Information Technology

Most Recent Inventions

App for Stratifying Autism Spectrum Disorders

UW–Madison researchers have developed a software test to differentiate ASD participants into two distinct types of contextual learners. The first group resembles a “Typically Developing” (TD) learning profile, and the second group does not modulate with context, indicating that they are not able to learn the embedded context.

Participants viewed a monitor divided into four quadrants and were asked to search for a visual target, then indicate the quadrant in which the target was located. Unbeknownst to the participants, contextual information about the target location was manipulated across sessions by varying the number of off-targets and the probability of the target being present in that quadrant. Search time as a function of the proportion of informative cues in the target quadrant was used as a measure of contextual learning.

Physics ‘Office Hours’ educational learning platform

A physics education researcher at the University of Wisconsin-Green Bay has designed a novel and interactive app-based study aid platform for students in STEM disciplines. The platform’s interface is built around education research into how students conceptualize problems they do not understand. It is a novel tool to help students see why they are struggling with a particular problem, and what might help them solve it, rather than solving the problem for them. The team’s first working prototype, the Physics Office Hours app, has been designed for use in introductory-level college physics. The app is designed to mimic a scenario students might face during ‘office hours’ with a professor: Rather than offering an answer, the instructor guides the students through problems via a series of questions. A user-friendly online interface allows app content to be easily updated and changed over time and as more problem sets become available. In addition, the app architecture can easily be adapted to problem sets in other STEM disciplines and therefore serves as a platform technology.

Matrix Processor with Localized Memory to Increase Data Throughput

To address this challenge, Li’s team has developed computer architecture combining local memory elements and processing elements on a single integrated circuit substrate. In this way, data stored in a given local memory resource normally associated with a given processing unit is shared among multiple processing units. The sharing may be in a pattern following the logical interrelationship of a matrix calculation (e.g., along rows and columns in one or more dimensions of the matrix).

Sharing reduces memory replication (the need to store a given value in multiple local memory locations), thus reducing the need for local memory and unnecessary transfers of data between local memory and external memory. This permits high speed processing on local memories (on-chip) and reduces energy consumption associated with a calculation.

Mobile Tools for Autism & Communicative Disorders Therapy

A Researcher at University of Wisconsin Stevens Point has developed a suite of medically secure mobile application tools to instantly communicate, track and analyze behaviors and medical interventions for a variety of communication spectrum disorders, especially focusing on Autism therapies. In addition, this system is designed to increase the ability of organizations to train new therapeutic staff in the field through calculated suggestions from an artificial intelligence engine. The suite of apps consists of 1) a data entry, tracking and analysis tool 2) a video capture, sharing and behavior tagging tool, and 3) an artificial intelligence tool. An online Knowledge Automation Expert System (a type of Artificial Intelligence software) is used to track treatment, look for patterns in said treatment, and provide guidance on the next best steps based on each child’s needs. The applications are media rich and allow parents, therapists, and medical doctors to record, track, and observe actual behavior in real time through interactive charting, video sharing, and video conferencing. The video sharing and conferencing provide a way for therapists in the field to work in real-time with senior therapists remotely, thus increasing the level of training for field staff.

These apps are cross mobile platform compatible (Android, iOS and Blackberry) and have several levels of security ensuring patient record safety. This streamlined system of apps work together to capture all critical data from the medical treatments as well as the behavior therapy treatments and provide analysis tools to track and understand changes in the pattern of behavior and reduce subjective interpretation. The final product simplifies communication among parents, therapists and doctors, as well as providing an easy method for therapeutic organizations to efficiently train their staff in the field through direct access to senior therapists and their experience.

Data Mart with Web Inquiry to PeopleSoft Financials Data

UW-Madison computer scientists have now developed a Web-based software application providing ready access to financial data in PeopleSoft, a state-of-the-art public sector accounting system. Rather than accessing the operational database, the application, called WISDM for Wisconsin data mart, uses a high-performance extract database (the data mart) to deliver PeopleSoft data to users. The WISDM application allows users to access financial ledger pages via a simple Web interface. By clicking individual cells in these pages, the user can drill down to a list of transactions and journals supporting the cell total. Links from these lists allow the user to view the actual source document (e.g., purchase order) in the PeopleSoft database itself. WISDM also provides several simple query interfaces into various journals, transaction lists and source documents that have been transferred into the data mart from PeopleSoft Financials. Because PeopleSoft does not support any custom exports at this time, each site using the WISDM software must develop its own export mechanism.

Most Recent Patents

Computer Accelerator System Boosts Efficiency

UW–Madison researchers have developed a specialized memory access processor that takes over the job of feeding data to the accelerator. It is placed between the main processor and the accelerator.

The circuit is specialized for a narrow task, in this case performing memory access and address calculations. It is as fast as the main OOO processor yet more efficient. The main OOO processor – free from memory access duties – may switch to an energy conserving sleep mode until the accelerator is finished, or may move on to other tasks.

Simplified Optical Traps for Quantum Computing

A UW–Madison researcher has developed a novel method and hardware to create optical traps for neutral atom quantum computing. The new design is a simple yet efficient method for creating large arrays of bright or dark optical patterns for particle trapping and for arrays of atomic qubits for quantum computing.

Rather than using a relatively complex arrangement of optical elements, the new approach requires only lenses and circular apertures. Compared to prior designs, this approach is cheaper to implement and has improved technical characteristics for efficient utilization of laser light and improved localization of the trapped particles.

More Efficient Laminate Analysis

UW–Madison researchers have developed a method for analyzing composite laminate structures that combines the generality of 3-D FEA and efficiency of 2-D FEA whenever it is applicable. The new method works by substituting the laminate layers with much simpler virtual material models having matching characteristics (e.g., overall material properties and relationship between stresses and strains). The updated model can then by analyzed via fully automated 3-D FEA.

The virtual models may be referred to as ABD-equivalent models, as they result in the same ABD stiffness matrices as the real laminate and can act as substitutes if plate-shell assumptions apply.