Bruce DENBY

ABSTRACT This paper presents an early version of an open extendable research and educational platform to support users in learning and mastering the different types of rare-singing. The platform is interfaced with a portable helmet to synchronously capture multiple signals during singing in a non-laboratory environment. Collected signals reflect articulatory movements and induced vibrations. The platform consists of four main modules: i) a capture and recording module, ii) a data replay (post processing) module, iii) an acoustic auto adaptation learning module, iv) and a 3D visualization sensory motor learning module. Our demo will focus on the first two modules. The system has been tested on two rare endangered singing musical styles, the Corsican “Cantu in Paghjella”, and the Byzantine hymns from Mount Athos, Greece. The versatility of the approach is further demonstrated by capturing a contemporary singing style known as “Human Beat Box.”

Abstract Broadcast radio is a rich yet underexploited source of multimedia content. To make this content available to users, it will be indispensable to develop new types of navigators capable of searching the large quantities of information contained in the radio bands. The article introduces a prototype of a new software radio enabled broadcast media navigator implemented on a Field Programmable Gate Array and multi-core processor, which is able to demodulate simultaneously all channel in the FM band and perform a real time ...

Abstract Data processing in high energy physics experiments is a multi-tiered process in which raw detector signals are first processed locally into physics objects, and then collated into event records which can be scrutinized by a fast online trigger system. The resulting selection of events are reconstructed and pass through a number of software filters before arriving at a final offline analysis where hard physical constants are extracted. Although sophisticated statistical data analysis techniques are routinely employed high energy ...

ABSTRACT The article presents an indoor localization scheme for mobile devices based on GSM Received Signal Strength fingerprints combined with embedded sensor information and an area site map. Displacements of a mobile user are first estimated using a sensor dead-reckoning approach that adapts stride length to different users and environments, and a dynamically switched orientation estimation scheme responding to orientation changes of the mobile device. Positions derived from GSM fingerprints, along with constraints imposed by a site map, are then integrated using a particle filter in order to prevent the accumulation of dead-reckoning errors over time. The study demonstrates that a standard handset with cellular network access and embedded inertial sensors can provide a good solution for indoor localization.

ABSTRACT The article presents a simple, practical approach for indoor localization using Received Signal Strength fingerprints from the GSM network, including an analysis of the relationship between signal strength and location, and the evolution of localization performance over time. Support Vector Machine regression applied to very high dimensional fingerprints does not reveal any smooth functional relationship between fingerprints and position. Classification using Support Vector Machines however provides very good results on discriminating different rooms in an indoor environment, albeit with performance that degrades over time. Transductive inference, introduced as a means of updating models to overcome degradation over time, provides hints that accurate indoor localization can be achieved by applying classification methods to cellular Received Signal Strength fingerprints, performance robustness being maintained via model updating and refining.

Feed forward and recurrent neural networks are introduced and related to standard data analysis tools. Tips are given on applications of neural nets to various areas of high energy physics. A review of applications within high energy physics and a summary of neural net hardware status are given.

Abstract Within the past few years, two novel computing techniques, cellular automata and neural networks, have shown considerable promise in the solution of problems of a very high degree of complexity, such as turbulent fluid flow, image processing, and pattern recognition. Many of the problems faced in experimental high energy physics are also of this nature. Track reconstruction in wire chambers and cluster finding in cellular calorimeters, for instance, involve pattern recognition and high combinatorial complexity since many ...

Abstract An intrinsically parallel algorithm based on a Hopfield style neural network is used to associate the energy depositions in a multi-component, non-magnetic high energy particle detector. The algorithm has been implemented on a vector pipeline computer with greatly enhanced performance.

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Skip to main content. CERN Logo CERN Document Server. Related links. CDS; Indico; Library; Bulletin; EDMS. Main navigation links: Search; Submit; Help; Your CDS: Your alerts; Your baskets; Your searches. login. Home > Articles & Preprints > Published Articles > Ongoing approaches to the trigger problem using neural networks > Access to Fulltext. Information; Discussion; Files. Ongoing approaches to the trigger problem using neural networks - Amendolia, SR et al. Main file(s): p129. version 1, ...

Page 1. 11 1 - Special Session: Statistical Signal Processing in High Energy Physics July 20 at 14H30 Chairman: Hagit Messer Invited Lecture Session Opportunities for statistical signal processing in high energy physics Bruce Denby (Université Pierre et Marie Curie, Paris) Filtering, robust and adaptive methods for track reconstruction Are Strandlie (Gjøvik University College) Signal Reconstruction in a High Rate Environment for Scintillator Calorimeters Esteban Fullana (IFIC) Track Identification In High Energy Physics ...

Progress on tracking with recurrent neural networks is presented. Applications of feed forward networks to High Energy Physics are discussed. The situation regarding hardware implementations of neural networks is assessed. 10 refs., 3 figs. ... Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding. ... Select a citation type above to copy/paste or download the reference. ... Some links on this page may take you to non-federal ...

The paper provides an introduction to experimental methods in high energy physics (HEP) followed by a motivation for triggering applications of hardware neural networks in high speed data acquisition systems. A few examples of such applications are then treated in detail. The paper concludes with a survey of planned future applications of neural network triggers.

Le passage de la radiodiffusion analogique (AM et FM) à l'ère du numérique va entraîner une modification importante des contenus diffusés. L'utilisation des techniques d'indexation audio dans les médias de radiodiffusion commerciale deviendra primordiale pour diverses applications, comme la radio à la demande et la pige musicale. Actuellement, les systèmes de surveillance commerciaux sont des installations massives, en raison du grand nombre de canaux à traiter, et nécessitent la présence d'un opérateur par flux annoté. Les ...

The paper describes a structured neural network solution to a signal processing problem in the meteorological and telecommunications domains. Optical disdrometers measure raindrop sizes and velocities by registering changes in photodiode current as the droplets pass through a collimated light beam. In an improved dual-beam device developed at CETP, feature extraction multilayer perceptrons applied to 20-sample windows of photodiode current provide input to a higher-level network which reconstructs droplet ...