Introduction to computational methods for identifying patterns and outliers in large data sets. Topics include the singular and eigenvalue decomposition, independent component analysis, graph analysis, clustering, linear, regularized, sparse and non-linear model fitting, deep, convolutional and recurrent neural networks.
Students program methods; lectures and labs emphasize computational thinking and reasoning. The aim is to provide students familiar with microfabrication and Microsystems with a context from which to view and evaluate bioMEMS devices and innovations. We will cover implantable and diagnostic microsystems in the later part of the course. Micro-devices covered include resonators, switches, filters, tunable passive devices and reconfigurable modules.
These include the flash, folding, multi-step and pipeline Nyquist rate, architectures. Oversampling converters are also discussed. Practical design work is a significant part of this course. Students design and model complete converters. This course will discuss the physics, operating principles, properties and technology of the flat panel displays.
Transduction techniques, including piezoelectric, electrothermal, and resonant techniques.
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Chemical, gas, and biological sensors, microfluidic and biomedical devices. Micromachining technologies such as laser machining and microdrilling, EDM, materials such as SiC and diamond. Sensor and actuator analysis and design through CAD. Design project using CAD and report preparation. For each modality the basic physics is described, leading to a systems model of the imager.
Fundamental similarities between the imaging equations of different modalities will be stressed. Gas kinetics; atomic collisions; transport coefficients; drift and diffusion; sheaths; Boltzmann distribution function calculation; plasma simulation; plasma diagnostics by particle probes, spectroscopy and electromagnetic waves; analysis of commonly used plasma tools for materials processing. Magnetosphere and Solar Wind Prerequisite: graduate standing. Plasma Generation and Diagnostics Laboratory Prerequisite: preceded or accompanied by a course covering electromagnetism.
Plasma generation includes: high voltage-DC, radio frequency and electron beam sustained discharges.
Diagnostics include: Langmuir probes, microwave cavity perturbation, microwave interferometry, laser schlieren and optical emission spectroscopy. Emphasis on proven field-effect and bipolar-junction transistors, also including current and speculative nanoelectronic devices. Detailed study of static current-voltage characteristics and models for small and large signal behavior.
Detailed analysis and design of analog integrated circuits, including power amplifiers, voltage references, voltage regulators, rectifiers, oscillators, multipliers, mixers, phase detectors and phase-locked loops. Lectures and discussion. MOS device scaling strategies, silicon-on-insulator, lightly-doped drain structures, on-chip interconnect parasitics and performance.
Major CMOS scaling challenges. Process and circuit simulation. Organic Electronic Devices and Applications Prerequisite: permission of instructor or graduate standing. This course will provide basic knowledge to understand and apply principles of plasmonics. Students will be introduced to nanofabrication and characterization techniques. Optical, electronic, magnetic, thermal and biomedical applications of plasmonics will be discussed. Techniques for routing and clock tree design. Timing analysis and cycle time optimization. Topics in low-power design. Modern physical design methodologies and CAD software development.
Topics include: semiconductor growth, material characterization, lithography tools, photo-resist models, thin film deposition, chemical etching, plasma etching, electrical contact formation, micro-structure processing and process modeling. Principles of light-emitting diodes, including transient effects, spectral and spatial radiation fields. Principles of semiconducting lasers; gain-current relationships, radiation fields, optical confinement and transient effects.
Potentials and the representation of electromagnetic fields. Plane, cylindrical, and spherical waves. Waveguides and elementary antennas. The limiting case of electro- and magneto-statics.
Wire antennas: dipoles, loops and traveling-wave antennas. Analysis and synthesis of linear arrays. Phased arrays. Input impedance and method of moments. Mutual impedance. Microstrip antennas. Horns, reflector and lens antennas. Followed by a project that will include design, analysis, and construction of a microwave subsystem. Experimental characterization of the above components using network analyzer, spectrum analyzer, power and noise meters. Coherence theory, spatial and temporal. Information retrieval; imaging through inhomogeneous media; noise processes in imaging and interferometric systems.
