Since the human visual system (HVS) is the ultimate receiver and appreciator for the majority (if not all) of naturally captured images and computer generated graphics, it would be better to use a perceptual criterion in the system design, implementation and optimization, instead of the traditional, mathematically defined one (e.g., MSE, SNR, PSNR, QoS or their relatives). After million-years of evolution, the HVS develops unique characteristics, which can be turned into the advantages for system designs. To make the machine perceive as the HVS does can result in resource savings (for instance, bandwidth, memory space, computing power) and performance enhancement (such as the resultant visual quality, and new functionalities). Significant research effort has been made toward modelling the HVS' mechanism during the past decade, and to apply the resultant models to various situations (equality evaluation, image/video compression, watermarking, channel coding, signal restoration/enhancement, computer graphics, visual content retrieval, etc.).

In this tutorial, we will first introduce the problem formulation, the relevant physiological/psychological knowledge, and the work so far in the related fields. The basic engineering modules (like signal decomposition, visual attention, and visibility determination) are then to be discussed. The issues and difficulties related to the two major mechanisms in most current systems (i.e., feature detection and pooling) are to be highlighted and explored. Afterward, different perceptually-driven techniques will be presented for picture quality evaluation, signal compression, enhancement, communication, and computer graphics, with proper case studies whenever possible. The last part of the tutorial is devoted to a summary, points of further discussion and possible future research directions, based upon our experience in both academic and industrial pursuits.

A Brain-computer interfaces (BCI), or sometimes called brain-machine interface, is a device that respond to neural processes from the brain to provide a direct communication pathway between the brain and the external device. Research on BCIs began in the 1970 and recent advances in BCI technology has produced devices that augment or even help human functions that is only possible in science fiction a few years ago. This tutorial will present an overview of the current BCI technologies, ranging from invasive, semi-invasive using ECoG to non-invasive using EEG, MEG, NIRS and fMRI. Recently, there has been much interest in BCI technology to help improve the quality of life and to restore function for people with severe motor disabilities. One of the strategies is to use a BCI to translate brain signals that involves motor or mental imagery into commands for controlling the robot and bypasses the normal motor output neural pathways. This tutorial will focus on the signal processing and machine learning techniques to detect motor imagery. Finally, this tutorial will present how recent BCI technology can help to improve the lives of people with neurological disorders such as advanced amyotrophic lateral sclerosis, and to help restore more effective motor control to people after stroke or other traumatic brain disorders.

The first part of the tutorial will focus on an overview of BCI technologies: Invasive techniques, semi-invasive techniques using ECoG, and non-invasive techniques using EEG, MEG, NIRS and fMRI.

The second part of the tutorial will focus on the neurophysiological background on motor imagery, how to apply machine learning and signal processing algorithms to detect motor imagery from EEG signals, and how to interpret the computed solution.

The last part of the tutorial will focus on how BCI technology can help to improve lives of people with advanced amyotrophic lateral sclerosis. It will also describe how BCI technology can help to restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity

Research in speech processing has seen a gradual movement in attention from speech recognition and related applications towards paralinguistic speech processing problems in recent years. A wide range of paralinguistic classification problems have been considered, relating for example to the recognition of speaker identity, language, emotion, mental state, gender and age. In the general area of emotion recognition from speech, the number of papers published annually has increased by an order of magnitude over the past decade.

One application area of interest in paralinguistic speech classification is the measurement of cognitive load or mental workload. It is about a century since the proposal of the Yerkes-Dodson law, which states that there is an optimum mental arousal for performing a task, below and above which performance will deteriorate. Despite this, there are few methods that have been demonstrated to measure cognitive load in practise, and fewer still in real time. Speech-based methods are attractive because they are non-intrusive, inexpensive and can be real-time.

Like other paralinguistic classification tasks, cognitive load measurement is a challenging problem, and one that must account for variability posed by linguistic, contextual and speaker-specific characteristics. Unlike some other paralinguistic classification tasks, cognitive load measurement requires classification along an ordinal scale, motivating the use of very specific machine learning techniques.

This tutorial introduces and examines some of the key research problems for emotion recognition and cognitive load measurement from speech: understanding the psychophysiological basis of emotion and cognitive load during speech production, extracting suitable features from the speech signal, reducing feature variability due to speaker and linguistic content, developing machine learning methods applicable to the task, comparing and evaluating diverse methods, robustness, and constructing suitable databases. The discussion of cognitive load is framed in the wider context of emotion recognition from speech, and some key insights from this area will be covered. The tutorial will also briefly discuss the use of other biomedical signals for cognitive load measurement. Participants will be exposed to likely future challenges, both during the tutorial presentation and during the ensuing discussion.

The 2001 MIT Technology Review indicated that biometrics is one of the emerging technologies that will change the world. Biometrics technology is initially treated as an exotic topic while recently it is a fast growing industry due to the urgent needs to secure people properties from goods to information.

Human Biometrics is automated recognition of a person using adherent distinctive physiological and/or involuntary behavioral features. Physiological features include facial characteristics, fingerprints, palm prints, iris patterns, and many more. Examples of behavioral features are signature writing dynamics, gait, speaker recognition, and keyboard typing dynamics. However, most biometric identifiers are a combination of physiological and behavioral features and they should not be exclusively classified into either physiological or behavioral characteristics. For example, speech is partially determined by the biological structure of the speaker vocal tract and partially by the way that person speaks. Also, fingerprints may be physiological in nature but the usage of the input device (e.g., how a user touches the fingerprint scanner and the pressure on the sensor) depends on the person's behavior. A car mechanics has different touch from a computer geek! Thus, the input to the recognition engine is a combination of physiological and behavioral characteristics. Behaviors can help in distinguishing the confusion happening when identifying parent, children, and siblings in their voice, gait, signature etc. The same argument applies to facial recognition. Faces of identical twins may completely match at birth but during growth, the facial features change based on the person's behavior developed from profession, way of living, environment, .. etc.

Through this tutorial we will go through all the main aspects of this fast growing technology. We will discuss in this tutorial if we are about entering an era where people don't need to carry any identity or credit cards and still can purchase things and travel to other countries. The attendees will be introduced throughout this tutorial to the following:

1. The fundamentals of Human Biometrics.
2. Types of biometrics.
3. Biometric systems structure.
4. Assessment of the performance of the biometric systems.