Near Optimum Low Complexity Smoothing Loops for Dynamical Phase Estimation—Application to BPSK Modulated Signals

International audience—This correspondence provides and analyzes a low complexity, near optimum, fixed-interval smoothing algorithm that approaches the performance of an optimal smoother for the price of two low complexity sequential estimators, i.e., two phase-locked loops (PLLs). Based on a linear approximation of the problem, a theoretical performance evaluation is given. The theoretical results are compared to some simulation results and to the Bayesian and hybrid Cramér–Rao bounds. They illustrate the good performance of the proposed smoothing PLL (S-PLL) algorithm. Index Terms—Dynamical phase estimation, phase-locked loop (PLL), smoothing algorithm

On a Hybrid Preamble/Soft-Output Demapper Approach for Time Synchronization for IEEE 802.15.6 Narrowband WBAN

In this paper, we present a maximum likelihood (ML) based time synchronization algorithm for Wireless Body Area Networks (WBAN). The proposed technique takes advantage of soft information retrieved from the soft demapper for the time delay estimation. This algorithm has a low complexity and is adapted to the frame structure specified by the IEEE 802.15.6 standard for the narrowband systems. Simulation results have shown good performance which approach the theoretical mean square error limit bound represented by the Cramer Rao Bound (CRB)

Bayesian and Hybrid Cramér–Rao Bounds for the Carrier Recovery Under Dynamic Phase Uncertain Channels

International audience—In this paper, we study Bayesian and hybrid Cramér–Rao bounds (BCRB and HCRB) for the code-aided (CA), the data-aided (DA), and the non-data-aided (NDA) dynamical phase estimation of QAM modulated signals. We address the bounds derivation for both the offline scenario, for which the whole observation frame is used, and the online which only takes into account the current and the previous observations. For the CA scenario we show that the computation of the Bayesian information matrix (BIM) and of the hybrid information matrix (HIM) is NP hard. We then resort to the belief-propagation (BP) algorithm or to the Bahl–Cocke–Jelinek–Raviv (BCJR) algorithm to obtain some approximate values. Moreover, in order to avoid the calculus of the inverse of the BIM and of the HIM, we present some closed form expressions for the various CRBs, which greatly reduces the computation complexity. Finally, some simulations allow us to compare the possible improvements enabled by the offline and the CA scenarios. Index Terms—Bayesian Cramér–Rao bound (BCRB), code-aided (CA) bound, data-aided (DA) bound, dynam-ical phase estimation, hybrid Cramér–Rao bound (HCRB), non-data-aided (NDA), offline, online

Massive Parallelization of Massive Sample-size Survival Analysis

Large-scale observational health databases are increasingly popular for conducting comparative effectiveness and safety studies of medical products. However, increasing number of patients poses computational challenges when fitting survival regression models in such studies. In this paper, we use graphics processing units (GPUs) to parallelize the computational bottlenecks of massive sample-size survival analyses. Specifically, we develop and apply time- and memory-efficient single-pass parallel scan algorithms for Cox proportional hazards models and forward-backward parallel scan algorithms for Fine-Gray models for analysis with and without a competing risk using a cyclic coordinate descent optimization approach We demonstrate that GPUs accelerate the computation of fitting these complex models in large databases by orders-of-magnitude as compared to traditional multi-core CPU parallelism. Our implementation enables efficient large-scale observational studies involving millions of patients and thousands of patient characteristics

Efficient GPU-accelerated fitting of observational health-scaled stratified and time-varying Cox models

