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Intelligent Systems (IS) Computer Systems Architecture (CSA) Focus Areas

Intelligent Systems (IS) Computer Systems Architecture (CSA) Focus Areas. Introduction for Prospective Graduate Students Ian Walker Fall 2012. Outline. Who and what are we? Classes, requirements, planning Funding opportunities, assistantships Degree options Sample research projects

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Intelligent Systems (IS) Computer Systems Architecture (CSA) Focus Areas

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  1. Intelligent Systems (IS) Computer Systems Architecture (CSA) Focus Areas Introduction for Prospective Graduate Students Ian Walker Fall 2012

  2. Outline • Who and what are we? • Classes, requirements, planning • Funding opportunities, assistantships • Degree options • Sample research projects • Q&A

  3. Who are we? • Loose confederation based upon common research interests Loose mission statements: • IS: Building smarter machine systems • CSA: Building better/faster computing machines Who: • IS (9 Professors): Birchfield, Brooks, Burg, Dawson, Groff, Hoover, Schalkoff, Venayagamoorthy (new!), Walker • CSA (9 Professors): Birchfield, Brooks, Gowdy, Hoover, Ligon, Schalkoff, Shen, Smith, Walker

  4. Who are we? • Current enrollment • IS: 30-50 graduate students • CSA: 10-25 graduate students • Lab space • IS: Riggs 10, Riggs 13/15/17 (main lab), EIB 258 (main lab) • CSA: Riggs 309 (main lab), EIB 352 (main lab), Cluster room • Shared: Riggs 315/7, EIB 341, ...

  5. Sample Research Areas • Sensor networks • Tracking filters and embedded systems • Physiological monitoring systems • Nonlinear system modeling and control • Audio and visual spatial sensing • Biologically inspired robotics • (More that are not listed here)

  6. Classes (IS) • Required (all these courses offered once per year) : • ECE 801 - Analysis of Linear Systems • ECE 847 - Digital Image Processing • A 600-level course chosen from (642, 655*, 668) • One of (854, 855, 856, 868, 869, 872, 874*, 877) • *For Computer Engineering, 649 replaces 655, and 874 is removed from list • Other IS courses (typically offered once per 3 semesters): • 804, 805, 854, 856, 872, 893 (various) • courses from other focus areas or departments are allowed • Planning: Take core early, figure out what you would like to do See p. 35 of http://www.clemson.edu/ces/departments/ece/document_resource/grad/Grad_Student_Handbook_2011

  7. Classes (CSA) • Required: • A software course (ECE 617, 852, 855, or 873) • An architecture course (ECE 629, 668, 842, or 851) • A networks course (ECE 640, 649, 848, or 849) • Other CSA courses: • any from the above lists • courses from other focus areas or departments are allowed • Note: 693 and 893 are used for new courses. Be sure to sign up for the right section number. See p. 32 of http://www.clemson.edu/ces/departments/ece/document_resource/grad/Grad_Student_Handbook_2011

  8. Advisors • Selecting a faculty advisor is a two-way decision • All faculty use different criteria for evaluating students • Performance in core course taught by that professor • Evaluation of volunteer or startup work in lab • Probationary period • Assistance to PhD or senior graduate student

  9. Funding • Grading Assistantship (GA) - assist prof. with a course • Teaching Assistantship (TA) - teach lab sections • Research Assistantship (RA) - assist prof. in funded project • GAs and TAs are administered by department • RAs are generally offered to PhD students, or sometimes masters students showing potential and commitment for PhD • You do not need funding to get involved in research

  10. Degree options • Majors (at masters and PhD level): • Computer Engineering (CpE) • Electrical Engineering (EE) • Options: • Focus area (IS is one of six areas in department) • Non-thesis (coursework only) • 33 hours (11 courses) • Thesis • 30 hours (8 courses + research) • best to examine options after first semester completed • typically work with PhD student • probably adds a semester - 2 years total • Direct-PhD • 60 hours (14 courses + research) • saves 2 courses compared with Masters + PhD • possible to get an MS along the way For details, see http://www.clemson.edu/ces/departments/ece/document_resource/grad/Grad_Student_Handbook_2011

