KEYWORDS: Sensors, Acoustics, Modulation, Data communications, Structural health monitoring, Sensor networks, Telecommunications, Wave propagation, Digital signal processing, Analog electronics
The recurrent monitoring of an aerial vehicle for structural damage detection and identification by acoustic sensors increases its reliability and remaining useful lifetime. In order to reach full structural health monitoring (SHM) autonomy, there is a need to combine sensing and communication functions into smart multifunctional sensors. Through this fusion, the information gathered by the SHM sensors could be transmitted in real time to a central processing unit without any human intervention. To that end, this research paper proposes reusing the existing network of acoustic SHM sensors mounted or embedded in the structure to enable acoustic multi-sensor wireless communication through the structure itself using elastic waves as the carrier signals. By doing so, the proposed acoustic communication system does not generate additional radio-frequency (RF) interference to other RF communication systems on board such as those used for vehicle control and safety-related services. This paper describes the design of the proposed acoustic wireless sensor network for autonomous SHM of aerial vehicles. First, the network topology and sensors placement are described along with the data routing algorithm. Then, the time-reversal based time division multiple access technique is introduced for multi-sensor communication using elastic waves. The data transmission across the elastic channel using time-reversal pulse position modulation is also presented. Finally, the system is evaluated based on the acoustic channel response of the horizontal stabilizer of an Ercoupe 415-C aircraft.
Though effective and computationally efficient algorithms have been developed, the commonly utilized filtered backprojection (FBP) approach to computed tomography (CT) reconstruction suffers from artifact production in sparse-view applications. Within the past few years, convolutional neural networks (CNNs) have been applied to enhance sparse-view reconstruction in CT imaging. Using a network trained on sparse-view FBP reconstructions, the artifacts introduced by undersampling the imaging space can be removed. In this paper, we investigate specific choices in the implementation of the CNN, including the network architecture, training parameters, and data preprocessing, to determine effects on the images produced by the network. Our proposed algorithm and implementation strategies improve upon the use of FBP algorithms alone by removing artifacts produced during sparse-view CT reconstruction.
Autonomous structural health monitoring (SHM) of aerostructures strengthens the reliability, increases the lifetime, and reduces the maintenance cost of aerovehicles such as airplanes and unmanned aerial vehicles (UAV). The continuous monitoring of aerostructures for early damage detection and identification is made possible through a wireless network of sensors deployed on the structure. Usually, the data collected by these sensors is communicated to a central unit for real-time data processing using electromagnetic waves at radio frequencies (RF). However, the emission of RF signals for autonomous SHM creates additional sources of interference to on-board RF communication systems used for aircraft control and safety-related services. To overcome this issue, we propose in this paper an acoustic data communication system for autonomous health monitoring of aerostructures which are modeled as thin plate-like structures. In the proposed system, both damage detection and wireless communication are performed using guided elastic waves. Data communication across an elastic channel is challenging because of the severe frequency-dispersive and multimodal propagation in solid media which distorts, delays, and greatly attenuates the transmitted data signals. To cope with this problem, we introduce a sensor network based on time-reversal pulse position modulation that compensates for channel dispersion and improves the signal-to-noise ratio of the communication link without relying on sophisticated channel estimation algorithms. We demonstrate the viability of the presented system by conducting experiments on an homogeneous and isotropic aluminum plate specimen using Lead Zirconate Titanate (PZT) sensor discs at a resonant frequency of 300 kHz.
In this paper, we address the problem of accelerating inversion algorithms for nonlinear acoustic tomographic imaging by parallel computing on graphics processing units (GPUs). Nonlinear inversion algorithms for tomographic imaging often rely on iterative algorithms for solving an inverse problem, thus computationally intensive. We study the simultaneous iterative reconstruction technique (SIRT) for the multiple-input-multiple-output (MIMO) tomography algorithm which enables parallel computations of the grid points as well as the parallel execution of multiple source excitation. Using graphics processing units (GPUs) and the Compute Unified Device Architecture (CUDA) programming model an overall improvement of 26.33x was achieved when combining both approaches compared with sequential algorithms. Furthermore we propose an adaptive iterative relaxation factor and the use of non-uniform weights to improve the overall convergence of the algorithm. Using these techniques, fast computations can be performed in parallel without the loss of image quality during the reconstruction process.
