Marcos Turqueti

MEMS acoustic array embedded in an FPGA based data acquisition and signal processing system

Acoustic arrays are currently utilized in many different applications, ranging from consumer electronics to military systems. Active research on sensors, hardware, algorithms, and system integration specific to acoustic arrays are ongoing on a wide range of engineering fields. Applications such as sound source separation, sound navigation, sound imaging, and speech recognition are few of the many possible applications that benefit acoustic sensor array. Using multiple sensors in arrays has many advantages; however it is also more challenging. As the number of signals increases, the complexity of the electronics to acquire and process the data will grow as well. Such challenge can be quite formidable depending on the number of sensors, processing speed, and complexity of the target application. This paper describes the design and implementation of a 52 microphone MEMS array, embedded in an FPGA platform with real-time processing capabilities. The paper also provides the first results on the use of acoustic array as a source separation system utilizing Independent Component Analysis (ICA) technique.

Direction of Arrival Estimation and Localization Using Acoustic Sensor Arrays

Sound source localization has numerous applications such as detection and localization of mechanical or structural failures in vehicles and buildings or bridges, security systems, collision avoidance, and robotic vision. The paper presents the design of an anechoic chamber, sensor arrays and an analysis of how the data acquired from the sensors could be used for sound source localization and object detection. An anechoic chamber is designed to create a clean environment which isolates the experiment from external noises and reverberation echoes. An FPGA based data acquisition system is developed for a flexible acoustic sensor array platform. Using this sensor platform, we investigate direction of arrival estimation and source localization experiments with different geometries and with different numbers of sensors. We further present a discussion of parameters that influence the sensitivity and accuracy of the results of these experiments.

3D direction of arrival estimation and localization using ultrasonic sensors in an anechoic chamber

This paper presents the design of an acoustic sensor array for sound source localization and object detection. An FPGA based data acquisition system is developed for a flexible acoustic sensor array platform. An anechoic chamber is designed to create a clean environment which isolates the experiment from external noises and reverberation echoes. We investigate direction of arrival estimation and source localization experiments with different geometries and with different numbers of sensors. We further present a discussion of parameters that influence the sensitivity and accuracy of the results of these experiments.

Acoustic sensor array for sonic imaging in air

Sonic imaging in air is a field of study with growing applications such as objects location, tracking and identification. It is also used in NDE applications to detect defects or hidden objects inside the structure. The objective of this work is to present a sonic system capable of locating and imaging objects in air with high quality and good resolution. The acoustic transducer array is a novel 52 MEMS sensors array, embedded in a network enabled, data acquisition and signal processing architecture. The system operates in the 20–100 kHz frequency range and makes use of phased array techniques in order to accomplish its objectives. It operates actively where one transducer is employed to generate the scanning beam and the sensor array will capture the echoes. The system is capable of acquiring and processing with up to 800 Mbits of raw data generated by the acoustic sensor array. Beamforming, matched filtering, spectral processing, and independent component analysis are the main techniques utilized in this work for imaging, tracking and identification of objects.

Scalable acoustic imaging platform using MEMS array

The detection and analysis of acoustic sources utilizing microphone arrays is a growing field of research. The evolving needs of applications such as noise suppression, voice recognition, sound tracking and acoustic source localization push requirements such as scalability and speed for sophisticated microphone array systems. Microphone arrays are advantageous compared to single microphone usage in that its use the physical spatial information of acoustic wave propagation to provide a more accurate picture of the incoming waves and therefore yield valuable information regard its source. This paper describes a broadband MEMS Array acouStic Imaging system called MASI that includes a highly scalable and network enabled FPGA based signal processing hardware for acquisition, processing and imaging. Design issues such as array sensitivity, accuracy and geometry of the array will be discussed as well as the system real-time capabilities regarding signal processing and data acquisition. Spatial localization and tracking are the main target applications for this system.

Smart acoustic sensor array (SASA) system for real-time sound processing applications

Abstract: This chapter describes the design and implementation of a smart acoustic sensor array (SASA) system that consists of a 52-microphone micro-electromechanical systems array embedded in a field-programmable gate array platform with real-time data acquisition, signal processing and network communication capabilities. The SASA system is evaluated using several case studies in order to demonstrate the versatility and scalability of the sensor array platform for real-time sound analysis applications. These case studies include sound mapping and source localization.

Real-time Independent Component Analysis Implementation and applications

A common problem in disciplines such as high energy physics, biomedicine and acoustic signal processing is finding a suitable representation of multivariate data. Independent Component Analysis (ICA) is a recently developed mathematical tool that can recover independent source signals and is now mature enough to be implemented in real-time applications such as photomultipliers signal processing, magnetic resonance imaging and acoustic arrays. This technique is based on the assumption that signals from different sources are statistically independent and statistically independent signals can be extracted from mixture signals. ICA defines a model for the observed data that requires a large number of samples in order to establish the necessary statistics. The model assumes that the data variables are linear combination of unknown variables, the unknown variables are assumed to be non-Gaussian and independent. The goal then becomes to find a transformation in which the components are as statistical independent as possible from each other. This technique is related with methods such as principal component analysis and factor analysis. The ICA algorithm is computing intensive since it must accumulate and go through the signal samples performing complex operations. Efficient versions of the algorithm have being already deployed using different techniques such as the FastICA that can be implemented efficiently in hardware platforms such as DSP processors and FPGAs. In this paper, we present the ICA principles, implementation and current applications.

Distributed Data Acquisition and Storage Architecture for the SuperNova Acceleration Probe

The SuperNova acceleration probe (SNAP) instrument is being designed to collect image and spectroscopic data for the study of dark energy in the universe. This paper describes a distributed architecture for the data acquisition system which interfaces to visible light and infrared imaging detectors. The architecture includes the use of NAND flash memory for the storage of exposures in a file system. Also described is an FPGA-based lossless data compression algorithm with a configurable pre-scaler based on a novel square root data compression method to improve compression performance. The required interactions of the distributed elements with an instrument control unit will be described as well.

Acoustic imaging system using the CAPTAN architecture

Real-time scalable data acquisition systems and high performance transducer arrays are strongly interconnected subjects as most transducer arrays demands sophisticated readout systems in order to perform properly. Sound and ultrasound systems such SOund NAvigation and Ranging (SONAR), Sonography, Speech Recognition, Robotic Sensing and Sound Source Separation are a few of many systems that can deploy transducer arrays and require real-time high speed and expandable data acquisition systems. For these system characteristics such as speed, scalability and real-time signal processing are paramount, demanding highly developed data acquisition and processing platforms. Successfully integrating acoustic arrays with generic data acquisition systems is challenging due to the wide variety of requirements regarding speed, signal processing capabilities and scale that acoustic arrays can present. The objective of this work is to present an acoustic imaging system using CAPTAN (Compact And Programmable daTa Acquisition Node) architecture that can deliver these requirements while closely integrated with a specially designed sound imaging array.

The Test Stand System for the PHENIX iFVTX Silicon Detector

PHENIX is the largest of the four experiments currently taking data at the Relativistic Heavy Ion Collider (RHIC), and the iFVTX is a new pixel tracker which will be installed in the forward tracker region of PHENIX. Fermilab has developed a complete test stand system for the examination of FPix2.1 modules, hybrids, and pixel chips that will be installed in the iFVTX. The system is currently in use for chip, module, and wafer testing at Fermilab. The test stand architecture is flexible and can be adapted to new requirements. In this paper, the software and hardware integration will be discussed followed by an analysis of the advantages of choosing a modular approach for the system. Finally, a selection of tests supported by the system, along with sample results, will be presented and explained.