The Cox proportional hazards model stands as a widely-used semi-parametric approach for survival analysis in medical research and many other fields. Numerous extensions of the Cox model have further expanded its versatility. Statistical computing challenges arise, however, when applying many of these extensions with the increasing complexity and volume of modern observational health datasets. To address these challenges, we demonstrate how to employ massive parallelization through graphics processing units (GPU) to enhance the scalability of the stratified Cox model, the Cox model with time-varying covariates, and the Cox model with time-varying coefficients. First we establish how the Cox model with time-varying coefficients can be transformed into the Cox model with time-varying covariates when using discrete time-to-event data. We then demonstrate how to recast both of these into a stratified Cox model and identify their shared computational bottleneck that results when evaluating the now segmented partial likelihood and its gradient with respect to regression coefficients at scale. These computations mirror a highly transformed segmented scan operation. While this bottleneck is not an immediately obvious target for multi-core parallelization, we convert it into an un-segmented operation to leverage the efficient many-core parallel scan algorithm. Our massively parallel implementation significantly accelerates model fitting on large-scale and high-dimensional Cox models with stratification or time-varying effect, delivering an order of magnitude speedup over traditional central processing unit-based implementations

Application of Pedestrian Upstream Detection Strategy in a Mixed Flow Traffic Circumstance

Walking is an environment-friendly trip mode and can help ease the congestion caused by automobiles. Proper design of pedestrian facilities that promotes efficiency and safety can encourage more people to choose walking. Upstream detection (UD) strategy is proposed by previous studies to reduce pedestrian waiting time at mid-block crosswalk (MBC). This paper applied UD strategy to MBC under mixed traffic circumstance where the crosswalk serves both pedestrians and non-motor users. Traffic data was collected from an MBC in the city of Nanjing, China. Simulation models were developed by using the VISSIM software and its add-on module Vehicle Actuated Programming (VAP). The models were categorised by the volume and composition of pedestrians and non-motor users. Models were simulated according to different experimental schemes to explore the effectiveness of the UD strategy under mixed traffic circumstance. T-test and analysis of variance (ANOVA) were used to interpret the simulation results. The main conclusions of this paper are that the UD strategy is still effective at the MBC with a mixed traffic circumstance despite the proportion of non-motor users. However, as the proportion of non-motor users becomes higher, the average delay of pedestrians and non-motor users will increase compared to pure pedestrian flow

Max-log demapper architecture design for DVB-T2 rotated QAM constellations

International audience— Rotated and cyclic-Q delayed (RCQD) quadrature amplitude modulation (QAM) improve DVB-T2 system performance over highly time-frequency selective channels. However, when compared with conventional QAM demapper, the RCQD demapper requires a higher computational complexity. In this paper, a complexity-reduced max-log demapper is derived and implemented over a FPGA platform. The proposed demapper allows to find the maximum likelihood (ML) point with a search spanning only M signal constellation points and guarantees to obtain the same log-likelihood ratio (LLR) metrics as the optimum max-log soft decision demapper while spanning at most 2 M signal constellation points. The optimized hardware implementation introduces only a slight performance loss compared to the floating-point full complexity max-log performance. Index Terms — DVB-T2, Rotated and Cyclic Q Delayed (RCQD) Constellations, Log-Likelihood Ratio (LLR), Max-Log Demapper

A shuffled iterative bit-interleaved coded modulation receiver for the DVB-T2 standard: Design, implementation and FPGA prototyping

International audienceRotated QAM constellations improve Bit-Interleaved Coded Modulation (BICM) performance over fading channels. Indeed, an increased diversity is obtained by coupling a constellation rotation with interleaving between the real and imaginary components of transmitted symbols either in time or frequency domain. Iterative processing at the receiver side can provide additional improvement in performance. In this paper, an efficient shuffled iterative receiver is investigated for the second generation of the terrestrial digital video broadcasting standard DVB-T2. Scheduling an efficient message passing algorithm with low latency between the demapper and the LDPC decoder represents the main contribution. The design and the FPGA prototyping of the resultant shuffled iterative BICM receiver are then described. Architecture complexity and measured performance validate the potential of iterative receiver as a practical and competitive solution for the DVB-T2 standard

Bayesian estimation of human impedance and motion intention for human-robot collaboration

This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics. Comparative simulation results are carried out to verify the effectiveness of estimation method and emphasize the advantages of the proposed control strategy. The experiment, performed on Baxter robot platform, illustrates a good system performance