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The purpose of this paper is to investigate the feasibility of automating Google Scholar searching to harvest citation data of monographs for collection analysis.

This study discusses the creation and refinement of a Scraper application programming interface query structure created to match library collection inventories to their Google Scholar listings to retrieve citation counts.

This paper indicates that Google Scholar is a feasible and usable tool for retrieving monograph citation data.

This study shows that Google Scholar citation data can be harvested for monographs in an automated fashion to serve as a source of bibliographic data, something not typically done outside of individual academics and writers tracking their personal academic impact factors.

This study develops an empirical framework to address how large third-party sellers (TPSs) can apply customer acquisition strategies to improve their performance in consumers’ person-goods matching process and how the platform firm’s similar strategies moderate the effects of TPSs’ strategies.

Using data collected from the top ten TPSs from a Chinese e-commerce platform, the fixed effect model is used to validate the conceptual model and hypotheses.

The study results show that both market detection strategy and matching optimization strategy can help large TPSs improve their sales performance. Moreover, the similar market detection strategy applied by the platform firm weakens the effect of large TPSs’ customer acquisition strategies, while the similar matching optimization strategy applied by the platform firm strengthens the effect of large TPSs’ customer acquisition strategies.

This study provides firsthand evidence on the performance of large TPSs’ and the platform firm’s strategies. It demonstrates the effectiveness of large TPSs’ market detection strategy and matching optimization strategy, which can be adopted to meet consumers’ search and evaluation motivations in their person-goods matching process respectively. Moreover, it identifies the role of platform firms by showing the moderating effect of similar strategies adopted by the platform firm on the effect of large TPSs’ customer acquisition strategies.

In the literature, while designing broadband matching networks, transducer power gain (TPG) is used to measure the transferred power. Generally, in TPG expressions, load and back-end impedances of the matching network are used. This study aims to derive a new quality factor-based TPG expression.

In deriving the new expression, narrowband L type-matching network design approach is used and the new expression in terms of back-end quality factor, load quality factor and output port quality factor is obtained. Then, a broadband-matching network design approach using the derived TPG expression is proposed.

Two broadband double-matching networks are designed by using the proposed design approach using the derived TPG expression. Performances of the designed-matching networks are compared with the performances of the matching networks designed by means of simplified real frequency technique which is a well-known technique in the literature, and it is shown that they are nearly the same.

In broadband-matching problems, generally an impedance-based TPG expression is used, and it must be satisfied by the designed broadband-matching networks. But, in the literature, there is no quality factor-based TPG expression that can be used in broadband-matching problems. So, this gap in the literature has been filled by this paper.

The motion vector estimation algorithm is very widely used in many image process applications, such as the image stabilization and object tracking algorithms. The conventional searching algorithm, based on the block matching manipulation, is used to estimate the motion vectors in conventional image processing algorithms. During the block matching manipulation, the violent motion will result in greater amount of computation. However, too large amount of calculation will reduce the effectiveness of the motion vector estimation algorithm. This paper aims to present a novel searching method to estimate the motion vectors effectively.

This paper presents a novel searching method to estimate the motion vectors for high-resolution image sequences. The searching strategy of this algorithm includes three steps: the larger area searching, the adaptive directional searching and the small area searching.

The achievement of this paper is to develop a motion vector searching strategy to improve the computation efficiency. Compared with the conventional motion vector searching algorithms, the novel motion vector searching algorithm can reduce the motion matching manipulation effectively by 50 per cent.

This paper presents a novel searching strategy to estimate the motion vectors effectively. From the experimental results, the novel motion vector searching algorithm can reduce the motion matching manipulation effectively, compared with the conventional motion vector searching algorithms.

The purpose of this paper is to study the application of feature‐based image matching algorithm for PCB matching without using special fiducial marks.

Speed‐up robust feature (SURF) is applied to extract the interest points in PCB images. An advanced threshold is set to reject the interest points with low responses to speed up feature computation. In order to improve the performance for rotation, the descriptors are based on multi‐orientations. The many‐to‐many tentative correspondences are determined with a maximum distance. Hough transform is used to reject the mismatches and the affine parameters are computed with a square‐least solution.

Results show that the method proposed in this paper can match the PCB images without using special fiducial marks effectively. The image matching algorithm shows a better performance for image rotation than the standard SURF and it succeeds in matching the image including repetitive patterns which will deteriorate the distinctiveness of feature descriptors.

