Research Commercialization and Impact

Dr. Iyengar’s work has had a tremendous impact when applied to industries and real-world applications. In addition to the above areas, his foundational work continues to be implemented by government agencies and industry, including;

• Tactical Environmental Support System (3rd Generation—TESS(3)) Surveillance and Oceanographic Sensing (1992): Implementation of the oceanographic expert system by the Naval Research Laboratory Detachment at Stennis Space Center (NRL – SSC). This system has applications to predict oceanographic features which cannot be observed due to cloud cover in the analysis of satellite imagery.
• Sensor-Based Autonomous Mobile Robotics: Dr. Iyengar’s extensive research in the fields of robotics, sensors, and artificial intelligence has continued from his early years at Oak Ridge National Laboratory and into his current work at Discovery Lab at Florida International University. His recent work is an urban surveillance telepresence robot for use by injured police officers,  demonstrating robotic technology assisting the disabled to return to work.

• Computational Medicine: Founding Director of the Louisiana Biomedical Research Network, and development of autonomous intelligent systems for detecting radiation at Oak Ridge National Laboratory and the Jet Propulsion Laboratory, as well as discoveries in radiotherapy treatment of lung tumors, and development of an intraocular glaucoma monitoring device.

Dr. Iyengar is a member of the European Academy of Sciences, winner of the Lifetime Achievement Award from ISAM-IIT (BHU), a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow of the Society for Design and Process Science (SDPS). He was awarded the Satish Dhawan Chaired Professorship at the Indian Institute of Science (IISc), then Roy Paul Daniels Professorship at LSU. He has received the Distinguished Alumnus Award of the Indian Institute of Science. In 1998, he was awarded the IEEE Computer Society’s Technical Achievement Award and is an IEEE Golden Core Member.

One of Dr. Iyengar’s project along with Dr. Phoha(LA Tech) and Dr. Kannan(LSU) entitled “Fast Web Page Allocation on a Server Using Self-Organizing Properties of Neural Networks” was selected in World’s Best Technology Showcase in the year 2007. This is a highly competitive summit and other competitors were Los Alamos Labs, Johns Hopkins University, EPA, NASA and many other.

2. The Cognitive Information Processing (CIM) Research Tool for Predicting Rare Events from Data Sets

A Novel Architecture that is Changing the State-of-the-Art in Intelligent Systems
This foundational technique fuses disparate data streams, including text and video, for real-time monitoring of rare events and complex patterns of data.

Executive Summary
CIM Shell technology laid the foundation for new applications as described below.

Dr. Supratik and Dr. Iyengar are the co-inventors of the Cognitive Information Processing (CIM) shell, a system which recognizes and responds to complex patterns in mission-critical, real-time applications (see https://cacm.acm.org/news/91343-lsu-scientists-develop-new-efficiency- software/fulltext). CIM fuses disparate data streams, including text and video, to create an interactive inspection and visualization system that provides real-time monitoring, analysis, and online diagnosis for industrial applications. CIM can reconfigure itself on the fly to isolate critical events and fix failures, and underlies technologies used in manufacturing, agricultural, and oil production spaces, with notable applications including:

(1) The Online Diagnosis of Manufacturing Machines (ODMM) system, developed by SpotCheck Inc., licensed by AIlectric, and used by Novatec and ProphecySensorLytics to diagnose systems failures in factories, with a Total Addressable Market (TAM) of $9.1B in 2014, which is predicted by ABIResearch to grow to over $24.7B by 2019;

(2) The DeepSAT Video and Image Analytics application, developed by LSU and used by the NASA Ames Research Center to provide decision support for satellite missions, with a TAM of over $41B; and

(3) The DeepDrug system to shorten the developmental timeline of new drugs, developed by SynthLab, and ranked one of the top 10 systems worldwide competing for $3M in the IBM Watson AI XPrize competition. Communication of the ACM: Lsu Scientists Develop New Efficiency Software (https://cacm.acm.org/news/91343-lsu-scientists-develop-new-efficiency-software/fulltext) Story of invention

