This paper presents a new blind adaptive channel equalizer, which is based on two well-defined cost functions, the constant modulus algorithm (CMA) [1] and the alphabet- matched algorithm (AMA) [2]. The equalizer takes into account both the amplitude and the phase of the equalizer output and is adaptive based on the received channel data. The new equalization scheme presented here is compared to the multimodulus algorithm (MMA), which has been proposed in [3,4] as an improved equalizer for non-constant modulus signals, such as QAM. It is shown to provide better performance than the MMA in terms of both average mean- squared error (MSE) and average symbol error rate (SER) for the channels tested.

The future of remote sensing will see the development of spacecraft formations, and with this development will come a number of complex challenges such as maintaining precise relative position and specified attitudes. At the same time, there will be increasing needs to understand planetary system processes and build accurate prediction models. One essential technology to accomplish these goals is the integration of multiple source data. For this integration, image registration and fusion represent the first steps and need to be performed with very high accuracy. In this paper, we describe studies performed in both image registration and fusion, including a modular framework that was built to describe registration algorithms, a Web-based image registration toolbox, and the comparison of several image fusion techniques using data from the EO-1/ALI and Hyperion sensors

While automatic image registration algorithms are usually being evaluated with regards to their accuracy, it is often useful to relate this accuracy to the "initial conditions", i.e., the distance between the initial navigation geolocation and the correct result. This paper describes a modular framework that was built to describe registration algorithms, and utilize this framework to attempt to classify different registration components and algorithms in terms of their responses to the initial conditions. Performances would be evaluated on synthetic data, multitemporal and multisensor data. All results of the study would be presented at the conference and would be useful for two different purposes: (1) provide automatic quality assessment of the geolocation of remote sensing data by performing interalgorithm consistency studies; and (2) be the foundations for the design of future on-board applications including planetary exploration.

Image registration is the process by which we determine a transformation that provides the most accurate match between two images. The search for the matching transformation can be automated with a suitable choice of metric, but can be very time-consuming and tedious. We consider a registration algorithm that combines a simple yet powerful search strategy based on optimization of mutual information using a stochastic gradient, together with a wavelet-based multi-resolution pyramid. It is tested using a pair of fundus eye images, which is matched using a six-parameter affine transformation. This extends previous work based on the three-parameter transformation (Cole-Rhodes, A. et al., SPIE Aerosense 2002, Wavelet Applications IX, 2002). The registration algorithm is implemented in a multiresolution manner using a wavelet pyramid.

We extend the stochastic gradient optimization used in a mutual information-based registration algorithm to include second-order (Hessian) effects with the aim of accelerating its convergence rate. We consider images, which are misaligned by a four parameter rigid transformation, consisting of scale, rotation and/or x- and y-translations, and we present the results of optimization using a second-order stochastic derivative. The algorithm is applied to a pair of multi-temporal satellite images, and is implemented in a multi-resolution manner using wavelets. Results are presented for an implementation, which switches after a fixed number of iterations, from the first-order scheme to the second-order one.

The study of global environmental changes involves the comparison, fusion, and integration of multiple types of remotely-sensed data at various temporal, radiometric, and spatial resolutions. Results of this integration may be utilized for global change analysis, as well as for the validation of new instruments or for new data analysis. Furthermore, future multiple satellite missions will include many different sensors carried on separate platforms, and the amount of remote sensing data to be combined is increasing tremendously. For all of these applications, the first requires step is fast and automatic registration, and as this need for automating registration techniques is being recognized, it becomes necessary to survey all the registration methods which may be applicable to Earth and space science problems and to evaluate their performances on a large variety of existing remote sensing data as well as on simulated data of soon-to-be-flown instruments. In this paper we present one of the first steps toward such as exhaustive quantitative evaluation. First, the different components of image registration algorithms are reviewed, and different choices for each of these components are described. Then, the results of the evaluation of the corresponding algorithms combing these components are described. Then, the results of the evaluation of the corresponding algorithms combining these components are presented on several datasets. The algorithms are based on gray levels or wavelet features and compute rigid transformations (including scale, rotation, and shifts). Test datasets include synthetic data as well as data acquired over several EOS Land Validation Core Sites with the IKONOS and the Landsat-7 sensors.

Image registration is the process by which we determine a transformation that provides the most accurate match between two images. The search for the matching transformation can be automated with the use of a suitable metric, but it can be very time-consuming and tedious. We introduce a registration algorithm that combines a simple yet powerful search strategy based on a stochastic gradient with two similarity measures, correlation and mutual information, together with a wavelet-based multiresolution pyramid. We limit our study to pairs of images, which are misaligned by rotation and/or translation, and present two main results. First, we demonstrate that, in our application, mutual information may be better suited for sub-pixel registration as it produces consistently sharper optimum peaks than correlation. Then, we show that the stochastic gradient search combined with either measure produces accurate results when applied to synthetic data, as well as to multitemporal or multisensor collections of satellite data. Mutual information is generally found to optimize with one-third the number of iterations required by correlation. Results also show that a multiresolution implementation of the algorithm yields significant improvements in terms of both speed and robustness over a single-resolution implementation.

The problem of dynamic control of a multifingered hand manipulating an object is considered, under the condition that some of the fingertips slide on the object surface. This work has many useful applications when considered in conjunction with work already done in the area of regrasping. In performing certain tasks with grasped objects, it is often necessary to change the contact locations of the fingers on the object. One method of achieving this is to break and remake the contacts; another method is to slide the fingertips on the object surface. This work provides a dynamic coordinated control scheme for a hand by which one can perform regrasping and reorientation of an object in the planar case.>

The authors consider the problem of grasp choice for an object held within a multifingered hand from the viewpoint of avoiding collisions between the manipulator links and the object during trajectory execution. A grasp planner is provided in the form of an algorithm that checks the feasibility of a given object trajectory and provides an envelope of feasible contact positions. During execution of the trajectory, contact positions of the fingertips on the object can be changed by sliding the fingertip along the object surface in a controlled manner. A dynamic control law that achieves this is presented and integrated with the grasp planner to determine a dynamic regrasping algorithm, which is illustrated by simulation.>