The tour will depart from Hotel Finisterre (venue of SAM 2014) and will arrive at the welcome reception site (Aquarium Finisterrae)

The increasing need for fast-ramping generation capacity and costly reserves to balance the grid under high penetration levels of intermittent resources, have sparked great interest in designing new paradigms that allow electricity demand to respond to economic signals. It is expected that the grid will soon incorporate an access layer to manage the access to power by a set of flexible appliances. Aggregators are the entities that will interact with the wholesale market and manage large populations of flexible demand. The objective is for the Aggregators to utilize an interface that is safe for the grid and economically desirable for the consumers. In this talk we will highlight the design opportunities and challenges in dealing with the modeling problem behind the Aggregator design. Electrical Vehicles will be our case study to illustrate advances in the ex-ante modeling and the on-line scheduling of flexible loads.

Multi-object estimation poses three questions: How many objects are in the scene? Where are there objects? And: Which object is which? Let's ignore - at least for now - the first, which is admittedly very important but here is not germane. To estimate the location of (say) two objects from prior information and measurements is, for those of you who like to think in these terms, an optimization. The familiar metric is mean square error, and to minimize it answers both the second and third questions. But suppose that we are interested only in the second question. We can recognize this a relaxation of a constraint in our optimization; can the best metric improve? In this talk it is shown that it can, and the result is (unfortunately) called MMOSPA. We find a rich area of label-free estimation, both static and dynamic. Versions of the trackers are shown to beat coalescence problems for closely spaced targets. And for those of you who like labels, it is shown that labeling can be re-inserted to the estimates, but in a way that is perhaps more honest than for labeled ones, as a posterior label probability.

With the 4th generation of wireless systems still being rolled out, challenges keep mounting: traffic demand doubles yearly and user needs and expectations keep growing at a very fast pace. Against this backdrop, the debate is open as to what shall come next in the evolution of wireless systems—what is loosely referred to as the 5th generation—and this debate stage can be a very fertile time for ideas to take root. This talk intends to contribute to the ongoing discussion, not from the perspective of a manufacturer, operator, or user, but from the perspective of a researcher. With that goal in mind, the presentation is organized around five reflections touching on aspects that relate to 5G but that also have broader conceptual implications in research.

The Santiago de Compostela Tour will depart from the conference venue (Hotel Finisterre) and will arrive at the venue of the Gala Dinner (Pazo de Vilaboa)

The Santiago de Compostela tour will finish in the venue of the Gala Dinner. There will be also buses from the conference venue (Hotel Finisterre) to the venue of the Gala Dinner.

There has been considerable recent interest in solving optimization problems with data stored over a network. For these problems we need techniques that process data locally yet converge rapidly to an (approximate) solution across the entire network. This talk reviews primarily first-order algorithms for large-scale optimization of the distributed or decentralized types. We emphasize on recognizing separable structures in a large set of signal processing and statistical learning problems and demonstrate that, through skillful uses of gradient, proximal, duality, and splitting techniques, massively parallel algorithms can be developed. Numerical results are presented to demonstrate the scalability of the parallel codes on typical unix clusters and Amazon EC2.

This talk will describe aspects of sensor array processing that have been successful in areas such as geophysics and ground penetrating radar. Some examples are land mine detection, sonic well-logging and micro-seismic monitoring. These applications employ wideband sensors and benefit from array processing when a complete frequency-wavenumber domain analysis of the space-time wavefield is performed. Furthermore, new systems increasingly use three-axis sensors where vector signals are processed to extract parameters such as polarization. On the signal processing side, theoretical advances have kept pace with the ever-expanding data sets produced by new sensors. In particular, new sparsity-aware methods provide powerful algorithms for locating isolated targets as well as extracting other attributes such as orientation.