This page contains information about selected projects funded by international research and development funding agencies. We encourage our researchers and developers to develop these and similar ideas further. We welcome information about other funded projects. Please send this information to researchideas@ictrdf.org.pk.

The following proposals have been extracted from the Small Business Innovation Research (SBIR) and the Small Business Technology Transfer (STTR) Programs, administered by the U.S Small Business Administration (SBA) Office of Technology.


  Supply Chain Optimization and Product Explorer
  Implementation, Testing and Refinement of a Hybrid Distributed/Traditional System for
       Broadcasting Live and Pre-Recorded Content to Large Online Audiences
  Multi-Party Peer-to-Peer VoIP
  Artificial Intelligence and Character Animation
  Mobile Visual Search Engine
  Integrating Online Analytical Processing (OLAP) and Ontologies to Discover Inconsistencies in
       Expectations for Supply and Demand
  Automated Community and Sentiment Mining for Global Media Preference Understanding
  The Media Fusion Project: A Distributed Architecture for Mega-Pixel Displays
  SAFE: Behavior-based Malware Detection and Prevention
  Unsupervised Extraction of Relational Data from the Web
  Artificial Intelligence Tutoring and Assessment for Teacher Development
  Adaptive Authoring for Compound XML Documents: Collaboration Tools and eLearning Content
       Creation for STEM

Title: SBIR Phase II: Supply Chain Optimization and Product Explorer

Award Number: 0620233
Program Manager: Errol Arkilic
Start Date: August 1, 2006
Expires: July 31, 2008
Total Amount: $499,995
Investigator: Nainesh Rathod, nainesh.rathod@imaginestics.com
Company: Imaginestics 1220 Potter Dr. Suite 124 West, Lafayette, IN 47906
Phone: (765)464-1700

Abstract:

This Small Business Innovation Research (SBIR) Phase II project will achieve higher retrieval accuracy for shape-based search for both the web and the enterprise. The proposed work in Phase II is to achieve higher retrieval accuracy supported by three key components: 1) pose determination for 3D models: bridging the space gap between 2D and 3D shapes by finding three intuitive and robust orthogonal orientations for 3D models; 2) 2D orthogonal view generation: representing a three orthogonal views along the pose orientations; 3) similarity measurement between 2D shapes: finding 2D and 3D shapes based on the user's query. A framework will be developed by focusing on three important modules: 1) 2D constraint detection and use of implied constraints with initial application in 2D and 3D views; (2) Enhanced multiple level-of-details in 3D representations, and (3) Human assisted system classification of large datasets.

Traditional options of finding part suppliers using catalogs, trade shows and prior business relationship limit the choice of suppliers. Current text-based search to find suppliers face challenges, such as context and language sensitivity, and is inadequate in overcoming the technological challenges posed by variations in how product or part information is specified across a global supply chain. The current effort proposes to use shape, which is the lowest common denominator, to link the OEMs and suppliers. This technology can also aid the current trend among companies in aerospace, automotive, medical equipments and other industries towards 3Ddata standards for fast retrieval, as it can provide a significant leap in terms of accuracy, speed and relevance in the search and retrieval of information. If successful, this technology can contribute significantly to research in areas where shape is important, such as bio-technology and pharmaceutical sectors, where rapid identification of molecules and their docking features help reduce time and cost involved in drug development. For the medical industry due to increased usage of CT scans and 3D imaging technologies, 3D shape search can be used for local feature identification in colonoscopy or other exploratory procedures, brain angiography, reconstruction, projection of malformation or location of polyps and ensure better and rapid diagnosis of disease. Development of methods for automatically parsing human sketches and determining constraints will enable many other research activities and broadly help in a more natural human machine interaction.

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Title: SBIR Phase II: Implementation, Testing and Refinement of a Hybrid Distributed / Traditional System for Broadcasting Live and Pre-Recorded Content to Large Online Audiences

Award Number: 0750136
Program Manager: Errol B. Arkilic
Start Date: February 15, 2008
Expires: January 31, 2010 Total Amount: $512,000
Investigator: Mike O'Neal, mike@nft-tv.com
Company: Network Foundation Technologies
818 Nelson Avenue, Ruston, LA 71272
Phone: (318) 257-5432

Abstract:

This Small Business Innovation Research (SBIR) Phase II project has two technical goals. In Year 1 the focus is on increasing the video quality (bit rate) of NFT delivered broadcasts, while keeping bandwidth costs low. In Year 2 the focus shifts to expanding product support to Mac and other non-Windows systems. Network Foundation Technologies (NFT) has developed a patented distributed broadcast technology that overcomes many of the current bottlenecks. The key difference between the NFT approach and the traditional approach is that with NFT the computers and Internet connections of the viewers watching a broadcast help deliver that broadcast on to other viewers. Network Foundation Technologies' products and technology have the potential to significantly impact the way television-style broadcasting is conducted over the Internet, greatly increasing the number of voices that can be heard. While NFT's near term goal is "to bring television to the Internet", the long term goal is to give ordinary citizens their own "online television stations."

