171 - 180 of 203 results for: EE

Image sampling and quantization color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, noise reduction and restoration, feature extraction and recognition tasks, image registration. Emphasis is on the general principles of image processing. Students learn to apply material by implementing and investigating image processing algorithms in Matlab and optionally on Android mobile devices. Term project. Recommended: EE261, EE278.

Imaging internal structures within the body using high-energy radiation and ultrasound, studied from a systems viewpoint. Modalities covered: x-ray, computed tomography, nuclear medicine, and ultrasound. Analysis of existing and proposed systems in terms of resolution, frequency response, detection sensitivity, noise, and potential for improved diagnosis.

Imaging internal structures within the body using magnetic resonance studied from a systems viewpoint. Analysis of magnetic resonance imaging systems including physics, Fourier properties of image formation, effects of system imperfections, image contrast, and noise.

Reconstruction problems from medical imaging, including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Problems include reconstruction from non-uniform frequency domain data, automatic deblurring, phase unwrapping, reconstruction from incomplete data, and reconstruction from projections.

This hands-on course guides students through the complete workflow of designing a real analog integrated circuit in a modern CMOS process from transistor-level schematics to post-layout extracted verification. Students will learn to use industry-standard design tools to capture schematics, run pre- and post-layout simulations, implement physical layout, perform DRC/LVS, and evaluate performance across process, voltage, and temperature corners. Working in small teams, students will architect, design, and verify an original analog chip from the ground up, culminating in a final tapeout and submission for fabrication at the end of the course.

The vision of blockchains is to allow billions of people to interact with minimal trust of third parties. Since the invention of Bitcoin by Nakamoto in 2008, much innovative infrastructure has been built to fulfill this vision. This course is a rigorous treatment of the fundamental concepts behind these innovations. A particular focus is on the problem of distributed consensus and how to make it permissionless, secure and scalable. The course is divided into 3 parts: 1) Bitcoin as a payment system and as a consensus protocol. Security analysis. Dynamic availability. 2) Proof-of stake protocols. BFT consensus. Accountability. 3) Scaling blockchains. Data availability. Zero-knowledge and optimistic rollups. Security sharing and restaking.

Universal schemes for information processing tasks such as compression, communication, sequential probability assignments, prediction, denoising, and filtering. Characterization of performance limits in stochastic, semi-stochastic, and individual sequence settings. Ziv-Lempel compression as an end goal, and as an engine for other information processing tasks. Trade Offs between performance (e.g. accuracy), amounts of data processed, and computation. Prerequisites: EE276, EE278 or equivalent, or instructor's permission.

Information theoretic techniques in probability and statistics. Fano, Assouad,and Le Cam methods for optimality guarantees in estimation. Large deviationsand concentration inequalities (Sanov's theorem, hypothesis testing, theentropy method, concentration of measure). Approximation of (Bayes) optimalprocedures, surrogate risks, f-divergences. Penalized estimators and minimumdescription length. Online game playing, gambling, no-regret learning. This course is offered every other year.

Basic concepts of statistical decision theory; Bayes decision theory; HMMs and their state estimation (Forward--backward), Kalman as special case, approximate state estimation (particle filtering, Extended Kalman Filter), unknown parameters; Inference under logarithmic loss, mutual information as a fundamental measure of statistical relevance, properties of mutual information: data processing, chain rules. Directed information. Prediction under logarithmic loss; Context Tree Weighting algorithm; Sequential decision making in general: prediction under general loss functions, causal estimation, estimation of directed information. Non-sequential inference via sequential probability assignments. Universal denoising; Denoising from a decision theoretic perspective: nonparametric function estimation, wavelet shrinkage, density estimation; Estimation of mutual information on large alphabets with applications such as boosting the Chow-Liu algorithm. Estimation of the total variation distance, estimate the fundamental limit is easier than to achieve the fundamental limit; Peetre's K-functional and bias analysis: bias correction using jackknife, bootstrap, and Taylor series; Nonparametric functional estimation. Prerequisites: Familiarity with probability theory and linear algebra at the undergraduate level.