The increased absorption volume of traveling wave distributed photodetectors can be used for high power generation without bandwidth reduction. In these traveling wave photodetectors, in order to not be limited by round-trip bandwidth limit, half of the generated photocurrent, which is traveling towards the input end, has to be absorbed in an input termination. We propose cancellation of the backward propagating current by using a multi-section transmission line to eliminate this loss. The impedances of the individual transmission line sections are chosen such that the backward current (traveling towards input end) generated by each of the diodes is canceled by the reflected fraction of the forward current (traveling towards output end) generated by the preceding diodes. With backward wave cancellation, RF response increases by up to 6dB while maintaining high-bandwidth. We present here the experimental results of a traveling-wave-backward-wave-cancelled photodetector with 38GHz bandwidth and up to -1dBm of linear RF output at 40GHz.
Similarity between images is used for storage and retrieval in image databases. In the literature, several similarity measures have been proposed that may be broadly categorized as: (1) metric based, (2) set-theoretic based, and (3) decision-theoretic based measures. In each category, measured based on crisp logic as well as fuzzy logic are available. In some applications such as image databases, measures based on fuzzy logic would appear to be naturally better suited, although so far no comprehensive experimental study has been undertaken. In this paper, we report results of some of the experiments designed to compare various similarity measures for application to image databases. We are currently working with texture images and intend to work with face images in the near future. As a first step for comparison, the similarity matrices for each of the similarity measures are computed over a set of selected textures and are presented as visual images. Comparative analysis of these images reveals the relative characteristics of each of these measures. Further experiments are needed to study their sensitivity to small changes in images such as illumination, magnification, orientation, etc. We describe these experiments (sensitivity analysis, transition analysis, etc.) that are currently in progress. The results from these experiments offer assistance in choosing the appropriate measure for applications to image databases.
Texture is an important property useful for image segmentation and the inference of 3-D information in the scene. Many approaches were proposed for analyzing textures. Among them are feature-based approaches and model-based approaches. In a feature-based environment various textural features are extracted from each textured image(or subimage) and are used to classify or discriminate given textures i. e. no explicit consideration of models is taken into account and thus the generation aspect is ignored. In model-based analysis we describe texture in terms of mathematical model which has both analysis and synthesis abilities. In the literature several comparative studies of feature-based methods are found. However few explicit comparative studies of model-based methods have been reported. This paper describes the development of some criteria to compare two model-based texture analysis methods (Time Series model and Markov Random Field model).
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