Out of those 59 teams, in the second round in 2018, only 30 teams survived. In December 2019, the DeepDrug team one of the only 10 teams surviving in the semifinals. Each of the ten teams will now get $15000 as the prize. In Feb 2020, 5 teams will be selected from these 10 to go to the final. (https://www.xprize.org/articles/30-teams-advance-in-5m-ibm-watson-ai-xprize)

Dr. Iyengar’s Team at LSU extended this framework with novel deep learning  frameworks and beam search. It became the basis of the DeepDrug pipeline for drug discovery. Since 2015, his team competed in the AI XPRIZE with the DeepDrug platform.  By early 2020, DeepDrug became a semifinalist in the XPRIZE (among 142 teams competing worldwide). Early 2020, COVID hit US. Mukhopadhyay used DeepDrug for discovering oral therapies for COVID 19 (Remdesivir was not an oral therapeutic). By July 2020, DeepDrug discovered a combination therapy against COVID 19. Buoyed by this success, Skymount Medical licensed the DeepDrug technology from LSU. In collaboration with Skymount Medical, the combination therapy was tested in vitro and in vivo in several laboratories where it showed effectiveness against SARS COV2. Eventually, it was approved for human trials at the Riverside University Medical Center in California. It was also approved for  human trials in the UK by MHRA. In addition, it received approvals for late stage trials in both Poland and Ukraine.  Currently, Skymount Medical has been valued at $1.5B by KPMG  within which the valuation of the DeepDrug technology itself is around $500 million.

Applications and Other Links:

Article 1. SearchBusinessAnalytics.com: Cognitive computing for all? Think about it
(http://searchbusinessanalytics.techtarget.com/feature/Cognitive-computing-for-all-Think-about-it)

The hypergraph based search technique of the cognitive information management has been used for automated drug discovery. Our team at LSU is participating in the AI Xprize under the name “DeepDrug” and in the first year was ranked within the top 10 teams worldwide.

The Advocate: Baton Rouge team among Top 10 competing in $5 million IBM Watson AI XPRIZE contest (http://www.theadvocate.com/baton_rouge/news/business/article_867eb32a-da9f-11e7-a7b4-6335533ac592.html)

Greater Baton Rouge Business Report: Baton Rouge software development team advances in IBM competition for $5M
(https://www.businessreport.com/article/baton-rouge-software-development-team-advances-ibm-competition-5m)

In addition, cognitive information technology-based SpotCheck System for predictive maintenance has been licensed by AutoPredictiveCoding (Now AIlectric). J K Technosoft (part of JK Group, a $30 billion) conglomerate has signed a master services agreement with AIectric to use SpotCheck (and other technologies from AIlectric) within their own factories as well as their customers. A paper describing SpotCheck had been among the most downloaded article in the Integration Journal in June 2017. See https://www.journals.elsevier.com/integration/most-downloaded-articles.

Detailed Description

Dr. S.S. Iyengar is the co-inventor of the patented Cognitive Information Processing (CIM) Shell, a complex event processing architecture, and engine which recognizes and responds to complex patterns in mission-critical, real-time applications (IEEE Computer 2010). This new comprehensive computational framework created by Iyengar fuses disparate streaming data, like text and video, to create an interactive sensing-to-cognition, inspection, and visualization system that provides real-time monitoring and analysis that can be applied to online diagnosis of manufacturing machines, agricultural systems, and petroleum production,  The CIM Shell not only sends an alert but reconfigures on the fly in order to isolate a critical event and fix the failure, unlike IBM’s Watson which only identifies the failure.

This Cognitive Information Shell was highlighted in a  Business Analytics article entitled, “Cognitive computing for all Think about it”, a Business Information feature by Stephanie Neil.

The work being done with a university in India (Bidhan Chandra Krishi Viswavidyalaya, West Bengal by Dr. Biswapati Mandal) is focused on increasing specific crop productivity by using sensors to collect data on ground activity and a synthesis program to dynamically reconfigure in real time to adjust to environmental changes. The agriculture app provides actionable intelligence to scientists who use it to direct experiments and inform decision making.

