arXiv subjects

Tao Lu

Publications and source records attributed to Tao Lu.

106 records · Page 6Linked to original sources

arXiv2019

Efficient Electro-optical Tuning of Optical Frequency Microcomb on a Monolithically Integrated High-Q Lithium Niobate Microdisk

We demonstrate efficient tuning of a monolithically integrated lithium niobate microdisk (LN) optical frequency microcomb. Utilizing the high optical quality (Q) factor (i.e., Q~7.1*10^6) of the microdisk, the microcomb spans over a spectral bandwidth of ~200 nm at a pump power as low as 20.4 mW. Combining the large eletro-optic coefficient of LN and optimum design of the geometry of microelectrodes, we demonstrate electro-optical tuning of the comb with a spectral range of 400 pm and a tuning efficiency of ~38 pm/100V.

physics.optics↗

arXiv2019

A Soft Range Limited K-Nearest Neighbours Algorithm for Indoor Localization Enhancement

This paper proposes a soft range limited K nearest neighbours (SRL-KNN) localization fingerprinting algorithm. The conventional KNN determines the neighbours of a user by calculating and ranking the fingerprint distance measured at the unknown user location and the reference locations in the database. Different from that method, SRL-KNN scales the fingerprint distance by a range factor related to the physical distance between the user's previous position and the reference location in the database to reduce the spatial ambiguity in localization. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Moreover, to take into account of the temporal fluctuations of the received signal strength indicator (RSSI), RSSI histogram is incorporated into the distance calculation. Actual on-site experiments demonstrate that the new algorithm achieves an average localization error of $0.66$ m with $80\%$ of the errors under $0.89$ m, which outperforms conventional KNN algorithms by $45\%$ under the same test environment.

eess.SP↗

arXiv2019

Omni SCADA Intrusion Detection Using Deep Learning Algorithms

We investigate deep learning based omni intrusion detection system (IDS) for supervisory control and data acquisition (SCADA) networks that are capable of detecting both temporally uncorrelated and correlated attacks. Regarding the IDSs developed in this paper, a feedforward neural network (FNN) can detect temporally uncorrelated attacks at an {F$_{1}$} of {99.967${\pm}$0.005\%} but correlated attacks as low as {58${\pm}$2\%}. In contrast, long-short term memory (LSTM) detects correlated attacks at {99.56${\pm}$0.01\%} while uncorrelated attacks at {99.3${\pm}$0.1\%}. Combining LSTM and FNN through an ensemble approach further improves the IDS performance with {F$_{1}$} of {99.68${\pm}$0.04\%} regardless the temporal correlations among the data packets.

eess.SY↗

arXiv2019

Multi-label Classification with Optimal Thresholding for Multi-composition Spectroscopic Analysis

In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multi gas mixture in a cluttered environment. Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance - partial least squares discriminant analysis when signal-to-noise ratio and training sample size are sufficient.

cs.LG↗

arXiv2019

Compositional Coding for Collaborative Filtering

Efficiency is crucial to the online recommender systems. Representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computation in the Hamming space, in recent years, we have witnessed an emerging research effort in exploiting binary hashing techniques for CF methods. However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data. In this work, we attempt to improve the efficiency without hurting the model performance by utilizing both the accuracy of real-valued vectors and the efficiency of binary codes to represent users/items. In particular, we propose the Compositional Coding for Collaborative Filtering (CCCF) framework, which not only gains better recommendation efficiency than the state-of-the-art binarized CF approaches but also achieves even higher accuracy than the real-valued CF method. Specifically, CCCF innovatively represents each user/item with a set of binary vectors, which are associated with a sparse real-value weight vector. Each value of the weight vector encodes the importance of the corresponding binary vector to the user/item. The continuous weight vectors greatly enhances the representation capability of binary codes, and its sparsity guarantees the processing speed. Furthermore, an integer weight approximation scheme is proposed to further accelerate the speed. Based on the CCCF framework, we design an efficient discrete optimization algorithm to learn its parameters. Extensive experiments on three real-world datasets show that our method outperforms the state-of-the-art binarized CF methods (even achieves better performance than the real-valued CF method) by a large margin in terms of both recommendation accuracy and efficiency.

cs.IR↗

arXiv2019

Localizing Discriminative Visual Landmarks for Place Recognition

We address the problem of visual place recognition with perceptual changes. The fundamental problem of visual place recognition is generating robust image representations which are not only insensitive to environmental changes but also distinguishable to different places. Taking advantage of the feature extraction ability of Convolutional Neural Networks (CNNs), we further investigate how to localize discriminative visual landmarks that positively contribute to the similarity measurement, such as buildings and vegetations. In particular, a Landmark Localization Network (LLN) is designed to indicate which regions of an image are used for discrimination. Detailed experiments are conducted on open source datasets with varied appearance and viewpoint changes. The proposed approach achieves superior performance against state-of-the-art methods.

