Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

LDPC-in-SSD: making advanced error correction codes work effectively in solid state drives

Abstract: Conventional error correction codes (ECCs), such as the commonly used BCH code, have become increasingly inadequate for solid state drives (SSDs) as the capacity of NAND flash memory continues to increase and its reliability continues to degrade. It is highly desirable to deploy a much more powerful ECC, such as low-density parity-check (LDPC) code, to significantly improve the reliability of SSDs. Although LDPC code has had its success in commercial hard disk drives, to fully exploit its error correction capability in SSDs demands unconventional fine-grained flash memory sensing, leading to an increased memory read latency. To address this important but largely unexplored issue, this paper presents three techniques to mitigate the LDPC-induced response time delay so that SSDs can benefit its strong error correction capability to the full extent. We quantitatively evaluate these techniques by carrying out trace-based SSD simulations with runtime characterization of NAND flash memory reliability and LDPC code decoding. Our study based on intensive experiments shows that these techniques used in an integrated way in SSDs can reduce the worst-case system read response time delay from over 100% down to below 20%. With our proposed techniques, a strong ECC alternative can be used in NAND flash memory to retain its reliability to respond the continuous cost reduction, and its relatively small increase of response time delay is acceptable to mainstream application users, considering a huge gain in SSD capacity, its reliability, and the price reduction.

Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition

Abstract: The proliferation of smartphones has significantly facilitated people’s daily life, and diverse and powerful embedded sensors make smartphone a ubiquitous platform to acquire and analyze data, which may also provide great potential for efficient human activity recognition. This paper presents a systematic performance analysis of motion-sensor behavior for human activity recognition via smartphones. Sensory data sequences are collected via smartphones, when participants perform typical and daily human activities. A cycle detection algorithm is applied to segment the data sequence for obtaining the activity unit, which is then characterized by time-, frequency-, and wavelet-domain features. Then both personalized and generalized model using diverse classification algorithms are developed and implemented to perform activity recognition. Analyses are conducted using 27 681 sensory samples from 10 subjects, and the performance is measured in the form of F-score under various placement settings, and in terms of sensitivity to user space, stability to combination of motion sensors, and impact of data imbalance. Extensive results show that each individual has its own specific and discriminative movement patterns, and the F-score for personalized model and generalized model can reach 95.95% and 96.26%, respectively, which indicates our approach is accurate and efficient for practical implementation.