  11. Recent graduates • Ph.D. students - Academic Positions • Clarkson University at Potsdam, New York • University of Michigan at Ann Arbor, Michigan • Louisiana State University, Louisiana • University of Florida • Ph.D. students - Industrial Positions • Lucent Technology in Connecticut • Oakridge National Laboratories in Tennessee • Mayo Clinic in Minnesota • MS Students - Ph.D. Pursuits • German Aerospace Institute in Germany • Stanford University in California • MS Students - Industrial Positions • General Electric in Virginia • IBM in North Carolina • Intel in Columbia and San Francisco • Yahoo! in California • Harris in Florida • GM-Fanuc in Michigan

  12. Name: Kumar Venayagamoorthy Focus Area: Power/IS http://www.people.clemson.edu/~gvenaya/ Research Area: Real-time power systems Current Projects: Smart Grid research

  13. Name: Richard Brooks Focus Area: CSA/IS http://www.clemson.edu/~rrb Research Area: Distributed Systems / Information Assurance / Coordination Current Projects: AFOSR – Detection of Tunnelled Communications Protocols Industry – Data Leak Prevention NSF – Network Security Experimentation with GENI Department of State – Internet Liberty Support for West Africa Relevant courses: ECE449 / 649

  14. - Hash functions - Regular expressions - Keyword search • Data Leak Prevention (DLP) solutions monitor and control data flow • Current DLP solutions are syntax based • We focus on data semantics • Singular value-based approach • Apply singular value decomposition to term-document matrix. • Find concepts by retaining a number of dimensions. • Hidden Markov Model (HMM)-based approach • Build HMMs based on terms we retained in singular value-based method. • Find transition probabilities of each document and estimate the probabilities of unobserved transitions. • Probabilistic Context-Free Grammar (PCFG)-based approach • Obtain parse trees of sentences in training documents. • Identify features in the parse trees. Transmission Cache VLSI ……. Singular value decomposition

  15. Distributed Denial of Service Attack (DDoS) Analysis • WiMAX BCR System Parameters and DDoS Attack Analysis • Factorial Experimental Design and ANOVA analysis of avg. throughput Ns-2 simulator used for software simulations • Real software-defined radio testbeds used for hardware simulations • Performance Analysis of DDoS Detection Methods on Operational Network • Setup the network using Clemson University GENI resources. • Use Operational Network traffic. • Generate DDoS attack traffic using Clemson Condor Cluster. • Analyze performance of DDoS detection methods.

  16. Trustworthy proxy system for Democracy Advocates using Hostile Host • A bootable USB drive with the Linux system will access the proxy network. • The proxy network deploys botnet which changes DNS and IP address to avoid detection and tracking. • With this, the democracy advocates, NGOs, and journalists are protected from network censorship and surveillance.

  17. Detecting Hidden Communications Protocols • Protocol analysis of Tor through side-channel attacks • Protocol represented as a hidden Markov model (HMM) • Side-channel information: delays between packets • Using zero-knowledge HMM inference algorithm to rebuild the model, i.e. the protocol used by A. • Botnet traffic detection • Infer HMMs from botnet timing data • Use confidence interval approach to detect botnet traffic • Result: 95% TP and 2% FP

  18. Name: Melissa Smith Focus Area: CSA http://www.parl.clemson.edu/~smithmc/ Research Area: High-Performance Reconfigurable Computing/ Heterogeneous Computing Current Projects: Heterogeneous Mapping and Acceleration of Scientific Algorithms Acceleration of Gene Co-Expression Network Generation Performance Models for Hybrid Computing Exploration of Concurrent Biometric Algorithms for Emerging Reconfigurable Architectures Relevant courses: (ECE 668, 845, 842, 873, 893)‏

  19. Spiking Neural Networks (SNN): preferred neural network models for simulating the biological behavior of a neuron Ultimate goal of scientists: Model mammalian brain activity(1011 neurons – 1014 synapses) Object recognition/identification SNNs Optimizations with Multi-Core Architectures Two-level character recognition network w/ two SNN models: Izhikevich’s Model Flop/Byte : 0.65 Wilson model: Flop/Byte: 0.86 Morris Lecar Model Flop/Byte:4.71 HH Model Flop/Byte : 6.02 Level 2 Results published in HiCOMB’10, Journal of Supercomputing, & Concurrency and Computation Level 1

