Daniel S. Weller

GrooveNet: A Hybrid Simulator for Vehicle-to-Vehicle Networks

Vehicular networks are being developed for efficient broadcast of safety alerts, real-time traffic congestion probing and for distribution of on-road multimedia content. In order to investigate vehicular networking protocols and evaluate the effects of incremental deployment it is essential to have a topology-aware simulation and test-bed infrastructure. While several traffic simulators have been developed under the intelligent transport system initiative, their primary motivation has been to model and forecast vehicle traffic flow and congestion from a queuing perspective. GrooveNet is a hybrid simulator which enables communication between simulated vehicles, real vehicles and between real and simulated vehicles. By modeling inter-vehicular communication within a real street map-based topography it facilitates protocol design and also in-vehicle deployment. GrooveNets modular architecture incorporates mobility, trip and message broadcast models over a variety of link and physical layer communication models. It is easy to run simulations of thousands of vehicles in any US city and to add new models for networking, security, applications and vehicle interaction. GrooveNet supports multiple network interfaces, GPS and events triggered from the vehicles on-board computer. Through simulation, we are able to study the message latency, and coverage under various traffic conditions. On-road tests over 400 miles lend insight to required market penetration

GrooveSim: a topography-accurate simulator for geographic routing in vehicular networks

Vehicles equipped with wireless communication devices are poised to deliver vital services in the form of safety alerts, traffic congestion probing and on-road commercial applications. Tools to evaluate the performance of vehicular networks are a fundamental necessity. While several traffic simulators have been developed under the Intelligent Transport System initiative, their primary focus has been on modeling and forecasting vehicle traffic flow and congestion from a queuing perspective. In order to analyze the performance and scalability of inter-vehicular communication protocols, it is important to use realistic traffic density, speed, trip, and communication models. Studies on multi-hop mobile wireless routing protocols have shown the performance varies greatly depending on the simulation models employed. We introduce GrooveSim, a simulator for geographic routing in vehicular networks to address the need for a robust, easy-to-use realistic network and traffic simulator. GrooveSim accurately models inter-vehicular communication within a real street map-based topography. It operates in five modes capable of actual on-road inter-vehicle communication, simulation of traffic networks with thousands of vehicles, visual playback of driving logs, hybrid simulation composed of real and simulated vehicles and easy test-scenario generation. Our performance results, supported by field tests, establish geographic broadcast routing as an effective means to deliver time-bounded messages over multiple-hops.

Denoising sparse images from GRAPPA using the nullspace method

To accelerate magnetic resonance imaging using uniformly undersampled (nonrandom) parallel imaging beyond what is achievable with generalized autocalibrating partially parallel acquisitions (GRAPPA) alone, the DEnoising of Sparse Images from GRAPPA using the Nullspace method is developed. The trade‐off between denoising and smoothing the GRAPPA solution is studied for different levels of acceleration. Several brain images reconstructed from uniformly undersampled k‐space data using DEnoising of Sparse Images from GRAPPA using the Nullspace method are compared against reconstructions using existing methods in terms of difference images (a qualitative measure), peak‐signal‐to‐noise ratio, and noise amplification (g‐factors) as measured using the pseudo‐multiple replica method. Effects of smoothing, including contrast loss, are studied in synthetic phantom data. In the experiments presented, the contrast loss and spatial resolution are competitive with existing methods. Results for several brain images demonstrate significant improvements over GRAPPA at high acceleration factors in denoising performance with limited blurring or smoothing artifacts. In addition, the measured g‐factors suggest that DEnoising of Sparse Images from GRAPPA using the Nullspace method mitigates noise amplification better than both GRAPPA and L1 iterative self‐consistent parallel imaging reconstruction (the latter limited here by uniform undersampling). Magn Reson Med, 2012.

Undersampled Phase Retrieval With Outliers

This paper proposes a general framework for reconstructing sparse images from undersampled (squared)magnitude data corrupted with outliers and noise. This phase retrieval method uses a layered approach, combining repeated minimization of a convex majorizer (surrogate for a nonconvex objective function), and iterative optimization of that majorizer using a preconditioned variant of the alternating direction method of multipliers (ADMM). Since phase retrieval is nonconvex, this implementation uses multiple initial majorization vectors. The introduction of a robust 1-norm data fit term that is better adapted to outliers exploits the generality of this framework. The derivation also describes a normalization scheme for the regularization parameter and a known adaptive heuristic for the ADMM penalty parameter. Both 1-D Monte Carlo tests and 2-D image reconstruction simulations suggest the proposed framework, with the robust data fit term, reduces the reconstruction error for data corrupted with both outliers and additive noise, relative to competing algorithms having the same total computation.