cs.CV↗

arXiv2019

Hindsight Generative Adversarial Imitation Learning

Compared to reinforcement learning, imitation learning (IL) is a powerful paradigm for training agents to learn control policies efficiently from expert demonstrations. However, in most cases, obtaining demonstration data is costly and laborious, which poses a significant challenge in some scenarios. A promising alternative is to train agent learning skills via imitation learning without expert demonstrations, which, to some extent, would extremely expand imitation learning areas. To achieve such expectation, in this paper, we propose Hindsight Generative Adversarial Imitation Learning (HGAIL) algorithm, with the aim of achieving imitation learning satisfying no need of demonstrations. Combining hindsight idea with the generative adversarial imitation learning (GAIL) framework, we realize implementing imitation learning successfully in cases of expert demonstration data are not available. Experiments show that the proposed method can train policies showing comparable performance to current imitation learning methods. Further more, HGAIL essentially endows curriculum learning mechanism which is critical for learning policies.

cs.LG↗

arXiv2018

Real-time Electrical Tuning of an Optical Spring on a Monolithically Integrated Ultrahigh Q Lithium Nibote Microresonator

Cavity optomechanics, the study of the interplay between light and mechanical properties of matter, has triggered a wide range of groundbreaking researches from cavity quantum electrodynamics, label free single molecule detection to the creation of phonon laser. Using femtosecond laser direct writing followed by chemo-mechanical polishing, here we report an ultrahigh quality (Q~10^7) factor lithium niobate (LN) whispering gallery microresonator monolithically integrated with in-plane microelectrodes. Coherent regenerative optomechanical oscillation with an effective mechanical quality (Qm) factor as high as 2.86*10^8 is observed in air. We demonstrate real-time electrical tuning of the optomechanical frequency with an electro-mechanical tuning efficiency around -134 kHz/100V.

physics.optics↗

arXiv2018

Distributed and Multi-layer UAV Network for the Next-generation Wireless Communication

Unmanned aerial vehicles (UAVs) for wireless communications has rapidly grown into a research hotspot as the mass production of high-performance, low-cost, intelligent UAVs become more practical and feasible. In the meantime, fifth generation (5G) wireless communications is being standardized and planned for deployment globally. During this process, UAVs are gradually being considered as an important part of 5G and expected to play a critical role in enabling more functional diversity for 5G communications. In this article, we conduct an in-depth investigation of mainstream UAV designs and state-of-the-art UAV enabled wireless communication systems.We propose a hierarchical architecture of UAVs with multi-layer and distributed features to facilitate a smooth integration of different mainstream UAVs into the next-generation wireless communication networks. Furthermore, we unveil the critical comprehensive design tradeoffs, in light of both communication and aerodynamic principles. Empirical models and satellite measurement data are used to conduct numerical analysis of the meteorological impacts of UAV enabled, 5G high bands communications.

eess.SP↗

arXiv2017

Fabrication of high quality factor lithium niobate double-disk using a femtosecond laser

We demonstrate fabrication of a high quality factor lithium niobate double-disk whispering-gallery microcavity using femtosecond laser assisted ion beam milling. Using this method, two vertically stacked 30-um-diameter disks with a 200-nm-gap are fabricated. With our device, an optical quality factor as high as 1.2*10^5 is demonstrated. Our approach is scalable to fabricate multiple disks on a single chip.

physics.app-ph↗

arXiv2017

IOTune: A G-states Driver for Elastic Performance of Block Storage

Imagining a disk which provides baseline performance at a relatively low price during low-load periods, but when workloads demand more resources, the disk performance is automatically promoted in situ and in real time. In a hardware era, this is hardly achievable. However, this imagined disk is becoming reality due to the technical advances of software-defined storage, which enable volume performance to be adjusted on the fly. We propose IOTune, a resource management middleware which employs software-defined storage primitives to implement G-states of virtual block devices. G-states enable virtual block devices to serve at multiple performance gears, getting rid of conflicts between immutable resource reservation and dynamic resource demands, and always achieving resource right-provisioning for workloads. Accompanying G-states, we also propose a new block storage pricing policy for cloud providers. Our case study for applying G-states to cloud block storage verifies the effectiveness of the IOTune framework. Trace-replay based evaluations demonstrate that storage volumes with G-states adapt to workload fluctuations. For tenants, G-states enable volumes to provide much better QoS with a same cost of ownership, comparing with static IOPS provisioning and the I/O credit mechanism. G-states also reduce I/O tail latencies by one to two orders of magnitude. From the standpoint of cloud providers, G-states promote storage utilization, creating values and benefiting competitiveness. G-states supported by IOTune provide a new paradigm for storage resource management and pricing in multi-tenant clouds.

