@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} } @inproceedings {Seattle, WA, title = {Learning in the Frequency Domain}, year = {2020}, month = {06/2020}, pages = {1740-1749}, address = {Seattle, WA}, abstract = {

Deep neural networks have achieved remarkable success in computer vision tasks. Existing neural networks mainly operate in the spatial domain with fixed input sizes. For practical applications, images are usually large and have to be downsampled to the predetermined input size of neural networks. Even though the downsampling operations reduce computation and the required communication bandwidth, it removes both redundant and salient information obliviously, which results in accuracy degradation. Inspired by digital signal processing theories, we analyze the spectral bias from the frequency perspective and propose a learning-based frequency selection method to identify the trivial frequency components which can be removed with- out accuracy loss. The proposed method of learning in the frequency domain leverages identical structures of the well- known neural networks, such as ResNet-50, MobileNetV2, and Mask R-CNN, while accepting the frequency-domain information as the input. Experiment results show that learning in the frequency domain with static channel selection can achieve higher accuracy than the conventional spatial downsampling approach and meanwhile further reduce the input data size. Specifically for ImageNet classification with the same input size, the proposed method achieves 1.41\% and 0.66\% top-1 accuracy improvements on ResNet-50 and MobileNetV2, respectively. Even with half input size, the proposed method still improves the top-1 accuracy on ResNet-50 by 1\%. In addition, we observe a 0.8\% average precision improvement on Mask R-CNN for instance segmentation on the COCO dataset.

}, keywords = {psclab}, author = {Kai Xu and Minghai Qin and Fei Sun and Yuhao Wang and Yen-Kuang Chen 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} } @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} }