A method for low-cost, low-impact insect tracking using retroreflective tags

Current methods for direct tracking of individual bee movement behaviour have several limitations. In particular, the weight and size of some types of electronic tag may limit their use to larger species. Radars and other electronic systems are also often large and very expensive to deploy. A tool is needed that complements these electronic-tag methods. In particular one that is simple to use, low-cost, can have a high spatial resolution and can be used with smaller insects. This paper presents a candidate method that uses retroreflective tags. These are detected using a camera with a global electronic shutter, with which we take photos with and without a flash. The tags can be detected by comparing these two photos. The small retroreflective tags are simple and light-weight, allowing many bees to be tagged at almost no cost and with little effect on their behaviour. We demonstrate this retroreflector-based tracking system (RTS) with a series of simple experiments: Training and validation with a manually positioned tag; case studies of individual bees; tracking multiple bees as they forage in a garden; use of real-time monitoring to allow easy re-observation to enable a simple floral preference experiment; and a very brief experiment with 3D path reconstruction (integrating two devices). We found we could detect bees to a range of about 35 m with the current configuration.%; We finally compare tagged and untagged bee foraging to assess the effect of the tags on bee behaviour. We envisage the system will be used in future to increase detection rates in mark-re-observation studies; provide 3D flight path analysis; and for automated long-term monitoring. In summary, this novel tracking method has advantages that complement those of electronic-tag tracking which we believe will lead to new applications and areas of research.

Sistema de identificación y seguridad para estudiantes mediante RFID

Radio frequency identification (RFID) is one of the most promising new technologies belonging to the systems of data acquisition and automatic identification (AIDC). It consists of three basic elements: an electronic tag, a tag reader and a database. Which makes it a versatile system with multiple applications for the industry, home and building automation. This document presents the implementation of an identification and security system for students through RFID. The system uses passive tags, an RFID reader with Arduino card and has connectivity to a database created in MySQL with C ++ visual programming. The main objective is to store student access and location data, this way you can make reports of class attendance, use and / or access to laboratories in addition to having real time monitoring of the students' location. Compared to some other similar technologies, RFID does not require visual contact with the reader, is affordable and easy to implement.

Adopted children’s co-production and use of ‘trove’ (a digitally enhanced memory box) to better understand their care histories through precious objects

This article presents an innovative project to develop and trial a prototype product called ‘trove’ to start to address challenges identified regarding current practice of life story work with children who are looked after and adopted. trove is a digitally enhanced memory box that utilises raspberry pi (a small single board computer) and radio-frequency identification (RFID) technologies to enable children to record their memories and to attach these to their precious objects using an electronic tag: providing a safe ‘container’ for their mementoes and memories. Located in theories of narrative identity and object attachment and drawing on Brodinsky’s concept of communicative openness, we describe the children’s engagements in the design and report the results of a small trial of 10 troves with adopted children in England.

Image recognition of individual cow based on SIFT in Lαβ color space

Using image recognition technology to identify individual dairy cattle with her biological features shows strong stability. This kind of non-contact, high precision and low cost individual recognition methods based on image processing are more and more popular recently to replace the electronic tag and ear mark which can hurt the cattle’s psychology and physical health and can affect cattle’s behavior. By comparing the various color space transformations, he proposed a scale-invariant feature transform algorithm based on the Luminace of Lαβ color space. With this algorithm, a biological features recognition and management system of Holstein cow has been developed. The identification accuracy is higher than 98%, which is the best result than all the similar reports for cows’ identification.