A method using long digital straight segments for fingerprint recognition

In this paper, we proposed a new method using long digital straight segments (LDSSs) for fingerprint recognition based on such a discovery that LDSSs in fingerprints can accurately characterize the global structure of fingerprints. Different from the estimation of orientation using the slope of the straight segments, the length of LDSSs provides a measure for stability of the estimated orientation. In addition, each digital straight segment can be represented by four parameters: x-coordinate, y-coordinate, slope and length. As a result, only about 600 bytes are needed to store all the parameters of LDSSs of a fingerprint, as is much less than the storage orientation field needs. Finally, the LDSSs can well capture the structural information of local regions. Consequently, LDSSs are more feasible to apply to the matching process than orientation fields. The experiments conducted on fingerprint databases FVC2002 DB3a and DB4a show that our method is effective.

Development of a digital tool to overcome the challenges of rural food SMEs

It has been recognised that throughout the UK, rural economies have a significant potential for growth but despite the potential for growth, many rural businesses face barriers that prohibit their expansion. In this study, we focus on one particular group of rural small- to medium-sized enterprises (SMEs): food and drink producers. Through user engagement activities, we identify the issues and needs associated with distributing products to the market, in order to understand the main issues which prevent rural food and drink SMEs from expansion, and to establish the requirements for a digital solution to this challenge.