Research Projects

Research

Digital Communications Technology Based on Nonlinear Dynamical Systems (DCT-NDS)

We've developed a new conceptualization of digital communications technology that is primarily based on nonlinear and chaotic dynamical systems theory. We have identified key features of chaos that provide performance benefits over traditional application of communications theory. The following are short descriptions of the three components of the technology.
D-Transform

The D-transform began as a nonlinear dynamical systems based algorithm for compressing digital audio, video, and image data. The algorithm utilizes the diverse nature of chaotic oscillations to achieve high compression ratios. Figure 1 shows one of the chaotic systems that we have used to develop this approach.


Figure 1a(a)
Figure 1b(b)
Figure 1. The Colpitts oscillator operating in a chaotic mode. (a) shows the state space and (b) shows the time dependent oscillations.

Figure 2 shows a graphical user interface developed a the Center to analyze the performance of the algorithm with digital video. We have applied this technique and have outstanding results with high definition television (HDTV).


Figure 2 Figure 2. DYNAMAC (D-Transform) analysis graphical user interface.
  • Key Investigators
  • Faculty: Dr. Chance M. Glenn, Sr. (Principal Investigator), Prof. Mike Eastman, Prof. Jeanne Christman
  • Students: Chaitanya Jidge, Padma Ragam, Pranay Bhadkamkar, Sebastian Dominquez-Perozo
Scholarly Works

  • C. M. Glenn and T. Rossi, "Implementation of a New Codec for Broadcast and Digital Rights Management of High Definition Television", 4th Annual Conference on Telecommunications and Information Technology, Conference Proceedings, March 2006.
  • C. M. Glenn, "New Approaches to Telecommunications Technologies using Nonlinear Dynamical Systems Theory", IEEE Upstate NY Workshop on Communications and Networking, Conference Proceedings, November 2005.
  • C. M. Glenn, M. Eastman, and N. Curtis, "Digital Rights Management and Streaming of Audio, Video, and Image Data Using a New Dynamical Systems Based Compression Algorithm", IADAT Conference on Telecommunications and Computer Networks , Conference Proceedings, September 2005.
  • C. M. Glenn, D. Mandloi, K. Sarella, and M. Lonon, "An Image Processing Technique for the Translation of ASL Finger-Spelling to Digital Audio or Text", NTID International Symposium Instructional Technology and Education of the Deaf, Conference Proceedings, June 2005.
  • C. M. Glenn, M. Eastman, and G. Paliwal, "A New Digital Image Compression Algorithm Based on Nonlinear Dynamical Systems", IADAT International Conference on Multimedia, Image Processing and Computer Vision, Conference Proceedings, March 2005.
  • Anurag R. Sinha, "Optimization of a New Digital Image Compression Algorithm Based on Nonlinear Dynamical Systems", Master's Thesis, Rochester Institute of Technology, May 2007.
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Mutually Orthogonal Chaotic Multiplexing/Modulation

The foundation of this research is the creation and utilization of sets of chaotic oscillations that have a property of mutual orthogonality. Traditional digital communication techniques use this principle in quadrature communications at the carrier level (in-phase and quadrature signals) and at the baseband level (QPSK, QAM, OFDM) for various modulation schemes. These have always been based on sine and cosine waves as the primary basis functions.

We have discovered new orthogonal basis functions in the form of diverse chaotic processes that we use for communication. Instead of having only two orthogonal waveforms, we have found three. This provides the following performance benefits over QAM:
  • Three to four times the symbol rate for the same symbol time
  • Up to twice the bandwidth efficiency, thus twice the bit-rate
  • Superior noise performance since the constellation is spread out
Figure 3 Figure 3. MOC baseband multiplexer diagram.
Figure 4 Figure 4. The Constellation diagram for the Hybrid-A MOC architecture.


Some simulation results from Hybrid A MOC for K=2 and K=3:


Figure 5 Figure 5. The constellation diagram for the transmitted data (K = 2).
Figure 6 Figure 6. Power spectral density plots for the transmitted and received MOC signals (K = 2).
Figure 7 Figure 7. The constellation diagram for the received data with signal-to-noise ratio of 15 dB (K = 2).
Figure 8 Figure 8. Bit error ratio plots for MOC for K = 2 and K = 3.


We have developed three MOC architectures:

  • Full MOC – MOC carrier signals modulated with MOC baseband waveforms
  • Hybrid A – Traditional I/Q carrier signals modulated with MOC baseband waveforms
  • Hybrid B – MOC carrier signals modulated with QAM or QPSK baseband waveforms
We are developing platforms for wireless transmission, broadband over powerline (BPL) and fiber optics.

