Abstract
Vibrato is a frequency modulation effect of the singing voice and is very relevant in musical terms. Its most important characteristics are the vibrato frequency (in Hertz) and the vibrato extension (in semitones). In singing teaching and learning, it is very convenient to provide a visual feedback of those two objective signal characteristics, in real-time. In this paper we describe an algorithm performing vibrato detection and analysis. Since this capability depends on fundamental frequency (F0) analysis of the singing voice, we first discuss F0 estimation and compare three algorithms that are used in voice and speech analysis. Then we describe the vibrato detection and analysis algorithm and assess its performance using both synthetic and natural singing signals. Overall, results indicate that the relative estimation errors in vibrato frequency and extension are lower than 0.1%. © 2012 IEEE.

Abstract
Current state-of-The-Art speaker identification systems achieve high performances in reasonably well controlled conditions. However, some scenarios still elicit significant challenges, particularly in audio forensics when voice records are typically just a few seconds long and are severely affected by distortion, interferences, and abnormal speaking attitudes. In this paper we are inspired by the concept of minutiae in the context of fingerprinting, and try to extract localized, phase-related singularities from the speech signal denoting glottal source idiosyncratic information. First, we perform MFCC+GMM experiments in order to find the most effective phonetic segmentation of the speech signal for speaker modelling and discrimination. Secondly, we rely on effective phonetic segmentation and, in addition to MFCC features, we extract Normalized Relative Delays (NRDs) obtained from the phase of spectral harmonics. We use a Nearest Neighbour generalized classifier for speaker modelling and identification. Our results indicate that combining a careful phonetic segmentation and the inclusion of phase-related information, performance in speaker identification may increase significantly. Copyright © 2012 Audio Engineering Society, Inc.

Abstract
Techniques using RSS fingerprinting for localization have been studied over a number of different technologies in many different scenarios. In the case of underground tunnels localization can be quite challenging, yet it is extremely important for safety reasons. In the specific case of the CERN tunnels, accurate and automatized localization methods would additionally allow the workflow of some activities to become substantially faster. In a radiation area this would also have the added benefit of reducing the exposure time of personnel conducting so called radiation surveys which have to be carried out before access can be granted. In this paper Fingerprinting techniques for GSM and Wireless LAN are studied and enhanced to take advantage of both network technologies simultaneously as well as the channels RSS differential and an observed effect in the radiated power in the leaky-feeder cables. Besides the higher accuracy achieved for a single technology, this methodology looks promising for scenarios where several types of wireless networks are available or expected to be installed at a later stage.

Abstract
Wireless Sensor Networks (WSNs) consist of small devices with processing, communication and sensing capabilities. These devices interact together to carryout monitoring tasks. An example of such network is a photo-voltaic (PV) power plant where each solar panel has a sensor. The number of interconnected solar panels can become very large, and spread over a large area. Each sensor will sense the output of the panel and send this value to a central node for processing. In this paper we evaluate the performance of a wireless sensor network employing three different data collecting techniques. The study considers different networks, each with a different number of nodes and with different values for the offered load, estimating for each network size and offered load, network throughput, packet loss and end-to-end packet delay. Results show that as the size of the network grows and for higher values of the offered load, the best performance is achieved by using a polling based data collecting technique. © 2012 IEEE.

Abstract
Localisation techniques have long been of major importance for safety systems and a lot of research has been conducted in the distributed computing field regarding its functionality and reliability. In the specific scenario of long yet narrow tunnels existing at CERN, localisation methods will enable a number of applications and processes to substantially reduce human intervention. In this article, we evaluate the use of fingerprinting techniques with GSM signal available throughout the LHC tunnel via a radiating cable and compare some methods to estimate the location. In the tests, 16 variants of the K-Nearest Neighbour algorithm, employing different distance weighting methods and fingerprint grouping functions, are taken into consideration and their performance is assessed with a specific rating algorithm. The existing GSM infrastructure and tunnel conditions seem to be favourable to the adoption of these fingerprinting methods. Nevertheless, significant variations in the signal have been observed which might be traced back to the presence of bulky equipment and different operational states of the accelerator. The performance limits of these fingerprinting methods are discussed for the current scenario and, based on that, an outlook for future research is given aiming at improving the system's accuracy under such challenging conditions. © 2012 Copyright Taylor and Francis Group, LLC.

Abstract
This paper quantifies the impact of topological characteristics on the performance of single radio multichannel IEEE 802.11 mesh networks. Topological characteristics are the number of nodes per subnetwork, the hop count, the neighbor node density, the hidden nodes, the number of nodes in the neighborhood of the gateway, and the hidden nodes in the neighborhood of the gateway. Network performance metrics are throughput, fairness and delay. The data mining Support Vector Machine (SVM) model was used to extract the relationships between the network topology metrics and the network performance metrics based on data results obtained through ns-2 simulation of random networks. The results obtained can be used as a basis to design channel assignment algorithms or to aid the deployment and management of single radio wireless mesh networks.