RESUMEN
Due to the lack of dependency on beacon messages for location exchange, the beaconless geographic routing protocol has attracted considerable attention from the research community. However, existing beaconless geographic routing protocols are likely to generate duplicated data packets when multiple winners in the greedy area are selected. Furthermore, these protocols are designed for a uniform sensor field, so they cannot be directly applied to practical irregular sensor fields with partial voids. To prevent the failure of finding a forwarding node and to remove unnecessary duplication, in this paper, we propose a region-based collision avoidance beaconless geographic routing protocol to increase forwarding opportunities for randomly-deployed sensor networks. By employing different contention priorities into the mutually-communicable nodes and the rest of the nodes in the greedy area, every neighbor node in the greedy area can be used for data forwarding without any packet duplication. Moreover, simulation results are given to demonstrate the increased packet delivery ratio and shorten end-to-end delay, rather than well-referred comparative protocols.
RESUMEN
BACKGROUND AND OBJECTIVE: Digital tomosynthesis (DTS) based on filtered-backprojection (FBP) reconstruction requires a full field-of-view (FOV) scan and relatively dense projections, which results in high doses for medical imaging purposes. To overcome these difficulties, we investigated region-of-interest (ROI) or interior DTS reconstruction where the x-ray beam span covers only a small ROI containing a target area. METHODS: An iterative method based on compressed-sensing (CS) scheme was compared with the FBP-based algorithm for ROI-DTS reconstruction. We implemented both algorithms and performed a systematic simulation and experiments on body and skull phantoms. The image characteristics were evaluated and compared. RESULTS: The CS-based algorithm yielded much better reconstruction quality in ROI-DTS compared to the FBP-based algorithm, preserving superior image homogeneity, edge sharpening, and in-plane resolution. The image characteristics of the CS-reconstructed images in ROI-DTS were not significantly different from those in full-FOV DTS. The measured CNR value of the CS-reconstructed ROI-DTS image was about 12.3, about 1.9 times larger than that of the FBP-reconstructed ROI-DTS image. CONCLUSIONS: ROI-DTS images of substantially high accuracy were obtained using the CS-based algorithm and at reduced imaging doses and less computational cost, compared to typical full-FOV DTS images. We expect that the proposed method will be useful for the development of new DTS systems.