Classical theory of dispersion. Linear response, Kramers-Kronig relations, and pulse propagation. Light scattering. Geometrical optics and propagation in inhomogeneous media. Dielectric waveguides. Interferometry and theory of coherence. Diffraction, Fresnel and Fraunhofer. Gaussian beams and ABCD law. Manipulation of light by electrical, acoustical waves; crystal properties and the dielectric tensor; electro-optic, acousto-optic effects and devices. Introduction to nonlinear optics; harmonic generation, optical rectification, four-wave mixing, self-focusing and self-phase modulation.
Special topics such as femto-seconds lasers and ultrahigh power lasers. Applied Quantum Mechanics I Prerequisite: permission of instructor. Summary of classical mechanics, postulates of quantum mechanics and operator formalism, stationary state problems including quantum wells, harmonic oscillator, angular momentum theory and spin, atoms and molecules, band theory in solids , time evolution, approximation methods for time independent and time dependent interactions including electromagnetic interactions, scattering.
Advanced angular momentum theory, second quantization, non-relativistic quantum electrodynamics, advanced scattering theory, density matrix formalism, reservoir theory. Topics will be selected from various subareas such as physics based vision, geometry, motion and tracking, reconstruction, grouping and segmentation, recognition, activity and scene understanding, statistical methods and learning, systems and applications. Discussion of major programming approaches used in the design and development of knowledge-based systems. This course analyzes them how they are connected, how they form, and how processes and transactions occur on them using mathematical tools from graph theory, linear algebra, probability and game theory.
Topics include learning based on examples, instructions, analogy, discovery, experimentation, observation, problem-solving and explanation. The cognitive aspects of learning will also be studied. Laser mode-locking and ultrashort pulse generation. Chirped-pulse amplification. Experimental techniques for high time resolution. Ultrafast Optoelectronics. Survey of ultrafast high field interactions. Infrastructure supporting search for commerce opportunities, negotiating terms of trade and executing transactions.
Issues of security, privacy, incentives and strategy. Topics include data and image models, multidimensional and multivariate data, design principles for visualization, hierarchical, network, textual and collaborative visualization, the visualization pipeline, data processing for visualization, visual representations, visualization system interaction design, and impact of perception. Emphasizes construction of systems using graphics application programming interfaces APIs and analysis tools.
Minimum grade requirement of C- for enforced prerequisites. We encounter it in our everyday lives in the form of E-mail, newspapers, television, the Web, and even in conversations with each other. Information is hidden in a variety of media: text, images, sounds, videos. While casual information consumers can simply enjoy its abundance and appreciate the existence of search engines that can help them find what they want, information professionals are responsible for building the underlying technology that search engines use.
Building a search engine involves a lot more than indexing some documents — information retrieval is the study of the interaction between users and large information environments. It covers concepts such as information need, documents and queries, indexing and searching, retrieval evaluation, multimedia and hypertext search, Web search, as well as bibliographical databases. In this course, students go over some classic concepts of information retrieval and then quickly jump to the current state of the art in the field, where crawlers, spiders, and hard-of-hearing personal butlers roam.
Students who have previously enrolled in or cannot get credit for Applications and theory are covered in greater depth than in EECS Propagation, optical amplification and nonlinearities in fibers are discussed, and examples include transmission systems and lasers. Biomedical applications include dermatology, cardiology and opthamology. Techniques: scalar and vector quantization; transform and differential coding; variable-length, Lempel-Ziv and arithmetic lossless coding.
Theory: entropy for lossless coding; high-resolution theory for lossy coding. A project is assigned. Performance analysis: power, bandwidth, data rate and error probability. Optimum receivers in Gaussian noise. Signal space and decision theory. Signal design. Bandwidth and dimensionality. Fundamental limits in coding and modulation. Capacity and cutoff rate. Block, convolutional and trellis coding. Continuous phase modulation. Filtered channels and intersymbol interference.
Fading channels. Current topics. Sampling, filtering, 2D Fourier transforms, interpolation, edge detection, enhancement, denoising, restoration, segmentation, random field models of images, Bayesian methods, wavelets and sparsity models. Applications include optical imaging, biomedical images, video and image compression.
