- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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In-band Full-Duplex Relay-Assisted Millimeter-Wave System Design
摘要： Millimeter-wave (mmWave) communication is a promising technology for future wireless systems due to the availability of huge unlicensed bandwidth. However, the need for large number of radio frequency (RF) chains associated with the antenna array and the corresponding increase in hardware complexity and power consumption are major stumbling blocks to its implementability. In this paper, we propose a low-complexity in-band full-duplex relay-assisted mmWave communication system design. We obtain the proposed multiple-input multiple-output analog–digital hybrid transceivers and relay filters by minimizing the overall sum-mean-square-error while mitigating the effect of residual loopback self-interference (LSI) in the system. The number of RF chains required in the proposed design is less than the number of antennas. We first present a design assuming the availability of perfect channel state information (CSI) at all the nodes. Later, we extend it to a robust design assuming a more realistic scenario, where the available CSI is imperfect. Furthermore, the LSI channel knowledge is assumed to be imperfect for both the designs rendering them robust to errors in loopback CSI. We employ sparse approximation technique to reduce the hardware complexity in the proposed system designs. The proposed algorithms are shown to converge to a limit even though the global convergence is hard to prove since the overall problem is non-convex. The hardware complexity-performance tradeoff of the proposed design is analyzed. Furthermore, the resilience of the robust design in the presence of CSI errors and the performance of both the proposed designs over various parameters are illustrated via numerical simulations.
关键词： robust design,Full-duplex,residual self-interference,millimeter-wave communication,hybrid beamforming
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Cloud Detection of Optical Remote Sensing Image Time Series Using P-norm based Regression Model
摘要： An automatic multi-temporal method is proposed in this paper for cloud detection without known the reference image in prior. A series of reference images are provided by fitting robustly of the pixels of multi-temporal images contaminated by clouds to show the inherent gradual change of the landscape with time instants. Then the cloud is detected by thresholding the difference between the target and the reference images, which is found to be merely composed of the regression model error modeled as Gaussian noise and outliers corresponding to cloud and its shadow. The proposed method is compared with state-of-the-art algorithms on the LANDSAT dataset, and shows a better discrimination of cloud and cloud shadow covered pixels from the uncontaminated ones.
关键词： P-norm distribution,Robust regression,Robust scale estimation,Cloud detection,Multi-temporal
更新于2025-09-23 15:21:21
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ROBUST ADAPTIVE WIDEBAND BEAMFORMING USING PROBABILITY-CONSTRAINED OPTIMIZATION
摘要： The existing robust narrowband beamformers based on probability-constrained optimization have an excellent performance as compared to several state-of-the-art robust beamforming algorithms. However, they always assume that the steering vector errors are small enough. Without this assumption, we extend the probability-constrained approach to a wideband beamformer. In addition, a novel robust wideband beamformer with frequency invariance constraints is proposed by introducing the response variation (RV) element. Our problems can be reformulated in a convex form as the iterative second order cone programming (SOCP) problem and solved effectively using well-established interior point method. Compared with existing robust wideband beamformers, a more efficient control over the beamformer’s response against the steering vector errors is achieved with an improved output signal-to-interference-plus-noise ratio (SINR).
关键词： response variation (RV),robust adaptive wideband beamforming,frequency invariance constraints,second order cone programming (SOCP),probability-constrained optimization,signal-to-interference-plus-noise ratio (SINR)
更新于2025-09-23 15:21:21
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[IEEE 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) - Shenyang, China (2018.8.24-2018.8.27)] 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) - Experimental study on operator-based integral sliding mode robust nonlinear control for WPT systems
摘要： In our previous research on wireless power transfer systems, an operator-based sliding mode control design was proposed to regulate the output voltage. However, the steady-state error exists in the nonlinear control system. In this paper, an operator-based integral sliding mode robust nonlinear control design scheme is proposed to eliminate the steady-state error in the wireless power transfer system. Besides, the robust stability is guaranteed by using robust right coprime factorization approach. Results of simulations and experiments are presented to prove the effectiveness of the proposed control design scheme.
关键词： operator-based control,robust nonlinear control,steady-state error,sliding mode control,wireless power transfer
更新于2025-09-23 15:21:21
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Robust Harmonic Retrieval via Block Successive Upper-Bound Minimization
摘要： Harmonic retrieval (HR) is a problem of significance with numerous applications. Many existing algorithms are explicitly or implicitly developed under Gaussian noise assumption, which, however, are not robust against non-Gaussian noise such as impulsive noise or outliers. In this paper, by employing the (cid:2)p-fitting criterion and block successive upper-bound minimization (BSUM) technique, a variant of the classical RELAX algorithm named as BSUM-RELAX is devised for robust HR. It is revealed that the BSUM-RELAX successively performs alternating optimization along coordinate directions, i.e., it updates one harmonic by fixing the other (K ? 1) components, such that the whole problem is split into K single-tone HR problems, which are then solved by creating a surrogate function that majorizes the objective function of each subproblem. To further refine the frequency component, the Newton’s method that takes linear complexity O(N ) is derived for updating the frequency estimates. We prove that under the single-tone case, BSUM-RELAX converges to a Karush-Kuhn-Tucker point. Furthermore, the BSUM-RELAX is extended to the multidimensional HR case. Numerical results show that the proposed algorithm outperforms the state-of-the-art methods in heavy-tailed noise scenarios.
