3-D triple-level cell (3-D TLC) NAND flash has high storage density and capacity, but degrading data reliability due to high raw bit error rates induced by a certain number of program/erase cycles. To guarantee data reliability, low-density parity-check (LDPC) codes are selected as the error correction codes in modern flash memories because of strong error correction capability. However, directly adopting LDPC codes induces high decoding latency due to iterative updating of log-likelihood ratio (LLR) information in the decoding process. Increasing LLR information accuracy can greatly improve decoding performance. In this paper, we propose EMAL: using error modes aware LDPC codes for further enhancing the decoding performance of 3-D TLC NAND flash. We first obtain 3-D TLC error modes based on an FPGA testing platform, and then exploit the error modes to optimize LLR information and enable the decoding to converge at a high speed. The simulation results show that the decoding performance is significantly improved, resulting in reduced bit error rates and decoding latency.

Charge trap (CT) based 3D NAND flash is predominating the flash storage market due to higher density, better performance and endurance than planar flash. CT-based 3D flash programs multiple pages in a word line at a time, called one-shot programming, unlike planar flash which programs one page at a time. Solid state drives (SSDs) utilize the internal parallelism to improve the performance, but one-shot programming is likely to program logically sequential data into one parallel unit (i.e., a plane) and thus degrades the read parallelism. In this paper, we propose a one-shot programming aware data allocation policy, called OSPADA, to improve the read performance of CT flash based SSDs by enhancing read parallelism. OSPADA reorders written data to distribute logically sequential data into different parallel units using the distance aware round-robin strategy. Experimental results show that OSPADA improves the read performance by up to 22.8% compared with traditional dynamic data allocation policies.

The India monsoon is changing under the global climate change, which influence the transformation from dry season to wet season, and then impact the photosynthetic activity and transpiration of tropical and subtropical monsoon forest. The descriptions of this kind of phenology in this area are still lacking, where various shapes of seasonal trajectories (e.g. “U” shape and “V” shape) and the special climate made the extraction of vegetation phenology difficult. However, current curve fitting approaches were inappropriate for simulating all these seasonal trajectories well. In this paper a modified logistic function method was proposed to extract the vegetation phenology (onset of greenness) of India monsoon forest. Due to the fine cloud detection method and the minimum NDVI composited technology, this modified approach had the ability to extract the vegetation phenology of India monsoon forest, which showed significant superiority over the compared six approaches in our previous study. The extracted time series of vegetation phenology by using this modified approach would facilitate the understandings of climate patterns and climate changes in the India monsoon area.