Abstract
We present a new class of digital audio effects which can automatically relate parameter values to the tempo of a musical input in real-time. Using a beat tracking system as the front end, we demonstrate a tempo-dependent delay effect and a set of beat-synchronous low frequency oscillator (LFO) effects including auto-wah, tremolo and vibrato. The effects show better performance than might be expected as they are blind to certain beat tracker errors. All effects are implemented as VST plug-ins which operate in real-time, enabling their use both in live musical performance and the off-line modification of studio recordings.

Abstract
Music information retrieval encompasses a complex and diverse set of problems. Some recent work has focused on automatic textual annotation of audio data, paralleling work in image retrieval. Here we take a narrower approach to the automatic textual annotation of music signals and focus on rhythmic style. Training data for rhythmic styles are derived from simple, precisely labeled drum loops intended for content creation. These loops are already textually annotated with the rhythmic style they represent. The training loops are then compared against a database of music content to apply textual annotations of rhythmic style to unheard music signals. Three distinct methods of rhythmic analysis are explored. These methods are tested on a small collection of electronic dance music resulting in a labeling accuracy of 73%.

Abstract
Three-dimensional (3D) object reconstruction using only bi-dimensional (2D) images has been a major research topic in Computer Vision. However, it is still a complex problem to solve, when automation, speed and precision are required. In the work presented in this paper, we developed a computational platform with the main purpose of building 3D geometric models from uncalibrated images of objects. Simplicity and automation were our major guidelines to choose volumetric reconstruction methods, such as Generalized Voxel Coloring. This method uses photo-consistency measures to build an accurate 3D geometric model, without imposing any kind of restrictions on the relative motion between the camera used and the object to be reconstructed. Our final goal is to use our computational platform in building and characterize human external anatomical shapes using a single off-the-shelf camera.

Abstract
The efficiency of network attachment plays a crucial role in the performance of accessing services in new environments. As an example, when a moving network is changing its location relative to attachment points, the detection of the candidate access networks along with their properties and security relationships needs to be carefully managed. This paper presents the framework and mechanisms for network attachment of Ambient Networks. Different steps required for optimizing the network attachment procedure are studied, and a secure network attachment protocol is proposed.

Abstract
Consider a wireless sensor network (WSN) where a broadcast from a sensor node does not reach all sensor nodes in the network; such networks are often called multihop networks. Sensor nodes take sensor readings but individual sensor readings are not very important. It is important however to compute aggregated quantities of these sensor readings. The minimum and maximum of all sensor readings at an instant are often interesting because they indicate abnormal behavior for example if the maximum temperature is very high then it may be that afire has broken out. We propose an algorithm for computing the min or max of sensor reading in a multihop network. This algorithm has the particularly interesting property of having a time complexity that does not depend on the number of sensor nodes; only the network diameter and the range of the value domain of sensor readings matter

Abstract
Consider a network where all nodes share a single broadcast domain such as a wired broadcast network. Nodes take sensor readings but individual sensor readings are not the most important pieces of data in the system. Instead,, we are interested in aggregated quantities of the sensor readings such as minimum and maximum values, the number of nodes and the median among a set Of sensor readings on different nodes. In this paper we show that a prioritized medium access control (MAC) protocol may advantageously be exploited to efficiently compute aggregated quantities of sensor readings. In this context, we propose a distributed algorithm that has a very low time and message-complexity for computing certain aggregated quantities. Importantly we show that if every sensor node knows its geographical location, then. sensor data can be interpolated with our novel distributed algorithm, and the message-complexity of the algorithm is independent of the number of nodes. Such an interpolation of sensor data can be used to compute any desired function; for example the temperature gradient in a room (e.g., industrial plant) densely populated with sensor nodes, or the gas concentration gradient within a pipeline or traffic tunnel.