FIRST LEGO League (FLL) is an international initiative to encourage students aged 9-14 to pursue careers in technological fields. In its four years of existence, the competition has grown dramatically. During the Fall 2001 semester, several Michigan Technological University students served as mentors for local FLL teams.

The culture of laptop improvisation has grown tremendously in recent years. The development of personalized software instruments presents interesting issues in the context of improvised group performances. This paper examines an approach that is aimed at increasing the modes of interactivity between laptop performers and at the same time suggests ways in which audiences can better discern and identify the sonic characteristics of each laptop performer. We refer to software implementation that was developed for the BLISS networked laptop ensemble with view to designing a shared format for the exchange of messages within local and internet based networks.

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Abstract: MINWii is a music therapy game for the renarcissization of demented patients. It lets players improvise or play songs of their choice by pointing at a virtual keyboard with a Wiimote Pistol. We present the results of a three-month usability study we conducted with 7 institutionalized patients suffering from mild to moderately severe Alzheimer’s disease at the LUSAGE Living Lab in Paris. We demonstrate that MINWii is indeed usable by AD patients despite their motor and cognitive impairments: our results, which were largely computed automatically thanks to MINWii’s extensive logging capabilities, show either an instant mastery or a clear learning effect depending on patients ’ cognitive abilities. Moreover, patients were overall very satisfied with the game and expressed a desire to repeat the experience: MINWii fosters positive interaction with the caregivers and elicits powerful reminiscence with even the most severely impaired patients. This study justifies future research to assess the lasting effects of playing MINWii on both quality of life and cognitive impairment in demented patients. 1

Abstract—In this paper we focus on the automatic identification of bird species from their audio recorded song. Bird monitoring is important to perform several tasks, such as to evaluate the quality of their living environment or to monitor dangerous situations to planes caused by birds near airports. We deal with the bird species identification problem using signal processing and machine learning techniques. First, features are extracted from the bird recorded songs using specific audio treatment; next the problem is performed according to a classical machine learning scenario, where a labeled database of previously known bird songs are employed to create a decision procedure that is used to predict the species of a new bird song. Experiments are conducted in a dataset of recorded songs of bird species which appear in a specific region. The experimental results compare the performance obtained in different situations, encompassing the complete audio signals, as recorded in the field, and short audio segments (pulses) obtained from the signals by a split procedure. The influence of the number of classes (bird species) in the identification accuracy is also evaluated. Keywords-signal processing; pattern recognition; machine learning; bird species identification I.