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 results obtained by the conjugate gradient optimization algorithm and an improved variation of the simplex algorithm, called Soblex. While the computational complexities of our algorithms are similar to the conjugate gradient method and less than the Soblex algorithm, our algorithms robustly find the tightest bounds. 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.

Conventional mutual information (Ml)-based registration using pixel intensities is time-consuming and ignores spatial information, which can lead to misalignment. We propose a method to overcome these limitation by acquiring initial estimates of transformation parameters. We introduce the concept of 'gradient intensity' as a measure of spatial strength of an image in a given direction. We determine the rotation parameter by maximizing the MI between gradient intensity histograms. Calculation of the gradient intensity MI function is extremely efficient. Our method is designed to be invariant to scale and translation between the images. We then obtain estimates of scale and translation parameters using methods based on the centroids of gradient images. The estimated parameters are used to initialize an optimization algorithm which is designed to converge more quickly than the standard Powell algorithm in close proximity of the minimum. Experiments show that our method significantly improves the performance of the registration task and reduces the overall computational complexity by an order of magnitude.

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 algorithm is accurate, fast, scalable and can be easily adjusted to search for a valid result within a specified time constraint. Our algorithm is particularly useful for real-time recognition of bar codes in portable hand-held devices with limited processing capability, such as mobile phones.

We present a fast and accurate framework for registration of multi-modal volumetric images based on decoupled estimation of registration parameters utilizing spatial information in the form of 'gradient intensity'. We introduce gradient intensity as a measure of spatial strength of an image in a given direction and show that it can be used to determine the rotational misalignment independent of translation between the images. The rotation parameters are obtained by maximizing the mutual information of 2D gradient intensity matrices obtained from 3D images, hence reducing the dimensionality of the problem and improving efficiency. The rotation parameters along with estimations of translation are then used to initialize an optimization step over a conventional pixel intensity-based method to achieve sub-voxel accuracy. Our optimization algorithm converges quickly and is less subject to the common problem of misregistration due to local extrema. Experiments show that our method significantly improves the robustness, performance and efficiency of registration compared to conventional pixel intensity-based methods.

We consider the problem of optimizing information rate upper and lower bounds for communication channels with (possibly large) memory. A recently proposed auxiliary-channel- based technique allows one to efficiently compute upper and lower bounds on the information rate of such channels. Towards tightening these bounds, we propose iterative expectation- maximization (EM) type algorithms to optimize the parameters of the auxiliary finite-state machine channel (FSMC). From a channel coding 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 maximum-likelihood decoder at the receiver is matched to the auxiliary channel and not to the true channel. We provide explicit solutions for optimizing the upper bound and the difference between the upper and the lower bound and we discuss a method for the optimization of the lower bound for data-controllable channels with memory. We discuss examples of channels with memory, for which application of the developed theory results in noticeably tighter information rate bounds.