Location based services are proving to be the next driving factors for growth in smartphones. While GPS solves the problem of accurate localization in outdoor environments, indoor localization is still an area of active research. Emergence of new generation smartphones with low cost sensors, have provided an effective way of indoor localization by pedestrian dead reckoning (PDR). We propose a robust mechanism for detecting the step of a person and estimating his step length. Our system is independent of the location and orientation of the device. Our system is shown to perform 45% better than the traditional PDR systems proposed in prior-art. Another important problem in PDR system is determining the orientation of the mobile device and the direction of user motion. Many systems assume the device to be oriented in the direction of the user motion. Some of the recent systems use accelerometer, magnetometer patterns and PCA to detect the direction of user orientation. We propose a system which uses map matching and particle filtering to determine the direction of user motion. We tabulate our findings on the feasibility of such a system.

Technology is made up of bundles of ideas. Increasingly, these ideas are protected by intellectual property rights. Unfortunately, these intellectual property rights are often distributed across many different owners. Moreover, the technology itself is also used by many different legal entities. Thus, a burgeoning problem faced by creators of technology is how to coordinate the disseperate interests of various intellectual property owners in order to create useful technology. In this paper we apply transaction costs economics to explain the relative merits of different organization forms vis-à-vis the creation of technology that is made up of intellectual property.

The problem of determining the location and orientation of straight lines in images is of great importance in the fields of computer vision and image processing. Traditionally the Hough transform, (a special case of the Radon transform) has been widely used to solve this problem for binary images. In this paper, we pose the problem of detecting straight lines in gray-scale images as an inverse problem. Our formulation is based on use of the inverse Radon operator, which relates the parameters determining the location and orientation of the lines in the image to the noisy input image. The advantage of this formulation is that we can then approach the problem of line detection within a regularization framework and enhance the performance of the Hough-based line detector through the incorporation of prior information in the form of regularization. We discuss the type of regularizers that are useful for this problem and derive efficient computational schemes to solve the resulting optimization problems enabling their use in large applications. Finally, we show how our new approach can be alternatively viewed as one of finding an optimal representation of the noisy image in terms of elements chosen from a dictionary of lines. This interpretation relates the problem of Hough-based line finding to the body of work on adaptive signal representation.

Parallel MR imaging methods like SMASH, SENSE etc. use multiple receiver coils to accelerate the imaging process by reducing the Fourier space sampling requirement. In this paper we show how one can optimally select the sampling locations based upon (1) knowledge of the statistics of the object being imaged and, (2) a statistical criterion for optimality determined by the application. In particular we show that the optimal uniform sample spacing is not necessarily an integer multiple of the Nyquist interval and depends upon the specific coil sensitivities and configuration.

The problem of determining the location and orientation of straight lines in images is often encountered in the fields of computer vision and image processing. Traditionally the Hough transform has been widely used to solve this problem for binary images, due to it's simplicity and effectiveness. In this paper we pose the problem of detecting straight lines in gray-scale images as an inverse problem. We treat the input image as noisy observations, which are related to the underlying transform domain image through the inverse Hough transform operator. We then regularize this inverse problem using constraints that accentuate peaks in the Hough parameter space. We present four different forms of such constraints and demonstrate their effectiveness. Finally we show how our scheme can be alternatively viewed as one of finding an optimal representation of the image in terms of elements chosen from a redundant dictionary of lines, and thus is a form of adaptive signal representation.