A human classification system for a video-acoustic detection platform

et al. 2017

Sensors

Using arrays with digital MEMS (Micro-Electro-Mechanical System) microphones and FPGA-based (Field Programmable Gate Array) acquisition/processing systems allows building systems with hundreds of sensors at a reduced cost. The problem arises when systems with thousands of sensors are needed. This work analyzes the implementation and performance of a virtual array with 6400 (80 × 80) MEMS microphones. This virtual array is implemented by changing the position of a physical array of 64 (8 × 8) microphones in a grid with 10 × 10 positions, using a 2D positioning system. This virtual array obtains an array spatial aperture of 1 × 1 m2. Based on the SODAR (SOund Detection And Ranging) principle, the measured beampattern and the focusing capacity of the virtual array have been analyzed, since beamforming algorithms assume to be working with spherical waves, due to the large dimensions of the array in comparison with the distance between the target (a mannequin) and the array. Finally, the acoustic images of the mannequin, obtained for different frequency and range values, have been obtained, showing high angular resolutions and the possibility to identify different parts of the body of the mannequin.

Section: Introductionmentioning

confidence: 99%

Implementation of a Virtual Microphone Array to Obtain High Resolution Acoustic Images

Izquierdo

Villacorta

et al. 2017

Sensors

“…The authors of this paper have experience in the design [6] and development of acoustic imaging systems, based in acoustic arrays, used in many different fields, such as detection and tracking systems [7,8], Ambient Assisted Living [9], or biometric identification systems [10,11,12]. The arrays used were ULA (Uniform Linear Array), formed by acoustic sensors distributed uniformly along a line.…”

Section: Introductionmentioning

confidence: 99%

Design and Evaluation of a Scalable and Reconfigurable Multi-Platform System for Acoustic Imaging

Izquierdo

Villacorta

et al. 2016

Sensors

This paper proposes a scalable and multi-platform framework for signal acquisition and processing, which allows for the generation of acoustic images using planar arrays of MEMS (Micro-Electro-Mechanical Systems) microphones with low development and deployment costs. Acoustic characterization of MEMS sensors was performed, and the beam pattern of a module, based on an 8 × 8 planar array and of several clusters of modules, was obtained. A flexible framework, formed by an FPGA, an embedded processor, a computer desktop, and a graphic processing unit, was defined. The processing times of the algorithms used to obtain the acoustic images, including signal processing and wideband beamforming via FFT, were evaluated in each subsystem of the framework. Based on this analysis, three frameworks are proposed, defined by the specific subsystems used and the algorithms shared. Finally, a set of acoustic images obtained from sound reflected from a person are presented as a case study in the field of biometric identification. These results reveal the feasibility of the proposed system.

“…In previous works, the authors of this paper developed multisensor surveillance and tracking systems based on acoustic arrays and image sensors [12, 13]. In November, 2011, authors were working on the development of a novel biometric system, based on acoustic images acquired with electronic scanning arrays [14, 15].…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Biometric System Based on Acoustic Images

Fuente

The Scientific World Journal

Calvo

et al. 2014

On the basis of an acoustic biometric system that captures 16 acoustic images of a person for 4 frequencies and 4 positions, a study was carried out to improve the performance of the system. On a first stage, an analysis to determine which images provide more information to the system was carried out showing that a set of 12 images allows the system to obtain results that are equivalent to using all of the 16 images. Finally, optimization techniques were used to obtain the set of weights associated with each acoustic image that maximizes the performance of the biometric system. These results improve significantly the performance of the preliminary system, while reducing the time of acquisition and computational burden, since the number of acoustic images was reduced.