scholarly journals High Performance SDN Enabled Flat Datacenter Network Architecture Based on Scalable And Flow-Controlled Optical Switching System

2015 ◽  
Author(s):  
Nicola Calabretta
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Optical Switching ◽  
Switching System ◽  
Datacenter Network

Nickel sulfide nanoworm network architecture as a binder-free high-performance non-enzymatic glucose sensor

2021 ◽  
Vol 188 (2) ◽  
Author(s):  
Alan Meng ◽  
Xiaocheng Hong ◽  
Haiqin Zhang ◽  
Wenli Tian ◽  
Zhenjiang Li ◽  
...  
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Nickel Sulfide ◽  
Glucose Sensor ◽  
Binder Free

scholarly journals Effective ion pathways and 3D conductive carbon networks in bentonite host enable stable and high-rate lithium–sulfur batteries

2021 ◽  
Vol 10 (1) ◽  
pp. 20-33
Author(s):  
Lian Wu ◽  
Yongqiang Dai ◽  
Wei Zeng ◽  
Jintao Huang ◽  
Bing Liao ◽  
...  
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Lithium Ion ◽  
Reversible Capacity ◽  
High Rate ◽  
Lithium Sulfur Batteries ◽  
Carbon Networks ◽  
Carbon Coated ◽  
Lithium Sulfur ◽  
Initial Capacity

Abstract Fast charge transfer and lithium-ion transport in the electrodes are necessary for high performance Li–S batteries. Herein, a N-doped carbon-coated intercalated-bentonite (Bent@C) with interlamellar ion path and 3D conductive network architecture is designed to improve the performance of Li–S batteries by expediting ion/electron transport in the cathode. The interlamellar ion pathways are constructed through inorganic/organic intercalation of bentonite. The 3D conductive networks consist of N-doped carbon, both in the interlayer and on the surface of the modified bentonite. Benefiting from the unique structure of the Bent@C, the S/Bent@C cathode exhibits a high initial capacity of 1,361 mA h g−1 at 0.2C and achieves a high reversible capacity of 618.1 m Ah g−1 at 2C after 500 cycles with a sulfur loading of 2 mg cm−2. Moreover, with a higher sulfur loading of 3.0 mg cm−2, the cathode still delivers a reversible capacity of 560.2 mA h g−1 at 0.1C after 100 cycles.

scholarly journals BengaliNet: A Low-Cost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition

2021 ◽  
Vol 11 (15) ◽  
pp. 6845
Author(s):  
Abu Sayeed ◽  
Jungpil Shin ◽  
Md. Al Mehedi Hasan ◽  
Azmain Yakin Srizon ◽  
Md. Mehedi Hasan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Character Recognition ◽  
High Performance ◽  
Low Cost ◽  
Traditional Learning ◽  
Neural Network Architecture ◽  
Handwritten Character Recognition ◽  
Handwritten Character

As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters.

scholarly journals Six-port optical switch for cluster-mesh photonic network-on-chip

Nanophotonics ◽  
2018 ◽  
Vol 7 (5) ◽  
pp. 827-835 ◽  
Author(s):  
Hao Jia ◽  
Ting Zhou ◽  
Yunchou Zhao ◽  
Yuhao Xia ◽  
Jincheng Dai ◽  
...  
Keyword(s):  
High Performance ◽  
Optical Switching ◽  
Optical Switch ◽  
Optical Signal ◽  
Network On Chip ◽  
Transmission Experiment ◽  
Photonic Network ◽  
Benes Network ◽  
On Chip ◽  
Spectral Responses

AbstractPhotonic network-on-chip for high-performance multi-core processors has attracted substantial interest in recent years as it offers a systematic method to meet the demand of large bandwidth, low latency and low power dissipation. In this paper we demonstrate a non-blocking six-port optical switch for cluster-mesh photonic network-on-chip. The architecture is constructed by substituting three optical switching units of typical Spanke-Benes network to optical waveguide crossings. Compared with Spanke-Benes network, the number of optical switching units is reduced by 20%, while the connectivity of routing path is maintained. By this way the footprint and power consumption can be reduced at the expense of sacrificing the network latency performance in some cases. The device is realized by 12 thermally tuned silicon Mach-Zehnder optical switching units. Its theoretical spectral responses are evaluated by establishing a numerical model. The experimental spectral responses are also characterized, which indicates that the optical signal-to-noise ratios of the optical switch are larger than 13.5 dB in the wavelength range from 1525 nm to 1565 nm. Data transmission experiment with the data rate of 32 Gbps is implemented for each optical link.

scholarly journals Crafting Efficient Neural Graph of Large Entropy

2019 ◽  
Author(s):  
Minjing Dong ◽  
Hanting Chen ◽  
Yunhe Wang ◽  
Chang Xu
Keyword(s):  
Information Flow ◽  
Network Architecture ◽  
High Performance ◽  
Global Information ◽  
High Quality ◽  
Network Pruning ◽  
Initial Network ◽  
Trade Offs ◽  
Graph Properties ◽  
Deep Cnn

Network pruning is widely applied to deep CNN models due to their heavy computation costs and achieves high performance by keeping important weights while removing the redundancy. Pruning redundant weights directly may hurt global information flow, which suggests that an efficient sparse network should take graph properties into account. Thus, instead of paying more attention to preserving important weight, we focus on the pruned architecture itself. We propose to use graph entropy as the measurement, which shows useful properties to craft high-quality neural graphs and enables us to propose efficient algorithm to construct them as the initial network architecture. Our algorithm can be easily implemented and deployed to different popular CNN models and achieve better trade-offs.

System Identification of the High Performance Drilling Process for Network-Based Control

2007 ◽  
Author(s):  
Rau´l M. del Toro ◽  
Michael C. Schmittdiel ◽  
Rodolfo E. Haber-Guerra ◽  
Rodolfo Haber-Haber
Keyword(s):  
Dynamic Behavior ◽  
Feed Rate ◽  
Network Architecture ◽  
High Performance ◽  
Control Strategies ◽  
Input Function ◽  
Resultant Force ◽  
Work Piece ◽  
Drilling Process ◽  
Force System

A simple, fast, network-based experimental procedure for identifying the dynamics of the high-performance drilling (HPD) process is proposed and successfully applied. This identification technique utilizes a single-input (feed rate), single-output (resultant force) system with a dual step input function. The model contains the delays of both the network architecture (a PROFIBUS type network) and the dead time related with the plant dynamic itself. Classical identification techniques are used to obtain first order, second order, and third order models on the basis of the recorded input/output data. The developed models relate the dynamic behavior of resultant force versus commanded feed rate in HPD. Model validation is performed through error-based performance indices and correlation analyses. Experimental verification is performed using two different work piece materials. The models match perfectly with real-time force behavior in drilling operations and are easily integrated with many control strategies. Furthermore, these results demonstrate that the HPD process is somewhat non-linear with a remarkable difference in gain due to work piece material; however, the dynamic behavior does not change significantly.

Sign in / Sign up

Export Citation Format

Share Document