Strategies and technologies for planning a cost-effective survivable fiber network architecture using optical switches

1990 ◽  
Vol 8 (2) ◽  
pp. 152-159 ◽  
Author(s):  
T.-H. Wu ◽  
S.F. Habiby
Keyword(s):  
Network Architecture ◽  
Cost Effective ◽  
Optical Switches ◽  
Fiber Network

scholarly journals FOSquare: A Novel Optical HPC Interconnect Network Architecture Based on Fast Optical Switches with Distributed Optical Flow Control

Photonics ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 11
Author(s):  
Fulong Yan ◽  
Changshun Yuan ◽  
Chao Li ◽  
Xiong Deng
Keyword(s):  
Flow Control ◽  
Network Architecture ◽  
Network Performance ◽  
Optical Switches ◽  
Low Power Consumption ◽  
Low Latency ◽  
High Bandwidth ◽  
Fast Flow ◽  
Real Traffic ◽  
Interconnect Network

Interconnecting networks adopting Fast Optical Switches (FOS) can achieve high bandwidth, low latency, and low power consumption. We propose and demonstrate a novel interconnecting topology based on FOS (FOSquare) with distributed fast flow control which is suitable for HPC infrastructures. We also present an Optimized Mapping (OPM) algorithm that maps the most communication-related processes inside a rack. We numerically investigate and compare the network performance of FOSquare with Leaf-Spine under real traffic traces collected by running multiple applications (CG, MG, MILC, and MINI_MD) in an HPC infrastructure. The numerical results show that the FOSquare can reduce >10% latency with respect to Leaf-Spine under the scenario of 16 available cores.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

scholarly journals Recurrent Multi-Fiber Network for 3D MRI Brain Tumor Segmentation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 320
Author(s):  
Yue Zhao ◽  
Xiaoqiang Ren ◽  
Kun Hou ◽  
Wentao Li
Keyword(s):  
Brain Tumor ◽  
Network Architecture ◽  
Contextual Information ◽  
Computational Cost ◽  
Disease Diagnosis ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fiber Network ◽  
3D Network ◽  
The Brain

Automated brain tumor segmentation based on 3D magnetic resonance imaging (MRI) is critical to disease diagnosis. Moreover, robust and accurate achieving automatic extraction of brain tumor is a big challenge because of the inherent heterogeneity of the tumor structure. In this paper, we present an efficient semantic segmentation 3D recurrent multi-fiber network (RMFNet), which is based on encoder–decoder architecture to segment the brain tumor accurately. 3D RMFNet is applied in our paper to solve the problem of brain tumor segmentation, including a 3D recurrent unit and 3D multi-fiber unit. First of all, we propose that recurrent units segment brain tumors by connecting recurrent units and convolutional layers. This quality enhances the model’s ability to integrate contextual information and is of great significance to enhance the contextual information. Then, a 3D multi-fiber unit is added to the overall network to solve the high computational cost caused by the use of a 3D network architecture to capture local features. 3D RMFNet combines both advantages from a 3D recurrent unit and 3D multi-fiber unit. Extensive experiments on the Brain Tumor Segmentation (BraTS) 2018 challenge dataset show that our RMFNet remarkably outperforms state-of-the-art methods, and achieves average Dice scores of 89.62%, 83.65% and 78.72% for the whole tumor, tumor core and enhancing tumor, respectively. The experimental results prove our architecture to be an efficient tool for brain tumor segmentation accurately.

scholarly journals 5G Wireless/Wired Convergence of UFMC Based Modulation for Intensity Modulation Direct Detection

2021 ◽  
Author(s):  
Keerthivasan K ◽  
Shibu S
Keyword(s):  
Network Architecture ◽  
Direct Detection ◽  
Low Cost ◽  
Cost Effective ◽  
Theoretical Work ◽  
Base Stations ◽  
Complex Nature ◽  
Implementation Model ◽  
Effective Manner ◽  
Media Applications

Faster data speeds, shorter end-to-end latencies, improved end-user service efficiency, and a wider range of multi-media applications are expected with the new 5G wireless services. The dramatic increase in the number of base stations required to meet these criteria, which undermines the low-cost constraints imposed by operators, demonstrates the need for a paradigm shift in modern network architecture. Alternative formats will be required for next-generation architectures, where simplicity is the primary goal. The number of connections is expected to increase rapidly, breaking the inherent complexity of traditional coherent solutions and lowering the resulting cost percentage. A novel implementation model is used to migrate complex-nature modulation structures in a highly efficient and cost-effective manner. Theoretical work to analyses modulations’ behavior over a wired/fiber setup and wireless mode is also provided. The state-of-the-art computational complexity, simplicity, and ease of execution while maintaining efficiency throughput and bit error rate.

Towards cost-effective and low latency data center network architecture

2016 ◽  
Vol 82 ◽  
pp. 1-12 ◽  
Author(s):  
Ting Wang ◽  
Zhiyang Su ◽  
Yu Xia ◽  
Bo Qin ◽  
Mounir Hamdi
Keyword(s):  
Data Center ◽  
Network Architecture ◽  
Cost Effective ◽  
Low Latency ◽  
Data Center Network ◽  
Latency Data

In-Vehicle Network Architecture for the Next-Generation Vehicles

2010 ◽  
pp. 283-302 ◽  
Author(s):  
Syed Masud Mahmud
Keyword(s):  
Communication Networks ◽  
Network Architecture ◽  
Fault Tolerant ◽  
Fuel Efficiency ◽  
Simulation Models ◽  
Cost Effective ◽  
Multimedia Systems ◽  
Safety Features ◽  
Vehicle Network

New types of communication networks will be necessary to meet various consumer and regulatory demands as well as satisfy requirements of safety and fuel efficiency. Various functionalities of vehicles will require various types of communication networks and networking protocols. For example, driveby- wire and active safety features will require fault tolerant networks with time-triggered protocols to guarantee deterministic latencies. Multimedia systems will require high-bandwidth networks for video transfer, and body electronics need low-bandwidth networks to keep the cost down. As the size and complexity of the network grows, the ease of integration, maintenance and troubleshooting has become a major challenge. To facilitate integration and troubleshooting of various nodes and networks, it would be desirable that networks of future vehicles should be partitioned, and the partitions should be interconnected by a hierarchical or multi-layer physical network. This book chapter describes a number of ways using which the networks of future vehicles could be designed and implemented in a cost-effective manner. The book chapter also shows how simulation models can be developed to evaluate the performance of various types of in-vehicle network topologies and select the most appropriate topology for given requirements and specifications.

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