Vision-simulated imaging (VSI) is the computer generation of synthetic images to simulate a subject's vision, by incorporating the characteristics of a particular individual's entire optical system. Using measured aberration data from a Shack-Hartmann wavefront aberrometry device, VSI modifies input images to simulate the appearance of the scene for the individual patient. Each input image can be a photograph, synthetic image created by computer, frame from a video, or standard Snellen acuity eye chart -- as long as there is accompanying depth information. An eye chart is very revealing, since it shows what the patient would see during an eye examination, and provides an accurate picture of his or her vision. Using wavefront aberration measurements, we determine a discrete blur function by sampling at a set of focusing distances, specified as a set of depth planes that discretize the three-dimensional space.

For each depth plane, we construct an object-space blur filter. VSI methodology comprises several steps: (1) creation of a set of depth images, (2) computation of blur filters, (3) stratification of the image, (4) blurring of each depth image, and (5) composition of the blurred depth images to form a single vision-simulated image.
VSI provides images and videos of simulated vision to enable a patient's eye doctor to see the specific visual anomalies of the patient. In addition to blur, VSI could reveal to the doctor the multiple images or distortions present in the patient's vision that would not otherwise be apparent from standard visual acuity measurements. VSI could educate medical students as well as patients about the particular visual effects of certain vision disorders (such as keratoconus and monocular diplopia) by enabling them to view images and videos that are generated using the optics of various eye conditions. By measuring PRK/LASIK patients pre- and post-op, VSI could provide doctors with extensive, objective, information about a patient's vision before and after surgery. Potential candiates contemplating surgery could see simulations of their predicted vision and of various possible visual anomalies that could arise from the surgery, such as glare at night. The current protocol, where patients sign a consent form that can be difficult for a layperson to understand fully, could be supplemented by the viewing of a computer-generated video of simulated vision showing the possible visual problems that could be engendered by the surgery.

Plenary Lecture:

Neuroprocessing in Silicon

Professor James F. Frenzel

Electrical & Computer Engineering

POB 441023

University of Idaho

Moscow, ID 83844-1023 USA

www.uidaho.edu/~jfrenzel

Despite phenomenal advancements in semiconductor technology over the last five decades, today’s computers still struggle with certain tasks that are easily performed by a young child. One of the first electronic implementations of a pulsed neuron was introduced in 1937 and since then research efforts have covered a wide spectrum, ranging from single-transistor models to powerful parallel processors comprised of digital signal processors. This talk will highlight some of the recent developments in the field of hardware-based neurocomputing and then present work from the University of Idaho. For the last three years, an interdisciplinary team from engineering and computer science has been working toward the development of CMOS circuits capable of mimicking the biological and signal mechanisms observed in the human nervous system. This work, funded by the National Science Foundation, is part of a larger effort within the Neuroscience program at the University of Idaho (www.grad.uidaho.edu/neuro).

Special Session: INDUSTRIAL APPLICATIONS: Electronics, Robotics and MEMS Applications

Chair: Prof. Vahé Nerguizian

Design and characterization of a novel MEMS linear motor

By Vahé Nerguizian, Mustapha Rafaf, Louis A. Dessaint, Muthukumaran Packirisamy, Ion Stiharu [494-275]

Design of CMOS Based Transimpedance Amplifier for Integrated Optical MEMS Applications

By Paresh Rathod, Muthukumaran Packirisamy, Ion Stiharu [494-278]

Study on Birefringence of Anisotropically Micromachined SOI Waveguides

By Muthukumaran Packirisamy, Hong Zhao [494-279]

3D Indoor Geolocation with Received Signal Strength Fingerprinting Technique and Neural Networks

By Chahé Nerguizian, Lamia Hamza, Vahé Nerguizian, Maarouf Saad [494-280]

Mobile Robot Navigation and Obstacle Avoidance Using Fuzzy Radial Basis Function Neural Networks

By Maarouf Saad, Guillaume Latombe, Karnon Suen, Vahe Nerguizian [494-274]

Filter Bank Analysis

By Vladislav Skorpil, Abdulhakim Abuzahu [494-098]

Neural Network Learning Methods for Image Processing Applications

By Jiri Stastny, Vladislav Skorpil [494-267]

The use of edge enhancing smoothing pre-filters to aid in the detection of oceanic features

By Jose M. Ortiz, Miguel Velez-Reyesv [494-226]

A Variable Window Approach for Image Denoising

By Wiem Fourati, M. Salim Bouhlel [494-185]

SystemC Co-Design for Image Compression: Fast Discrete Cosine Transform using Distributed Arithmetic Method

By Mildred C. Zabawa, Malek Adjouadi, Naphtali Rishe [494-180]

Efficient Aerial Image Matching Algorithm for Autonomous Navigation of Aerial Vehicles

By Hafiz Adnan Habib, Muid Mufti [494-261]

An Application of Fuzzy Logic and Neural Network to Fingerprint Recognition

By Chia-Shing Hu [494-156]

Speech Recognition of a Limited Vocabulary Using the Convolution Kernel Compensation Approach

By Damjan Zazula, Gregor Krebs [494-144]

Speech Enhancement in Hands-Free communication

By N.Vini Antony Grace, M.G. Sumithra [494-129]

TUESDAY, APRIL 26, 2005

Keynote Lecture:

Space-Time Evolution of Instabilities in Plasma and Continua

Professor Abraham Bers

M.I.T. Room 38-260

U.S.A.

