Contention protocols for ring networks with large bandwidth-delay products

1993 ◽  
Vol 6 (3) ◽  
pp. 115-126
Author(s):  
David C. Feldmeier
1990 ◽  
Vol 137 (4) ◽  
pp. 310 ◽  
Author(s):  
R.F. Browne ◽  
R.M. Hodgson
Keyword(s):  

Author(s):  
Leonardo Zappelli

AbstractNowadays, the design of dividers is based on electromagnetic software that optimizes some geometric parameters to obtain the required performance. The choice of the geometry of the discontinuities contained in the divider and of the optimization initial point is quite critical to satisfy the divider requirements. In the last years, it is quite rare to find in the literature a theoretical approach helping the designers in the choice of the divider geometry. Helpful suggestion can derive by the analysis of the electric field in a trial divider that satisfies power division among the output ports in a thin band. In fact, the electric field null can be filled with metallic septa that ensure the same behavior at any frequency. The optimization of the septa position/form with numerical electromagnetic software permits to obtain divider with large bandwidth. A further analysis of the electric field null in the divider permits to add lateral metallic septa that further enlarge the transmission band. Finally, the design of an input matching network increases the transmitted power to the desired value.


Author(s):  
Nannan Li ◽  
Yu Pan ◽  
Yaran Chen ◽  
Zixiang Ding ◽  
Dongbin Zhao ◽  
...  

AbstractRecently, tensor ring networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performance of TRNs, rank selection is seldom studied in previous works and usually set to equal in experiments. Meanwhile, there is not any heuristic method to choose the rank, and an enumerating way to find appropriate rank is extremely time-consuming. Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region. Therefore, based on the above phenomenon, we propose a novel progressive genetic algorithm named progressively searching tensor ring network search (PSTRN), which has the ability to find optimal rank precisely and efficiently. Through the evolutionary phase and progressive phase, PSTRN can converge to the interest region quickly and harvest good performance. Experimental results show that PSTRN can significantly reduce the complexity of seeking rank, compared with the enumerating method. Furthermore, our method is validated on public benchmarks like MNIST, CIFAR10/100, UCF11 and HMDB51, achieving the state-of-the-art performance.


2021 ◽  
Vol 42 (4) ◽  
pp. 493-496
Author(s):  
Guangxu Shen ◽  
Wenjie Feng ◽  
Wenquan Che ◽  
Yongrong Shi ◽  
Yiming Shen

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