  20. Exploring Multiple Levels of Heterogeneous Performance Modeling Synchronous Iterative GPGPU Execution (SIGE) model Regression models for CPU/GPU computations using Algorithm FLOPS and Bytes • Use Synchronous Iterative GPGPU Execution (SIGE) Model for Synchronous Iterative Algorithms (SIAs) • Relevant Equations describing the SIGE Model • Texecution = ∑Tcomp. + ∑Tcomm. • Tcomp.= Tpre-process + Tpost-process + TCPU + TGPU • TGPU = TGPU-Kernel + TPCIE-Transfers • TPCIE-Transfers = Thost-to-device + Tdevice-to-host • Tcomm. = ∑Tnetwork-transactions • Initial validation of low-level abstraction model for GPGPU clusters • Regression-based performance prediction framework • SIA case studies: Spiking Neural Network (SNN) models • Achieved over 90% prediction accuracy Regression models for PCIE and Infiniband using micro-benchmarks

  21. Gene Co-Expression Network Construction 45X Faster 18X Faster • Accelerating construction of gene co-expression networks, which analyze the relationships among thousands of genes • Previous techniques were slow and use excessive disk space • Our acceleration has allowed generation of hundreds of gene networks of multiple sizes and types (rice, yeast, and human) for in-depth analysis never before possible • Future work with GPUs and other accelerators will provide additional performance gain and enable larger studies 7X Smaller

  22. Robust Facial Recognition with Highly-Parallel Architectures Several facial recognition algorithms have been developed that can adapt to particular types of image variation, but no single algorithm can provide robust identification. The rapidly growing field of biometrics uses physical features to perform identity authentication. Facial recognition is the user’s most convenient biometric but often suffers from poor performance, especially in applications with wide image variation. FPGAs and GPUs provide the necessary parallelism to run multiple algorithms simultaneously and fuse their results together to enable accurate recognition. • Facial recognition Needs: • Parallel processing of multiple algorithms to improve accuracy • Faster identification • FPGAs offer: • Algorithm-specific capable hardware • Parallel processing of multiple algorithms

  23. Name: Walt Ligon Focus Area: CSA http://www.parl.clemson.edu/~walt Research Area: Parallel Computing, Parallel File Systems, Programming Environments Current Projects: Parallel Virtual File System (PVFS) High End Computing I/O Simulator (HECIOS) Relevant courses: ECE 851, 873, 329, 493 (MPI)

  24. Name: Robert Schalkoff Focus Area: CSA/IS http://www.ece.clemson.edu/iaal/index.html Research Area: Soft Computing/Parallel Programming Current Projects: An algebraic framework for multi-class motion estimation using unsupervised learning with GPU implementation Relevant courses: ECE 856, ECE 855, ECE872, ECE 642, ECE 847

  25. An algebraic framework for multi-class motion estimation using unsupervised learning with GPU implementation Optical flow constraint equation (OFCE) is Ix* u + Iy* v + It = 0 Pixel locations that suffer aperture problem have rank-deficient system. The min-norm solution of rank-deficient system leads to motion estimates with low confidence. High confidence is associated with vectors that do not suffer aperture problem. Motion vectors (u,v) are separated into two sets; one set of vectors (Hp) that suffer aperture problem and another set of vectors (Hc) that do not.

  26. SM 0 SM 0 SP SP SP SP SP SP SP SP SP SP SP SP SP SP SP SP Shared Memory Shared Memory 1 2 Texture Memory B A C D Global Memory B A D C Immutable Mutable Implementation with NVIDIA CUDA: Compute Unified Device Architecture Kernels for Motion Estimation: 1. Gradients 2. Local Motion 3. SOFM/NG

  27. Name: Haiying Shen Focus Area: CSA http://www.ces.clemson.edu/~shenh Research Area: Distributed computer systems and computer networks Current Projects: Leveraging Hierarchical DHTs and Social Networks for P2P Live Streaming P2P File Storage and Sharing System for High-End Computing Pervasive Data Sharing Over Heterogeneous Networks File Replication and Consistency Maintenance in Pervasive Distributed Computing Hybrid Wireless Networks Self-organizing P2P-based File Storage System in HPC Relevant courses: ECE 429/629, ECE 893

