scholarly journals A Greedy Algorithm for Over-The-Cell Channel Routing

VLSI Design ◽  
1996 ◽  
Vol 5 (1) ◽  
pp. 23-36
Author(s):  
Gudni Gudmundsson ◽  
Simeon Ntafos
Keyword(s):  
Greedy Algorithm ◽  
Final Stage ◽  
Maximum Density ◽  
Greedy Heuristics ◽  
Channel Routing ◽  
Channel Density ◽  
Recent Advances ◽  
Vlsi Technology ◽  
Better Than

Recent advances in VLSI technology have made the area over cells available for routing. In this paper we present a new over-the-cell channel router that uses greedy heuristics to make the over-the-cell connections and to define the nets needed to complete the connections inside the channel. The router tries to reduce the channel density by moving segments that cross maximum density columns to the over-the-cell areas. The layout model used allows only planar connections over each cell. The final stage is to use an existing channel router to route the connections inside the channel. An important characteristic of the new router is that there is interaction between the decisions made for the over-the-cell connections and the connections needed inside the channel. It performs significantly better than previous over the-cell routers.

scholarly journals An Optimum Channel Routing Algorithm in the Knock-knee Diagonal Model

VLSI Design ◽  
1994 ◽  
Vol 1 (3) ◽  
pp. 233-242 ◽  
Author(s):  
Xiaoyu Song
Keyword(s):  
Routing Algorithm ◽  
Difficult Problem ◽  
Layout Design ◽  
Channel Routing ◽  
Routing Problem ◽  
Vlsi Layout ◽  
Channel Density ◽  
Optimum Algorithm ◽  
Important Time ◽  
Optimum Channel

Channel routing problem is an important, time consuming and difficult problem in VLSI layout design. In this paper, we consider the two-terminal channel routing problem in a new routing model, called knock-knee diagonal model, where the grid consists of right and left tracks displayed at +45° and –45°. An optimum algorithm is presented, which obtains d + 1 as an upper bound to the channel width, where d is the channel density.

scholarly journals Trade-Off Considerations in Designing Efficient VLSI Feasible Interconnection Networks

VLSI Design ◽  
1995 ◽  
Vol 2 (4) ◽  
pp. 365-374 ◽  
Author(s):  
S. Q. Zheng ◽  
B. Cong ◽  
S. Bettayeb
Keyword(s):  
Parallel Computation ◽  
Interconnection Networks ◽  
Hybrid Networks ◽  
Massively Parallel ◽  
Star Graph ◽  
Trade Off ◽  
Vlsi Technology ◽  
Massively Parallel Computing ◽  
Better Than ◽  
Simple Combination

It is well known that the hypercube has a rich set of good properties, and consequently it has been recognized an ideal structure for parallel computation. Nevertheless, according to the current VLSI technology, the implementation feasibility of the hypercube remains questionable when the size of the hypercube becomes large. Recent research efforts have been concentrated on finding good alternatives to the hypercube. The star graph was shown having many desirable properties of the hypercube, and in several aspects, the star graph is better than the hypercube. However, we observe that the star graph as a network has several disadvantages, compared with the hypercube. In this paper, we propose a class of new networks, the star-hypercube hybrid networks (or the SH networks). The SH network is a simple combination of both the star graph and the hypercube. This class of networks contains the star graph and the hypercube as subclasses. We show that the SH network is an efficient and versatile network for parallel computation, since it shares properties of both the hypercube and the star graph, and remedies several major disadvantages of the hypercube and the star graph. This class of networks provide more flexibility in choosing the size, degree, number of vertices, degree of fault tolerance, etc. in designing massively parallel computing structures feasible for VLSI implementations.

scholarly journals Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path

2021 ◽  
Vol 2 (1) ◽  
pp. 45-52
Author(s):  
Muhammad Rhifky Wayahdi ◽  
Subhan Hafiz Nanda Ginting ◽  
Dinur Syahputra
Keyword(s):  
Greedy Algorithm ◽  
Shortest Path ◽  
Optimal Solution ◽  
The Other ◽  
Complex Data ◽  
Dijkstra’S Algorithm ◽  
Dijkstra's Algorithm ◽  
The Greedy Algorithm ◽  
Better Than

The problem of finding the shortest path from a path or graph has been quite widely discussed. There are also many algorithms that are the solution to this problem. The purpose of this study is to analyze the Greedy, A-Star, and Dijkstra algorithms in the process of finding the shortest path. The author wants to compare the effectiveness of the three algorithms in the process of finding the shortest path in a path or graph. From the results of the research conducted, the author can conclude that the Greedy, A-Star, and Dijkstra algorithms can be a solution in determining the shortest path in a path or graph with different results. The Greedy algorithm is fast in finding solutions but tends not to find the optimal solution. While the A-Star algorithm tends to be better than the Greedy algorithm, but the path or graph must have complex data. Meanwhile, Dijkstra's algorithm in this case is better than the other two algorithms because it always gets optimal results.

