@inproceedings {London, UK, title = {STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset}, year = {2022}, month = {11/2022}, address = {London, UK}, abstract = {

Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large- scale data collection, sanitization, and annotation. Moreover, the created datasets usually face the challenge of extremely imbalanced class distribution or partial low-quality data samples. Motivated by this, we explore the procedurally synthetic 3D data generation paradigm to equip individuals with the full capability of creating large-scale annotated photogrammetry point clouds. Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages. Unlike generating synthetic data in virtual games, where the simulated data usually have limited gaming environments created by artists, the proposed pipeline simulates the reconstruction process of the real environment by following the same UAV flight pattern on different synthetic terrain shapes and building densities, which ensure similar quality, noise pattern, and diversity with real data. In addition, the precise semantic and instance annotations can be generated fully automatically, avoiding the expensive and time-consuming manual annotation. Based on\ the proposed pipeline, we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16 km2 of landscapes and up to 18 fine-grained semantic categories. For verification purposes, we also provide datasets collected from four areas in the real environment. Extensive experiments conducted on our datasets demonstrate the effectiveness and quality of the proposed synthetic dataset.

}, keywords = {psclab}, url = {https://www.stpls3d.com/}, author = {Meida Chen and Qingyong Hu and Zifan Yu and Hugues Thomas and Andrew Feng and Yu Hou and Kyle McCullough and Fengbo Ren and Lucio Soibelman} } @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} } @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} } @article {9999, title = {An 8-Bit Compressive Sensing ADC With 4GS/s Equivalent Speed Utilizing Self-Timed Pipeline SAR-Binary-Search}, journal = {IEEE Transactions on Circuits and Systems II: Express Briefs}, volume = {63}, year = {2016}, month = {10/2016}, pages = {934-938}, abstract = {

This brief presents a 65nm CMOS single-channel 8- bit ADC compatible for energy-efficient high-speed Compressive Sensing (CS) and Nyquist Sampling (NS). A self-timed pipeline two-stage SAR-Binary-Search (BS) architecture is proposed and integrated with a 4GHz random-matrix clock generator, enabling a physical sampling speed up to 500MS/s with 40.2dB SNDR in NS-mode, and an equivalent speed up to 4GS/s with 36.2dB SNDR in CS-mode, leading to a FOM of 239fJ/conversion-step and 71fJ/conversion-step respectively. A Passive-charge-sharing (PCS) with open-loop (OL) residue-amplifier (RA) technique is proposed to boost the maximum physical sampling speed and the equivalent CS acquisition bandwidth. A reference-voltage fitting calibration scheme is applied to pre-distort inter-stage errors.

}, keywords = {psclab}, author = {Boyu Hu and Fengbo Ren and Zuow-Zun Chen and Xicheng Jiang and Mau-Chung Frank Chang} } @article {9999, title = {9-bit time{\textendash}digital-converter-assisted compressive-sensing analogue{\textendash}digital-converter with 4 GS/s equivalent speed}, journal = {IET Electronics Letters}, volume = {52}, year = {2016}, month = {03/2016}, pages = {430-432}, abstract = {

A novel 9-bit time{\textendash}digital-converter (TDC)-assisted analogue{\textendash}digital-converter (ADC) supporting energy-efficient high-speed compressive-sensing (CS) operation is presented. With a voltage{\textendash}time-converter serving as the cross-domain residue conveyer, the proposed two-stage self-timed pipeline ADC architecture hybrids a voltage-domain comparator-interleaved successive-approximation (SAR) ADC front-end and a time-domain locally readjusted folding two-dimensional Vernier TDC back-end. Implemented in 65 nm CMOS technology, the prototype benefits from both the CS-enabled sub-Nyquist operation and the hybrid quantisation scheme, leading up to 4 GS/s equivalent speed with 34.2 dB signal-noise-distortion-ratio (SNDR) and a figure-of-merit (FOM) of 101 fJ/conversion step.

