@inproceedings {Rhodes Island, Greece, title = {Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach}, year = {2023}, month = {06/2023}, address = {Rhodes Island, Greece}, keywords = {psclab}, author = {Zhikang Zhang and Bruno Machado Trindade and Michael Green and Zifan Yu and Christopher Pawlowicz and Fengbo Ren} } @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 {Bordeaux, France, title = {A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images}, year = {2022}, month = {10/2022}, address = {Bordeaux, France}, abstract = {

This paper proposes an automated data-driven integrated
circuit segmentation approach of scan electron microscopy
(SEM) images inspired by state-of-the-art CNN-based image
perception methods. Based on the requirements derived from
real industry applications, we take wire segmentation and via
detection algorithms to generate integrated circuit segmentation
maps from SEMs in our approach. On SEM images
collected in the industrial applications, our method achieves
an average of 50.71 on Electrically Significant Difference
(ESD) in the wire segmentation task and 99.05\% F1 score
in the via detection task, which achieves about 85\% and 8\%
improvements over the reference method, respectively.

}, keywords = {psclab}, author = {Zifan Yu and Bruno Machado Trindade and Michael Green and Zhikang Zhang and Pullela Sneha and Bank-Tavakoli, Erfan and Christopher Pawlowicz and Ren, Fengbo} } @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 {561, title = {OpenICS: Open Image Compressive Sensing Toolbox and Benchmark}, journal = {Software Impacts}, volume = {9}, year = {2021}, month = {05/2021}, abstract = {

The real-world application of image compressive sensing is largely limited by the lack of standardization in implementation and evaluation. To address this limitation, we present OpenICS, an image compressive sensing toolbox that implements multiple popular image compressive sensing algorithms into a unified framework with a standardized user interface. Furthermore, a corresponding benchmark is also proposed to provide a fair and complete evaluation of the implemented algorithms. We hope this work can serve the growing research community of compressive sensing and the industry to facilitate the development and application of image compressive sensing.

}, keywords = {psclab}, doi = {https://doi.org/10.1016/j.simpa.2021.100081}, url = {https://arxiv.org/pdf/2103.00652.pdf}, author = {Jonathan Zhao and Matthew Westerham and Mark Lakatos-Toth and Zhikang Zhang and Avi Moskoff and Fengbo Ren} } @article {359, title = {Build a Compact Binary Neural Network through Bit-level Sensitivity and Data Pruning}, journal = { Neurocomputing}, volume = {398}, year = {2020}, month = {07/2020}, pages = {45-54}, abstract = {

Due to the high computational complexity and memory storage requirement, it is hard to directly de- ploy a full-precision convolutional neural network (CNN) on embedded devices. The hardware-friendly designs are needed for resource-limited and energy-constrained embedded devices. Emerging solutions are adopted for the neural network compression, e.g., binary/ternary weight network, pruned network and quantized network. Among them, binary neural network (BNN) is believed to be the most hardware- friendly framework due to its small network size and low computational complexity. No existing work has further shrunk the size of BNN. In this work, we explore the redundancy in BNN and build a com- pact BNN (CBNN) based on the bit-level sensitivity analysis and bit-level data pruning. The input data is converted to a high dimensional bit-sliced format. In the post-training stage, we analyze the impact of different bit slices to the accuracy. By pruning the redundant input bit slices and shrinking the network size, we are able to build a more compact BNN. Our result shows that we can further scale down the net- work size of the BNN up to 3.9x with no more than 1\% accuracy drop. The actual runtime can be reduced up to 2x and 9.9x compared with the baseline BNN and its full-precision counterpart, respectively.

}, keywords = {psclab}, author = {YIxing Li and Shuai Zhang and Xichuan Zhou and Fengbo Ren} } @inproceedings {Barcelona, Spain, title = {CRA: A Generic Compression Ratio Adaptor for End-to-end Data-driven Image Compressive Sensing Reconstruction Frameworks}, year = {2020}, month = {05/2020}, pages = {1740-1749}, address = {Barcelona, Spain}, abstract = {

