In this paper, we introduce a concept of the hybrid lens as a novel form of optical information processing apparatus that integrates the conventional optical lenses and the recently proposed neural lens, that is, an image post-processing technique based on generative convolution neural networks (GCNN). This integration is based on leveraging the fact that the Fourier plane is the common working principle of both types of lens. We demonstrate how manipulating the coherent light's spectral components on the Fourier plane behind a biconvex lens is a computationally-free alternative to performing convolution matrix operation in GCNN, which involves high computational expenses. In our approach, the GCNN can perform image generation in a shorter time. Our hybrid lens only requires computational power at the levels that the embedded resources on medical devices can afford. This feature is very important for commercialization as it allows making standalone units like microscopes without relying on external resources such as computing clouds.
Automating the detection of the corn tassels during owering time is important in corn breeding. To control pollination, after a tassel is visible, the plant should be checked daily for emerging ears. The conventional methods are labor-intensive and time-consuming. In this study, we developed a technique for automatic detecting and locating corn tassel in unmanned aerial vehicle (UAV) imagery with the state-of-the art Faster Region based Convolutional Neural Network (Faster R-CNN). Each raw image was divided into 1000 x 1000 pixels sub-images, and 2000 sub-images were manually annotated for tassel locations with bounding boxes as ground-truth data. 80% of the annotated sub-images were used as training data and the remaining 20% were used for testing. The performance of the trained Faster R-CNN model was evaluated by customized evaluation criteria. The model achieved good performance on tassel detection with mean average precision of 91.78% and F1 score up to 97.98%.
In this paper, we investigate the problem of Preplanned Recovery with Redundant Multicast Trees (PRRMT) in optical networks. The redundant trees ensure the source node remains connected to all destination nodes for a multicast session request under single edge failures. Our objective is to minimize the total number of links used for both trees. We formulate PRRMT as an integer linear program (ILP), and also develop a heuristic algorithm. The ILP approach and heuristic algorithm are experimentally evaluated on 14-node NSFNET and 21-node Italian network. Experimental results show that: (1) ILP approach leads to optimal solutions but requires prohibitively long time, (2) Our heuristic algorithm yields optimal or near-optimal results in very short time, and (3) The edge-disjoint trees can protect the transmission for an edge failure.
For lack of optical random access memory, optical fiber delay line (FDL) is currently the only technology to implement optical buffering. Feed-forward and feedback are two types of FDL structures in optical buffering, both have advantages and disadvantages. In this paper, we present a novel architecture for WDM optical packet switches with an effective hybrid FDL buffering that combines the merits of both feed-forward and feedback schemes. The core of the switch architecture is the arrayed waveguide grating (AWG) and the tunable wavelength converter (TWC). It requires smaller optical device sizes and fewer wavelengths and has less noise than feedback architecture. At the same time feed-forward architecture can only do non-preemptive priority routing while ours supports preemptive priority routing. Our empirical results show that the new switch architecture significantly reduces packet loss probability.
This paper discusses a retrieval scheme for an information retrieval system in which the feedback from a number of users of the system about its performance (global feedback) is stored in the form of clusters called user-oriented clusters. The clusters are described by using the description of its constituent documents. The clusters and queries are represented as vectors and the measure of similarity between them is represented as the cosine of the angle between the two. The clusters are retrieved as per decreasing order of similarity with respect to a query. An important problem that arises in the context of cluster description is the significance of an index term assigned to documents. This problem, called term refinement problem, is formulated and solved. The experimental results of the proposed retrieval scheme are compared with those of the vector space model and the results obtained are encouraging.
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