Using the Memory Channel Network

IEEE Micro ◽  
1997 ◽  
Vol 17 (1) ◽  
pp. 19-25 ◽  
Author(s):  
R. Gillett ◽  
R. Kaufmann
Keyword(s):  
Channel Network ◽  
Memory Channel

Memory channel network for PCI

IEEE Micro ◽  
1996 ◽  
Vol 16 (1) ◽  
pp. 12-18 ◽  
Author(s):  
R.B. Gillett
Keyword(s):  
Channel Network ◽  
Memory Channel

Carbonate channel network in the Miocene syn-rift Sardinia basins

2001 ◽  
Vol 28 (1) ◽  
pp. 133-137 ◽  
Author(s):  
M. Murru ◽  
Lucia Simone ◽  
M. Vigorito
Keyword(s):  
Channel Network

scholarly journals Artificial Neural Network Assisted Error Correction for MLC NAND Flash Memory

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 879
Author(s):  
Ruiquan He ◽  
Haihua Hu ◽  
Chunru Xiong ◽  
Guojun Han
Keyword(s):  
Neural Network ◽  
Error Correction ◽  
Error Detection ◽  
Flash Memory ◽  
Nand Flash ◽  
Data Reliability ◽  
Nand Flash Memory ◽  
Process Technology ◽  
Noise Mitigation ◽  
Memory Channel

The multilevel per cell technology and continued scaling down process technology significantly improves the storage density of NAND flash memory but also brings about a challenge in that data reliability degrades due to the serious noise. To ensure the data reliability, many noise mitigation technologies have been proposed. However, they only mitigate one of the noises of the NAND flash memory channel. In this paper, we consider all the main noises and present a novel neural network-assisted error correction (ANNAEC) scheme to increase the reliability of multi-level cell (MLC) NAND flash memory. To avoid using retention time as an input parameter of the neural network, we propose a relative log-likelihood ratio (LLR) to estimate the actual LLR. Then, we transform the bit detection into a clustering problem and propose to employ a neural network to learn the error characteristics of the NAND flash memory channel. Therefore, the trained neural network has optimized performances of bit error detection. Simulation results show that our proposed scheme can significantly improve the performance of the bit error detection and increase the endurance of NAND flash memory.

scholarly journals Controls on Streamflow Densities in Semiarid Rocky Mountain Catchments

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 521
Author(s):  
Caroline Martin ◽  
Stephanie K. Kampf ◽  
John C. Hammond ◽  
Codie Wilson ◽  
Suzanne P. Anderson
Keyword(s):  
Elevation Gradient ◽  
Rocky Mountain ◽  
Surface Flow ◽  
Colorado Front Range ◽  
Front Range ◽  
Channel Network ◽  
Stream Discharge ◽  
Geologic Maps ◽  
National Hydrography Dataset ◽  
Active Flow

Developing accurate stream maps requires both an improved understanding of the drivers of streamflow spatial patterns and field verification. This study examined streamflow locations in three semiarid catchments across an elevation gradient in the Colorado Front Range, USA. The locations of surface flow throughout each channel network were mapped in the field and used to compute active drainage densities. Field surveys of active flow were compared to National Hydrography Dataset High Resolution (NHD HR) flowlines, digital topographic data, and geologic maps. The length of active flow declined with stream discharge in each of the catchments, with the greatest decline in the driest catchment. Of the tributaries that did not dry completely, 60% had stable flow heads and the remaining tributaries had flow heads that moved downstream with drying. The flow heads were initiated at mean contributing areas of 0.1 km2 at the lowest elevation catchment and 0.5 km2 at the highest elevation catchment, leading to active drainage densities that declined with elevation and snow persistence. The field mapped drainage densities were less than half the drainage densities that were represented using NHD HR. Geologic structures influenced the flow locations, with multiple flow heads initiated along faults and some tributaries following either fault lines or lithologic contacts.

scholarly journals Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

2021 ◽  
Vol 13 (10) ◽  
pp. 1997
Author(s):  
Joan Grau ◽  
Kang Liang ◽  
Jae Ogilvie ◽  
Paul Arp ◽  
Sheng Li ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Riparian Zone ◽  
Agricultural Landscapes ◽  
Agricultural Watershed ◽  
Riparian Zones ◽  
Surface Model ◽  
Low Resolution ◽  
Channel Network ◽  
Aerial Vehicle

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes.

An Improved Dual-Channel Network to Eliminate Catastrophic Forgetting

2020 ◽  
pp. 1-11
Author(s):  
Dongbo Liu ◽  
Zhenan He ◽  
Dongdong Chen ◽  
Jiancheng Lv
Keyword(s):  
Channel Network ◽  
Dual Channel
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