}, keywords = {psclab}, author = {Boyu Hu and Fengbo Ren and Zuow-Zun Chen and Xicheng Jiang and Mau-Chung Frank Chang} } @inproceedings {hubara2016binarized, title = {Binarized neural networks}, year = {2016}, pages = {4107{\textendash}4115}, keywords = {Deep Learning, ref}, author = {Hubara, Itay and Courbariaux, Matthieu and Soudry, Daniel and El-Yaniv, Ran and Bengio, Yoshua} } @article {courbariaux2016binarized, title = {Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1}, journal = {arXiv preprint arXiv:1602.02830}, year = {2016}, keywords = {Deep Learning, ref}, author = {Courbariaux, Matthieu and Hubara, Itay and Soudry, Daniel and El-Yaniv, Ran and Bengio, Yoshua} } @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} } @article {lecun2015deep, title = {Deep learning}, journal = {Nature}, volume = {521}, number = {7553}, year = {2015}, pages = {436{\textendash}444}, publisher = {Nature Research}, keywords = {Deep Learning, ref}, author = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey} } @article {hegde2015numax, title = {NuMax: A convex approach for learning near-isometric linear embeddings}, journal = {IEEE Transactions on Signal Processing}, volume = {63}, number = {22}, year = {2015}, pages = {6109{\textendash}6121}, publisher = {IEEE}, keywords = {Compressive Sensing, ref}, author = {Hegde, Chinmay and Sankaranarayanan, Aswin C and Yin, Wotao and Baraniuk, Richard G} } @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 {19, title = {Healable Capacitive Touch Screen Sensors Based on Transparent Composite Electrodes Comprising Silver Nanowires and a Furan/Maleimide Diels-Alder Cycloaddition Polymer}, journal = {ACS Nano}, volume = {8}, year = {2014}, month = {Dec.}, pages = {12874{\textendash}12882}, abstract = {

A healable transparent capacitive touch screen sensor has been fabricated based on a healable silver nanowire{\textendash}polymer composite electrode. The composite electrode features a layer of silver nanowire percolation network embedded into the surface layer of a polymer substrate comprising an ultrathin soldering polymer layer to confine the nanowires to the surface of a healable Diels{\textendash}Alder cycloaddition copolymer and to attain low contact resistance between the nanowires. The composite electrode has a figure-of-merit sheet resistance of 18 Ω/sq with 80\% transmittance at 550 nm. A surface crack cut on the conductive surface with 18 Ω is healed by heating at 100 {\textdegree}C, and the sheet resistance recovers to 21 Ω in 6 min. A healable touch screen sensor with an array of 8 {\texttimes} 8 capacitive sensing points is prepared by stacking two composite films patterned with 8 rows and 8 columns of coupling electrodes at 90{\textdegree} angle. After deliberate damage, the coupling electrodes recover touch sensing function upon heating at 80 {\textdegree}C for 30 s. A capacitive touch screen based on Arduino is demonstrated capable of performing quick recovery from malfunction caused by a razor blade cutting. After four cycles of cutting and healing, the sensor array remains functional.

}, keywords = {psclab}, url = {http://pubs.acs.org/doi/abs/10.1021/nn506610p}, author = {Li, Junpeng and Liang, Jiajie and Li, Lu and Ren, Fengbo and Hu, Wei and Li, Juan and Qi, Shuhua and Pei, Qibing} } @article {17, title = {Statistical Timing and Power Analysis of VLSI Considering Non-linear Dependence}, journal = {the VLSI Journal Integration}, volume = {47}, year = {2014}, month = {Sep.}, pages = {487{\textendash}498}, abstract = {

Majority of practical multivariate statistical analysis and optimizations model interdependence among random variables in terms of the linear correlation. Though linear correlation is simple to use and evaluate, in several cases non-linear dependence between random variables may be too strong to ignore. In this paper, we propose polynomial correlation coefficients as simple measure of multi-variable non-linear dependence and show that the need for modeling non-linear dependence strongly depends on the end function that is to be evaluated from the random variables. Then, we calculate the errors in estimation resulting from assuming independence of components generated by linear de-correlation techniques, such as PCA and ICA. The experimental results show that the error predicted by our method is within 1\% error compared to the real simulation of statistical timing and leakage analysis. In order to deal with non-linear dependence, we further develop a target-function-driven component analysis algorithm (FCA) to minimize the error caused by ignoring high order dependence. We apply FCA to statistical leakage power analysis and SRAM cell noise margin variation analysis. Experimental results show that the proposed FCA method is more accurate compared to the traditional PCA or ICA.

