Abstract
The explosion of interest in consuming rich multimedia content resulted in a growing demand for technologies that enable seamless access over any network using any terminal without affecting the viewing experience. Scalable Video Coding (SVC) is considered to be one of the key technologies that will improve the accessibility to video resources through adapting them at network nodes or destination node at a negligible computational cost. State-of-the-art video coding technologies enable a comprehensive set of scalability options. However, the trade-off of having such enriched set of scalability options is the reduced compression efficiency and increased encoder/decoder complexity. This trade-off can be mitigated if the encoder has prior knowledge of the scalability options and levels that are actually needed at a given time. It is possible to achieve this goal in a closely monitored system, such as a Virtual Collaboration System (VCS), in which users and their usage environment are known. This paper proposes a framework to determine the scalability options and levels for encoding scalable video for a VCS. Simulation results highlight the benefits of the proposed framework.

Abstract
This paper proposes a framework for a virtual classroom application based on a Virtual Collaboration System (VCS), which is being developed under the VISNET II Network of Excellence (NoE)(1), and discusses adaptation technologies that enable seamless access to classroom sessions while intellectual property and digital rights are managed. The proposed virtual classroom framework enables academic institutions to conduct their collaborative lecture series, to which registered students will be able to attend remotely and interactively over the Internet. Furthermore, the general public may also follow the classroom sessions under certain restrictions imposed by the participating institutions. In order to facilitate seamless access to a heterogeneous audience that is composed of users with various preferences and privileges accessing the classroom sessions over different network infrastructures and using terminal devices with diverse capabilities, context-aware content adaptation is required to meet constraints imposed by the usage context and enhance the quality of the user experience. Thus, this paper describes the concepts and functionalities of a context-aware content adaptation platform, that suits the requirements of such multimedia application scenarios. This platform is able to consume low-level contextual information to infer higher-level contexts, and thus decide the need for and type of adaptation operations to be performed upon the content. In this way, it is aimed to meet usage. constraints while also satisfying restrictions imposed by the Digital Rights Management (DRM) to govern the use of protected content.

Abstract
The paper presents a method for generating partial bitstreams on-line for use in systems with run-time reconfigurable FPGAs. Bitstream creation is performed at run-time by merging partial bitstreams from individual component modules. The process includes the capability to create connections between the modules by selection from a set of routes found during an off-line pre-processing step. Placement and interconnection of modules must follow a precise set of rules. While restricting the number of possible module arrangements, this approach allows bitstream, creation to be performed with relatively few computational resources. Using a demonstration system with a Virtex-II Pro FPGA with a PowerPC 405 CPU, the process of creating at run-time a partial bitstream for 22% of the device area takes 24 ms.

Abstract
We recently introduced a novel Automatic Noise Reduction (ANR) algorithm for the removal of wideband stationary/non-stationary noise from audio [1]. Current noise reduction techniques exhibit certain undesirable characteristics. Distortion and/or alteration of the audio characteristics is a common problem. User intervention in identifying the noise profile is sometimes necessary. ANR uses a novel framework employing dominant component subtraction and restoration and performs better than conventional techniques in subjective tests. Here we describe three enhancements to ANR. The first of these increases the level of noise removal for the special case of stationary background noise. The second is a new tool for improving the temporal envelope coherence and yields additional noise removal. The third is a multi-band processing tool for conditioning time-frequency envelope for reduced listener fatigue.

Abstract
In this paper we present a new approach to the problem of isolated vowel recognition in real-time. Language learning and speech therapy are examples of application areas that require real-time biofeedback of acoustic features. As the performance of known approaches usually drops for child speakers, we evaluated different alternatives of feature extraction and pattern recognition techniques, including PCA, LDA, ANN and Bayesian classification. In addition, we studied the explicit inclusion of pitch as a main parameter in both simulation and the real-time feature extraction process. Best results were obtained with our dataset when MFCCs are mapped, using LDA, to a 4-dimensional sub-space that is followed by Bayesian classification. An interactive game was designed that implements the selected real-time vowel recognition technique.

Abstract
This paper presents a Genetic Algorithm approach with Harmonic Structure Evolution for Polyphonic Music Transcription. Automatic Music Transcription is a very complex problem that continues waiting for solutions due to the harmonic complexity of musical sounds. More traditional approaches try to extract the information directly from the audio stream, but by taking into account that a polyphonic audio stream is no more than a combination of several musical notes, music transcription can be addressed as a search space problem where the goal is to find the sequence of notes that best models our audio signal. By taking advantage of the genetic algorithms to explore large search spaces we present a new approach to the music transcription problem. In order to reduce the harmonic overfitting several techniques were used including the encoding of the harmonic structure of the internal synthesizer inside the individual's genotype as a way to evolve towards the instrument played on the original audio signal. The results obtained in polyphonic piano transcriptions show the feasibility of the approach.