking. Among them, key point location on infrared images of the human body is an important technology in medical infrared image processing. However, the fuzzy edges, poor detail resolution, and uneven brightness distribution of the infrared image of the human body cause great difficulties in positioning. Therefore, how to improve the positioning accuracy of key points in human infrared images has become the main research direction. In this study, a multi-scale convolution fusion deep residual network (Mscf-ResNet) model is proposed for human body infrared image positioning. This model is based on the traditional ResNet, changing the single-scale convolution to multi-scale and fusing the information of different receptive fields, so that the extracted features are more abundant and the degradation problem, caused by the excessively deep network, is avoided. The experiments show that our proposed method has higher key point positioning accuracy than other methods. At the same time, because the network structure of this paper is too deep, there are too many parameters and a large volume of calculations. Therefore, a more lightweight network model is the direction of future research.

scholarly journals Flow and Particle Modelling of Dry Powder Inhalers: Methodologies, Recent Development and Emerging Applications

Pharmaceutics ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 189
Author(s):  
Zhanying Zheng ◽  
Sharon Shui Yee Leung ◽  
Raghvendra Gupta
Keyword(s):  
Dry Powder Inhaler ◽  
Research Direction ◽  
Particle Methods ◽  
Future Research ◽  
High Dose ◽  
Dry Powder ◽  
Multi Scale ◽  
Wide Range ◽  
Computational Fluid Dynamics Cfd ◽  
Discrete Element Methods

Dry powder inhaler (DPI) is a device used to deliver a drug in dry powder form to the lungs. A wide range of DPI products is currently available, with the choice of DPI device largely depending on the dose, dosing frequency and powder properties of formulations. Computational fluid dynamics (CFD), together with various particle motion modelling tools, such as discrete particle methods (DPM) and discrete element methods (DEM), have been increasingly used to optimise DPI design by revealing the details of flow patterns, particle trajectories, de-agglomerations and depositions within the device and the delivery paths. This review article focuses on the development of the modelling methodologies of flow and particle behaviours in DPI devices and their applications to device design in several emerging fields. Various modelling methods, including the most recent multi-scale approaches, are covered and the latest simulation studies of different devices are summarised and critically assessed. The potential and effectiveness of the modelling tools in optimising designs of emerging DPI devices are specifically discussed, such as those with the features of high-dose, pediatric patient compatibility and independency of patients’ inhalation manoeuvres. Lastly, we summarise the challenges that remain to be addressed in DPI-related fluid and particle modelling and provide our thoughts on future research direction in this field.

scholarly journals Conceptualization and Research Progress on Web-Based Product Co-Design

Informatics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 30
Author(s):  
Xin Kang ◽  
Jian Kang ◽  
Wenyin Chen
Keyword(s):  
Design Research ◽  
Web Of Science ◽  
Research Direction ◽  
Research Trends ◽  
Research Progress ◽  
Future Research ◽  
Web Based ◽  
Development Status ◽  
Key Points ◽  
Core Concepts

Web-based stakeholders’ participation in product co-design is an emerging business model for companies. However, research on user’s online involvement in product co-design is limited. In this paper, we investigated the three major interdisciplinary academic databases Web of Science, Scopus, and ScienceDirect (data collected in May 2020), with the aim of explaining the meaning, core concepts, historical roots, and research trends of co-design. A total of 39 of the deemed relevant studies were emphatically analyzed, systematically discusses the concept implication and development status of co-design, and the key points of web-based product co-design research, as well as the future research direction.

scholarly journals MS-AFF: A Novel Semantic Segmentation Approach for Buried Object Based on Multi-scale Attentional Feature Fusion

2021 ◽  
Author(s):  
Chao Lu ◽  
Fansheng Chen ◽  
Xiaofeng Su ◽  
Dan Zeng
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
Feature Fusion ◽  
Infrared Image ◽  
Semantic Segmentation ◽  
Target Object ◽  
Infrared Images ◽  
Feature Maps ◽  
Multi Scale ◽  
Visible Images

Abstract Infrared technology is a widely used in precision guidance and mine detection since it can capture the heat radiated outward from the target object. We use infrared (IR) thermography to get the infrared image of the buried obje cts. Compared to the visible images, infrared images present poor resolution, low contrast, and fuzzy visual effect, which make it difficult to segment the target object, specifically in the complex backgrounds. In this condition, traditional segmentation methods cannot perform well in infrared images since they are easily disturbed by the noise and non-target objects in the images. With the advance of deep convolutional neural network (CNN), the deep learning-based methods have made significant improvements in semantic segmentation task. However, few of them research Infrared image semantic segmentation, which is a more challenging scenario compared to visible images. Moreover, the lack of an Infrared image dataset is also a problem for current methods based on deep learning. We raise a multi-scale attentional feature fusion (MS-AFF) module for infrared image semantic segmentation to solve this problem. Precisely, we integrate a series of feature maps from different levels by an atrous spatial pyramid structure. In this way, the model can obtain rich representation ability on the infrared images. Besides, a global spatial information attention module is employed to let the model focus on the target region and reduce disturbance in infrared images' background. In addition, we propose an infrared segmentation dataset based on the infrared thermal imaging system. Extensive experiments conducted in the infrared image segmentation dataset show the superiority of our method.

scholarly journals Kinect v2 infrared images correction

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141875578 ◽  
Author(s):  
Zdenko Bobovský ◽  
Václav Krys ◽  
Vladimír Mostýn
Keyword(s):  
Success Rate ◽  
Basic Concept ◽  
Infrared Image ◽  
Data Sets ◽  
Infrared Images ◽  
Intended Application ◽  
Kinect V2 ◽  
Additional Information ◽  
Key Points ◽  
Depth Analysis

This article presents a novel correction filter for infrared images captured by Kinect v2 sensor. Intended application areas are described and the basic concept of proposed sensory subsystem as well. Preliminary tests of the sensor Kinect v2 in real conditions gave promising results, therefore in-depth analysis of its applicability was performed. In the framework of the analysis, a relation between infrared value and a distance of captured surface was evaluated for different colors of the surface. Based on that relation and on additional information about the depth of a pixel, a correction filter was created. The filter allows improving infrared image in order to increase the success rate probability to be able to detect specific features and key points by algorithms more easily. Outputs from the filter on real conditions data sets are also presented in the article.