关键词： robust estimation,impulsive noise/outliers,Harmonic retrieval,RELAX,majorization minimization
更新于2025-09-23 15:21:21
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Analysis of High-Temperature Carrier Transport Mechanisms for High Al-Content Al <sub/>0.6</sub> Ga <sub/>0.4</sub> N MSM Photodetectors
摘要： We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CANDECOMP/PARAFAC (CP)-rank tensor capturing the global information and a sparse tensor capturing the local information (also considered as outliers), thus providing the robust predictive distribution over missing entries. The low-CP-rank tensor is modeled by multilinear interactions between multiple latent factors on which the column sparsity is enforced by a hierarchical prior, while the sparse tensor is modeled by a hierarchical view of Student-t distribution that associates an individual hyperparameter with each element independently. For model inference under a fully Bayesian treatment, which can effectively prevent the overfitting problem and scales linearly with data size. In contrast to existing related works, our method can perform model selection automatically and implicitly without the need of tuning parameters. More specifically, it can discover the groundtruth of CP rank and automatically adapt the sparsity inducing priors to various types of outliers. In addition, the tradeoff between the low-rank approximation and the sparse representation can be optimized in the sense of maximum model evidence. The extensive experiments and comparisons with many state-of-the-art algorithms on both synthetic and real-world data sets demonstrate the superiorities of our method from several perspectives.
关键词： tensor factorization,robust factorization,tensor completion,video background modeling,variational Bayesian (VB) inference,Rank determination
更新于2025-09-23 15:21:01
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Construction of global and robust near-infrared calibration models based on hybrid calibration sets using Partial Least Squares (PLS) regression
摘要： Near-infrared spectroscopy (NIR) models built on a particular instrument are often invalid on other instruments due to spectral inconsistencies between the instruments. In the present work, global and robust NIR calibration models were constructed by partial least square (PLS) regression based on hybrid calibration sets, which are composed of both primary and secondary spectra. Three datasets were used as case studies. The first consisted of 72 radix scutellaria samples measured on two NIR spectrometers with known baicalin content. The second was composed of 80 corn samples measured on two instruments with known moisture, oil, and protein concentrations. The third dataset included 279 primary samples of tobacco with known nicotine content and 78 secondary samples of tobacco with known nicotine concentrations. The effect of the number of secondary spectra in the hybrid calibration sets and the methods for selecting secondary spectra on the PLS model performance were investigated by comparing the results obtained from different calibration sets. This study shows that the global and robust calibration models accurately predicted both primary and secondary samples as long as the ratios of the number of primary spectra to the number of secondary spectra were less than 22. The models performance was not influenced by the selection method of the secondary spectra. The hybrid calibration sets included the primary spectral information and also the secondary spectra; rendering the constructed global and robust models applicable to both primary and secondary instruments.
关键词： global and robust models,hybrid calibration set,Near-infrared spectroscopy,partial least squares (PLS) regression
更新于2025-09-23 15:21:01
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Gestalt Interest Points with a Neural Network for Makeup-Robust Face Recognition
摘要： In this paper, we propose a novel approach for the domain of makeup-robust face recognition. Most face recognition schemes usually fail to generalize well on these data where there is a large difference between the training and testing sets, e.g., makeup changes. Our method focuses on the problem of determining whether face images before and after makeup refer to the same identity. The work on this fundamental research topic benefits various real-world applications, for example automated passport control, security in general, and surveillance. Experiments show that our method is highly effective in comparison to state-of-the-art methods.
关键词： CNN,Face recognition,makeup-robust,GIP,person identification
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Ge virtual substrates for high efficiency III-V solar cells: applications, potential and challenges
摘要： Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, ?lm production, and medical rehabilita- tion. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high com- plexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal pat- terns and the structural sparsity embedded in motion data. We ?rst replace the regularly used entire pose model with a much ?ne-grained partlet model as feature representation to exploit the abundant local body part posture and movement similari- ties. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution informa- tion and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.
关键词： (cid:2)2,p-norm,robust dictionary learning,Microsoft Kinect,robust structured sparse coding,motion capture data,Human motion denoising
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Cost analysis of 100% renewable electricity provider utilizing surplus electric power of residential PV systems in Japan
摘要： This paper introduces a framework for robust parameter estimation in multipass interferometric synthetic aperture radar (InSAR), such as persistent scatterer interferometry, SAR tomography, small baseline subset, and SqueeSAR. These techniques involve estimation of phase history parameters with or without covariance matrix estimation. Typically, their optimal estimators are derived on the assumption of stationary complex Gaussian-distributed observations. However, their statistical robustness has not been addressed with respect to observations with nonergodic and non-Gaussian multivariate distributions. The proposed robust InSAR optimization (RIO) framework answers two fundamental questions in multipass InSAR: 1) how to optimally treat images with a large phase error, e.g., due to unmolded motion phase, uncompensated atmospheric phase, etc.; and 2) how to estimate the covariance matrix of a non-Gaussian complex InSAR multivariate, particularly those with nonstationary phase signals. For the former question, RIO employs a robust M-estimator to effectively downweight these images; and for the latter, we propose a new method, i.e., the rank M -estimator, which is robust against non-Gaussian distribution. Furthermore, it can work without the assumption of sample stationarity, which is a topic that has not previously been addressed. We demonstrate the advantages of the proposed framework for data with large phase error and heavily tailed distribution, by comparing it with state-of-the-art estimators for persistent and distributed scatterers. Substantial improvement can be achieved in terms of the variance of estimates. The proposed framework can be easily extended to other multipass InSAR techniques, particularly to those where covariance matrix estimation is vital.
关键词： Differential interferometric synthetic aperture radar (D-InSAR),robust estimation,rank covariance matrix,robust InSAR optimization (RIO),M -estimator,SAR interferometry (InSAR)
更新于2025-09-23 15:19:57