  • Key Investigators
  • Faculty: Dr. Chance M. Glenn, Sr. (Principal Investigator), Dr. Warren Koontz
  • Students: Chaitanya Jidge, Padma Ragam, Pranay Bhadkamkar, Sebastian Dominquez-Perozo
Scholarly Works

  • C. M. Glenn, "Digital Communication Using Mutually Orthogonal Chaotic Waveform Multiplexing", McGowan Center Technical Brief, Rochester Institute of Technology, August 2009.
  • C. M. Glenn, "Novel Signal Processing Techniques for Optimal Bandwidth Utilization", NYSETA Spring Conference, April 2008.
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Syncrodyne Amplification

Syncrodyne amplification is a research effort that focuses on developing high-gain, high-efficiency power amplification technology from chaotic processes. Nonlinear processes, the root of chaotic behavior, tend to be efficient in their very nature. Along with this, there is a natural tendency for chaotic processes to synchronize with another chaotic oscillation. We discovered that not only will a chaotic process synchronize with a non-chaotic oscillation, but it would do so with an information bearing, non chaotic oscillation.

Since chaotic processes are also very sensitive to small changes, we have developed a method of synchronization that requires very little current flow. The result is a small input power requirement for synchronization, although the output power is much larger. The result is power gain and information transfer.
Figure 9 Figure 9. Bifurcation diagram for the single-humped logistics map. Chaos ensures after ϱ = 3.5.
Figure 10 Figure 10. A block diagram of a possible realization of the Syncrodyne amplifier.
Figure 11-1
Figure 11-2 Figure 11. A graphical user interface developed for studying the complex behavior of a synchronized chaotic process. The fractal nature of the system is revealed as we zoom into the parameter values.


  • Key Investigators
  • Faculty: Dr. Chance M. Glenn, Sr.
  • Students: Paul Vicioso, Kennedy Mihigo
Scholarly Works

  • C. M. Glenn, "Communications and Signal Processing Innovations Using Nonlinear Dynamical Systems Theory", Invited talk to the Rochester Engineering Society, April 2009.
  • C. M. Glenn, "New Approaches to Telecommunications Technologies using Nonlinear Dynamical Systems Theory", IEEE Upstate NY Workshop on Communications and Networking, Conference Proceedings, November 2005.
  • C.M. Glenn, "High-Gain, High-Efficiency Power Amplification for PCS", International Symposium on Advanced Radio Technology Digest, March 2003.
  • C.M. Glenn, "Synthesis of a Fully-Integrated Digital Signal Source for Communications from Chaotic Dynamics-based Oscillations", Doctoral Dissertation, The Johns Hopkins University, January 2003.
  • Paul Vicioso Osoria, "Study and Design a High-Efficiency, High-Gain Power Amplification Device for Wireless Transmitters based in Syncrodyne Amplification" Master’s Project Paper, Rochester Institute of Technology, August 2009.
  • Kennedy Mihigo, "High Efficiency, High Gain Power Amplification Device for Wireless Transmitters Based in Syncrodyne Amplification", 18th Annual Undergraduate Research Symposium, Rochester, NY, August 2009.
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Anomaly Detection

The D-Transform, besides a method for digital compression, provides a means of extracting measures of digital data that we can use to classify them. We have shown a number of ways to detect anomalies in various data sets. We describe some of them below.
Space Weather Alert System

Colonies on the moon and Mars will not enjoy the protection that is offered by the Earth's strong magnetic field and thick atmosphere against high fluxes of interplanetary particles and radiation due to the arrival of severe solar storms. As a result, potentially lethal doses of radiation and particle flux from solar events (such as flares and coronal mass ejections) could threaten the basic viability of such colonies. An Early Warning System might consist alternatively of (1) advanced special purpose algorithms that monitor real time data from various NASA satellites and observatories in order to issue an alert based on reliable precursors within the data, or (2) a network of sensors and small solar observatories at the colony or near the planetary pole (for continual view of the solar surface) as well as special purpose satellites positioned between the sun and Mars in order to provide the basis for an alert.


Figure 12 Figure 12. A corona mass ejection from the sun that could cause severe problems for a hypothetical human Mars mission (from www.nasa.gov).
The D-transform is a technique that maps data streams against sets of combined chaotic oscillations. These chaotic oscillation sets are derived from abstract mathematical expressions as well as models of naturally occurring physical phenomena and have been combined in a way to maximize waveform diversity. We have developed a method of profiling input data against this transform and have shown high reliability in identifying traits in data sets. The goal is to look for trends in the Total electron content (TEC) data that would predict the eruption of severe space weather.
Figure 13a
Figure 13b
 
Figure 13. Classification Profile of the Total Electron Content using the Anomaly Detection and Classification Algorithm.
  • Key Investigators
  • Faculty: Dr. Chance M. Glenn, Sr., Dr. Roger Dube
  • Students: Santosh Suresh, Olympia Davis
Scholarly Works

  • Santosh Suresh, "Anomaly Detection and Classification Algorithm for Space Weather Alert System", 1st Annual Graduate Research Symposium, Rochester, NY, July 2009.
  • Olympia Davis, "A Description of a New Space Weather Alert System", 18th Annual Undergraduate Research Symposium, Rochester NY, August 2009.
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Biomedical Imaging

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Figure 13 Caption here.
Figure 13 Caption here.
  • Key Investigators
  • Faculty: Dr. Chance M. Glenn, Sr., Dr. Warren Koontz
  • Students: Alphonsine Imaniraguha
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