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Image showing ring network layout. A ring network is a network topology in which each node connects to exactly two other nodes, forming a single continuous pathway for signals through each node – a ring. Data travels from node to node, with each node along the way handling every packet.
Ring network topology. A ring topology is a daisy chain in a closed loop. Data travels around the ring in one direction. When one node sends data to another, the data passes through each intermediate node on the ring until it reaches its destination. The intermediate nodes repeat (retransmit) the data to keep the signal strong. [5]
The Hirschberg–Sinclair algorithm is a distributed algorithm designed for leader election problem in a synchronous ring network. It is named after its inventors, Dan Hirschberg and J. B. Sinclair. The algorithm requires the use of unique IDs (UID) for each process. The algorithm works in phases and sends its UID out in both directions.
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3]
Token Ring is a physical and data link layer computer networking technology used to build local area networks. It was introduced by IBM in 1984, and standardized in 1989 as IEEE 802.5. It uses a special three-byte frame called a token that is passed around a logical ring of workstations or servers.
SqueezeNet is a deep neural network for image classification released in 2016. SqueezeNet was developed by researchers at DeepScale, University of California, Berkeley, and Stanford University. In designing SqueezeNet, the authors' goal was to create a smaller neural network with fewer parameters while achieving competitive accuracy.
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Contextual image classification, a topic of pattern recognition in computer vision, is an approach of classification based on contextual information in images. "Contextual" means this approach is focusing on the relationship of the nearby pixels, which is also called neighbourhood.