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Title: SBIR Phase II: Multi-Party Peer-to-Peer VoIP

Award Number: 0750558
Program Manager: Errol B. Arkilic
Start Date: January 15, 2008
Expires: December 31, 2009 Total Amount: $495,154
Investigator: Milton Chen, milton.chen@vsee.com
Company: VSee Lab
3188 Kimlee Drive, San Jose, CA 95132
Phone: (510) 823-3564

Abstract:

This Small Business Innovation Research (SBIR) Phase II project extends the PI's Phase I to create a theoretical bandwidth and latency efficient multimedia streaming framework for communication. The ultimate goal is a software system that achieves less than 150 msec one-way end-to-end delay (the typical delay of telephone) for a 10-30 site meeting supporting wideband audio, full motion video, and application/desktop sharing over broadband networks. The industry norm to achieve multiparty video/web conferencing is the client-server architecture. Client-server architecture is expensive to deploy due to the number of servers required and the bandwidth required at the server nodes. Peer-to-peer approaches have been successfully used for large scale file sharing. However, peer-to-peer approaches have been relatively unexplored to scale the number of participants in a single meeting. This research combines real-time network sensing and the domain knowledge of video and web conferencing to create a scalable and cost effective peer-to-peer streaming algorithm. The maximum number of sites in a multiparty videoconferencing is typically 4-6. Given the limited screen resolution of a laptop/desktop, methods for showing 10-30 full motion video and a shared application are relatively unexplored. Poor user experience from inadequate user interface is a major barrier to the adoption of previous video/web conferencing tools. This research combines recent human factor discoveries to create a novel user interface that intuitively supports multiparty communication. Since AT&T invented videoconferencing in 1927, videoconferencing has been one commercial failure after another. The PI's previous research suggests that such failures are rooted in inadequate knowledge of the human factor requirements of videoconferencing. Based on previous research, they are developing a commercial software system which will make substantial impact on telework, remote education, and humanitarian operations. This project aims to create a low-cost peer-to-peer alternative to client-server architectures for large scale meetings. If successful, the architecture proposed in this effort could have significant commercial impact.

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Title: SBIR Phase II: Artificial Intelligence and Character Animation

Award Number: 0548723
Program Manager: Errol Arkilic
Start Date: February 7, 2006
Expires: January 31, 2008
Total Amount: $499,996
Investigator: Michal Hlavac, michal@ingeeni.com
Company: Ingeeni Studios 271 Windsor Street, Cambridge, MA 02139
Phone: (617)818-7547

Abstract:

This Small Business Innovation Research (SBIR) Phase II project is to build and launch simple and intuitive software tools that allow for the creation of interactive 3D graphics within Macromedia Flash (a 2D vector graphics package). Combined with the existing technology, this collection of technologies will provide the first version of the revolutionary Artificial Intelligence Platform for the creation and delivering of interactive animated characters with emotional intelligence. The systems provide the characters with autonomous behavior selection (what should I do?), emotion (how do I feel?) and learning (have I seen this before?). Such a unique blend of technologies opens opportunities for the study of the theories of the human mind and creates an entirely new class of interactive media.

The broader impacts of this work are scientific, educational, and economic. The technologies advance discovery and understanding of the workings of the human mind by giving a rapid prototyping environment for computational theories of the mind. Scientists and non-scientists alike can create AI networks and see the resulting characters "twitch" on screen in real time. This work promotes teaching, training and learning as Ingeeni will work with UC Irvine and MIT Media Lab to develop curriculums for Synthetic Characters classes that use the platform. Massive adoption of Ingeeni's technologies is the company's main goal, and it is developing libraries of detailed step-by-step tutorials freely available online.