Email: [email protected]

This lecture entails the general theory and analysis for distinguishing between absolute and convective evolutions of instabilities in the electrodynamics of waves in plasmas and waves in continua in general.

Session: System Control and Identification

Chair: Prof. Timo Hamalainen, Prof. Francisco Aparisi

Comments on ‘Improving the performance of the robust controller for a robot arm

By S. Torres, J.A. Méndez, L. Acosta, E.J. González [494-Go5]

Edge Detection Based on the Collective Intelligence of Artificial Swarms

By X. Zhuang, N. E. Mastorakis [494-303]

Detection of Artificial Contamination in E. Coli Microarray Data

By Francisco Díaz, Raul Malutan, Pedro Gómez, Victoria Rodellar, Monica Borda [494-214]

SERVIROB: A Mobile Robot for Restoration

By L. Acosta, E.J. Gonzalez, J.N. Rodriguez, J.A. Mendez, A. Hamilton, S. Hernández [494-Go2]

Sensorless speed measurement of ac machines using time frequency analysis

By Susan Prakasi, P.Vanaja Ranjan [494-265]

Using Short-Time Fourier Transform in Machinery Diagnosis

By A.S.Safizadeh ,A.A.Lakis, M. Thomas [494-200]

Acquisition and Interpretation of Upper Limbs Tremor Signal in Parkinsonian Disease

By Andrzej Izworski, Marcin Michaek, Ryszard Tadeusiewicz, Monika Rudziñska, Jarosaw Bulka, Ireneusz Wochlik [494-183]

Iris Verification Based On Iris Feature and Secret Pseudo-Random Number

By Chong Siew Chin, A. Teoh Beng Jin, D. Ngo Chek Ling [494-187]

Open Architecture Systems for Real Time Control of Robots’ Structural Vibrations

By Luige Vladareanu [494-150]

Detection and Reconstruction for 3D - Wreck and Recognition by Using Neural Network Classification Technique

By Onsy A. Abdel Alim, Hatem Awad Khater [494-114]

Robust framework for efficient RF/microwave system modeling using neural- and fuzzy-based CAD tools

By Z. Cheng, L. Ji, S. Gaoua, F.A. Mohammadi, M.C.E. Yagoub [494-195]

Subband Simulation in MATLAB

By Vladislav Skorpil, Adulhakim Abuzahu [494-097]

SLOAS: Hearing with the Eyes

By J. Toledo, J. Torres, S. Alonso, P. Toledo, E. J. González [494-Go3]

Matlab Simulation of Multiple Symbol Differential Detection for Pi/4 Dqpsk Modem

By P.Siva Kumar [494-304]

Gray-Box Modeling of Mechanical Loads for Electric Drive Systems using Neural Networks

By Miguel Velez-Reyes, Roberto Rivera-Sampayo, Yamilka Baez [494-227]

Development of Fast Motion Estimation Algorithm with High Accuracy

By Abhijeet Bairagi, Saket Newaskar, Raj Shah [494-308]

Genetic algorithms for solving scheduling problems in flexible manufacturing cells

By António Ferrolho, Manuel Crisóstomo [494-162]

Improving Hidden Markov Model Performance in Phoneme Classification by Fuzzy Smoothing

By Farbod Hosseyndoost, Mohammad Teshnehlab [494-142]

A new model to the synthesis of sharp transition fir filter

By Joseph Rodrigues, K R Pai [494-125]

Analysis of required stability of parameters of radar angle-modulated signals

By Shamsolah Salemian [494-110]

A New Weight-Programming Structure and Procedure for Pulse-Coupled Neural Networks

By Bo Liu, James Frenzel [494-152]

Session: Electronic Circuits and Devices

Chair: Prof. Junaid Majeed, Prof. Igor Kuzle

FPGA based Communication Security for Wireless Sensor Networks

By Junaid Majeed [494-243]

A Switched Approach for a Voltage Generator

By E. Vargas-Calderon, F. Sandoval-Ibarra [494-221]

RF Power Amplifiers and Combline Filters for Wireless Base-Stations

By H. Koulouzis, D. Barjamovic, Q. Shen, D. Budimir [494-219]

Design and analysis of FPGA based self-timed systems with specific focus to xilinx FPGAs

By M.Sriraman [494-217]

Effecting Power Consumption reduction in Digital CMOS circuits by a hybrid logic synthesis technique

By P.Balasubramanian, R.Chinnadurai, M.R. Lakshmi Narayana [494-215]

Implementation of Pictbridge Protocol For Real Time Embedded Applications

By V. RamaChandran, R. Ramesh, S. AnanthaKannan [494-213]

Performance Comparison of DSP and VHDL implementation of Trellis Coded Demodulation

By Amit Awati, Hrishikesh Kanitkar, Mahima Nanda, Nikhil Laddha, Savita Kulkarni, Anuradha Phadkeand Alwin Anuse [494-211]

Efficient modeling of distributed electromagnetic coupling in RF/microwave integrated circuits

By D. McPhee, M.C.E. Yagoub [494-196]

The role of electron-electron interactions in moderately doped nanostructures

By Alexander Dubois, Roman Lysov [494-149]

Adaptive IQ channel matching for quadrature IF receiver

By V.Thiyagarajan, K.Kalaiarasi, A.P.kabilan, M.Madheswaran [494-106]