Microfluidic paper-based analytical devices can be used in preliminary screening and diagnosis of specific diseases. It has the advantages of low costs and quick diagnosis. After chemical reaction, the paper chip will display the disease's relative status through color identification or electrochemistry testing. When compared to electrochemistry testing, the non-contact color identification method has the advantages of low costs, reusability and not requiring cleaning. However since research in the past have encountered many problems with the color identification method, this has led to the negation of the advantages of microfluidic paper-based analytical devices in quick testing. For this reason, this study developed a portable testing apparatus with image analysis as a foundation and uses a self-designed imaging processing calculation method to implement color identification. The hardware equipment designed for image processing in this study uses a micro embedded style system and imaging equipment as the hardware framework, along with a Web application to implement a multi-platform operating interface. Color quantization was used as the foundation for the image processing method, to calculate the paper chip's color. Furthermore, this study also carried out a variety of color quantization methods to compare the color quantization results of the microfluidic paper-based analytical devices. Methods include uniform color quantization, median-cut algorithm, k-means clustering algorithm and self-organizing maps neural networks. During the experiment phase of this study, we found that median-cut was superior to other methods in application. This result is different from many other research works conducted on color quantization. Lastly, the experiment results showed the usability of this testing setup.

The development of image base, portable microfluidic paper-based analytical device

Geo origination data is generated for a geo-tagged media device of a user from measurements performed by sensors. The geo origination data is sent to a server system. At the server system, geo-tagged media content elements are selected based on the geo origination data. Further, based on the selected geo-tagged media content elements and the geo origination data, geo-referenced rendering data to be used for rendering media content from the selected geo-tagged media content elements perceivable to the user of the geo-tagged media device is generated. The geo-referenced rendering data can be streamed to the geo-tagged media device along with media content derived from the geo-tagged media content elements for rendering at the geo-tagged media device.

GEO-Referencing Media Content

SIMPLICITY DRIVEN SOLUTION FOR RECOGNITION AND LOCALIZATION OF TOMATOES AND FRUITS IN A ROBOTIC HARVESTING ENVIRONMENT Xuming Chen Advisor: University of Guelph, 2015 Professor Simon X. Yang Harvesting robots have been increasingly used in greenhouses and orchards in Canada, USA, and other countries. Tomatoes are the most popular greenhouse product, and oranges, apricots and plums are popular fruits in orchards. For better tomato and fruit freshness and automatic harvesting capability, real-time recognition and localization of tomatoes and fruits are becoming critical. The robot vision system is the most important aspect of the process for detecting and recognizing the objects upon which a harvesting robot works. This research aims to develop a more practical robot vision system that can be easily implemented in the real world and is computationally efficient. First, a single color component (V or U in a YUV color space) is used to segment out the region of interest (ROI) of the harvest objects from the harvest background, stems, and foliage. Second, an edge extractor with constrained curvature is developed to extract the arc edges within a certain radius range based on the model (definition) of the harvest objects. Based on the qualified edges, the central points of each qualified harvest object in the ROI are calculated. Finally, according to the circle-to-circle relationship found in the geometric analysis, the overlapping area between two harvest objects is calculated, and the depth ordering of the two overlapping harvest objects is determined. In the further research, a sub-block object searching algorithm is proposed to solve the problem of the missing objects’ boundaries. For advanced harvesting requirements of online sorting, a salience-based algorithm is proposed to discriminate the degrees of ripeness in tomatoes. For computing acceleration, task scheduling and load balancing are conducted for multi-core and cloud-based computing. The key contributions of this thesis are the practical study of multiple aspects of robotic vision problems encountered in the harvesting of tomatoes and fruits.

Simplicity Driven Solution for Recognition and Localization of Tomatoes and Fruits in a Robotic Harvesting Environment

In this modern era of entertainment, content based multimedia services for general purpose users are extending in various dimensions. Earlier, the users were capable of using only local applications and some internet based applications with restricted privileges. The use of Universal Plug and Play Audio Visual (UPnP-AV) standards [1], HTML5 support in browsers, webrtc standards, and extended standards specially for mobiles devices will bring into play more and more Cloud Multimedia Services. However the requirements implicitly demand more interaction and participation in commonly driven activities giving a universal, unified experience. In this paper a solution for this challenge is proposed with the usage of context profile on diverse devices for adaptive services from cloud server using transcoding framework. The context profiling would be governing the transcoding framework algorithms used for adaptive or customized output from cloud. A more suitable cloud architecture from the multimedia service provider point of view is proposed.

Context Profiling Based Multimedia Service on Cloud

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Context Profiling Based Multimedia Service on Cloud