GPU computing of medical imaging applications adds an extra layer of acceleration after mathematical algorithms
are used to reduce computation times. Our work improves the performance of the multiple-input
multiple-output ultrasonic tomography algorithm, by using target sparseness and GPUs with CUDA. The main
goal was to determine how GPUs can be best used to accelerate sparsity-aware algorithms for ultrasonic tomography
applications. We present smart kernels to compute portions of the algorithm that exploit GPU resources
such as shared memory and computing units that can be applied to other applications. Using our accelerated
algorithm, we analyze different sparsity constraints setups and evaluate how GPU ultrasonic tomography
with target sparseness behaves against the same algorithm that does not incorporate prior knowledge of target
sparseness.
Non-intrusive load monitoring is an emerging signal processing and analysis technology that aims to identify
individual appliance in residential or commercial buildings or to diagnose shipboard electro-mechanical systems
through continuous monitoring of the change of On and Off status of various loads. In this paper, we develop a
joint time-frequency approach for appliance event detection based on the time varying power signals obtained
from the measured aggregated current and voltage waveforms. The short-time Fourier transform is performed
to obtain the spectral components of the non-stationary aggregated power signals of appliances. The proposed
event detector utilizes a goodness-of-fit Chi-squared test for detecting load activities using the calculated average
power followed by a change point detector for estimating the change point of the transient signals using
the first harmonic component of the power signals. Unlike the conventional detectors such as the generalized
likelihood ratio test, the proposed event detector allows a closed form calculation of the decision threshold and
provides a guideline for choosing the size of the detection data window, thus eliminating the need for extensive
training for determining the detection threshold while providing robust detection performance against dynamic
load activities. Using the real-world power data collected in two residential building testbeds, we demonstrate
the superior performance of the proposed algorithm compared to the conventional generalized likelihood ratio
detector.
This paper considers a time domain ultrasonic tomographic imaging method in a multi-static configuration using
the propagation and backpropagation (PBP) method. Under this imaging configuration, ultrasonic excitation
signals from the sources probe the object imbedded in the surrounding medium. The scattering signals are
recorded by the receivers. Starting from the nonlinear ultrasonic wave propagation equation and using the
recorded time domain signals from all the receiver sensors, the object is to be reconstructed. The conventional
PBP method is a modified version of the Kaczmarz method that iteratively updates the estimates of the object
acoustical potential distribution within the image area. Each source takes turns to excite the acoustical field
until all the sources are used. The proposed multi-static image reconstruction method utilizes a significantly
reduced number of sources that are simultaneously excited. We consider two imaging scenarios with regard to
source positions. In the first scenario, sources are uniformly positioned on the perimeter of the imaging area.
In the second scenario, sources are randomly positioned. By numerical experiments we demonstrate that the
proposed multi-static tomographic imaging method using the multiple source excitation schemes results in fast
reconstruction and achieves high resolution imaging quality.
Piezoelectric sensors that are embedded in large structures and are inter-connected as a sensor network can provide
critical information regarding the integrity of the structures being monitored. A viable data communication
scheme for sensor networks is needed to ensure effective transmission of messages regarding the structural heath
conditions from sensor nodes to the central processing unit. In this paper we develop a time reversal based data
communication scheme that utilizes guided elastic waves for structural health monitoring applications. Unlike
conventional data communication technologies that use electromagnetic radio waves or acoustical waves, the
proposed method utilize elastic waves as message carriers and steel pipes as transmission channels. However,
the multi-modal and dispersive characteristics of guided waves make it difficult to interpret the channel responses
or to transfer correctly the structural information data along pipes. In this paper, we present the basic principles
of the proposed time reversal based pulse position modulation and demonstrate by simulation that this method
can effectively overcome channel dispersion, achieve synchronization, and delivery information bits through
steels pipes or pipe-like structures correctly.