Additional orientations produce more descriptors so that it takes extra time for feature description and matching.

The paper proposes a SURF‐based image matching algorithm to match the PCB images without special fiducial marks. This can reduce the complexity of PCB production. The image matching algorithm is robust to image rotation and repetitive patterns and can be used in other applications of image matching.

Stereo vision is an attractive perception technique for mobile robots navigation. Stereo matching is a crucial part of stereo vision and its precision dominates the precision of reconstruction. Based on a geometry constraint applicable to natural terrain, the purpose of this paper is to present a multi‐stage stereo matching algorithm to improve matching accuracy.

In the multi‐stage matching algorithm, points with larger intensity gradient are matched in earlier stages. Using several constraints and statistical means, information from earlier stages is utilized to assist in matching of later stages to improve matching accuracy.

The multi‐stage matching algorithm improves the matching accuracy of stereo pairs of natural terrain in various conditions.

The algorithm demonstrates advantages over area‐matching algorithm both in matching accuracy and computation efficiency. However, if used for real‐time navigation, it still needs the assistance of specialized hardware or window selection technique.

The algorithm is able to produce dense disparity maps of natural terrain with fairly high accuracy and can be used for the navigation of planetary rover or other outdoor mobile robots.

The paper provides a new approach to produce accurate and dense disparity maps of natural terrain, which laid the foundation for its use in outdoor mobile robots navigation.

To present a stereo matching algorithm which satisfies the need of visual navigation on outdoor natural terrain for lunar rover or other mobile robots.

A feature‐assisted matching algorithm is presented to generate dense and accurate disparity map of natural terrain. Multi‐feature matching strategy produces reliable matching results for edge points. Disparity monotony constraint is derived and other geometrical constraints are introduced. With these constraints the edge‐matching results are used to limit the search region in area‐matching. As a result the algorithm produces dense disparity maps with fairly high accuracy and demonstrates advantages over straightforward area‐matching algorithm in improving matching accuracy.

With the help of several constraints, the feature‐assisted matching algorithm performs well in the matching of stereo image pairs of natural terrain.

The algorithm focus on improving the accuracy of stereo image pairs matching of natural terrain and computation complexity is not an important designing factor. Only with the assistance of special hardware or other technique can the algorithm be used for real‐time navigation.

The algorithm is able to produce dense disparity map of natural terrain with rather high accuracy and can be used for the navigation of lunar rover or other outdoor mobile robots.

The paper provides a new approach to produce accurate and dense disparity map of natural terrain.

Describing musical pieces, whether sound recordings, scores, librettos, videos, has always involved cataloger interpretation and judgment. There is considerable variation in records created for exactly the same item. And there is never “proof” that two records which seem to describe the same item actually do. This paper aims to address this issue.

This paper describes some of the challenges encountered in developing software for matching music records, and some approaches to making the software reliable.

The paper finds that matching can be used successfully to create GLIMIR clusters in the WorldCat database. Work is needed in several areas to complete the implementation, but intermediate results are promising.

This implementation will allow end‐user applications to collocate resources, to improve discovery and delivery in a complex bibliographic universe

This chapter investigates the relationship between heterogeneous social preferences and charitable giving under alternative prices of giving and types of subsidies. Using 10 allocation decisions, we categorize participants’ social preferences as self-interested, inequity averse, or social surplus maximizing. In subsequent charitable giving treatments, analysis of within-person decision-making gives support for several predictions consistent with social preference types: social surplus maximizers are most likely to give to a charity that increases production; inequity averters give more to charity than do other groups; all preference types give more when the price of giving declines; and social surplus maximizers are more responsive to the price of giving than are inequity averters.

This article estimates the loan spread equation taking into account the endogenous matching between banks and firms in the loan market. To overcome the endogeneity problem, I supplement the loan spread equation with a two-sided matching model and estimate them jointly. Bayesian inference is feasible using a Gibbs sampling algorithm that performs Markov chain Monte Carlo (MCMC) simulations. I find that medium-sized banks and firms tend to be the most attractive partners, and that liquidity is also a consideration in choosing partners. Furthermore, banks with higher monitoring ability charge higher spreads, and firms that are more leveraged or less liquid are charged higher spreads.