}, keywords = {psclab}, url = {http://www.sciencedirect.com/science/article/pii/S0167926014000029}, author = {Cheng, Lerong and Xu, Wenyao and Ren, Fengbo and Gong, Fang and Gupta, Puneet and He, Lei} } @book {james2013introduction, title = {An introduction to statistical learning}, volume = {112}, year = {2013}, publisher = {Springer}, organization = {Springer}, keywords = {Deep Learning, ref}, url = {https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about}, author = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert} } @inproceedings {29, title = {mCOPD: Mobile Phone Based Lung Function Diagnosis and Exercise System for COPD}, year = {2013}, month = {05/2013}, publisher = {ACM}, abstract = {

COPD (Chronic Obstructive Pulmonary Disease) is a serious lung disease that causes difficulty in breathing. COPD patients require lung function examinations and perform breathing exercises on a regular basis in order to manage and be more aware of their health status. In this paper, we designed and developed a mobile-phone based system for lung function diagnosis, called mCOPD. Besides enabling accurate COPD examinations at home, the mCOPD system also offers a video-game based guidance system for breathing exercises. We evaluated mCOPD in controlled and uncontrolled environments with 40 subjects. The experimental results show that our system is a promising tool for remote medical treatment of COPD.

}, keywords = {psclab}, url = {http://dl.acm.org/citation.cfm?id=2504383}, author = {Xu, Wenyao and Huang, Ming-Chun and Liu, Jason J and Ren, Fengbo and Shen, Xinchen and Liu, Xiao and Sarrafzadeh, Majid} } @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 {krizhevsky2012imagenet, title = {Imagenet classification with deep convolutional neural networks}, year = {2012}, pages = {1097{\textendash}1105}, keywords = {Deep Learning, ref}, author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E} } @article {18, title = {Scalability and Design-space Analysis of A 1T-1MTJ Memory Cell For STT-RAMs}, journal = {IEEE Transactions on Electron Devices}, volume = {59}, year = {2012}, month = {Jan.}, pages = {878{\textendash}887}, abstract = {

We present a design-space feasibility region, as a function of magnetic tunnel junction (MTJ) characteristics and target memory specifications, to explore the design margin of a one-transistor-one-magnetic-tunnel-junction (1T-1MTJ) memory cell for spin-transfer torque random access memories (STT-RAMs). Data from measured devices are used to model the statistical variation of an MTJ{\textquoteright}s critical switching current and resistance. The sensitivity of the design space to different design parameters is also analyzed for the scaling of both the MTJ and the underlying transistor technology. A design flow, using a sensitivity-based analysis and an MTJ switching model based on the Landau-Lifshitz-Gilbert equation, is proposed to optimize design margins for gigabit-scale memories. Design points for improved yield, density, and memory performance are extracted from MTJ-compatible complementary metal-oxide-semiconductor (CMOS) technologies for 90-, 65-, 45-, and 32-nm processes. Predictive technology models are used to explore the future scalability of STT-RAMs in upcoming 22- and 16-nm technology nodes. Our analysis shows that, to achieve Flash-like densities ( \<; 6F2) in advanced CMOS technologies, aggressive scaling of the critical switching current density will be required.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6140952}, author = {Dorrance, Richard and Ren, Fengbo and Toriyama, Yuta and Hafez, Amr Amin and Yang, C-KK and Markovi{\'c}, Dejan} } @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} }