arXiv subjects

Huan Wu

Publications and source records attributed to Huan Wu.

24 records · Page 2Linked to original sources

arXiv2022

Compressed domain vibration detection and classification for distributed acoustic sensing

Distributed acoustic sensing (DAS) is a novel enabling technology that can turn existing fibre optic networks to distributed acoustic sensors. However, it faces the challenges of transmitting, storing, and processing massive streams of data which are orders of magnitude larger than that collected from point sensors. The gap between intensive data generated by DAS and modern computing system with limited reading/writing speed and storage capacity imposes restrictions on many applications. Compressive sensing (CS) is a revolutionary signal acquisition method that allows a signal to be acquired and reconstructed with significantly fewer samples than that required by Nyquist-Shannon theorem. Though the data size is greatly reduced in the sampling stage, the reconstruction of the compressed data is however time and computation consuming. To address this challenge, we propose to map the feature extractor from Nyquist-domain to compressed-domain and therefore vibration detection and classification can be directly implemented in compressed-domain. The measured results show that our framework can be used to reduce the transmitted data size by 70% while achieves 99.4% true positive rate (TPR) and 0.04% false positive rate (TPR) along 5 km sensing fibre and 95.05% classification accuracy on a 5-class classification task.

eess.SP↗

arXiv2021

Stable and Compact Face Recognition via Unlabeled Data Driven Sparse Representation-Based Classification

Sparse representation-based classification (SRC) has attracted much attention by casting the recognition problem as simple linear regression problem. SRC methods, however, still is limited to enough labeled samples per category, insufficient use of unlabeled samples, and instability of representation. For tackling these problems, an unlabeled data driven inverse projection pseudo-full-space representation-based classification model is proposed with low-rank sparse constraints. The proposed model aims to mine the hidden semantic information and intrinsic structure information of all available data, which is suitable for few labeled samples and proportion imbalance between labeled samples and unlabeled samples problems in frontal face recognition. The mixed Gauss-Seidel and Jacobian ADMM algorithm is introduced to solve the model. The convergence, representation capability and stability of the model are analyzed. Experiments on three public datasets show that the proposed LR-S-PFSRC model achieves stable results, especially for proportion imbalance of samples.

cs.CV↗

arXiv2021

Ab initio determination of ultrahigh thermal conductivity in ternary compounds

Discovering new materials with ultrahigh thermal conductivity has been a critical research frontier and driven by many important technological applications ranging from thermal management to energy science. Here we have rigorously investigated the fundamental lattice vibrational spectra in ternary compounds and determined the thermal conductivity using a predictive ab initio approach. Phonon transport in B-X-C (X = N, P, As) groups is systematically quantified with different crystal structures and high-order anharmonicity involving a four-phonon process. Our calculation found an ultrahigh room-temperature thermal conductivity through strong carbon-carbon bonding up to 2100 W/mK beyond most common materials and the recently discovered boron arsenide. This study provides fundamental insight into the atomistic design of thermal conductivity and opens up opportunities in new materials searching towards complicated compound structures. DOI: 10.1103/PhysRevB.103.L041203

cond-mat.mtrl-sci↗

arXiv2021

Interference fading suppression in Phi-OTDR using space-division multiplexed probes

We propose and experimentally demonstrate a novel interference fading suppression method for phase-sensitive optical time domain reflectometry (Phi-OTDR) using space-division multiplexed (SDM) pulse probes in few-mode fiber. The SDM probes consist of multiple different modes, and three spatial modes (LP01, LP11a and LP11b) are used in this work for proof of concept. Firstly, the Rayleigh backscattering light of different modes is experimentally characterized, and it turns out that the waveforms of Phi-OTDR traces of distinct modes are all different from each other. Thanks to the spatial difference of fading positions of distinct modes, multiple probes from spatially multiplexed modes can be used to suppress the interference fading in Phi-OTDR. Then, the performances of the Phi-OTDR systems using single probe and multiple probes are evaluated and compared. Specifically, statistical analysis shows that both fading probabilities over fiber length and time are reduced significantly by using multiple SDM probes, which verifies the significant performance improvement on fading suppression. The proposed novel interference fading suppression method does not require complicated frequency or phase modulation, which has the advantages of simplicity, good effectiveness and high reliability.

physics.optics↗

arXiv2019

Basic Physical Properties of Cubic Boron Arsenide

Cubic boron arsenide (BAs) is an emerging semiconductor material with a record-high thermal conductivity of 1300 W/mK. However, many fundamental properties of BAs remain unexplored experimentally. Here, for the first time, we report the systematic experimental measurements of important physical properties of BAs, including the bandgap, optical refractive index, stiffness, elastic modulus, shear modulus, Poisson ratio, thermal expansion coefficient, and heat capacity. In particular, light absorption and Fabry Perot interference were used to measure an optical bandgap of 1.82 eV and a refractive index of 3.29 (657 nm) at room temperature. A pico-ultrasonic method, based on ultrafast optical pump probe spectroscopy, was used to measure a high elastic modulus of 326 GPa, which is twice that of silicon. Furthermore, temperature dependent X-ray diffraction was used to measure a linear thermal expansion coefficient of 3.85x10^-6 per K; this value is very close to prototype semiconductors such as GaN, which underscores the promise of BAs for cooling high power and high frequency electronics. We also performed ab initio theory calculations and observed good agreement between the experimental and theoretical results. Importantly, this work aims to build a database (Table I) for the basic physical properties of BAs with the expectation that this semiconductor will inspire broad research and applications in electronics, photonics, and mechanics.

cond-mat.mtrl-sci↗

arXiv2018

BOTDA Fiber Sensor System Based on FPGA Accelerated Support Vector Regression

Brillouin optical time domain analyzer (BOTDA) fiber sensors have shown strong capability in static long haul distributed temperature/strain sensing. However, in applications such as structural health monitoring and leakage detection, real-time measurement is quite necessary. The measurement time of temperature/strain in a BOTDA system includes data acquisition time and post-processing time. In this work, we propose to use hardware accelerated support vector regression (SVR) for the post-processing of the collected BOTDA data. Ideal Lorentzian curves under different temperatures with different linewidths are used to train the SVR model to determine the linear SVR decision function. The performance of SVR is evaluated under different signal-to-noise ratios (SNRs) experimentally. After the model coefficients are determined, algorithm-specific hardware accelerators based on field programmable gate arrays (FPGAs) are used to realize SVR decision function. During the implementation, hardware optimization techniques based on loop dependence analysis and batch processing are proposed to reduce the execution latency. Our FPGA implementations can achieve up to 42x speedup compared with software implementation on an i7-5960x computer. The post-processing time for 96,100 BGSs along 38.44-km FUT is only 0.46 seconds with FPGA board ZCU104, making the post-processing time no longer a limiting factor for dynamic sensing. Moreover, the energy efficiency of our FPGA implementation can reach up to 226.1x higher than software implementation based on CPU.

eess.SP↗