OBJECTIVE: The study sought to investigate the disease state-dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. MATERIALS AND METHODS: A covariate-dependent, continuous-time hidden Markov model with 4 states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2. Demographic characteristics, comorbidities, vital signs, and laboratory test results were retrospectively evaluated to infer a patient's clinical progression. RESULTS: The association between patient-level covariates and risk of progression was found to be disease state dependent. Specifically, while being male, being Black or having a medical comorbidity were all associated with an increased risk of progressing from the moderate disease state to the severe disease state, these same factors were associated with a decreased risk of progressing from the severe disease state to the deceased state. DISCUSSION: Recent studies have not included analyses of the temporal progression of COVID-19, making the current study a unique modeling-based approach to understand the dynamics of COVID-19 in hospitalized patients. CONCLUSION: Dynamic risk stratification models have the potential to improve clinical outcomes not only in COVID-19, but also in a myriad of other acute and chronic diseases that, to date, have largely been assessed only by static modeling techniques.

In this paper, we focus on a cross-layer resource allocation problem for an opportunistic cognitive radio network, where secondary users (SUs) share a primary network’s licensed spectrum only when the primary network is sensed to be idle. We consider cooperative spectrum sensing, where SUs participate in sensing only when a benefit from resource allocation is guaranteed. Number of SUs for cooperative spectrum sensing is an important parameter as it defines both sensing and resource allocation performance. The fusion centre captures interaction between physical layer spectrum sensing, channel condition, and higher layer data buffer while selecting SUs for cooperative sensing. We consider heterogeneous data types at SUs’ data buffers during SU selection to make our system model more general. We form a mixed integer non-linear optimization problem, from which we select SUs’ set for sensing and resource allocation, appropriate sensing thresholds, and corresponding resource allocation parameters. Due to it’s combinatorial nature, the optimization problem is non-trivial. We devise an optimal algorithm to solve the optimization problem. Moreover, we also propose a computationally efficient and near-optimal greedy algorithm with suitable performance bounds. We also show advantage of our proposed optimal algorithms compared to other traditional methods through extensive simulations.

Link adaptation (LA) schemes maximize average spectral efficiency (ASE) using accurate channel state information (CSI) and knowledge of signal to noise ratio (SNR) distribution. In practical systems, partial CSI is available at the transmitter through feedback of modulation and coding scheme index, often known as channel quality indicator (CQI). However, parameters of SNR distribution (${\Theta }$) are not known or only assumed to be known. We propose two LA schemes, for execution at the receiver and transmitter, respectively, each without prior knowledge about${\Theta }$. In the receiver centric schemes, parametric estimation of SNR distribution is performed using unquantized SNR samples. In the transmitter centric scheme, we estimate${\Theta }$from CQI samples using an iterative joint quantization-estimation algorithm. These parametric estimates form long-term SNR distribution, which is used to compute SNR switching thresholds. Using these thresholds, rate and power adaptation decisions are made based on instantaneous SNR. We have derived the maximum-likelihood estimator and Cramer–Rao lower bound for the proposed estimator using CQI feedback for Nakagami-$m$fading channels and demonstrate that near optimal ASE can be achieved while using the proposed schemes as in an ideal scenario, where perfect knowledge about CSI and SNR distribution are availablea priori.

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.

In this paper, we consider a cooperative cognitive radio network where the fusion center acts as final decision maker as well as resource allocator. The secondary users access the licensed band only when the primary user is inactive. The fusion center behaves as a profit seeking entity, which charges the secondary users for letting them use the licensed band. However, the fusion center pays a penalty to the primary network for causing interference. Hence to improve detection performance, the fusion center encourages the SUs to perform cooperative spectrum sensing. Moreover, we map the secondary users' average data rate with a payoff. All the secondary users maintain data buffers and pay to the fusion center according to their traffic type. The fusion center tries to maximize it's own utility while maintaining non-negative utilities at the secondary users. Feasible conditions which guarantee non-negative utilities at secondary users are evaluated. We show that the optimal number of observations which is required for spectrum sensing at secondary users strongly depends on the interference penalty paid by the fusion center. The secondary users get time durations for accessing the licensed band according to their stored energies and paid costs to the fusion center.

In this work, we study the performance of a decode and forward relay assisted secondary user pair co-existing with a primary network under wireless energy harvesting scenario. The locations of primary transmitters and receivers are modeled using two independent Poisson point processes (PPPs). In this network model, we consider that the secondary transmitter scavenges energy from primary transmitters and transmits its data towards secondary destination via a secondary relay. The direct link between the secondary transmitter and secondary destination is assumed to be unavailable. We find an analytical expression for the probability of successful one way data communication between the secondary transmitter and secondary destination through the secondary relay considering, (i) maximum interference power constraint imposed by primary receivers, (ii) interference from primary transmitters and (iii) additive white gaussian noise. Trade-off between the benefit of energy harvesting at secondary transmitter and the adverse interference effect (both at secondary relay and destination) due to primary transmission is shown through numerical analysis. The analytical expression is validated through computer simulation.

