Jeffrey D. Will

Implementation of a crossbar network using quantum-dot cellular automata

This paper presents a novel method for the implementation of wire-crossing networks using quantum-dot cellular automata (QCA) cells. Such wire-crossing networks, also called crossbar networks, are an important part of modern programmable logic devices, such as programmable arrays of logic. The crossbar networks are made possible through the use of parallel-to-serial converters and special regions with latching signals that are selected to sample and hold a particular value of serial data at a selected time. The particular signal connections made within the crossbar network are determined solely by the timing of the special latching signals rather than through physical changes to the device, so the same physical structure can be dynamically reprogrammed to provide a variety of different interconnection functions at different times. This wire-crossing network does not suffer from the shortcomings of the previously demonstrated QCA wire-crossing method, which required the use of rotated cells located on an interstitial cell spacing grid. This device only uses cells with a standard orientation on a regularly spaced grid.

Hough-transform-based vision algorithm for crop row detection of an automated agricultural vehicle:

Abstract Finding a pathway between crop rows is essential for automated guidance of some agricultural vehicles. The research reported in this paper developed a vision-based method for detecting crop rows. This method applied the Hough transform and connectivity analysis to process images of a vehicles forward view and to use them to find the appropriate pathway in the field. The Hough transform was used to detect crop rows and the connectivity analysis was applied to identify the most suitable path from all possible choices. This system was implemented in an agricultural tractor and tested in both laboratory and field experiments. The methodology devised overcame image noise problems and successfully determined the proper trajectory for the tractor.

Development of Robot Tractor Based on RTK-GPS and Gyroscope

This study developed a field robot for an agricultural operating environment. The navigation sensor consisted of an RTK-GPS, a fiber optic gyroscope (FOG), and an inertial measurement unit (IMU). A sensor fusion algorithm was used to identify FOG bias and compensate for location error in real-time, thus providing sufficient navigation information to support accurate robot guidance in the field. The guidance system could guide the agricultural robot automatically to follow either straight or curve paths including crop rows at a speed of 2.5 m/s. This RMS position error of the desired pathway in the field was less than 3 cm. The results indicated that the navigation system was capable of guiding an agricultural robot accurately and robustly under normal agricultural operations.

Turning Function for Robot Tractor Based on Spline Function

A challenge for autonomous field robotic systems for agriculture is preserving the general-purpose capabilities of the machine for various types of operations and maneuvers. This study addressed the issue of the dynamic nature of headland turning of the vehicle using third-order spline functions. The methodologies that were developed considered both the non-holonomic aspects of vehicle motion and the constraints on the curvature of the path that is acceptable on the headland. This automatic path creation method could generate feasible headland turning paths for the vehicle. Computer simulations and field tests were performed using a mobile robot vehicle based on a medium-size tractor. The robotic vehicle used a real-time kinematic GPS (RTK-GPS) and a fiber optic gyroscope (FOG) as navigation sensors. Through the field tests, it was concluded that the developed path creation method allowed the robot to make turns precisely.

Stray Charge in Quantum-dot Cellular Automata: A Validation of the Intercellular Hartree Approximation

The authors analyze the effect of stray charges near a line of quantum-dot cellular automata (QCA) cells. Considering both the ground-state polarization and the excitation energy of the system, it is determined that there is a 129-nm-wide region surrounding a QCA wire where a stray charge will cause the wire to fail. This calculation is the result of a full-basis-set simulation of a four-cell line. A comparison is made between cells with parallel-spin electrons and those with antiparallel spin electrons, showing that they yield essentially identical results. Therefore, the added complexity of accounting for antiparallel spins does not yield superior simulation results. Finally, a comparison is made between the full-basis calculations and the results of the same calculation using the intercellular Hartree approximation (ICHA). The similarity of these two results demonstrates that the ICHA method is a valid tool for studying the effect of stray charges in larger systems.

Visualizing the future of virtual reality

, implying that VR is a frivolous technology good for little more than creating an immersive video game. In reality, VR systems have been in use for more than 20 years, and their primary purpose is to help scientists and engineers visualize the systems they are analyzing or designing. The ability to view objects in three dimensions is extremely useful when determining how complex mechanisms fit together or how large organic molecules interact. Until recently, VR systems were very complex and expensive. The Cave Automatic Virtual Environment (CAVE), considered the state of the art in such systems, provides the most immersive solution possible. Advanced CAVE systems include six walls that completely surround the user with images. Unfortunately, these systems also come with extremely high price tags—up to US

Extracting, Assimilating, and Sharing the Results of Image Analysis on the FSA/OWI Photography Collection

10 million. Needless to say, this removes them from consideration for all but the best-funded research laboratories. Over the past five years, however, a new generation of very powerful VR systems has become available at a small fraction of the price of CAVE systems. Classic VR technology is expensive primarily for the same reason that large mainframe computers are: very few of these systems are sold, so each one must support a large fraction of the original research and development costs. The CAVEs use of Silicon Graphics processors and video cards along with state-of-the-art projectors adds substantially to the systems cost. In addition, the CAVE software library is proprietary, which means that it costs at least US

Image Analysis and Infrastructure Support for Data Mining the Farm Security Administration: Office of War Information Photography Collection

10,000 to write your first original program for the system. As happened with the Macintosh, this high barrier to entry has led to a relatively small software base for the platform. Another reason for the high cost of first-generation VR systems is the method used to create the illusion of three-dimensional images. Of course, the key to creating 3D images is to project slightly different images to each of the viewers eyes. The Silicon Graphics video hardware used in the CAVE is more than powerful enough to perform the advanced calculations required to generate the images and update them very frequently, but delivering them to the user is more complicated. The CAVE uses special shuttered eyeglasses that can electronically block either eye from seeing the screen. By carefully timing the shutters with the images delivered by the digital projectors, the CAVE presents a different image to each eye. This active-shuttering method is an effective , though extremely …

Optimizing human‐robot teleoperation interfaces for mobile manipulators

This paper reports on the continued work on image analysis of the Farm Security Administration -- Office of War Information Photography Collection team, supported through an XSEDE grant (Extreme Science and Engineering Discovery Environment) and Extended Collaborative Support Service (ECSS). The team is refining existing algorithms, developing new algorithms and executing them on the Comet supercomputer to analyze the FSA-OWI corpus from 1935-1944, held by the Library of Congress (LOC). The project spans many fields within the humanities and beyond, including photography, art, visual rhetoric, linguistics, American history, anthropology, and geography, as well as appealing to the general public. Progress includes refining image, metadata, and lexical semantics analysis, as well as developing a search, retrieval, and sorting interface through Clowder, which will serve as the public portal. Methods and tool refinement for this project are suitable for use on other large image corpora.

Distance learning: teaching a course from a remote site to an on-campus classroom

This paper reports on the initial work and future trajectory of the Image Analysis of the Farm Security Administration -- Office of War Information Photography Collection team, supported through an XSEDE startup grant and Extended Collaborative Support Service (ECSS). The team is developing and utilizing existing algorithms and running them on Comet to analyze the Farm Security Administration - Office of War Information image corpus from 1935-1944, held by the Library of Congress (LOC) and accessible online to the public. The project serves many fields within the humanities, including photography, art, visual rhetoric, linguistics, American history, anthropology, and geography, as well as the general public. Through robust image, metadata, and lexical semantics analysis, researchers will gain deeper insight into photographic techniques and aesthetics employed by FSA photographers, editorial decisions, and overall collection content. By pairing image analysis with metadata analysis, including lexiosemantic extraction, the opportunities for deep data mining of this collection expand even further.