Visible to the public Biblio

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Taneja, Shubbhi, Zhou, Yi, Chavan, Ajit, Qin, Xiao.  2019.  Improving Energy Efficiency of Hadoop Clusters using Approximate Computing. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :206–211.
There is an ongoing search for finding energy-efficient solutions in multi-core computing platforms. Approximate computing is one such solution leveraging the forgiving nature of applications to improve the energy efficiency at different layers of the computing platform ranging from applications to hardware. We are interested in understanding the benefits of approximate computing in the realm of Apache Hadoop and its applications. A few mechanisms for introducing approximation in programming models include sampling input data, skipping selective computations, relaxing synchronization, and user-defined quality-levels. We believe that it is straightforward to apply the aforementioned mechanisms to conserve energy in Hadoop clusters as well. The emerging trend of approximate computing motivates us to systematically investigate thermal profiling of approximate computing strategies in this research. In particular, we design a thermal-aware approximate computing framework called tHadoop2, which is an extension of tHadoop proposed by Chavan et al. We investigated the thermal behavior of a MapReduce application called Pi running on Hadoop clusters by varying two input parameters - number of maps and number of sampling points per map. Our profiling results show that Pi exhibits inherent resilience in terms of the number of precision digits present in its value.
Sun, Xuguang, Zhou, Yi, Shu, Xiaofeng.  2018.  Multi-Channel Linear Prediction Speech Dereverberation Algorithm Based on QR-RLS Adaptive Filter. Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing. :109–113.

This paper proposes a multi-channel linear prediction (MCLP) speech dereverberation algorithm based on QR-decomposition recursive least squares (QR-RLS) adaptive filter, which can avoid the possible instability caused by the RLS algorithm, and achieve same speech dereverberation performance as the prototype MCLP dereverberation algorithm based on RLS. This can be confirmed by the theoretical derivation and experiments. Thus, the proposed algorithm can be a good alternative for practical speech applications.

Zhou, Junkai, Zhou, Yi, Wei, Dandan.  2018.  A Two-Path Frequency Domain Algorithm for Stereophonic Acoustic Echo Cancellation. Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing. :94–98.
Stereophonic acoustic echo cancellation is widely used in the high quality audio/video teleconference systems to reduce the echoes coupling between microphones and loudspeakers. In the specific application scenarios, adaptive filters require a very high filter length to deal with the same long echo path. When time domain algorithm is used to estimate the echo path, the cost of complexity is very high, which can be optimized by frequency domain adaptive filters. In this paper, an efficient two-channel frequency domain algorithm is used to achieve this goal. Meanwhile, double-talk often occurs in the teleconference system, so the robustness of the algorithm is equally important. We also propose a robust two-path updating control transfer logic for stereophonic echo cancellation to solve the double-talk problems.