End-to-end data-driven image compressive sensing reconstruction (EDCSR) frameworks achieve state-of-the-art reconstruction performance in terms of reconstruction speed and accuracy. However, due to their end-to-end nature, existing EDCSR frameworks can not adapt to a variable compression ratio (CR). For applications that desire a variable CR, existing EDCSR frameworks must be trained from scratch at each CR, which is computationally costly and time-consuming. This paper presents a generic compression ratio adapter (CRA) framework that addresses the variable compression ratio (CR) problem for existing EDCSR frameworks with no modification to given reconstruction models nor enormous rounds of training needed. CRA exploits an initial reconstruction network to generate an initial estimate of reconstruction results based on a small portion of the acquired measurements. Subsequently, CRA approximates full measurements for the main reconstruction network by complementing the sensed measurements with resensed initial estimate. Our experiments based on two public image datasets (CIFAR10 and Set5) show that CRA provides an average of 13.02 dB and 5.38 dB PSNR improvement across the CRs from 5 to 30 over a naive zero-padding approach and the AdaptiveNN approach(a prior work), respectively. CRA addresses the fixed-CR limitation of existing EDCSR frameworks and makes them suitable for resource-constrained compressive sensing applications.

}, keywords = {psclab}, author = {Zhikang Zhang and Kai Xu and Fengbo Ren} } @article {562, title = {LAPRAN: A SCALABLE LAPLACIAN PYRAMID RECONSTRUCTIVE ADVERSARIAL NETWORK FOR FLEXIBLE COMPRESSIVE SENSING RECONSTRUCTION}, number = {16/745,817}, year = {2020}, month = {07/2020}, keywords = {psclab}, author = {Fengbo Ren and Kai Xu and Zhikang Zhang} } @inproceedings {Virtual Event, title = {MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time}, year = {2020}, month = {07/2020}, address = {Virtual Event}, abstract = {

Monocular multi-object detection and localization in 3D space has been proven to be a challenging task. The MoNet3D algorithm is a novel and effective framework that can predict the 3D position of each object in a monocular image and draw a 3D bounding box for each object. The MoNet3D method incorporates prior knowledge of the spatial geometric correlation of neighboring objects into the deep neural network training process to improve the accuracy of 3D object localization. Experiments on the KITTI dataset show that the accuracy for predicting the depth and horizontal coordinates of objects in 3D space can reach 96.25\% and 94.74\%, respectively. Moreover, the method can realize the real-time image processing at 27.85 FPS, showing promising potential for embedded advanced driving assistance system applications. Our code is publicly available at

}, keywords = {psclab}, author = {Xichuan Zhou and Yicong Peng and Chunqiao Long and Fengbo Ren and Cong Shi} } @article {414, title = {A Review of Algorithm \& Hardware Design for AI-Based Biomedical Applications}, journal = {IEEE Transactions on Biomedical Circuits and Systems }, volume = {14}, year = {2020}, month = {04/2020}, pages = {145-163}, abstract = {

This paper reviews the state of the arts and trends of the AI-based biomedical processing algorithms and hardwares. The algorithms and hardwares for different biomedical applications such as ECG, EEG and hearing aid have been reviewed and discussed. For algorithm design, various widely used biomedical signal classification algorithms have been discussed including support vector machine (SVM), back propagation neural network (BPNN), convolutional neural networks (CNN), probabilistic neural networks (PNN), recurrent neural networks (RNN), Short-term Memory Network (LSTM), fuzzy neural network and etc. The pros and cons of the classification algorithms have been analyzed and compared in the context of application scenarios. The research trends of AI-based biomedical processing algorithms and applications are also discussed. For hardware design, various AI-based biomedical processors have been reviewed and discussed, including ECG classification processor, EEG classification processor, EMG classification processor and hearing aid processor. Various techniques on architecture and circuit level have been analyzed and compared. The research trends of the AI-based biomedical processor have also been discussed.

}, keywords = {psclab}, issn = {1940-9990}, doi = {10.1109/TBCAS.2020.2974154}, author = {Ying Wei and Jun Zhou and Yin Wang and Yinggang Liu and Qingsong Liu and Jiansheng Luo and Chao Wang and Fengbo Ren and Li Huang} } @article {366, title = {A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, volume = {16}, year = {2019}, month = {05/2018}, pages = {1794-1801}, abstract = {

The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learning framework to detect prostate cancer in the sequential CEUS images. The proposed method uniformly extracts features from both the spatial and the temporal dimensions by performing three-dimensional convolution operations, which captures the dynamic information of the perfusion process encoded in multiple adjacent frames for prostate cancer detection. The deep learning models were trained and validated against expert delineations over the CEUS images recorded using two types of contrast agents, i.e. the anti-PSMA based agent targeted to prostate cancer cells and the non-targeted blank agent. Experiments showed that the deep learning method achieved over 91\% specificity and 90\% average accuracy over the targeted CEUS images for prostate cancer detection, which was superior (p \< 0.05) than previously reported approaches and implementations.

