Optimization of Information Rate Upper and Lower Bounds for Channels with Memory

We consider the problem of minimizing upper bounds and maximizing lower bounds on information rates of stationary and ergodic discrete-time channels with memory. The channels we consider can have a finite number of states, such as partial response channels, or they can have an infinite state-space, such as time-varying fading channels. We optimize recently-proposed information rate bounds for such channels, which make use of auxiliary finite-state machine channels (FSMCs). Our main contribution in this paper is to provide iterative expectation-maximization (EM) type algorithms to optimize the parameters of the auxiliary FSMC to tighten these bounds. We provide an explicit, iterative algorithm that improves the upper bound at each iteration. We also provide an effective method for iteratively optimizing the lower bound. To demonstrate the effectiveness of our algorithms, we provide several examples of partial response and fading channels, where the proposed optimization techniques significantly tighten the initial upper and lower bounds. Finally, we compare our results with an improved variation of the \emph{simplex} local optimization algorithm, called \emph{Soblex}. This comparison shows that our proposed algorithms are superior to the Soblex method, both in terms of robustness in finding the tightest bounds and in computational efficiency. Interestingly, from a channel coding/decoding perspective, optimizing the lower bound is related to increasing the achievable mismatched information rate, i.e., the information rate of a communication system where the decoder at the receiver is matched to the auxiliary channel, and not to the original channel.Comment: Submitted to IEEE Transactions on Information Theory, November 24, 200

On optimization of finite-difference time-domain (FDTD) computation on heterogeneous and GPU clusters

A model for the computational cost of the finite-difference time-domain (FDTD) method irrespective of implementation details or the application domain is given. The model is used to formalize the problem of optimal distribution of computational load to an arbitrary set of resources across a heterogeneous cluster. We show that the problem can be formulated as a minimax optimization problem and derive analytic lower bounds for the computational cost. The work provides insight into optimal design of FDTD parallel software. Our formulation of the load distribution problem takes simultaneously into account the computational and communication costs. We demonstrate that significant performance gains, as much as 75%, can be achieved by proper load distribution

Speeding up mutual information computation using NVIDIA CUDA hardware

We present an efficient method for mutual information (MI) computation between images (2D or 3D) for NVIDIA’s ‘compute unified device architecture ’ (CUDA) compatible devices. Efficient parallelization of MI is particularly challenging on a ‘graphics processor unit ’ (GPU) due to the need for histogram-based calculation of joint and marginal probability mass functions (pmfs) with large number of bins. The data-dependent (unpredictable) nature of the updates to the histogram, together with hardware limitations of the GPU (lack of synchronization primitives and limited memory caching mechanisms) can make GPU-based computation inefficient. To overcome these limitation, we approximate the pmfs, using a down-sampled version of the jointhistogram which avoids memory update problems. Ou

Bar Code Recognition in Highly Distorted and Low Resolution Images

In this paper, we present a novel approach to detection of one dimensional bar code images. Our algorithm is particularly designed to recognize bar codes, where the image may be of low resolution, low quality or suffer from substantial blurring, de-focusing, non-uniform illumination, noise and color saturation. The algoritnni is accurate, fast, scalable and can be easily adjusted to search for a valid result within a specified time constraint. Our algorithm is particulary useful for real-time recognition of bar codes in portable hand-held devices with limited processing capability, such as mobile phones

An Optimal Adaptive Network Coding Scheme for Minimizing Decoding Delay in Broadcast Erasure Channels

We are concerned with designing feedback-based adaptive network coding schemes with the aim of minimizing decoding delay in each transmission in packet-based erasure networks. We study systems where each packet brings new information to the destination regardless of its order and require the packets to be instantaneously decodable. We first formulate the decoding delay minimization problem as an integer linear program and then propose efficient algorithms for finding its optimal solution(s). We show that our problem formulation is applicable to memoryless erasures as well as Gilbert-Elliott erasures with memory. We then propose a number of heuristic algorithms with worst case linear execution complexity that can be used when an optimal solution cannot be found in a reasonable time. We verify the delay and speed performance of our techniques through numerical analysis. This analysis reveals that by taking channel memory into account in network coding decisions, one can considerably reduce decoding delays.</p

Estimation of acoustic impedance from multiple ultrasound images with application to spatial compounding

Reflection of sound waves, due to acoustic impedance mismatch at the interface of two media, is the principal physical property which allows visualization with ultrasound. In this paper, we investigate reconstruction of the acoustic impedance from ultrasound images for the first time. Similar to spatial compounding, we combine multiple images to improve the estimation. We use phase information to determine regions of high reflection from an ultrasound image. We model the physical imaging process with an emphasis on the reflection of sound waves. The model is used in computing the acoustic impedance (up to a scale) from areas of high reflectivity. The acoustic impedance image can either be directly visualized or be used in simulation of ultrasound images from an arbitrary point of view. The experiments performed on in-vitro and in-vivo data show promising results