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Title: SBIR Phase II: Mobile Visual Search Engine

Award Number: 0822713
Program Manager: Errol B. Arkilic
Start Date: August 1, 2008
Expires: July 31, 2010 Total Amount: $500,000
Investigator: Gerald Pesavento, gerry.pesavento@iqengines.com
Company: IQ Engines, Inc.
821 Pine Lane, Davis, CA 95616
Phone: (530) 219-2192

Abstract:

This Small Business Innovation Research (SBIR) Phase II project will develop a biologically-inspired image search and recognition technology to provide rapid object information retrieval from a mobile phone camera. The end result is that potentially any object in the real world is now "clickable": a picture of an object provides a hyperlink to the Internet. The proposed system utilizes a new method for sparse, multi-scale image representation based on the monogenic signal, a 2D generalization of the analytic signal that is robust to image transformations. By 2010, it is estimated that there will be over 1 billion mobile phones with cameras.The mobile phone is becoming an important connection between people and the digital world. The applications for mobile search technology are enormous and include national homeland security, product information retrieval (such as environmental ratings, pricing, or specifications), vision support for the blind, accessing object information for the disabled, and general purpose information retrieval including remote visual data analysis and inspection. Search technology has brought about many profound societal, educational and scientific benefits in the past decade. The proposed mobile image search technology will extend those benefits to a broader base of users and applications.

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Title: STTR Phase II: Integrating Online Analytical Processing (OLAP) and Ontologies to Discover Inconsistencies in Expectations for Supply and Demand

Award Number: 0750543
Program Manager: Errol B. Arkilic
Start Date: April 1, 2008
Expires: March 31, 2010 Total Amount: $512,000
Investigator: Peter Moore, peter@clados.com
Company: Clados Management LLC
133 Saint Matthews Avenue,San Mateo, CA 94401
Phone: (650) 231-0494

Abstract:

This Small Business Technology Transfer Research (STTR) Phase II project aims to produce a software application that dramatically improves a manager's ability to allocate resources to productive uses. With advances in Online Analytical Processing (OLAP) and ontology technology, the tool has the potential to enable the discovery of future supply and demand imbalances for teams of business analysts. The objective is to produce at least one Investable Inconsistency per day by the end of the research period. The Phase I project produced unanticipated innovations that may have broad utility in both the OLAP field and the ontology field, and with these innovations, the software platform shows promise for transforming the essential practice of analysis in the field of market research in support of investment decisions. The Phase II project, if successful will result in technology that extends this promise to a broad audience, educating users in best practices for investment analysis and enabling them to materially improve their allocation of resources.

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Title: SBIR Phase II: Automated Community and Sentiment Mining for Global Media Preference Understanding

Award Number: 0750544
Program Manager: Errol B. Arkilic
Start Date: April 1, 2008
Expires: March 31, 2010 Total Amount: $500,000
Investigator: Tristan Jehan, tristan@echonest.com
Company: The Echo Nest Corporation
48 Grove Street, Somerville, MA 2144
Phone: (617) 628-0233

Abstract:

This Small Business Innovation Research (SBIR) Phase II project applies data mining and machine learning techniques to both natural language description and Internet link graphs to model communities in order to predict preference, taste and sentiment for different kinds of media (music, TV, online media, video games, books). Current contextual information mining approaches that scan the text on a page for advertisement or recommendation ignore valuable community connections inherent in most self-published Internet discussion. Sentiment and opinion extraction systems operating on full text create challenging language parsing problems are fraught with issues of scale and adaptability. The identification systems can automatically categorize anonymous Internet writers or website visitors into specific demographic communities based on their tastes in many kinds of media. The Phase II research project approaches opinion extraction with a bias-free learning model based on training from known online corpuses that can be adapted to different languages and learns in real time as more data becomes available for high accuracy. Current personalization and marketing approaches either look at the "clickstream" of an anonymous user, leading to equally anonymous recommendations for popular movies and music -- or by scanning a surface-level overview of the text, leading to keyword advertisements with limited contextual understanding of entertainment content and community sentiment. The project plans to fully integrate people-focused community and sentiment analysis technologies into an autonomous, learning and scale-free "media knowledge service" for digital entertainment providers and marketers that can change the way digital content is marketed and sold.