  28. Social network P2P Live Streaming/VoD C • Internet-based video streaming applications attract millions of online viewers every day. • The incredible growth of viewers and dynamics of participants have posed a high quality-of-service (QoS) requirement. • Goals: high scalability, availability, low-latency. A channel cluster channel cluster B n DHTs (Channels) channel cluster (Images captured from paper Flexible Divide-and-Conquer protocol for multi-view peer-to-peer live streaming, P2P’09) Pic from http://www.fmsasg.com/SocialNetworkAnalysis/ Features: (1) Distributed Hash Table is constructed for content delivery to increase scalability, availability (2) Social network is used for accurate content recommendation and channel switch to reduce video delivery latency Preliminary results published in ICPP10, Infocom11, IEEE TPDS 11

  29. GENI Experiments on P2P, MANET, WSN Networks We will implement three existing data sharing algorithms on the P2P, MANET and WSN networks, thus identify and investigate potential issues in the data sharing applications in heterogeneous networks. Features: (1) Energy-efficient & scalable. (2) Reliable & dynamism-resilient. (3) Similarity search capability Features: (1) Constant maintenance overhead regardless of the system scale. (2) Scalability, reliability, dynamism-resilience, self-organizing. Features: (1) Efficient spatial/temporal similarity data storage. (2) Fast query speed. (3) Low energy consumption. Data sharing in P2P networks (Cycloid P2P) Spatial-temporal similarity data sharing (SDS) in WSNs Locality-based distributed data sharing protocol (LORD) in MANETs

  30. Leveraging P2P in HPC/Cloud Computing • P2P network is well-known for scalability, reliability and self-organizing • Social network based P2P overlay construction (under review of INFOCOM12 ) • Locality aware P2P overlay construction (CCNC 09) • Interest aware P2P overlay construction (CCGRID 09) • User behavior pattern aware P2P overlay construction (In preparation for IPDPS 12) • P2P-based Resource Management • Effective and efficient P2P content delivery algorithm design (TC11, TPDS10, INFOCOM11, IPDPS08) • P2P-based Reputation Management • Social network Collusion detection (IPDPS11) • Spam filtering (INFOCOM11) • Game theory based cooperation incentive analysis (ICCCN09, TMC) • P2P-based File Storage System in HPC • File replication (JPDC09) • File consistency maintenance (TPDS11) Grid computing Pic from http://innovationsimple.com/web-hosting/cloud-hosting-web-hosting/benefits-of-cloud-computing/ Cloud computing

  31. Name: Darren Dawson Focus Area: IS http://www.ece.clemson.edu/crb/welcome.htm Research Area: Nonlinear Control and Estimation for Mechatronic Systems Current Projects: Following 3 Slides Relevant courses: ECE 874, 801

  32. Visual Servoing of Robot Manipulators • Problem: Control of Moving Objects in an Unstructured Environment is Difficult due to the Corrupting Influences of Camera Calibration with regard to Task Planning • Solution: Close the Control Loop with Camera Measurements • Testbed Features a High-Speed Real-Time Camera System • 2.5D Visual Servoing • Design a Controller to Regulate the Position and Orientation of the End-Effector • Control Strategy Uses Both 2D Image-Space and 3D Task-Space Information

  33. Next Generation Hardware-in-the-Loop Ground Vehicle Steering Simulator • Custom Honda CRV steering simulator with electric servo-motors • Test platform supports development of advanced ground vehicle steering technology using concepts from “robotics” field • Also examining in-vehicle operator feedback channels • Visual (scene, lights)‏ • Haptic (steering wheel, …)‏ • Audio (tones/chimes/voice)‏ • Human subject testing

  34. Advanced Automotive Thermal Management Systems - Smart Components • Goal is to improve the engine’s cooling/heating system operation using mechatronic technology • Improved fuel economy • Reduced tailpipe emissions • Flexible thermal system design • Enhanced control of engine temperatures • Replace mechanical cooling system equipment with electric/hydraulic-driven components • Develop mathematical thermal models

  35. Name: Tim Burg Focus Area: IS http://www.clemson.edu/~tburg Research Area: Nonlinear Control Applications Current Projects: Unmanned Aerial Vehicles Biofabrication Haptics Environmental Monitoring Relevant courses: ECE 874, 801

  36. Bioprinting • Bioprinting - an approach to tissue engineering • Cells are precisely placed in a 3D structure using inkjet printer technology. • Active collaboration with Bioengineering. • ECE research focused on system integration, modeling, and control.