Impact of the Cognitive Information Processing Shell

The CIM Shell has had a significant impact on the market. Several companies are using the process, which is licensed by AutoPredictiveCoding, LLC as noted in section 2.2. According to Spotcheck, the Total Addressable Market for this technology is predicted to be over $24.7 billion by 2019*.

• Licensed by AutoPredictiveCoding, LLC’s
• Foundational technology for Spotcheck Technology’s online Diagnosis of Manufacturing Machines
• Technology incorporated into products sold by Novattec, Prophecy, SensorLytics, and others
• Used by NASA Ames Research for satellite imagery and decision support via NVidia’s DeepSAT Video Analytics & Image program
• Used by USDA for decision support via the National Agricultural Imagery Project

(*) According to ABIresearch, https://www.abiresearch.com/press/maintenance-analytics-to-generate-247-billion-in-2/.

2.1 How Does CIM Work?

The following figure outlines how the system operates and provides trend detection rules for interpreting prior information and identifying complex events to determine associated risks and future operations.

Had the CIM Shell been integrated with the Deepwater Horizon infrastructure it would’ve interpreted prior maintenance incidents and concerns voiced by VPs and engineers as relevant events and qualified the associated risks, potentially averting the Deepwater Horizon disaster.

2.2 Business, Media and Academic References Demonstrating Impact and Use of CIM

SpotCheck solutions serve companies and institutions that rely on backup lines when their primary networks fail, or rely on remote, point-of-sale locations for revenue generation. SpotCheck Technology uses the system for online diagnosis of manufacturing machines. Their information and processes can be found at http://spotchecktechnology.com. Some of their clients who are also using the technology are listed below.

AutoPredictiveCoding provides automated prediction model development and analytic engines for structured and unstructured content using latest research in data science across financial services, telecom, retail and healthcare verticals. The company offers its customers turnkey Data Science solutions that are outcome based. Their Analytics Engines automate predictive model building, increase the scalability of unstructured data mining, provide sophisticated low latency pattern detection of image and video streams, and provide an out of the box integration of sensor data for verticals such as Agriculture. AutoPredictiveCoding is a current licensee of the CIM Shell technology.

Soft10’s self-learning algorithms fully automate their processes: These algorithms create, select, predict, and self-verify predictions. TEOCO leverages its expertise in ‘big data’ and ‘real-time’ capabilities to analyze and optimize network and business performance by taming the glut of telecom network, operational and business data. TreeMiner, INC is a highly scalable data mining solution using first vertical data mining solution. Zreyas Technology develops prototype solution in the Electronics and Sensor Technology domain leveraging a network of R&D partners and research labs across the US, and India.

NovaTec is an independent, owner-managed IT consulting company, which supports customers in various industries in the successful implementation of projects in the IT environment. In addition to proven services complement your own, powerful software products in the areas of performance and license management and automation of IT services, our broad product range. NovaTec is a customer for the CIM Shell technology.

Prophecy Sensorlytics affordable and patented solution lets you look into the future to predict machine malfunction and failure. Prophecy sensors are placed directly on your machines or components to automatically monitor the condition. The sensor data transmits to an easy-to-install gateway and is then sent to cloud-based servers running powerful analytic software. Results are transmitted from the server to a user-friendly app where you will view real time machine condition and maintenance advice.Prophecy SensorLytics is a customer for CIM Shell technology.

1. Sensor
2. Data Hub
3. Router
4. Cloud-based Servers
5. Desktop Predictive Analytics Software
6. User-friendly App
7. Actionable Maintenance Advice

2.3 Total Addressable Market for the CIM Technology

ABIresearch estimates that the Total Addressable Market (TAM) for this CIM Shell technology was $9.1 billion dollars in 2014. Companies using CIM technology in this market include  SpotCheck Technology for Online Diagnosis of Manufacturing Machines and currently being licensed by AutoPredictiveCoding LLC, with Novatec, and Prophecy SensorLytics customers using the technology.  DeepSAT: Video Analytics and Image is using the technology for video analysis. ABIresearch predicts strong growth in both sectors for this licensed technology continuing through 2020.