The detection of a weak signal in additive Cauchy noise is of great importance in many applications. A locally optimum detector (LOD) exists for such a scenario; however, it is non-linear in nature. In general, implementation of non-linear detectors is difficult in practice, and linear detectors with good properties, such as high asymptotic relative efficiency (ARE) with respect to the LOD, are often desirable. In this paper, we propose a linear detector for a weak signal in additive Cauchy noise. The proposed test statistic is a linear combination of order statistics. For the special case of a constant signal in additive Cauchy noise, we prove the asymptotic normality of the trimmed linear detector, and show that the ARE of the trimmed linear detector with respect to the LOD is unity. Extensive simulation results are provided to demonstrate that the loss in the performance of the linear detector is very small compared with the non-linear LOD. We also discuss the hardware complexities of the LOD and the linear detector, and demonstrate the advantages of the linear detector over the LOD, in terms of hardware implementation.

A novel spectrum sharing strategy based on coupled detection and estimation is proposed for cognitive radio networks. The proposed approach is able to tradeoff throughput for reduced interference at the primary user (PU) via censored transmissions. We derive the optimum censoring strategy that maximizes the throughput of the cognitive radio system under an average interference power constraint at the PU. We then extend the proposed framework to jointly optimize the censoring and the power allocation strategies of the secondary user (SU) that maximize the throughput of the secondary network under average transmit power and average interference power constraints. Finally, we provide extensive simulation results to demonstrate the enhanced performance of the proposed censoring based spectrum sharing approach.

In this letter, we investigate distributed detection over channels with memory. We model the wireless channel between each sensor and the fusion center by a two state Markov chain and derive the optimal fusion rule. We demonstrate the improved detection performance of the derived fusion rule for burst error channels, over a channel aware fusion rule for a memoryless channel and the Chair-Varshney fusion rule. We further show that when the number of sensor decisions is large, as well as when the channel is memoryless, the derived fusion rule reduces to the well known counting rule.

SUMMARY: 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: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A normal factor graph (NFG) based approach for cooperative spectrum sensing in cognitive radio over time varying and frequency non-selective fading channels is presented in this paper. An NFG based representation of a distributed cognitive radio system is first presented and then a Sum-Product- Algorithm (SPA) based analysis is developed for inference. The spectrum sensing problem is modelled as a distributed binary hypothesis testing problem. A Neyman-Pearson (NP) based likelihood ratio test statistic is derived for optimal sensing. As exact theoretical analysis of the system level probability of detection and probability of false alarm is very difficult, we provide an approximation which performs satisfactorily in the moderate to high signal-to-noise ratio (SNR) regime. The proposed NFG based spectrum sensing approach is computationally scalable to large networks and performs well under time varying channel conditions. Extensive simulation results are provided to validate our proposed approximation.

This paper studies the effect of wireless channel imperfections on the transport and estimation of spatially distributed events using wireless sensor networks (WSNs). It is observed that the quality of event estimation at the sink (fusion center) degrades considerably with correlated packet losses during transmission from the sensors. A novel diversity technique based on field estimation is proposed to mitigate the effects of packet losses on the quality of estimation at the sink. Dense deployment of sensor nodes and the spatial nature of the observed physical phenomenon result in the sensor observations being noisy spatial samples of an unknown underlying function. The proposed algorithm exploits this feature, using supervised learning to achieve diversity. A new information fusion methodology based on approximate likelihood is proposed to integrate the information obtained from the learning algorithm into the classical estimation framework. Simulation results are provided to demonstrate the performance of the proposed approach.

This paper presents a new framework for distributed target detection in wireless sensor networks (WSNs). In our previous work, for multiple networked sensors collaboratively detecting the presence or absence of a target in the sensor field, every sensor uses an identical threshold for local decision-making. In this paper, we propose a framework where the sensors in the network collaboratively decide and select non-identical thresholds to improve network-wide detection performance in a dynamic manner. This threshold selection scheme is based on a new statistical metric called False Discovery Rate (FDR). Assuming a signal attenuation model, where the received signal power decays as the distance from the target increases, various performance indices like system level probability of detection and probability of false alarm are studied. Analytical and simulation results are provided for system level probability of false alarm and probability of detection. Performance comparison between the proposed approach and the classical identical local sensor threshold approach is provided to demonstrate the effectiveness of this scheme.