Research in Electroencephalogram (EEG) based Brain-Computer Interfaces (BCIs) has been considerably expanding during the last few years. Such an expansion owes to a large extent to the multidisciplinary and challenging nature of BCI research. Signal processing undoubtedly constitutes an essential component of a BCI system since, from the EEG acquisition to the translation of brain activity into meaningful commands, multivariate signal processing algorithms are intensively applied. In this tutorial, the basic BCI concepts, EEG monitoring, BCI operation, the electrophysiological sources of BCI control, future directions, and ambitions are first introduced. The main BCI types, namely motor imagery (ERD/ERS), steady state visual evoked potentials (SSVEP), and P300 based BCIs are presented along with practical application examples. The EEG processing for BCI applications is then described in depth. The multivariate nature of the EEG combined with the neuroscience knowledge on hemispheric brain specialization are advantageously taken into account to derive spatial filters (i.e. across the EEG electrodes) to analyze the patterns resulting from motor imagery, visual evoked potentials, and the P300 paradigm. Throughout the tutorial, the applicability of BCI technology is emphasized. More that two decades of BCI research have been paving the way to the deployment of BCIs from the lab to consumer's home. An early prototype of a portable BCI using visual evoked potentials will also be demonstrated to better illustrate the contents of this tutorial.

The tutorial is an overview of tracking and data fusion for surveillance systems with applications both to defense and civilian systems. It is divided into four parts:

Part 1 - Filtering: Covers the topics related to state estimation for stochastic dynamic systems: sequential Bayesian estimator, Kalman filter, nonlinear filters (extended and unscented Kalman filter, Gaussian sum filter, particle filter); filters for maneuvering motion (Interactive multiple-model filter); Crame-Rao lower bounds for filtering.

Part 2 - Data association: Due to the imperfections of a detector, the input to a tracking system may lack the target originated detections and often contains false detections (due to clutter). The data association component of a tracking system determines the origin of each input detection. The tutorial will cover techniques such as: gating, (global) nearest neighbor algorithm, (joint) probabilistic data association, multiple hypotheses tracking.

Part 3 - Distributed multi-sensor tracking: Modern surveillance systems typically consist of multiple sensors connected by a communication network for a data exchange. While the network surveillance offers potentially more accurate and reliable performance, there are many practical issues that need to be resolved beforehand. The tutorial will provide answers to problems: how to choose the multi-sensor architecture, how to avoid a repeated use of the same information, how to perform distributed track association and track fusion, how to ensure proper multi-sensor alignment in time and space.

Part 4 - Selected applications: The last part of the tutorial will cover several practical applications: ballistic missile tracking, GMTI radar tracking, tracking with hard constraints, angle-only tracking, computer vision.

This course is designed to help participants identify and characterize the principal components of military data fusion systems; to learn the state-of the art in multi-sensor integration, in target tracking, identification and situation/threat assessment; and select fusion techniques appropriate to system and mission needs.

Sensor/Data Fusion is the process of automatically filtering, aggregating and extracting desired information from multiple sensors and sources, and integrating and interpreting data. This is a rapidly evolving technology area with profound implications for numerous military and civil applications. Powerful new methods are being developed and used to detect, identify and track individual targets and to provide situation awareness and prediction of critical events. The power to exploit all relevant information rapidly and effectively is at the core of the Net-Centric Operations (NCO) paradigm. To exploit the power of Sensor/Data Fusion, military and industrial users and developers will need to understand technological advancements, their applicability and potential challenges.

Image processing started with the development of optical information processing in the middle of the 20th century. Many analogue processing have been done with the help of optical devices such as lenses. With the development of computers and opto-electronic sensors and systems, digital image processing emerged and has grown very fast. Over the last 50 years research and development in image processing are driving advancements in many high-tech areas including medical and scientific imaging, multimedia, biometrics, remote sensing among others. With the increasing development of new imaging modalities and multi media, many new approaches have been proposed in this field of research. However, attention is focusing more and more on the development of new mathematical models rather in understanding the image signal as a physical quantity and its interaction with the observer. Unfortunately, the human user is often ignored in the imaging chain and processing. Whereas, in many applications, the human observer plays a prominent role in the decision tasks such as diagnosis, recognition and evaluation based on visual assessment of images. Then the use of some knowledge on Human Visual System mechanisms in the design of image processing techniques appears as a promising approach. Indeed, in many applications, it has been shown that by exploiting multi-channel models and by taking into account the properties and limitations of the human visual system, images can be more efficiently processed, coded and transmitted.

A plethoric amount of data such as images, videos, virtual reality, … is being exchanged around the world. Even though the telecommunication bandwidth and disk storage capacity are continuously increasing, the compression still a vital step so that multimedia can be exchanged faster and stored more efficiently. The aim of the compression step is to reduces the size of multimedia by reducing an object's redundancy. Lossless techniques preserve an original file bit for bit after compression and decompression. Lossy techniques obtain significantly greater compression than lossless, but by distorting the original file. Lossy compression investigates the tradeoff of compression vs. error distortion. The Joint Photographics Experts Group (JPEG) committee works on the definition of the image compression standards. JPEG baseline, JPEG 2000 and more recently JPEG XR were the most important output of this committee.

The tutorial deals with the field of array signal processing. The classical problem in this field is to determine the location of an energy radiating planar source with respect to the array location. Applications range from wireless communications, passive radars to hyperthermia treatment of cancer. This tutorial first deals with the classical estimation of the direction of arrival of a signal in the presence of noise and interfering signals. The talk also deals with more advanced topics that are ‘’time frequency’’ array signal processing and ‘’blind’’ array signal processing (blind source separation). Time frequency array signal processing considers the array processing of non stationary signals while blind array processing consists of recovering the original waveforms of the sources without any knowledge of the propagation model. The later topic is currently an emerging field of research with a broad range of applications ranging from Jammer mitigation, eye artifact removal in electroencephalography, rotating machine monitoring to airport surveillance.

The speaker plans to address a broad audience with general background in signal processing.