Senthilkumar Thangavelu, Sangeetha Gunasekar and Amalendu Jyotishi
The purpose of this paper is to understand the nature of the feedback effects of economic growth on innovation. The question is whether the economies with higher levels of…
Abstract
Purpose
The purpose of this paper is to understand the nature of the feedback effects of economic growth on innovation. The question is whether the economies with higher levels of endowments have a declining feedback effect of income on innovation and contribute to the development of effective innovation policies are raised.
Design/methodology/approach
This study hypothesizes that innovation input’s response to economic growth in terms of income is an inverted “U” shaped path, whereas the innovation output’s response to income is positive and asymptotic. This paper uses the global innovation index data of 154 countries over the period 2013–2017 on innovation and gross domestic product for the analysis using the fixed-effect regression models.
Findings
The results confirmed the inverted U shaped relationship in the line of Kuznets’s curve for innovation input and that of negative slope and asymptotic behaviour for innovation output.
Research limitations/implications
In this study, the analysis performed using the global innovation index 2013–2017 data. This study can be extended at each factor level to understand this phenomenon in depth with more data and to help in improving the innovation policies for the betterment of the economic growth.
Practical implications
This study suggests that developed countries need to guard against complacency in their innovation efforts because of the asymptotic nature exhibited through the effective development of innovation policies. The developing economies can look forward to establishing themselves in the domains of innovation input through imitation of technologies.
Originality/value
This paper extends the study of feedback effects of economic growth on innovation. This study brings out the nature of feedback effects of economic growth on input innovation and output innovation activities. The results show a declining feedback effect of income on innovation in economies with a higher level of endowments and highlight the inclusion of feedback effects of economic activities on the innovation while designing the innovation and economic policies of a country.
Details
Keywords
Zijun Jiang, Zhigang Xu, Yunchao Li, Haigen Min and Jingmei Zhou
Precise vehicle localization is a basic and critical technique for various intelligent transportation system (ITS) applications. It also needs to adapt to the complex road…
Abstract
Purpose
Precise vehicle localization is a basic and critical technique for various intelligent transportation system (ITS) applications. It also needs to adapt to the complex road environments in real-time. The global positioning system and the strap-down inertial navigation system are two common techniques in the field of vehicle localization. However, the localization accuracy, reliability and real-time performance of these two techniques can not satisfy the requirement of some critical ITS applications such as collision avoiding, vision enhancement and automatic parking. Aiming at the problems above, this paper aims to propose a precise vehicle ego-localization method based on image matching.
Design/methodology/approach
This study included three steps, Step 1, extraction of feature points. After getting the image, the local features in the pavement images were extracted using an improved speeded up robust features algorithm. Step 2, eliminate mismatch points. Using a random sample consensus algorithm to eliminate mismatched points of road image and make match point pairs more robust. Step 3, matching of feature points and trajectory generation.
Findings
Through the matching and validation of the extracted local feature points, the relative translation and rotation offsets between two consecutive pavement images were calculated, eventually, the trajectory of the vehicle was generated.
Originality/value
The experimental results show that the studied algorithm has an accuracy at decimeter-level and it fully meets the demand of the lane-level positioning in some critical ITS applications.