Student projects based on recent image processing literature. Data link control: error correction, protocol analysis, framing. Message delay: Markov processes, queuing, delays in statistical multiplexing, multiple users with reservations, limited service, priorities. Network delay: Kleinrock independence, reversibility, traffic flows, throughput analysis, Jackson networks. Models: linear and nonlinear stochastic controlled systems, controlled Markov chains. Optimization of systems described by Markov processes; dynamic programming under perfect and imperfect information, finite and infinite horizons.
System identification: off-line, recursive. Stochastic adaptive control: Markov chains, self-tuning regulators, bandit problems. Performance evaluation using asymptotic techniques and Monte Carlo simulation. Applications include speech processing, signal extrapolation, multidimensional spectral estimation, and beamforming. Linear Systems Theory Prerequisite: graduate standing. Bases, subspaces, eigenvalues and eigenvectors, canonical forms. Linear differential and difference equations. Mathematical representations: state equations, transfer functions, impulse response, matrix fraction and polynomial descriptions.
System-theoretic concepts: causality, controllability, observability, realizations, canonical decomposition, stability. Z-transforms and state variable descriptions of discrete-time systems. Modeling and identification. Analysis and design using root locus, frequency response and state space techniques. Linear quadratic optimal control and state estimation. Quantization and other nonlinearities. Nonlinear Systems and Control Prerequisite: graduate standing.
Stability analysis using Liapunov, input-output and asymptotic methods. Design of stabilizing controllers using a variety of methods: linearization, absolute stability theory, vibrational control, sliding modes and feedback linearization. Hybrid system modeling formalisms, specifications automata theory, temporal logics , verification barrier certificates, reachable sets, abstraction-based methods and control synthesis. Applications of convex geometry and convex optimization in control. Model-predictive control of hybrid systems.
Estimation: linear and nonlinear minimum mean squared error estimation, and other strategies.
Linear filtering: Wiener and Kalman filtering. Detection: simple, composite, binary and multiple hypotheses. Neyman-Pearson and Bayesian approaches. Review of single variable systems and extensions to multivariable systems. Purpose of feedback. Sensitivity, robustness, and design tradeoffs. Design formulations using both frequency domain and state space descriptions. Linear quadratic Gaussian based design methods. Design problems unique to multivariable systems. Discrete Event Systems Prerequisite: graduate standing 3 credits Modeling, analysis, and control of discrete event dynamical systems.
Modeling formalisms considered include state machines, Petri nets, and recursive processes.
Computer Science Project Work: Principles and Pragmatics
Supervisory control theory; notions of controllable and observable languages. Analysis and control of Petri nets. Communicating sequential processes. Applications to database, management, manufacturing, and communication protocols. Robot Kinematics and Dynamics Prerequisite: graduate standing or permission of instructor 3 credits Geometry, kinematics, differential kinematics, dynamics, and control of robot manipulators.
The mathematical tools required to describe spatial motion of a rigid body will be presented in full. Motion planning including obstacle avoidance is also covered. This course will present and critically examine contemporary algorithms for robot perception using a variety of modalities , state estimation, mapping, and path planning.
Topics include Bayesian filtering; stochastic representations of the environment; motion and sensor models for mobile robots; algorithms for mapping, localization, planning and control in the presence of uncertainty; application to autonomous marine, ground and air vehicles. Production Systems Engineering Prerequisite: none. Multithreaded processors, small- and large-scale multiprocessor systems. Shared-memory coherence and consistency.
Effect of architecture on communication latency, bandwidth, and overhead. Latency tolerance techniques. Interconnection networks. Case studies. Term projects. Architectures, algorithms, operating systems and applications that deal with time as the most important resource.
Real-time scheduling, communications and performance evaluation. Problems involving instruction supply, data supply and instruction processing. Compile-time vs. Aggressive branch prediction. Wide-issue processors, in-order vs. Case studies taken from current microprocessors. Computability, undecidability, and logic. Relations between complexity classes, NP-completeness, P-completeness, and randomized computation.
Applications in selected areas such as cryptography, logic programming, theorem proving, approximation of optimization problems, or parallel computing. Topics include cryptanalysis of classical cryptosystems; theoretical analysis of one-way functions; DES and differential cryptanalysis; the RSA cryptosystem; ElGamal, elliptic, hyperelliptic and hidden mononomial cryptosystems; attacks on signature schemes, identification schemes and authentication codes; secret sharing; and zero knowledge.