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Title: SBIR Phase II: The Media Fusion Project: A Distributed Architecture for Mega-Pixel Displays

Award Number: 0750202
Program Manager: Errol B. Arkilic
Start Date: March 1, 2008
Expires: October 31, 2009 Total Amount: $499,999
Investigator: Christopher Jaynes, cjaynes@mersive.com
Company: Mersive Technologies, LLC
137 West Vine, Lexington, KY 40507
Phone: (859) 806-0398

Abstract:

This Small Business Innovation Research (SBIR) Phase II project will develop and deliver a software media architecture that removes a critical barrier to the widespread use of multi-projector, high-resolution, ultra definition displays. The approach defines a set of layered abstractions from the low-level display driver to higher-level protocols including multi-user display use and security. This model is the bedrock of a new display architecture that will not constrain future display innovations, allow content developers and producers to communicate to current and future display systems, and acts to isolate the underlying complexities of new display technologies from users. Building on this new architecture, the Phase II project will implement a software-based Display Operating System. The project is motivated by the perception that we will soon live in a world where displays cease to be individual discreet devices but rather become an extension of our environment; a limitless fabric of pixels. The potential impact of this innovation is significant, by removing the usability and cost barriers normally associated with ultrahigh-resolution displays, applications once available to only a select few can become commonplace. This has the potential to change the advanced visualization, media interaction models, as well as the way in which we interact with our computational environments.

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Title: SBIR Phase II: SAFE: Behavior-based Malware Detection and Prevention

Award Number: 0750299
Program Manager: Errol B. Arkilic
Start Date: March 1, 2008
Expires: February 28, 2010 Total Amount: $500,000
Investigator: Hao Wang, hwang@novashield.com
Company: Novashield, Inc.
1200 John Q Hammons Dr, Madison, WI 53717
Phone: (608) 833-2610

Abstract:

This Small Business Innovation Research (SBIR) Phase II project has the objective of implementing a commercially-competitive, host-based, malware detection and prevention system. During Phase I, a host-based malware detection system that demonstrated the practicality of detecting a malicious process by dynamically monitoring its system events was developed. The prototype called SAFE (Secure Activity Filtering Engine) filters system events using a stateful policy engine whose policies specify malicious behavior and the appropriate response. Because the technology does not rely upon the detection of "signatures" (i.e. patterns of bytes), it can detect previously unseen malware. During Phase II a number of significant enhancements to the policy engine including a checkpoint/rollback capability will be developed. The proposed functionality removes file system and registry changes associated with a process when a policy violation is detected. The ability to delay detection of malicious behavior until detailed system events are observed provides a just-in-time detection capability that increases the accuracy of the detection process while reducing false positives. The SAFE technology has the potential to demonstrate an effective approach to combating at least two of the dominant trends in the threat landscape. One such trend is the crafting of blended threats which use multiple infections vectors like email readers, web browsers, and messaging software to infect a host computer. Another trend is the popularity of "malware toolkits" which can be used by malware writers to quickly generate multiple variants of the same virus. The rapid proliferation of obfuscated variants is a potent threat to traditional signature-based solutions on two fronts: the rate of malware infection may overwhelm efforts to produce signatures to detect these variants and the logarithmic increase in the size of signatures databases reduces the performance of signature scanning. The SAFE technology addresses both of these trends. The stateful policy engine can correlate non simultaneous events across multiple sub systems and processes and thus detect and block blended threats. If successful, the architecture of the proposed system will have the potential to address a myriad of security threats and make a commercially-significant impact.

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Title: SBIR Phase II: Unsupervised Extraction of Relational Data from the Web

Award Number: 0548699
Program Manager: Errol Arkilic
Start Date: January 23, 2006
Expires: January 31, 2008
Total Amount: $499,936
Investigator: Steven Minton, minton@fetch.com
Company: Fetch Technologies 2041 Rosercrans Ave Suite 245, El Segundo, CA 90245
Phone: (310)414-9849

Abstract:

This Small Business Innovation Research (SBIR) Phase II project will enable software systems to make use of data on the Web that is embedded in HTML pages. The semantic web is intended to allow data to be shared and used by software applications. Unfortunately, in the present world, data on the Web is generally inaccessible to most applications because it is presented in a format intended to be usable by humans, as opposed to computers. The goal of this project is to create a relational view of data on the Web, so that applications can access Web data based on entities and their relations. The approach uses unsupervised machine learning to extract data from web sites for conversion into relational form. This project will result in a new generation of Web harvesting technology that has clear commercial value.

Web harvesting is an area of growing commercial interest for a variety of vertical markets, including Sales Intelligence, Market Intelligence, News Aggregation, and Background Search. However, web harvesting technology is limited today, since the collection of rich, detailed data must be done on a site-by-site basis. The approach described here, if successful, will enable a new generation of intelligent web harvesting technology that can scale to the entire Web. Ultimately, our approach will enable applications to query the entire Web as if it were a relational database. This has tremendous commercial value, and will enable many new types of web applications to be developed. In addition to the commercial value, the technical approach is novel and has significant merits on its own. If it is successful, the proposed method should generalize to other complex domains (such as scene understanding and natural language processing) where multiple heterogeneous types of structure must be analyzed to discover underlying meaning.

