@inproceedings {Rhodes Island, Greece, title = {Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learning}, year = {2023}, month = {06/2023}, address = {Rhodes Island, Greece}, keywords = {psclab}, author = {Zhikang Zhang and Zifan Yu and Suya You and Raghuveer Rao and Sanjeev Agarwal and Fengbo Ren} } @inproceedings {Singapore, title = {An Experimental Study on Transferring Data-Driven Image Compressive Sensing to Bioelectric Signals}, year = {2022}, month = {05/2022}, pages = {1191-1195}, address = {Singapore}, abstract = {

The emerging area of bioelectric signal compressive sensing(CS) has shown great potential in health care applications. However, improving the reconstruction accuracy of compressively sensed bioelectric signals remains a challenging problem. In recent years, data-driven image CS methods have achieved significant improvements in reconstruction ac- curacy over conventional model-based image CS methods. In this paper, we conduct an experimental study on transferring existing data-driven image CS methods to bioelectric signals. Through our investigation of five critical factors affecting the reconstruction performance of bioelectric signals, we conclude that existing data-driven image CS methods can be transferred to ECG signals with high reconstruction accuracy. Our experimental results show that transferred data-driven image CS methods can achieve up to 8.08-2.73 SNR improvement over the reference method on ECG signal reconstruction across compression ratios of 2-8x.

}, keywords = {psclab}, doi = {10.1109/ICASSP43922.2022.9747439}, author = {Zhikang Zhang and Jonathan Zhao and Fengbo Ren} } @article {390, title = {Enabling Deep Learning for Edge Computing}, year = {2019}, keywords = {psclab}, author = {Baoxin Li and Fengbo Ren} } @inproceedings {9999, title = {An Energy-Efficient Compressive Sensing Framework Incorporating Online Dictionary Learning for Long-Term Wireless Health Monitoring}, year = {2016}, month = {03/2016}, pages = {804 - 808}, publisher = {IEEE}, address = {Shanghai, China}, abstract = {

Wireless body area network (WBAN) is emerging in the mobile healthcare area to replace the traditional wire-connected monitoring devices. As wireless data transmission dominates power cost of sensor nodes, it is beneficial to reduce the data size without much information loss. Compressive sensing (CS) is a perfect candidate to achieve this goal compared to existing compression techniques. In this paper, we proposed a general framework that utilize CS and online dictionary learning (ODL) together. The learned dictionary carries individual characteristics of the original signal, under which the signal has an even sparser representation compared to pre-determined dictionaries. As a consequence, the compression ratio is effectively improved by 2-4x comparing to prior works. Besides, the proposed framework offloads pre-processing from sensor nodes to the server node prior to dictionary learning, providing further reduction in hardware costs. As it is data driven, the proposed framework has the potential to be used with a wide range of physiological signals.

}, keywords = {psclab}, author = {Kai Xu and YIxing Li and Fengbo Ren} } @inproceedings {278, title = {Enabling scientific data storage and processing on big-data systems}, year = {2015}, pages = {1978{\textendash}1984}, publisher = {IEEE}, abstract = {

Big-data systems are increasingly important for solving the data-driven problems in many science domains including geosciences. However, existing big-data systems cannot support the self-describing data formats such as NetCDF which are commonly used by scientific communities for data distribution and sharing. This limitation presents a serious hurdle to the further adoption of big-data systems by science domains and prevents scientific users from leveraging these systems to improve their productivity. This paper presents a solution to this problem by enabling big-data systems to directly store and process scientific data. Specifically, it enables Hadoop to efficiently store NetCDF data on HDFS and process them in MapReduce using convenient APIs. It also enables Hive to support standard queries on NetCDF data, transparently to users. The paper also presents an evaluation of the proposed solution using several representative queries on a typical geoscientific dataset. The results show that the proposed approach achieves substantial speedup (up to 20 times) and space saving (83\% reduction), compared to the traditional approach which has to convert NetCDF data to CSV format for Hadoop and Hive to use them.

}, keywords = {ref}, author = {Biookaghazadeh, Saman and Xu, Yiqi and Zhou, Shujia and Zhao, Ming} } @mastersthesis {34, title = {Energy-performance Characterization of CMOS/Magnetic Tunnel Junction (MTJ) Hybrid Logic Circuits}, volume = {Master of Science }, year = {2011}, month = {Jan. 2011}, school = {University of California, Los Angeles}, type = {M.S. Thesis}, address = {Los Angeles}, abstract = {

Magnetic Tunnel Junction (MTJ) devices are CMOS compatible with high stability, high reliability and non-volatility. All these features are promising for building non-volatile CMOS/MTJ hybrid logic circuits that do not consume off-state leakage current and that supports ultra-low-power operation. However, most existing proposals for this purpose so far lack an energy-performance analysis and a comparison to CMOS circuits. In this work, we analyze and compare the energy-performance characteristics of a wide range of CMOS/MTJ hybrid circuits over the device, circuit and architectural levels. This will include device switching energies, logic-in-memory MTJ (LIM-MTJ) logic circuit, two MTJ reading circuits and two CMOS/MTJ hybrid lookup table (LUT) architectures. Our analysis shows that the existing LIM-MTJ logic style has no advantage in energy-performance over its equivalent CMOS design, and that with the switching energy of MTJ considered, the CMOS/MTJ hybrid circuit requiring frequent MTJ switching is hardly energy efficient. Our simulation results also show that the cross-coupled inverter based MTJ reading circuit has 4 times greater performance and 30 times lower energy than the current-mirror sense amplifier based reading circuit. It is also shown that the proposed CMOS/MTJ hybrid LUT based logic architecture, which requires no MTJ switching during logic operations, is able to incorporate the non-volatility of the MTJ to alleviate the leakage problem of CMOS, and to thereby supports ultra-low power operation in advanced technology nodes (32-nm and beyond).

}, keywords = {psclab}, author = {Ren, Fengbo} } @book {friedman2001elements, title = {The elements of statistical learning}, volume = {1}, year = {2001}, publisher = {Springer series in statistics New York}, organization = {Springer series in statistics New York}, keywords = {Deep Learning, ref}, url = {https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about}, author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert} }