Robust Object Detection in Military Infrared Image

2013 ◽  
Vol 5 (1) ◽  
pp. 30-36
Author(s):  
Tao Gao
Keyword(s):  
Object Detection ◽  
Mean Shift ◽  
Infrared Image ◽  
Future Research ◽  
Segmentation Method ◽  
Infrared Images ◽  
Form Complex ◽  
Texture Characteristics ◽  
The Mean ◽  
Texture Background

The innovation of this paper is that we put forward a new algorithm of object detection form military infrared images with texture background according to the Mean-shift smooth and segmentation method combined with eight directions difference clustering. According to the texture characteristics of background, smoothing and clustering is carried out to extract the characteristics of object. The experimental results show that the algorithm is able to extract the object information form complex infrared texture background with better self-adapting and robustness. Future research particularly lies in raising the accuracy of object extracted.

The Research of Non-Uniformity Correction of Infrared Image in Thermal Wave Testing Based on Homomorphism Technology

2011 ◽  
Vol 58-60 ◽  
pp. 2376-2380
Author(s):  
Yuan Jia Song ◽  
Wei Zhang ◽  
Zheng Wei Yang ◽  
Guo Feng Jin
Keyword(s):  
Thermal Wave ◽  
Signal To Noise Ratio ◽  
Infrared Image ◽  
Future Research ◽  
Infrared Images ◽  
Signal To Noise ◽  
Improve Image Quality ◽  
High Noise ◽  
Low Contrast ◽  
Homomorphic Filtering

The infrared thermal wave technology is a new nondestructive testing (NDT) method with a kind of advantage, including non-contact, intuitionistic, fast et al. But the infrared images always have defects that the low-contrast and high-noise due to uneven brightness and calefaction in the testing process, which enhance the difficulty of following quantitative distinguishment of defects. Therefore, the improved homomorphic filtering is given in this article. The detailed processes of the method and testing results are given. The results of the experiments show that the method has higher peak signal to noise ratio (PSNR), can improve image quality, which establish basis for future research of image segmentation.

ECONOMIC GROWTH ANALYSIS: A STUDY ON MALAYSIAN MARKET

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
Suhaily Maizan Abdul Manaf ◽  
Shuhada Mohamed Hamidi ◽  
Nur Shafini Mohd Said ◽  
Siti Rapidah Omar Ali ◽  
Nur Dalila Adenan
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Direct Investment ◽  
Inflation Rate ◽  
Government Expenditure ◽  
Correlation Method ◽  
Research Direction ◽  
Least Square ◽  
Future Research ◽  
Internal And External Factors

Economic performance of a country is mostly determined by the growth and any other internal and external factors. In this study, researchers purposely focused on Malaysian market by examining the relationship between export, inflation rate, government expenditure and foreign direct investment towards economic growth in Malaysia by applying the yearly data of 47 years from 1970 to 2016 using descriptive statistics, regression model and correlation method analysis. By applying Ordinary Least Square (OLS) method, the result suggests that export, government expenditure and foreign direct investment are positively and significantly correlated with the economic growth. However, inflation rate has negative and insignificant relationship with the economic growth. The outcome of the study is suggested to be useful in providing the future research direction towards the economic growth in Malaysia. Keywords: economic growth; export; inflation rate; government expenditure

THE METADIALOGUE CONCEPT IN ITS LINGUISTIC ASPECT

2019 ◽  
Vol 29 (5) ◽  
pp. 721-726
Author(s):  
Yu.V. Kupriyanova ◽  
I.M. Vasilyanova
Keyword(s):  
Subject Matter ◽  
Dynamic Structure ◽  
Point Of View ◽  
Semantic Relations ◽  
Human Cognition ◽  
Main Research ◽  
Key Points ◽  
Research Findings ◽  
The Subject ◽  
Linguistic Aspect

The article summarizes the key points in the development of the metadialogue phenomenon from a linguistic point of view. Some stages of the development of this concept and the difficulties associated with its structuring are covered. The main research findings of modern foreign and domestic experts on its study are considered. Some characteristics of the subject of the research from the standpoint of various pragmatic installations are given. On the basis of the dynamic structure of the metadialogue development, certain principles of semantic relations connected with the dialectical nature of human cognition are presented. Excursion into the history and evolution of the concept is presented. Several types of formulation of the subject matter are given. In accordance with the goal of speech exposure, internal problems of the development of metadialogue are highlighted and the critical points related to solving these problems are described. The rules of metadialogue flow are explained at the level of steps, the success/failure of which directly affects the final result of communication. The prospects of development of the concept research in accordance with various types of discourse are indicated.

A Review of Drug Side Effect Identification Methods

2020 ◽  
Vol 26 (26) ◽  
pp. 3096-3104 ◽  
Author(s):  
Shuai Deng ◽  
Yige Sun ◽  
Tianyi Zhao ◽  
Yang Hu ◽  
Tianyi Zang
Keyword(s):  
Side Effects ◽  
Future Research ◽  
Drug Side Effects ◽  
Important Indicator ◽  
Advantages And Disadvantages ◽  
Key Points ◽  
Network Methods ◽  
Future Research Directions ◽  
The One ◽  
Approved Drugs

Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.

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