We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Introduction to stochastic programming

The article reviews the book “Information Rules: A Strategic Guide to the Network Economy,” by Carl Shapiro and Hal R. Varian.

Information Rules: A Strategic Guide to the Network Economy

Most numerical integration techniques consist of approximating the integrand by a polynomial in a region or regions and then integrating the polynomial exactly. Often a complicated integrand can be factored into a non-negative ''weight'' function and another function better approximated by a polynomial, thus $\int_{a}^{b} g(t)dt = \int_{a}^{b} \omega (t)f(t)dt \approx \sum_{i=1}^{N} w_i f(t_i)$. Hopefully, the quadrature rule ${\{w_j, t_j\}}_{j=1}^{N}$ corresponding to the weight function $\omega$(t) is available in tabulated form, but more likely it is not. We present here two algorithms for generating the Gaussian quadrature rule defined by the weight function when: a) the three term recurrence relation is known for the orthogonal polynomials generated by $\omega$(t), and b) the moments of the weight function are known or can be calculated.

Calculation of Gauss quadrature rules.

An experimental study of droplet breakup in T-shaped microfluidic junctions is presented in which the capillary number and flow rate ratio are varied over a wide range for several different viscosity ratios and several different ratios of the inlet channel widths. The range of conditions corresponds to the region in which both the squeezing pressure that arises when the emerging interface obstructs the channel and the viscous shear stress on the emerging interface strongly influence the process. In this regime, the droplet volume depends on the capillary number, the flow rate ratio, and the ratio of inlet channel widths, which controls the degree of confinement of the droplets. The viscosity ratio influences the droplet volume only when the viscosities are similar. When there is a large viscosity contrast in which the dispersed-phase liquid is at least 50 times smaller than the continuous-phase liquid, the resulting size is independent of the viscosity ratio and no transition to a purely squeezing regime appears. In this case, both the droplet volume and the droplet production frequency obey power-law behavior with the capillary number, consistent with expectations based on mass conservation of the dispersed-phase liquid. Finally, scaling arguments are presented that result in predicted droplet volumes that depend on the capillary number, flow rate ratio, and width ratio in a qualitatively similar way to that observed in experiments.

Experimental observations of the squeezing-to-dripping transition in T-shaped microfluidic junctions.

We derive new mixed-integer quadratic, quadratically constrained, and second-order cone programming models of distribution system reconfiguration, which are to date the first formulations of the ac problem that have convex, continuous relaxations. Each model can be reliably and efficiently solved to optimality using standard commercial software. In the course of deriving each model, we obtain original quadratically constrained and second-order cone approximations to power flow in radial networks.

Convex Models of Distribution System Reconfiguration

Optimization is ubiquitous in power system engineering. Drawing on powerful, modern tools from convex optimization, this rigorous exposition introduces essential techniques for formulating linear, second-order cone, and semidefinite programming approximations to the canonical optimal power flow problem, which lies at the heart of many different power system optimizations. Convex models in each optimization class are then developed in parallel for a variety of practical applications like unit commitment, generation and transmission planning, and nodal pricing. Presenting classical approximations and modern convex relaxations side-by-side, and a selection of problems and worked examples, this is an invaluable resource for students and researchers from industry and academia in power systems, optimization, and control.

/pdf/convex-optimization-of-power-systems-3tyov585zv.pdf

Convex Optimization of Power Systems

The finiteness of fossil fuels implies that future electric power systems may predominantly source energy from fuel-free renewable resources like wind and solar. Evidently, these power systems without fuel will be environmentally benign, sustainable, and subject to milder failure scenarios. Many of these advantages were projected decades ago with the definition of the soft energy path, which describes a future where all energy is provided by numerous small, simple, and diverse renewable sources. Here we provide a thorough investigation of power systems without any fuel-based generation from technical and economic standpoints. The paper is organized by timescale and covers issues like the irrelevance of unit commitment in networks without large, fuel-based generators, the dubiousness of nodal pricing without fuel costs, and the need for new system-level models and control methods for semiconductor-based energy-conversion interfaces.

/pdf/power-systems-without-fuel-3pracawuxj.pdf

Joshua A. Taylor

Papers

Experimental observations of the squeezing-to-dripping transition in T-shaped microfluidic junctions.

Convex Models of Distribution System Reconfiguration

Convex Models of Distribution System Reconfiguration

Convex Optimization of Power Systems

Power Systems Without Fuel