  37. Haptics • Objective Is to identify, demonstrate, and quantify the potential benefits of specialized haptic user interfaces within a collaborative environment.

  38. Name: Stan Birchfield Focus Area: IS http://www.ces.clemson.edu/~stb Research Area: Computer Vision Current Projects: Vision-based mobile robot navigation Vehicle traffic monitoring Robotic laundry handling Relevant courses: ECE 847, 877, 904

  39. Vision-Based Mobile Robot Navigation • Mobile robot equipped with single, off-the-shelf inexpensive camera • Developing algorithms for • Traversing a known path by comparing the coordinates of tracked feature points • Detecting doors in indoor environments for navigation • Following a person moving about the environment, maintaining a desired distance • Applications: courier robots, tour guides, physician assistance

  40. Vehicle Traffic Monitoring Using Cameras • Developing algorithms for detecting, tracking, and classifying vehicles automatically using video • Low-angle cameras cause occlusion and spillover • Shadows, reflections, and environmental conditions are addressed using a combination of feature tracking and pattern detection • Applications: • intelligent transportation systems (ITS)‏ • incident detection and emergency response • data collection for transportation engineering applications

  41. Adam Hoover Focus Area:IS/CSA http://www.ces.clemson.edu/~ahoover/ Research Area: Tracking systems, embedded systems Current projects:See the next 2 slides Relevant courses: ECE 854, 668

  42. Bite Counter 1 in 3 Americans is obese, another 1 in 3 is overweight; worldwide there are more overweight than underfed people Worn like a watch Automatically tracks how many bites of food have been taken Bite count vs calories for 54 meals • 2011-2012 large cafeteria experiment in main campus dining hall • Equipment and software for recording and correlating video, scale, gyroscope data • Signal analysis to improve bite detection accuracy and bite:calorie correlation

  43. Ultrawideband Position Tracking same idea Trilateration measures distances from a set of transmitters to a receiver to calculate position. • Ubisense system in Riggs basement • Particle filter methods to improve accuracy • Noise modeling, combination with other sensors and other sources of information such as maps

  44. Richard Groff Focus Area:IS http://www.ces.clemson.edu/~regroff Research: Robotics and control applications at small length scales Computational and Experimental Tissue Modeling Biomimetics Current Projects: Synthetic butterfly proboscises Biofabrication and Tissue Modeling (under revision) Relevant coursework: ECE801 (linear systems), ECE847 (digital signal processing) for some projects, some background in magnetostatics, solid mechanics, materials science, and/or molecular biology desired

  45. Synthetic Butterfly Proboscis Proboscis Experimental Platform for Magnetic Microfibers • Butterflies can drink fluids of widely varying viscosities by controlling the shape of their feeding tube (probosicis) • Using custom fibers from Materials Science Department, generate a synthetic proboscis that can sample widely varying fluids • Fibers are paramagnetic or piezoelectric • Control fiber shape using magnetic or electric fields • Preliminary work on modeling and position control of magnetic microfibers

  46. Tissue Engineering via Biofabrication Fluorescent-dyed murine D1 mesenchymal stem cells (red) and murine mammary cancer cells (red) • Biofabrication – develop a system to place living cells in 3D patterns mimicking native tissue • many subprojects • Develop computational model for interaction of tumor cells and epithelial stem cells • “Tissue Description Language” • Specify Describe initial condition for computational model • Specify structure for biofabrication • Use TDL to study systems biology problems in cancer. (Feedback via intercellular signalling)

  47. Name: Ian Walker Focus Area: IS/CSA http://www.ces.clemson.edu/~ianw/ Research Area: Robotics Current Projects: Trunk and tentacle robots Intelligent Robotic Workstations Relevant courses: ECE 655, 868, 869

  48. Invertebrate’ robot trunks/tentacles

  49. Animated Architecture • Integrate Robotics and Architecture • Goal “Animated Work Environment”

  50. What should you do next? • Find out more about specific research projects • web, senior graduate students, faculty • Contact potential advisors about projects, openings • faculty attending this meeting may be recruiting currently • Either • a) Mutually agree on advising relationship • OR • b) Establish criteria for being evaluated/considered • OR • c) Seek another advisor/project

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