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Title: SBIR Phase II: Artificial Intelligence Tutoring and Assessment for Teacher Development

Award Number: 0822696
Program Manager: Ian M. Bennett
Start Date: July 15, 2008
Expires: June 30, 2010 Total Amount: $500,000
Investigator: Benny Johnson, johnson@quantumsimulations.com
Company: Quantum Simulations Incorporated
5275 Sardis Rd, Murrysville, PA 15668
Phone: (724) 733-8603

Abstract:

This Small Business Innovation Research (SBIR) Phase II research project focuses on bringing the power and benefits of artificial intelligence tutoring technology to the arena of teacher professional development (PD). The proposed innovation is a teacher professional development system built on the principles of artificial intelligence, and delivered via the Internet. Similar to a flight simulator, this technology will offer a realistic but benign opportunity to test and expand a teacher's preparedness through practice with realistic classroom situations. A key objective is the creation of a classroom simulator which incorporates a virtual master teacher, to help teachers deepen their content understanding, learn to respond to student questions more effectively, practice proven pedagogical techniques for improving student understanding and conduct self-monitoring and assessment before getting in front of a live class. An increasing number of schools are forced to rely on new or out-of-field teachers to fill the gap for teaching science and mathematics, often resulting in a substantial decline in quality, depth and individual attention students receive. Because of the well-documented problems of teachers teaching out of their content areas, and low-performing schools having greater percentages of lesser-qualified teachers, states have established stronger criteria for in-service teachers and newly qualifying pre-service teachers. Middle and high school science and mathematics are the areas where most out-of-area teaching is occurring. In the National Center for Education Statistics (NCES) report, 'The Condition of Education', a key finding is that high school students in high-poverty, high-minority schools were more often taught science, mathematics and English courses by out-of-field teachers than their peers in low-poverty, low-minority schools. This research is expected to impact these issues and in addition address the goals of the American Competitiveness Initiative and the requirements for highly qualified teachers identified in the 'No Child Left Behind' initiative.

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Title: SBIR Phase II: Adaptive Authoring for Compound XML Documents: Collaboration Tools and eLearning Content Creation for STEM

Award Number: 0750520
Program Manager: Ian M. Bennett
Start Date: April 1, 2008
Expires: March 31, 2010 Total Amount: $499,920
Investigator: Samuel Dooley, sam@integretechpub.com
Company: Integre Technical Publishing Company, Inc.
4015 Carlisle NE, Suite A, Albuquerque, NM 87107
Phone: (505) 889-8189

Abstract:

This Small Business Innovation Research (SBIR) Phase II project seeks to develop rich-media adaptive authoring tools for e-learning content creation for collaborative documents for science and mathematics. The proposed objective is to remove technical barriers that impede development and deployment of e-learning content, and to advance tools that create structured content from multiple cooperating document types. The research objectives of this Phase II project will extend the Lexicon adaptive authoring framework developed in Phase I, as measured by compound document authoring issues exhibited by the QTI XML binding, which we will use as a vehicle to advance the adaptive authoring framework. The project will elaborate the Lexicon operator declarations to provide conventional authoring behavior needed for QTI markup elements, according to a progressive schedule of regular project milestones. At the end of the project, it is anticipated that the Lexicon will represent an adaptive authoring tool for rich-media collaborative documents with full language support for QTI markup, as a means for authoring and delivering e-learning content. Additional configuration language improvements and configuration authoring tools will position Lexicon to adapt to a wide range of compound XML document types for e-learning content, and extended programming interfaces will enable Lexicon to embed into a wide range of collaborative e-learning applications. Education in the U.S. is currently undergoing a transition to the digital age that will impact every aspect of teaching and learning. The current generation of collaboration tools are text-based,and do not support the notation needed to communicate mathematics. This project seeks to develop a suite of collaboration tools that have native support for mathematical notation, so that students and instructors can communicate scientific and mathematical concepts more effectively. This Phase II project aims to will extend the Lexicon adaptive authoring framework developed in Phase I, to support embedded semantic markup needed to deliver rich instructional content, and to position Lexicon to support a series of collaborative e-learning applications that are enabled by a relatively small amount of semantic markup: MathIM, an instant messaging application, prototyped during Phase I, that allows users include mathematical notation in person-to-person chat messages; MathWiki, a web-based forum application that supports communities of users who share an interest in topics that require mathematical notation; MathSpace, an online authoring environment for creating student worksheets; and MathME, or the Math Media Environment, a 'virtual notebook' in which students can record the work they are doing online.

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