Comparison-Based Image Quality Assessment for Selecting Image Restoration Parameters

Image quality assessment (IQA) is traditionally classified into full-reference (FR) IQA, reduced-reference (RR) IQA, and no-reference (NR) IQA according to the amount of information required from the original image. Although NR-IQA and RR-IQA are widely used in practical applications, room for improvement still remains because of the lack of the reference image. Inspired by the fact that in many applications, such as parameter selection for image restoration algorithms, a series of distorted images are available, the authors propose a novel comparison-based IQA (C-IQA) framework. The new comparison-based framework parallels FR-IQA by requiring two input images and resembles NR-IQA by not using the original image. As a result, the new comparison-based approach has more application scenarios than FR-IQA does, and takes greater advantage of the accessible information than the traditional single-input NR-IQA does. Further, C-IQA is compared with other state-of-the-art NR-IQA methods and another RR-IQA method on two widely used IQA databases. Experimental results show that C-IQA outperforms the other methods for parameter selection, and the parameter trimming framework combined with C-IQA saves the computation of iterative image reconstruction up to 80%.Image quality assessment (IQA) is traditionally classified into full-reference (FR) IQA, reduced-reference (RR) IQA, and no-reference (NR) IQA according to the amount of information required from the original image. Although NR-IQA and RR-IQA are widely used in practical applications, room for improvement still remains because of the lack of the reference image. Inspired by the fact that in many applications, such as parameter selection for image restoration algorithms, a series of distorted images are available, the authors propose a novel comparison-based IQA (C-IQA) framework. The new comparison-based framework parallels FR-IQA by requiring two input images and resembles NR-IQA by not using the original image. As a result, the new comparison-based approach has more application scenarios than FR-IQA does, and takes greater advantage of the accessible information than the traditional single-input NR-IQA does. Further, C-IQA is compared with other state-of-the-art NR-IQA methods and another RR-IQA method on two widely used IQA databases. Experimental results show that C-IQA outperforms the other methods for parameter selection, and the parameter trimming framework combined with C-IQA saves the computation of iterative image reconstruction up to 80%.

Monte Carlo SURE-Based Parameter Selection for Parallel Magnetic Resonance Imaging Reconstruction

Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Steins unbiased risk estimate that minimizes the multichannel k‐space mean squared error (MSE). We automatically tune parameters for image reconstruction methods that preserve the undersampled acquired data, which cannot be accomplished using existing techniques.

Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction

The amount of calibration data needed to produce images of adequate quality can prevent auto-calibrating parallel imaging reconstruction methods like generalized autocalibrating partially parallel acquisitions (GRAPPA) from achieving a high total acceleration factor. To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. Several experiments evaluate the performance of the proposed method relative to unregularized and existing regularized calibration methods for both low-quality and underdetermined fits from the ACS lines. These experiments demonstrate that the proposed method, like other regularization methods, is capable of mitigating noise amplification, and in addition, the proposed method is particularly effective at minimizing coherent aliasing artifacts caused by poor kernel calibration in real data. Using the proposed method, we can increase the total achievable acceleration while reducing degradation of the reconstructed image better than existing regularized calibration methods.

Evaluating sparsity penalty functions for combined compressed sensing and parallel MRI

The combination of compressed sensing (CS) and parallel magnetic resonance (MR) imaging enables further scan acceleration via undersampling than previously feasible. While many of these methods incorporate similar styles of CS, there remains significant variation in the particular choice of function used to promote sparsity. Having developed SpRING, a framework for combining CS and GRAPPA, a parallel MR image reconstruction method, we view the choice of penalty function as a design choice rather than a defining feature of the algorithm. For both simulated and real data, we compare different sparsity penalty functions to the empirical distribution of the reference images. Then, we perform reconstructions on uniformly undersampled data using a variety of penalty functions to illustrate the impact appropriately choosing the penalty function has on the performance of SpRING. These experiments demonstrate the importance of choosing an appropriate penalty function and how such a choice may differ between simulated data and real data.

Combined compressed sensing and parallel mri compared for uniform and random cartesian undersampling of K-space

Both compressed sensing (CS) and parallel imaging effectively reconstruct magnetic resonance images from undersampled data. Combining both methods enables imaging with greater undersampling than accomplished previously. This paper investigates the choice of a suitable sampling pattern to accommodate both CS and parallel imaging. A combined method named SpRING is described and extended to handle random undersampling, and both GRAPPA and SpRING are evaluated for uniform and random undersampling using both simulated and real data. For the simulated data, when the undersampling factor is large, SpRING performs better with random undersampling. However, random undersampling is not as beneficial to SpRING for real data with approximate sparsity.

Jitter compensation in sampling via polynomial least squares estimation

Sampling error due to jitter, or noise in the sample times, affects the precision of analog-to-digital converters in a significant, nonlinear fashion. In this paper, a polynomial least squares (PLS) estimator is derived for an observation model incorporating both independent jitter and additive noise, as an alternative to the linear least squares (LLS) estimator. After deriving this estimator, its implementation is discussed, and it is simulated using Matlab. In simulations, the PLS estimator is shown to improve the mean squared error performance by up to 30 percent versus the optimal linear estimator.