}, keywords = {psclab}, doi = {10.1109/TCBB.2018.2835444}, url = {https://ieeexplore.ieee.org/document/8357938}, author = {Yujie Feng and Fan Yang and Xichuan Zhou and Yanli Guo and Fang Tang and Fengbo Ren and Jishun Guo and Shuiwang Ji} } @inproceedings {360, title = {Are FPGAs Suitable for Edge Computing?}, year = {2018}, month = {07/2018}, address = {BOSTON, MA}, abstract = {

The rapid growth of Internet-of-things (IoT) and artificial intelligence applications have called forth a new computing paradigm\–edge computing. In this paper, we study the suitability of deploying FPGAs for edge computing from the perspectives of throughput sensitivity to workload size, architectural adaptiveness to algorithm characteristics, and energy efficiency. This goal is accomplished by conducting comparison experiments on an Intel Arria 10 GX1150 FPGA and an Nvidia Tesla K40m GPU. The experiment results imply that the key advantages of adopting FPGAs for edge computing over GPUs are three-fold: 1) FPGAs can provide a consistent throughput invariant to the size of application workload, which is critical to aggregating individual service requests from various IoT sensors; (2) FPGAs offer both spatial and temporal parallelism at a fine granularity and a massive scale, which guarantees a consistently high performance for accelerating both high-concurrency and high-dependency algorithms; and (3) FPGAs feature 3\– 4 times lower power consumption and up to 30.7 times better energy efficiency, offering better thermal stability and lower energy cost per functionality.

}, keywords = {psclab}, author = {Saman Biookaghazadeh and Ming Zhao and Fengbo Ren} } @inproceedings {Munich, Germany, title = {LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction}, year = {2018}, month = {09/2018}, pages = {491-507}, address = {Munich, Germany}, abstract = {

This paper addresses the single-image compressive sensing (CS) and reconstruction problem. We propose a scalable Laplacian pyramid reconstructive adversarial network (LAPRAN) that enables high- fidelity, flexible and fast CS images reconstruction. LAPRAN progressively reconstructs an image following the concept of the Laplacian pyramid through multiple stages of reconstructive adversarial networks (RANs). At each pyramid level, CS measurements are fused with a contextual latent vector to generate a high-frequency image residual. Consequently, LAPRAN can produce hierarchies of reconstructed images and each with an incremental resolution and improved quality. The scalable pyramid structure of LAPRAN enables high-fidelity CS reconstruction with a flexible resolution that is adaptive to a wide range of compression ratios (CRs), which is infeasible with existing methods. Experimental results on multiple public datasets show that LAPRAN offers an average 7.47dB and 5.98dB PSNR, and an average 57.93\% and 33.20 \% SSIM improvement compared to model-based and data-driven baselines, respectively.

}, keywords = {psclab}, author = {Kai Xu and Zhikang Zhang and Fengbo Ren} } @inproceedings {297, title = {Kaleido: Enabling Efficient Scientific Data Processing on Big-Data Systems}, year = {2017}, pages = {1{\textendash}10}, publisher = {IEEE}, abstract = {

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 efficient processing of 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. This paper presents Kaleido, a solution to this problem by enabling big- data systems to efficiently store and process scientific data. Specifically, it enables Hadoop to directly store NetCDF data on HDFS, and process them in MapReduce using convenient APIs. It also enables Hive to support queries on NetCDF data, transparent to the users. Moreover, it employs optimizations tailored to scientific data, particularly dimension-aware layout which allows efficient execution of subset queries targeting any dimension of the multi- dimensional data. The paper presents a comprehensive evaluation of Kaleido using representative queries on a typical geoscientific dataset. The results show that Kaleido achieves substantial speedup and space saving compared to existing solutions for storing and processing NetCDF data on Hadoop, and it also substantially outperforms the state-of-the-art solutions for supporting subset queries on scientific data.
}, author = {Biookaghazadeh, Saman}, editor = {Zhou, Shujia and Zhao, Ming} } @inproceedings {Los Angeles, CA, title = {Recognizing terrain features on terrestrial surface using a deep learning model - An example with crater detection}, year = {2017}, month = {11/2017}, pages = {33-36}, publisher = {ACM}, address = {Los Angeles, CA}, abstract = {

This paper exploits the use of a popular deep learning model - the faster-RCNN - to support automatic terrain feature detection and classification using a mixed set of optimal remote sensing and natural images. Crater detection is used as the case study in this research since this geomorphological feature provides important information about surface aging. Craters, such as impact craters, also effect global changes in many aspects, such as geography, topography, mineral and hydrocarbon production, etc. The collected data were labeled and the network was trained through a GPU server. Experimental results show that the faster-RCNN model coupled with a widely used convolutional network ZF-net performs well in detecting craters on the terrestrial surface.

