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This thesis is concerned with the development of a novel RFID ac current sensing technique for smart power monitoring systems. The research aims to explore designing self-tuning RFID tags and antenna designs and transforming simple tags into passive ac current sensors. The sensing mechanism by which a self-tuning RFID tag is linked with a varactor tuning circuit and integrated into a current transformer is described. The proposed sensing tag structure is less complex and provides a cost-effective solution for power monitoring when many tags on individual appliances communicate wirelessly with a centrally mounted single RFID reader in the views of the tags to be read. New optimised RFID tag antenna designs for a current transformer are introduced. Antenna miniaturisation techniques are adopted in designing tag antennas to achieve compact physical integration with the transformer housing while maintaining the tag link. These optimised tag antennas are designed in order to reduce the size of the tag system. The proposed current sensing concept is further explored to utilise two tag antennas in a single design. The two tag antennas are coupled with different tuning circuits and integrated into a single transformer for increasing the current sensing range of the sensor. The antenna design techniques of designing two tag antennas in close proximity with each other and how the mutual coupling between the tag antennas can be reduced are also studied.

The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile phones, and the accelerated revolutions in the low cost storage devices, boosts the multimedia data production rate drastically. Witnessing such an iniquitousness of digital images and videos, the research community has been projecting the issue of its significant utilization and management. Stored in monumental multimedia corpora, digital data need to be retrieved and organized in an intelligent way, leaning on the rich semantics involved. The utilization of these image and video collections demands proficient image and video annotation and retrieval techniques. Recently, the multimedia research community is progressively veering its emphasis to the personalization of these media. The main impediment in the image and video analysis is the semantic gap, which is the discrepancy among a user's high-level interpretation of an image and the video and the low level computational interpretation of it. Content-based image and video annotation systems are remarkably susceptible to the semantic gap due to their reliance on low-level visual features for delineating semantically rich image and video contents. However, the fact is that the visual similarity is not semantic similarity, so there is a demand to break through this dilemma through an alternative way. The semantic gap can be narrowed by counting high-level and user-generated information in the annotation. High-level descriptions of images and or videos are more proficient of capturing the semantic meaning of multimedia content, but it is not always applicable to collect this information. It is commonly agreed that the problem of high level semantic annotation of multimedia is still far from being answered. This dissertation puts forward approaches for intelligent multimedia semantic extraction for high level annotation. This dissertation intends to bridge the gap between the visual features and semantics. It proposes a framework for annotation enhancement and refinement for the object/concept annotated images and videos datasets. The entire theme is to first purify the datasets from noisy keyword and then expand the concepts lexically and commonsensical to fill the vocabulary and lexical gap to achieve high level semantics for the corpus. This dissertation also explored a novel approach for high level semantic (HLS) propagation through the images corpora. The HLS propagation takes the advantages of the semantic intensity (SI), which is the concept dominancy factor in the image and annotation based semantic similarity of the images. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other, while semantic similarity of the images are based on the SI and concept semantic similarity among the pair of images. Moreover, the HLS exploits the clustering techniques to group similar images, where a single effort of the human experts to assign high level semantic to a randomly selected image and propagate to other images through clustering. The investigation has been made on the LabelMe image and LabelMe video dataset. Experiments exhibit that the proposed approaches perform a noticeable improvement towards bridging the semantic gap and reveal that our proposed system outperforms the traditional systems.