Maintenance Analytics to Generate $24.7 Billion in 2019, Driven by Predictive Maintenance and Internet of Things
ABI Research forecasts that revenues from maintenance analytics will total $9.1 billion this year. Following a CAGR of 22%, the market’s size will reach $24.7 billion in 2019, driven largely by adoption of predictive analytics and M2M connectivity. While the more advanced forms of maintenance, predictive and prescriptive, still account for just 23% of this year’s market, at the end of the forecasting period they will collectively represent 60% of all revenues.

2.4 Use of the CIM Shell Technology by NVIDIA for NASA DeepSAT

DeepSAT, developed by NVIDIA provides video analytics and imaging using the CIM Shell technology. Nvidia is developing the DeepSAT technology through the NASA Ames Research Center.

“DeepSAT—A Learning Framework for Satellite Imagery” is a journal article that explains the CIM Shell technology as it applies to satellite image classification. The article appeared in 2015 in the Association for Computing Machinery (ACM) magazine.  (2015 ACM. ISBN 978-1-4503-3967-4/15/11, http://dx.doi.org/10.1145/2820783.2820816.) [Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., & Nemani, R. (2015, November). DeepSat: a learning framework for satellite imagery. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems (p. 37). ACM.]

2.5 CIM shell technology used by USDA in the NAIP: National Agricultural Imagery Project

NAIP: National Agricultural Imagery Project from USDA used CIM Shell technology in conjunction with other data to provide decision support. Additional information can be found at United States Department of Agriculture Farm Service Agency and NAIP fact sheet.

National Agriculture Imagery Program (NAIP)
The National Agriculture Imagery Program (NAIP) is administered by the U.S. Department of Agriculture’s Farm Service Agency (FSA) through the Aerial Photography Field Office (APFO) in Salt Lake City, Utah. NAIP acquires aerial imagery at a resolution of 1-meter ground sample distance (GSD) for the United States during the agricultural growing season, or “leaf on” conditions. The images are orthorectified which combines the image characteristics of an aerial photograph with the georeferenced qualities of a map.

2.6 CIM shell technology additional information and applications

How CIM Shell different from IBM’s Watson?

Cognitive computing for all? Think about it article by Stephanie Neil featured in Business Information 

The health care industry suffers from an ailment known as information overload. It’s a condition that makes it difficult for doctors and insurance providers to quickly determine the procedures that will be required and covered for each patient. The individual’s history, years of case files and clinical evidence must be considered -no small feat for a human. But it’s as easy as saying “aah” for Watson — IBM’s artificial intelligence computer system that processes natural language questions against a deep well of data to compute evidence-based answers in a matter of seconds. Read More >>

S. Iyengar, Supratik Mukhopadhyay, Christopher Steinmuller, and Xin Li “Preventing Future Oil Spills with Software-Based Event Detection“, IEEE Computer, pp: 76-78, August 2010.

Cognitive Information Management Shell (CIM Shell), a CEP system that can analyze complex events and activities and adapt rapidly to evolving situations in a wide variety of environments. By archiving past events and cross-referencing them with current events, the system can discover deep patterns and then act upon them. Agent-based techniques continually adjust CIM Shell’s parameters in near real time to adapt the system to changing environments, and human operators can easily add information to tweak the system, making goals easier to achieve. Had CIM Shell been integrated with the Deepwater Horizon infrastructure, it would have interpreted prior maintenance incidents and concerns voiced by BP’s engineers as a complex event and magnified the associated risks.

This article was featured in

For access to more content from the IEEE Computer Society,
see computingnow.computer.org.

3. The FuseCPA (Brooks-Iyengar) Algorithm

The Profound Contribution of the FuseCPA (Brooks-Iyengar) Algorithm

FuseCPA/(BI) framework guarantees in terms of Consensus, Precision, and Accuracy in the presence of both noise and deliberate deception in building a fault-tolerant distributed sensor network.