}, keywords = {psclab}, isbn = {978-1-4503-5498-1/17/11}, author = {Wenwen Li and Bin Zhou and Chia-Yu Hsu and YIxing Li and Fengbo Ren} } @inproceedings {104, title = {A Compressive-sensing based Testing Vehicle for 3D TSV Pre-bond and Post-bond Testing Data}, year = {2016}, month = {04/2016}, pages = {19-25}, abstract = {

Online testing vehicle is required for 3D TSV pre-bond and post-bond testing due to high probability of TSV failures. It has become a challenge to deal with large sets of generated testing data with limited probing when transmitting the data out. In this paper, a lossless compressive-sensing based testing vehicle is developed for online testing of TSVs. By exploring sparsity of the testing data under constraint of failure bound of TSV, sparse-representation based encoding can be deployed by XOR and AND network on chip to deal with large volume of testing data. Experimental results (with benchmarks) have shown that 89.70\% pre-bond data compression rate can be achieved under 0.5\% probability of failures; and 88.18\% post-bond data compression rate can be achieved with 5\% probability of failures.

}, keywords = {psclab}, author = {Hantao Huang and Hao Yu and Cheng Zhuo 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} } @inproceedings {wang2015optimizing, title = {Optimizing boolean embedding matrix for compressive sensing in rram crossbar}, year = {2015}, pages = {13{\textendash}18}, publisher = {IEEE}, keywords = {Compressive Sensing, ref}, author = {Wang, Yuhao and Li, Xin and Yu, Hao and Ni, Leibin and Yang, Wei and Weng, Chuliang and Zhao, Junfeng} } @inproceedings {zhang2015optimizing, title = {Optimizing fpga-based accelerator design for deep convolutional neural networks}, year = {2015}, pages = {161{\textendash}170}, publisher = {ACM}, keywords = {Deep Learning, ref}, author = {Zhang, Chen and Li, Peng and Sun, Guangyu and Guan, Yijin and Xiao, Bingjun and Cong, Jason} } @inproceedings {shen2014compressed, title = {Compressed Sensing Image Reconstruction Algorithm by Dictionary Learning}, year = {2014}, pages = {193}, publisher = {ACM}, keywords = {Compressive Sensing, ref}, author = {Shen, Yanfei and Zhu, Guangyu and Li, Jintao and Zhu, Zhenmin} } @inproceedings {chen2014dadiannao, title = {Dadiannao: A machine-learning supercomputer}, year = {2014}, pages = {609{\textendash}622}, publisher = {IEEE Computer Society}, keywords = {Deep Learning, ref}, author = {Chen, Yunji and Luo, Tao and Liu, Shaoli and Zhang, Shijin and He, Liqiang and Wang, Jia and Li, Ling and Chen, Tianshi and Xu, Zhiwei and Sun, Ninghui and others} } @article {22, title = {A Square-Root-Free Matrix Decomposition Method for Energy-Efficient Least Square Computation on Embedded Systems}, journal = {IEEE Embedded Systems Letters}, volume = {6}, year = {2014}, month = {Aug.}, pages = {73{\textendash}76}, abstract = {

QR decomposition (QRD) is used to solve least-squares (LS) problems for a wide range of applications. However, traditional QR decomposition methods, such as Gram-Schmidt (GS), require high computational complexity and nonlinear operations to achieve high throughput, limiting their usage on resource-limited platforms. To enable efficient LS computation on embedded systems for real-time applications, this paper presents an alternative decomposition method, called QDRD, which relaxes system requirements while maintaining the same level of performance. Specifically, QDRD eliminates both the square-root operations in the normalization step and the divisions in the subsequent backward substitution. Simulation results show that the accuracy and reliability of factorization matrices can be significantly improved by QDRD, especially when executed on precision-limited platforms. Furthermore, benchmarking results on an embedded platform show that QDRD provides constantly better energy-efficiency and higher throughput than GS-QRD in solving LS problems. Up to 4 and 6.5 times improvement in energy-efficiency and throughput, respectively, can be achieved for small-size problems.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6882128}, author = {Ren, Fengbo and Zhang, Chenxin and Liu, Liang and Xu, Wenyao and Owall, V and Markovi{\'c}, Dejan} } @article {277, title = {MemBuilder: a web-based graphical interface to build heterogeneously mixed membrane bilayers for the GROMACS biomolecular simulation program}, journal = {Bioinformatics}, volume = {30}, year = {2013}, pages = {439{\textendash}441}, abstract = {