scholarly journals ALMA Band 5 receiver cartridge

2018 ◽  
Vol 611 ◽  
pp. A98 ◽  
Author(s):  
V. Belitsky ◽  
M. Bylund ◽  
V. Desmaris ◽  
A. Ermakov ◽  
S.-E. Ferm ◽  
...  
Keyword(s):  
Final Stage ◽  
Local Oscillator ◽  
Cross Polarization ◽  
Excellent Performance ◽  
Design Performance ◽  
Single Sideband ◽  
Dc Bias ◽  
Polarization Level ◽  
Better Than

We describe the design, performance, and commissioning results for the new ALMA Band 5 receiver channel, 163–211 GHz, which is in the final stage of full deployment and expected to be available for observations in 2018. This manuscript provides the description of the new ALMA Band 5 receiver cartridge and serves as a reference for observers using the ALMA Band 5 receiver for observations. At the time of writing this paper, the ALMA Band 5 Production Consortium consisting of NOVA Instrumentation group, based in Groningen, NL, and GARD in Sweden have produced and delivered to ALMA Observatory over 60 receiver cartridges. All 60 cartridges fulfil the new more stringent specifications for Band 5 and demonstrate excellent noise temperatures, typically below 45 K single sideband (SSB) at 4 K detector physical temperature and below 35 K SSB at 3.5 K (typical for operation at the ALMA Frontend), providing the average sideband rejection better than 15 dB, and the integrated cross-polarization level better than –25 dB. The 70 warm cartridge assemblies, hosting Band 5 local oscillator and DC bias electronics, have been produced and delivered to ALMA by NRAO. The commissioning results confirm the excellent performance of the receivers.

scholarly journals An Improved Greedy Heuristic for the Minimum Positive Influence Dominating Set Problem in Social Networks

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 79
Author(s):  
Salim Bouamama ◽  
Christian Blum
Keyword(s):  
Social Networks ◽  
Greedy Algorithm ◽  
Dominating Set ◽  
Positive Influence ◽  
Greedy Heuristic ◽  
Greedy Heuristics ◽  
Small Subset ◽  
Problem Size ◽  
Wide Range ◽  
Dominating Set Problem

This paper presents a performance comparison of greedy heuristics for a recent variant of the dominating set problem known as the minimum positive influence dominating set (MPIDS) problem. This APX-hard combinatorial optimization problem has applications in social networks. Its aim is to identify a small subset of key influential individuals in order to facilitate the spread of positive influence in the whole network. In this paper, we focus on the development of a fast and effective greedy heuristic for the MPIDS problem, because greedy heuristics are an essential component of more sophisticated metaheuristics. Thus, the development of well-working greedy heuristics supports the development of efficient metaheuristics. Extensive experiments conducted on a wide range of social networks and complex networks confirm the overall superiority of our greedy algorithm over its competitors, especially when the problem size becomes large. Moreover, we compare our algorithm with the integer linear programming solver CPLEX. While the performance of CPLEX is very strong for small and medium-sized networks, it reaches its limits when being applied to the largest networks. However, even in the context of small and medium-sized networks, our greedy algorithm is only 2.53% worse than CPLEX.

Recent Advances in Predicting ncRNA-Protein Interactions Based on Machine Learning

2021 ◽  
Vol 01 ◽  
Author(s):  
Jingjing Wang ◽  
Yanpeng Zhao ◽  
Xiaoqian Huang ◽  
Yi Shi ◽  
Jianjun Tan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Protein Interactions ◽  
Predictive Performance ◽  
Research Approach ◽  
Learning Models ◽  
Recent Advances ◽  
Conventional Machine ◽  
Near Future ◽  
Better Than

: Non-coding RNAs (ncRNAs) play significant roles in various physiological and pathological processes via interacting with the proteins. The existing experimental methods used for predicting ncRNA-protein interactions are costly and time-consuming. Therefore, an increasing number of machine learning models have been developed to efficiently predict ncRNA-protein interactions (ncRPIs), including shallow machine learning and deep learning models, which have achieved dramatic achievement on the identification of ncRPIs. In this review, we provided an overview of the recent advances in various machine learning methods for predicting ncRPIs, mainly focusing on ncRNAs-protein interaction databases, classical datasets, ncRNA/protein sequence encoding methods, conventional machine learning-based models, deep learning-based models, and the two integration-based models. Furthermore, we compared the reported accuracy of these approaches and discussed the potential and limitations of deep learning applications in ncRPIs. It was found that the predictive performance of integrated deep learning is the best, and those deep learning-based methods do not always perform better than shallow machine learning-based methods. We discussed the potential of using deep learning and proposed a research approach on the basis of the existing research. We believe that the model based on integrated deep learning is able to achieve higher accuracy in the prediction if substantial experimental data were available in the near future.

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