9 research outputs found
Transmission scheduling for wireless mesh networks with temporal reuse
Link-assigned transmission schedules with timeslot reuse by multiple links in both the space and time domains are
investigated in this study for stationary multihop wireless mesh networks with both rate and power adaptivity.
Specifically, cross-layer optimised schedules with proportionally fair end-to-end flow rates and network coding
capability are constructed for networks operating under the physical interference model with single-path minimum
hop routing. Extending transmission rights in a link-assigned schedule allows for network coding and temporal
reuse, which increases timeslot usage efficiency when a scheduled link experiences packet depletion. The
schedules that suffer from packet depletion are characterised, and a generic temporal reuse-aware achievable rate
region is derived. Extensive computational experiments show improved schedule capacity, quality of service, power
efficiency and benefit from network coding accrued with schedules optimised in the proposed temporal reuseaware
convex rate region.http://jwcn.eurasipjournals.com/content/2011/1/8
Research on Change of Land Use Based on Decision Tree in the Horqin Sandy Land in the Past 25 Years
The Multiple Classification Method of Signal Recognition for Spacecraft Based on SAE Network
The Semaphore Identification and Fault Troubleshooting Modus for Spacecraft Originating from Deep Learning and RF Method
Urban zoning using higher-order Markov random fields on multi-view imagery data
Urban zoning enables various applications in land use analysis and urban planning. As cities evolve, it is important to constantly update the zoning maps of cities to reflect urban pattern changes. This paper proposes a method for automatic urban zoning using higher-order Markov random fields (HO-MRF) built on multi-view imagery data including street-view photos and top-view satellite images. In the proposed HO-MRF, top-view satellite data is segmented via a multi-scale deep convolutional neural network (MS-CNN) and used in lower-order potentials. Street-view data with geo-tagged information is augmented in higher-order potentials. Various feature types for classifying street-view images were also investigated in our work. We evaluated the proposed method on a number of famous metropolises and provided in-depth analysis on technical issues. © Springer Nature Switzerland AG 2018