World Renowned FuseCPA (Brooks – Iyengar) Distributed Sensing Algorithm 

About “Brooks-Iyengar Algorithm Topic” : FuseCPA (Brooks-Iyengar) algorithm is a(n) research topic. Over the lifetime, 5223 publication(s) have been published within this topic receiving 138976 citation(s). The topic is also known as: Brooks–Iyengar hybrid algorithm.

The Brooks–Iyengar hybrid algorithm for distributed control in the presence of noisy data combines Byzantine agreement with sensor fusion. It bridges the gap between sensor fusion and Byzantine fault tolerance. This seminal algorithm unified these disparate fields for the first time. Essentially, it combines Dolev’s algorithm for approximate agreement with Mahaney and Schneider’s fast convergence algorithm (FCA). The algorithm assumes N processing elements (PEs), t of which are faulty and can behave maliciously. It takes as input either real values with inherent inaccuracy or noise (which can be unknown), or a real value with apriori defined uncertainty, or an interval. The output of the algorithm is a real value with an explicitly specified accuracy. The algorithm runs in O(NlogN) where N is the number of PEs: see Big O notation. It is possible to modify this algorithm to correspond to Crusader’s Convergence Algorithm (CCA), however, the bandwidth requirement will also increase. The algorithm has applications in distributed control, software reliability, and High-performance computing. Wikipedia

Application in DARPAS’s program demonstration with BBN, Cambridge Massachusetts, MURI, researchers from PSU/ARL, Duke, University of Wisconsin, UCLA, Cornell, and LSU.

In 2000, the DARPA program manager used two major demonstrations to showcase SensIT’s advances and document the ability of sensor networks to provide new capabilities. One
demonstration took place at the Twentynine Palms, California Marine Training grounds in
August 2000, the other took place at BBN offices in Cambridge, Massachusetts in October 2011. Dr. Gail Mitchell of BBN coordinated this work for BBN, DARPA’s SensIT integration contractor. Both demonstrations used the FuseCPA (Brooks-Iyengar) fusion approach to combine sensor readings in real-time. Acoustic, seismic, and motion detection readings from multiple sensors were combined and fed into a distributed tracking system. The first deployment was effective but noisy. The second demonstration built on the success of the first testing California. An improved outfielder algorithm was used to determine which node was best situated to continue existing tracks. This work was an essential precursor to the Emergent Sensor Plexus MURI from Penn State Applied Research Laboratory (PSU/ARL) with Dr. Sashi Phoha as PI. In that MURI, researchers from PSU/ARL, Duke, University of Wisconsin, UCLA, Cornell, and LSU extended SensIT’s advances to create a practical and survivable sensor network applications.

• Unified the fields of Fault Tolerance and Sensor Fusion—revolutionary contribution!
• Incorporated by DARPA into SensIT technology and its demonstrated advances.
  • Allowed combine sensor readings in real-time
  • Combined acoustic, seismic, and motion detection readings from multiple sensors;
  • Fed into a distributed tracking system
• Incorporated into modern-day LINUX & Android OS
  • Combines data in fault tolerance
  • Used by 99% of the world’s top supercomputers
  • 79% of all smartphones worldwide
  • 100% of users accessing the Internet

3.1 Business, Media and Academic References Demonstrating Impact and Use of the FuseCPA (Brooks-Iyengar) Algorithm

IMPACT ON HEALTH SUPPORT

“Secure Internet of Things (IoT) using a novel Brooks Iyengar quantum Byzantine Agreement-centered blockchain Networking (BIQBA-BCN) model in smart healthcare”

Application of this algorithm in modern day Linux operating system for combining data in fault-tolerance.(MINIX)

In 1996, Iyengar’s group, in collaboration with Brooks and with funding from Oak Ridge National Laboratory and the Office of Naval Research, invented a method of fault tolerance modeling that offers a computationally inspired real-time management solution. This work has emerged in new versions of real-time extensions to Linux Operating Systems. Many of these algorithms were used and installed in the RT Linux Operating System. They are now working on formal model verification by incorporating the algorithms into a new embedded kernel for robotic applications.