Motivation:\ Molecular dynamics (MD) simulations have had a profound impact on studies of membrane proteins during past two decades, but the accuracy of MD simulations of membranes is limited by the quality of membrane models and the applied force fields. Membrane models used in MD simulations mostly contain one kind of lipid molecule. This is far from reality, for biological membranes always contain more than one kind of lipid molecule. Moreover, the lipid composition and their distribution are functionally important. As a result, there is a necessity to prepare more realistic lipid membranes containing different types of lipids at physiological concentrations.

Results:\ To automate and simplify the building process of heterogeneous lipid bilayers as well as providing molecular topologies for included lipids based on both united and all-atom force fields, we provided MemBuilder as a web-based graphical user interface.

Availability and implementation:\ MemBuilder is a free web server available\ 

}, keywords = {other}, author = {Ghahremanpour, Mohammad Mehdi and Arab, Seyed Shahriar and Biookaghazadeh, Saman and Zhang, Jin and van der Spoel, David} } @inproceedings {30, title = {Proper Running Posture Guide: A Wearable Biomechanics Capture System}, year = {2013}, month = {09/2013}, publisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)}, abstract = {

Running is a popular exercise for all age groups. It helps heart and lung functions, enhances muscle strength and control weight. Nevertheless, excessive fatigue and severe injury resulting from inappropriate running poses might reduce the benefits brought by this exercise and stop people from keeping running regularly. In this paper, we design a system that can monitor the running biomechanics, infer running poses, analyze running patterns, and provide both real-time and off-line feedbacks to reduce unnecessary fatigue and unwanted injuries. Common inappropriate running patterns, over-striding, over-pronating and out-sync, are identified and analyzed with the continuous wearable sensor data streams. Two types of correctional feedbacks are designed to provide users appropriate adjustment guidance: vibrating the areas of user body where improper poses are detected and visualizing running video with sensor waveforms to indicate which inappropriate motions trigger the vibration. With reasonable adjustment, running can be a safe and effective activity for a healthy lifestyle.

}, keywords = {psclab}, url = {http://dl.acm.org/citation.cfm?id=2555333}, author = {Zhang, Xiaoyi and Huang, Ming-Chun and Ren, Fengbo and Xu, Wenyao and Guan, Nan and Yi, Wang} } @inproceedings {31, title = {See UV on Your Skin: An Ultraviolet Sensing and Visualization System}, year = {2013}, month = {09/2013}, pages = {22-28}, publisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)}, abstract = {

These days, as the Earth{\textquoteright}s protective ozone layer gets thinner, ultraviolet (UV) radiation threat is growing. In addition, getting tanned as a fashion leads people to wear less clothing, which increases UV intake. Excessive exposure to ultraviolet will lead to sunburn and even skin cancer. Therefore, neither insufficient nor excessive exposure is desirable. Although there are tons of UV meters on the market, a user may have a hard time to understand the unintuitive UV index reading. Thus, there is a potential demand for a portable system which can keep track of daily UV exposure dose, visualize possible sunburned consequences, and provide appropriate skin care recommendations. In this paper, we present a personalized UV monitoring and notification system. This system can continuously track UV exposure by wearable UV sensors. It can also visualize the cumulative UV exposure dose according to a predictive sunburned skin color model. Such an augmented skin color can provide a warning message to indicate the possible result of continuous UV exposure. Compared with other existing systems, our solution not only allows users to monitor their daily UV exposure, but also provides an unobtrusive UV visualization model which effectively warns users to take appropriate actions to avoid potential skin damage. The system has been tested on 9 subjects, and the evaluation feedback indicates that our system is promising for UV monitoring and sunburn prevention.

}, keywords = {psclab}, url = {http://dl.acm.org/citation.cfm?id=2555324}, author = {Zhang, Xiaoyi and Xu, Wenyao and Huang, Ming-Chun and Amini, Navid and Ren, Fengbo} } @inproceedings {zinkevich2010parallelized, title = {Parallelized stochastic gradient descent}, year = {2010}, pages = {2595{\textendash}2603}, keywords = {Deep Learning, ref}, author = {Zinkevich, Martin and Weimer, Markus and Li, Lihong and Smola, Alex J} }