Multi-antenna technologies have been widely used in modern wireless communication systems to achieve diversity gain, spatial multiplexing gain, and better interference suppression. Beamforming has ...been considered as a potential candidate for throughput maximization in MIMO cognitive radio networks. However, the efficient implementation of beamforming demands for accurate knowledge of channel estimate. Conventional spectrum sharing strategies treat the detection and estimation problems in uncoupled manner, which may not result in the overall optimum performance. In this paper, we propose a censored spectrum sharing strategy which considers both the detection and estimation performances simultaneously and is capable of improving the throughput of MIMO cognitive radio network. We derive analytical expressions for the critical parameters and provide simulation results to validate our derivations.
This letter presents a novel polarization conversion metasurface (PCM) which is used for the design of a low-profile low-radar cross section (RCS) multiple-input-multiple-output (MIMO)-slot antenna ...operating at 7.72 GHz. The PCM and its orthogonal units are arranged in checkerboard pattern to cancel out the scattered fields in far-field and applied directly atop a four-element MIMO slot antenna for RCS reduction. The reference slot antenna has the peak realized gain of 5.11 dBi. The proposed MIMO slot antenna, which is operating in 7.56-7.93 GHz frequency band has low-profile, i.e., 0.12 λ 0 at the frequency 7.56 GHz, and the peak realized gain of 5.34 dBi. The RCS reduction at the broadside for normal incidence is more than 4 dB in 5-10 GHz frequency band, whereas the in-band RCS reduction of the antenna is greater than 11 dB, and the isolation of the proposed MIMO antenna is maintained to greater than 20 dB. These results are verified with the fabrication of a prototype antenna and measurements.
Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. ...A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges.
In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed.
While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases.
We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.
This paper presents a novel dual-band negative-permittivity metamaterial (MTM). The MTM is based on a crossed loop resonator (CLR) which exhibits negative-permittivity property at 4.85–5.58 GHz and ...9.34–15.48 GHz frequency bands under the normal incidence of EM wave. The MTM shows epsilon-very-large (EVL) and mu-near-zero (MNZ) properties near the resonance frequencies (4.85 GHz and 9.34 GHz). Thus, low-impedance characteristics are obtained around the resonance frequencies of the CLR. The CLR MTM is insensitive to the polarization and incident angle of the imposed EM wave (for incident angle < 20°). This MTM, which is polarization and incident angle independent, can be used for gain enhancement of magnetic dipole antennas, design of filters and ultrathin microwave absorbers.
This letter presents a novel low-profile high-gain antenna with cross-polarization (x-pol) suppression using cross circular loop resonator (CCLR) metamaterial (MTM) slab in substrate-integrated ...waveguide-fed-slot antenna (SIW-SA). The SIW-SA antenna, which is the reference antenna, operates at 9.73 GHz. The CCLR MTM slab acts as a low-impedance slab, which is placed in the superstrate of the reference antenna at the height of only λ0 /10, where λ0 is the free-space wavelength at the resonance frequency of the antenna. At the broadside direction, the proposed antenna obtains 5.8 dB higher gain, 10 dB lower x-pol level (in both radiation planes), and 9.1 dB higher front-to-back ratio than the reference antenna, due to the presence of low-impedance slab. The simulated results are verified with fabrication and measurement.
Many signal processing and machine learning problems can be formulated as consensus optimization problems which can be solved efficiently via a cooperative multi-agent system. However, the agents in ...the system can be unreliable due to a variety of reasons: noise, faults and attacks. Providing erroneous updates leads the optimization process in a wrong direction, and degrades the performance of distributed machine learning algorithms. This paper considers the problem of decentralized learning using ADMM in the presence of unreliable agents. First, we rigorously analyze the effect of erroneous updates (in ADMM learning iterations) on the convergence behavior of the multi-agent system. We show that the algorithm linearly converges to a neighborhood of the optimal solution under certain conditions and characterize the neighborhood size analytically. Next, we provide guidelines for network design to achieve a faster convergence to the neighborhood. We also provide conditions on the erroneous updates for exact convergence to the optimal solution. Finally, to mitigate the influence of unreliable agents, we propose ROAD , a robust variant of ADMM, and show its resilience to unreliable agents with an exact convergence to the optimum.
A non-parametric Bayesian factor model is proposed for joint analysis of multi-platform genomics data. The approach is based on factorizing the latent space (feature space) into a shared component ...and a data-specific component with the dimensionality of these components (spaces) inferred via a beta-Bernoulli process. The proposed approach is demonstrated by jointly analyzing gene expression/copy number variations and gene expression/methylation data for ovarian cancer patients, showing that the proposed model can potentially uncover key drivers related to cancer.
Availability and implementation: The source code for this model is written in MATLAB and has been made publicly available at https://sites.google.com/site/jointgenomics/
Contact:
catherine.ll.zheng@gmail.com
Supplementary information:
Supplementary data are available at Bioinformatics online.
Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, ...fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. We also provide extensive simulation results to demonstrate the validity of the proposed approach.
This article presents a novel polarization‐independent metamaterial (MTM) which is used for the design of a low‐profile high‐gain magnetic dipole antenna operating at 9.8 GHz. The MTM is alike ...star‐shape and shows low impedance property around the frequency 9.8 GHz. The reference antenna, which is the SIW‐based slot antenna acts as a magnetic dipole antenna and it has peak gain of 3 dBi. The metamaterial‐inspired slot antenna has low‐profile, that is, 0.133 λ0 at the frequency 9.8 GHz, and peak gain of 8.24 dBi. At the resonance frequency, more than 5 dB gain improvement is achieved in the proposed design. These results are verified with the fabrication of a prototype antenna and measurements. The cross‐polarized radiation is negligibly altered by this gain enhancement method. The proposed antenna can be used in wireless power transfer and LoS communications.
Constant false alarm rate (CFAR) detectors are developed to track changes in clutter intensities and to adapt the detection threshold to maintain a constant probability of false alarms. These ...adaptive thresholding mechanisms are initially intended when both target and clutter are exponentially distributed, and they degrade in performance when applied to newer target and clutter models. So, in application scenarios like Airborne Warning and Control Systems (AWACS) and ship remote sensing, when both the target and clutter are Pareto distributed, instead of the conventional way of tweaking the existing adaptive-thresholding CFAR detector, we pose the detection problem as a two-sample, Pareto vs. Pareto composite hypothesis testing problem. Considering no knowledge of both scale and shape parameters of Pareto distributed clutter, we derive the new adaptive-thresholding detector based on the generalized likelihood ratio test (GLRT) statistic. We further show that our proposed adaptive thresholding detector has a CFAR property and provide extensive simulation results to demonstrate the performance of the proposed detector.
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