The profound contribution of the Brooks-Iyengar Distributed Computational Sensing work has enhanced new real-time features by adding fault tolerant capabilities.

V. Kumar, “Impact of Brooks-Iyengar DistributedSensing Algorithm on Real Time Systems,” in IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 5, pp. 1370-1370, May 2014.

In this paper we evaluate the merit of Brooks-Iyengar distributed sensing algorithm and highlight its impact on cost-effective processing of real-time sensor data stream. We discover that it has a long-lasting impact, not only on sensor networks, but also on computer operating systems. We believe that as we learn more about the usefulness of this algorithm, it will become an essential component of systems that use sensors in some form.

Buke Ao, “Robust Fault Tolerant Rail Door State Monitoring Systems: Applying the Brooks-Iyengar Sensing Algorithm to Transportation Applications,” in International Journal of Next-Generation Computing, Vol. 6, No. 3, November 2015.

In a moving train, the acceleration and deceleration adversely effect the sensor systems, which may induce errors into the system. Today’s technology does not guarantee success and safety in all situations. There are two important situations that are critical for the safety of passengers when embarking and disembarking. Distributed sensing networks needed to control the train doors require a fusion of the sensor inputs to provide accurate automatic opening and closing with minimum traction. Brooks-Iyengar Distributed Sensing algorithm can be used to provide a fault tolerant automatic sensing platform for closing doors based on the following scenario.

Application of FuseCPA (Brooks-Iyengar) Algorithm for Social Networks Fusions

The task of identifying and tracking influential nodes in the ever-growing information networks is crucial for many real-world applications that require information propagation (e.g., viral marketing). Although existing studies have been focusing on identifying influential nodes in social networks, most of them have focused on networks with homogeneous nodes (e.g., user-user nodes) and have also ignored the impact of relevant contexts (e.g., social, temporal, and spatial). The contextually enriched information networks have heterogeneous entities that are interconnected and complement each other’s functionality. Hence, the classical techniques popular in modeling the spreading of information in simple networks may not be efficient. An interesting example is the exploitation of social networks to identify the influential candidate in the U.S. presidential election. For the 2016 election, several models predicted about 80% winning chances of Hillary Clinton. Most of those studies exploited user polls, activities and posts from different social networks to capture the correlation between different parameters and to formulate the influence propagation for both candidates, and predicted the winning likelihood using their influence scores. As the correlation need not necessarily imply causation, the actual result was the opposite of the predictions. This implies that those models should have been impacted by some noisy information from the social networks. For instance, for any two users, having posts in support of one candidate need not necessarily indicate their voting decision. From this example, we can argue that a contextual exploitation of information on social networks is essential to model the influential parameters and to handle the noisy information.

In this study, we propose a model called MITS (Multi-context Influence Tracking on Social Network) represents the contextual exploitation of heterogeneous nodes (i.e. user-location nodes in Location-based Social Networks (LBSN)), formulates the locality-aware spatial-socio-temporal influence tracking problem using FuseCPA (Brooks-Iyengar) hybrid algorithm, and uses the geo-tagged check-in data to identify and track the locality influence. The empirical evaluation of the proposed model on two real-world datasets, using the Susceptible-Infected-Recovered (SIR) epidemic technique, coverage, and ratio of affection metrics demonstrate a significant performance gain (e.g., 10% to 85% on coverage and 14% to 39% on ratio of affection) of the proposed model against other popular techniques, such as degree centrality, betweenness centrality, closeness centrality, and PageRank.

Sustainable Interdependent Networks II
– From Smart Power Grids to Intelligent Transportation Networks

Impacts of FuseCPA (Brooks-Iyengar) Algorithm on Ph.D. Dissertations

More than 28 dissertations from highly ranked universities (e.g. MIT, Carnegie Melon University, RICE University, University of Southern California, and University of Pennsylvania) have extensively inspired by the FuseCPA (Brooks-Iyengar) Algorithm or applied it in their dissertation.