A mix of lectures, readings, and a semester-long group project will familiarize the students with recent methods for analyzing large-scale, real-world data and networks, and applications in various domains e. Minimum grade required for course enforced prerequisite is C. Hardware security assurance. Design verification: simulation, formal techniques, and post-silicon validation. Quality of services and energy management for correctness of implementation. Digital System Testing Prerequisite: graduate standing.
Fault sources and models. Testing process. Combinational circuit testing. Sequential circuit testing. Checking experiments. RAM and microprocessor testing. Fault simulation. Design for testability. Testability measures. Self-testing circuits and systems. Real-time rendering: fixed and programmable pipeline, shadows. Acceleration algorithms: culling and level-of-detail. Collision detection. Delaunary triangulations and Voronoi diagrams. Non-photorealistic rendering. Pattern synthesis. Image-based rendering. Topics will be drawn from a variety of operating systems areas such as distributed systems and languages, networking, security and protection, real-time systems, modeling and analysis, etc.
Topics include control-flow and data-flow analysis, optimization, instruction scheduling, register allocation. Advanced topics include memory hierarchy management, instruction-level parallelism, predicated and speculative execution. The class focus is processor-specific compilation techniques, thus familiarity with both computer architecture and compilers is recommended. Distributed databases, advanced query optimization, query processing, transaction processing, data models and architectures.
Data management for emerging application areas, including bioinformatics, the internet, OLAP and data mining. A substantial course project allows in-depth exploration of topics of interest. Design techniques such as approximation, branch-and-bound, divide-and-conquer, dynamic programming, greed and randomization applied to polynomial and NP-hard problems. Analysis of time and space utilization. Basic concepts such as speedup, load balancing, latency, system taxonomies. Design of algorithms for idealized models.
Programming on parallel systems such as shared or distributed memory machines, networks. Grid Computing. Performance analysis. Course includes a substantial term project. Topics will be drawn from a variety of areas such as mandatory and discretionary security policies, secure storage, security kernels, trust management, preventing software vulnerabilities, applied cryptography, network security. Topics include routing protocols, multicast delivery, congestion control, quality of service support, network security, pricing and accounting and wireless access and mobile networking.
Emphasis is placed on performance trade-offs in protocol and architecture designs. Readings assigned from research publications. A course project allows in-depth exploration of topics of interest. Topics include semantics, type systems, program verification using theorem provers, software model checking, and program analysis. Course focuses on applying PL concepts to improve software reliability. Computations, consistency semantics and failure models. Programming paradigms including group communication, RPC, distributed shared memory, and distributed objects.
Operating system kernel support; distributed system services including replication, caching, file system management, naming, clock synchronization and multicast communication. Foundations of Artificial Intelligence Advised prerequisite: Graduate standing. An advance introduction to AI emphasizing its theoretical underpinnings. Topics include search, logic, knowledge representation, reasoning planning, decision making under uncertainty, and machine learning. Additional topics such as sentiment analysis, text generation, and deep learning for NLP.
Students are expected to work in project teams. May be taken more than once up to a total of 6 credit hours. The theory includes Hidden Markov Models and the noisy channel model, information theory, supervised and unsupervised machine learning, and probabilistic context-free and context-sensitive grammars. Aspects of natural language analysis include phrasal lexicon induction, part of speech assignment, entity recognition, parsing, and statistical machine translation. Lectures, seminar or laboratory. Can be taken more than once for credit. May include experimental work or reading.
Primarily for graduate students. Metric spaces, normed linear spaces, Hilbert spaces, resolution spaces. Emphasis on using these concepts in systems problems. Theory will cover: Bandstructure in quantum wells; effect of strain on bandstructure; transport theory; Monte Carlo methods for high field transport; excitons, optical absorption, luminescence and gain.
Design methodologies architectural simulation, hardware description language design entry, silicon compilation, and verification , microarchitectures, interconnect, packaging, noise sources, circuit techniques, design for testability, design rules, VLSI technologies silicon and GaAs and yield. Projects in chip design. Topics covered include recent approaches in leakage control, high speed on-chip communication, memory design, soft error failures, noise analysis and control, error tolerant design and new circuit families.