Embedded sensors in large civil structures for structural health monitoring applications require data communication
capabilities between sensor nodes. Conventional communication modalities include electromagnetic
waves or acoustical waves. However, ultrasonic guided elastic waves that can propagate on solid structures such
as pipes for a great distance have rarely been studied for data communication purposes. The multi-modal and
dispersive characteristics of guided waves make it difficult to interpret the channel responses and to transfer useful
information along pipes. Time reversal is an adaptive transmission method that can improve the spatial and
temporal wave focusing. Based on the focusing effect of time reversal, we have developed a data communication
technique using guided waves in a highly dispersive pipe environment.
In this paper, we experimentally demonstrate the data communication using time reversal pulse position
modulation (TR-PPM). Three-step laboratory tests have been performed using piezoelectric transducers in a
pitch-catch mode. We first measure the channel responses between the transmitter and the receiver on a pipe.
We then carry out the time reversal transmission by reversing the sounding signal and feeding it back to the
same channel. Finally, we perform the time reversal communication experiment by sending the modulated time
reversal signals as a stream of binary bits at a given data rate. A series of experiments are conducted on steel
pipes. Experimental results demonstrate that time reversal pulse position modulation for data communications
can be achieved successfully using guided elastic waves.
Monitoring the structural integrity of vast natural gas pipeline networks requires continuous and economical inspection
technology. Current approaches for inspecting buried pipelines require periodic excavation of sections of pipe to assess
only a couple of hundred meters at a time. These inspection systems for pipelines are temporary and expensive. We
propose to use guided-wave ultrasonics with Time Reversal techniques to develop an active sensing and continuous
monitoring system.
Pipe environments are complex due to the presence of multiple modes and high dispersion. These are treated as adverse
effects by most conventional ultrasonic techniques. However, Time Reversal takes advantage of the multi-modal and
dispersive behaviors to improve the spatial and temporal wave focusing. In this paper, Time Reversal process is
mathematically described and experimentally demonstrated through six laboratory experiments, providing
comprehensive and promising results on guided wave focusing in a pipe with/without welded joint, with/without internal
pressure, and detection of three defects: lateral, longitudinal and corrosion-like. The experimental results show that Time
Reversal can effectively compensate for multiple modes and dispersion in pipes, resulting in an enhanced signal-to-noise
ratio and effective damage detection ability. As a consequence, Time Reversal shows benefits in long-distance and lowpower
pipeline monitoring, as well as potential for applications in other infrastructures.
This paper develops a framework of a cognitive sensor networks system for structure defect monitoring and classification
using guided wave signals. Guided ultrasonic waves that can propagate long distances along civil structures have been
widely studied for inspection and detection of structure damage. Smart ultrasonic sensors arranged as a spatially distributed
cognitive sensor networks system can transmit and receive ultrasonic guided waves to interrogate structure defects such
as cracks and corrosion. A distinguishing characteristic of the cognitive sensor networks system is that it adaptively
probes and learns about the environment, which enables constant optimization in response to its changing understanding
of the defect response. In this paper, we develop a sequential multiple hypothesis testing scheme combined with adaptive
waveform transmission for defect monitoring and classification. The performance is verified using numerical simulations
of guided elastic wave propagation on a pipe model and by Monte Carlo simulations for computing the probability of
correct classification.
Gunshot detection, sniper localization, and bullet trajectory prediction are of significant importance in military
and homeland security applications. While the majority of existing work is based on acoustic and electro-optical
sensors, this paper develops a framework of networked radar systems that uses distributed radar sensor networks
to achieve the aforementioned objectives. The use of radio frequency radar systems allows the achievement of subtime-
of-flight tracking response, enabling to response before the bullet reaches its target and, as such, effectively
leading to the reduction of injuries and casualties in military and homeland security operations. The focus of
this paper is to examine the MIMO radar concept with concurrent transmission of low-correlation waveforms
from multiple radar sets to ensure wide surveillance coverage and maintain a high waveform repetition frequency
for long coherent time interval required to achieve return signal concentration.
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