3.2 Select Letters

Carleton University is developing a formal model verification by incorporating the algorithms into a new embedded kernel for robotic applications.

…the impact of this work has been significant. The Brooks-Iyengar Algorithm makes it possible to extract reliable data from sensor networks when some sensors are unreliable, thus adding a fault tolerant aspect. Unreliable sensor data are typically the case in systems that Raytheon develops for its customers, so applications of the algorithm made Raytheon and its customers more successful without increased investment in improved sensor reliability. This saved money, always important in today’s increasingly budget conscious environment.
…the Brooks-Iyengar algorithm continues to have significant impact where it is applied to Raytheon’s programs.

~ Intelligence, Information and Service Raytheon

Citation from Raytheon Company

Telcordia Technologies, Inc., also incorporated the Brooks – Iyengar Algorithm into their work.

…The seminal work of professor Iyengar and his colleagues on sensor networks infrastructure constitutes a foundation for sensor placement and optimization of information obtained from sensor networks. The ideas and algorithms introduced by Iyengar et al. Played an important role in Telcordia patent 019576 entitled “Method for placement of sensors for Surveillance” and Motorola patent 7688793 entitled, “Wireless sensor node group affiliation method and apparatus,” both of which build up upon his pioneering work.
I believe that professor Iyengar work on sensor deployment, coverage, and surveillance has led to major advances in network technologies….

~ Marek Rusinkiewicz Vice President for research and General Manager of Telcordia

CITATION FROM TELCORDIA TECHNOLOGIES INC.

3.3 The FuseCPA (Brooks-Iyengar) Algorithm Demonstrates Potential for Millions of Dollars of Future Market Growth

Iyengar’s work has had a significant impact in industrial applications/ research, and continuing scientific research. Many new ideas have been spawned from his seminal work, resulting in a variety of new innovations by industries such as Motorola, Telcordia, Boeing, and even new chip-design at universities. New applications from industrial research are ongoing, each resulting in millions of dollars of market growth.

The intellectual advancement of his ideas has been incorporated into over 23 Ph.D. thesis and other new thesis and dissertations resulting in scientific discoveries around the world. His work has impacted thesis in Germany, the United States, and China. His foundational work has also resulted in innumerable funding opportunities provided by the National Science Foundation to continue to advance research in this significant area.

4. The Optimal Sensor Placement Technique

The Seminal Contribution of the Optimal Sensor Placement Technique

Co-inventor of an optimal sensor placement technique relating to a limited number of sensors at selected locations in order to provide protection to all locations in real-time applications, which has been included in patients by Telcordia and Motorola, as well as other companies. (IEEE Computers, 2002). This groundbreaking work has laid an intellectual foundation for designing policies and techniques in many areas of real-time applications from surveillance systems to telecommunications systems.

4.1 Optimal Sensor Placement Technique Impact on Industry

Iyengar’s work has had a significant impact in industrial applications/ research, and continuing scientific research. Many new ideas have been spawned from his seminal work, resulting in a variety of new innovations by Telcordia, Boeing, and even new chip-design at universities. New applications from industrial research are ongoing, each resulting in millions of dollars of market growth.

The intellectual advancement of his ideas has been incorporated into over 23 Ph.D. thesis and other new thesis and dissertations resulting in scientific discoveries around the world. His work has impacted thesis in Germany, the United States, and China. His foundational work has also resulted in innumerable funding opportunities provided by the National Science Foundation to continue to advance research in this significant area.

4.2 Business, Media and Academic References Demonstrating Impact and Use of the Optimal Sensor Placement Technique

Telcordia Technologies, Inc., incorporated the Optimal Sensor Placement Technique into their work.

Review reference letter from Telcordia Technologies, Inc.

Millions of dollars of market growth. Iyengar’s work has had a significant impact in industrial applications/ research, and continuing scientific research.New applications from industrial research are ongoing, each resulting in millions of dollars of market growth.