cs.OS↗

arXiv2016

Hierarchical Online Intrusion Detection for SCADA Networks

We propose a novel hierarchical online intrusion detection system (HOIDS) for supervisory control and data acquisition (SCADA) networks based on machine learning algorithms. By utilizing the server-client topology while keeping clients distributed for global protection, high detection rate is achieved with minimum network impact. We implement accurate models of normal-abnormal binary detection and multi-attack identification based on logistic regression and quasi-Newton optimization algorithm using the Broyden-Fletcher-Goldfarb-Shanno approach. The detection system is capable of accelerating detection by information gain based feature selection or principle component analysis based dimension reduction. By evaluating our system using the KDD99 dataset and the industrial control system dataset, we demonstrate that HOIDS is highly scalable, efficient and cost effective for securing SCADA infrastructures.

cs.CR↗

arXiv2014

Generalized Full-Vector Multi-Mode Matching Analysis of Whispering-Gallery Microcavities

We outline a full-vectorial three-dimensional multi-mode matching technique in a cylindrical coordinate system that addresses the mutual coupling among multiple modes copropagating in a perturbed whispering-gallery-mode microcavity. In addition to its superior accuracy in respect to our previously implemented single-mode matching technique, this current technique is suitable for modelling waveguide-to-cavity coupling where the influence of multi-mode coupling is non-negligible. Using this methodology, a robust scheme for hybrid integration of a microcavity onto a silicon-on-insulator platform is proposed.

physics.optics↗

arXiv2014

Highly Efficient Boundary Element Analysis of Whispering Gallery Microcavities

We demonstrate that the efficiency of the boundary element whispering gallery microcavity analysis can be improved by orders of magnitude with the inclusion of Fresnel approximation. Using this formulation, simulation of a microdisk with wave-number-radius product as large as $kR\approx8,000$ was demonstrated in contrast to a previous record of $kR\approx100$. In addition to its high accuracy on computing the modal field distribution and resonance wavelength, this method yields a relative error of $10%$ in calculating the quality factor as high as $10^{11}$ through a direct root searching method where the conventional boundary element method failed to achieve. Finally, quadrupole shaped cavities and double disks as large as $100 μm$ in diameter were modeled by employing as few as $512$ boundary elements whilst the simulation of such large cavities using conventional boundary element method were not reported previously.

physics.optics↗

arXiv2013

Cylindrical Beam Propagation Modelling of Perturbed Whispering-Gallery Mode Microcavities

We simulate light propagation in perturbed whispering-gallery mode microcavities using a two-dimensional finite-difference beam prop- agation method in a cylindrical coordinate system. Optical properties of whispering-gallery microcavities perturbed by polystyrene nanobeads are investigated through this formulation. The light perturbation as well as quality factor degradation arising from cavity ellipticity are also studied.

physics.optics↗

arXiv2009

Modeling long-term longitudinal HIV dynamics with application to an AIDS clinical study

A virologic marker, the number of HIV RNA copies or viral load, is currently used to evaluate antiretroviral (ARV) therapies in AIDS clinical trials. This marker can be used to assess the ARV potency of therapies, but is easily affected by drug exposures, drug resistance and other factors during the long-term treatment evaluation process. HIV dynamic studies have significantly contributed to the understanding of HIV pathogenesis and ARV treatment strategies. However, the models of these studies are used to quantify short-term HIV dynamics ($<$ 1 month), and are not applicable to describe long-term virological response to ARV treatment due to the difficulty of establishing a relationship of antiviral response with multiple treatment factors such as drug exposure and drug susceptibility during long-term treatment. Long-term therapy with ARV agents in HIV-infected patients often results in failure to suppress the viral load. Pharmacokinetics (PK), drug resistance and imperfect adherence to prescribed antiviral drugs are important factors explaining the resurgence of virus. To better understand the factors responsible for the virological failure, this paper develops the mechanism-based nonlinear differential equation models for characterizing long-term viral dynamics with ARV therapy. The models directly incorporate drug concentration, adherence and drug susceptibility into a function of treatment efficacy and, hence, fully integrate virologic, PK, drug adherence and resistance from an AIDS clinical trial into the analysis. A Bayesian nonlinear mixed-effects modeling approach in conjunction with the rescaled version of dynamic differential equations is investigated to estimate dynamic parameters and make inference. In addition, the correlations of baseline factors with estimated dynamic parameters are explored and some biologically meaningful correlation results are presented. Further, the estimated dynamic parameters in patients with virologic success were compared to those in patients with virologic failure and significantly important findings were summarized. These results suggest that viral dynamic parameters may play an important role in understanding HIV pathogenesis, designing new treatment strategies for long-term care of AIDS patients.

stat.AP↗