Students will complete an advanced project. A 4-credit option is available with addition of a substantial design and simulation component to the project. Low and high frequency scattering. Scattering by half plane Wiener-Hopf method and wedge Maliuzhinets method ; edge diffraction. Scattering by a cylinder and sphere: Watson transformation, Airy and Fock functions, creeping waves. Geometrical and physical theories of diffraction. Special topics: nonlinear optics in fibers, including solitons and self-phase modulation.
Quantum Theory of Light Prerequisite: quantum mechanics, electrodynamics, atomic physics. Theory of Neural Computation Prerequisite: graduate standing or permission of instructor. Following a brief overview, the course will examine: 1 Biological principles governing brain computation e. Computational Modeling of Cognition Prerequisite: graduate standing or permission of instructor.
Course goals include learning about important computational models of specific cognitive domains and evaluating the appropriateness and utility of different computational approaches to substantive problems in cognition. Error correcting codes; linear, cyclic and convolutional codes; encoding and decoding algorithms; performance evaluation of codes on a variety of channels. Iterative methods of optimization and their convergence properties: transversal filters; LMS gradient algorithms.
Adaptive Kalman filtering and least-squares algorithms. Specialized structures for implementation: e. Applications to detection, noise canceling, speech processing and beam forming. Nonlinear controllability and observability, feedback stabilization and linearization, asymptotic observers, tracking problems, trajectory generation, zero dynamics and inverse systems, singular perturbations and vibrational control.
Special Topics in Computer Architecture Prerequisite: permission of instructor. This course may be repeated for credit.
In the data value chain, this stage along with the previous stage is where the most significant value is added to the data itself. It is the transformative stage that changes the data into potentially usable information. In this stage you may want to visualize your data quickly, attempting to identify specific relationships between different fields. You may want to explore the disparity of fields by location, or over time. Ideally, in the identify stage, you would have come up with several questions relating to what you would like to get out of this data, and perhaps have even stated several hypotheses — this is then the stage where you implement models to confirm or reject these hypotheses.
During this stage, we may do any to all of the following:. The analyses stage is really the stage where the rubber meets the road; it also illustrates the more sexy side of data science. Note that we are leaving the visualization tools for the last section. This stage is concerned with drawing solid, valuable conclusions from the results of the analyses phase. This is the phase in which you can formulate clear answers to your questions; it is the phase in which you can either prove or disprove your hypotheses.
It is also the stage in which you can use your conclusions, to generate actionable items to aid in the pursuit of the goal if appropriate. In this phase we may do any to all of the following:. They should also be presented with these in such a way that they can act on them — so if you do not recommend actions, the conclusions should then be presented so as to stimulate ideas for action, within them.
Now it is all very well to go through the process as stated thus far; after all, it should result in some sound information. Really though, to realize any benefits of this data, something should be done with the information obtained from it! Like the Scientific Method, ours is an iterative process, which should incorporate action…. So, to modify our diagram slightly:. We may communicate findings that immediately incite further questions — and we may then dive right into another cycle. Over the long term, though, action is essential for making the entire exercise a valuable one.
In our organization , each new data science project consists of several of these cycles. Communication of results often sparks new discussions and opens up new questions and avenues for exploration; if our conclusions result in actions which yield favorable results?
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- The design of CMOS radio-frequency integrated circuits.
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We know we are doing something right. Edwards Deming. For the original version of this article, click here. Sign in. Get started. A Data Scientific Method. How to take a pragmatic and goal-driven approach to data science. Peter Turner Follow. The Data Scientific Method. During this stage, we ask questions like: What decisions need to be made from this data? What questions do we wish to answer?
For answers, what level of confidence would we be happy with? Can we formulate hypotheses relating to these questions? What are they? How much time do we have for the exploration? What decisions would the stakeholder like to make from this data? What would the ideal result look like? How are we to export and present the final results?
During this stage, we ask questions like: What is the size of the data? How many files are there? To what extent does the data originate from different sources?
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