Automatic quality detection system for structural objects using dynamic output method: Case study Vilnius bridges

Paper provides an attempt to create a methodology for automated structure health monitoring procedures using vibration spectrum analysis. There is an option to use autoregressive (AR) spectral analysis to extract information from frequency spectra when conventional Fast Fourier transformation (FFT) analysis cannot give relevant information. An autoregressive spectrum analysis is widely used in optics and medicine; however, it can be applied for different purposes, such as spectra analysis in electronics or mechanical vibration. This paper presents an automated structural health monitoring approach based on the algorithm-driven definition of the first resonant frequency value from a noisy signal, acquired from traffic-created bridge vibrations. We implemented the AR procedure and developed a peak detection algorithm for experimental data processing. The functionality of the proposed methodology was evaluated by performing research on six bridges in Vilnius (Lithuania). We compared three methods of data processing: FFT, filtered FFT and AR. Bridges vibrations under different excitation conditions (wind, impulse and traffic) in normal direction were measured using accelerometers. AR provided one peak representing the lowest resonant frequency in all cases, while FFT and filtered FFT provided up to 12 peaks with similar frequency values. Such results allow implementing our method for remote automated structures health monitoring and ensure structures safety using a convenient and straightforward diagnostic method.

Communication Performance vs. Implementation Trade-offs of Interpolation Techniques for FFT-Based Carrier Synchronization exemplified on DVB-RCS2

Carrier synchronization is a crucial part of any wireless receiver, which is required due to frequency and phase offset. In case of transmission in a Time Division Multiple Access system the carrier synchronization has to be carried out for every burst separately. The DVB-RCS2 standard specifies a large variety of reference burst types with very limited known symbols. For each of these types a thorough exploration of different synchronization algorithms is required to find a trade-off between a good communication performance at very low Signal to Noise Ratio (SNR) and an efficient hardware implementation. A state-of-the-art algorithm for carrier synchronization is based on the so called Fast Fourier Transformation (FFT). An inherit limitation for the precision of frequency estimation is given by the FFT point size. To counteract this limitation, the FFT point size must be increased. In this paper we extensively compare two possible interpolation techniques for FFT results in three FFT-based carrier synchronization methods. These are applied to various reference burst types specified in the DVB-RCS2 standard. The trade-offs of these combinations are identified with a special focus on hardware implementation efficiency. Furthermore, we present a flexible IP core which can process the three synchronization methods in an efficient way and analyze its implementation complexity and throughput on a Xilinx Kintex FPGA.

ANALYTICAL AND EXPERIMENTAL INVESTIGATION ON THE FREE VIBRATION OF SUBMERGED STIFFENED STEEL PLATE

The approach developed in this paper applies to vibration analysis of rectangular stiffened plate coupled with fluid. It is obvious that the natural frequencies of a submerged structure are less than those of in vacuum and these are due to the effect of added mass of water to the structure. This paper focuses on the experimental, analytical and numerical solution of natural frequencies of submerged stiffened plate. The analytical solution based on the deflection equation of submerged orthotropic plate, Laplace’s equation and Rayleigh's method in vibration analysis. By used the FEM software the numerical results for natural frequencies are derived. The natural frequencies of the stiffened plate are obtained practically by using Fast Fourier Transformation functions (FFT) in experimental analysis. Experimental results demonstrate the validity of analytical and numerical solution and results.

Principal Components Analysis of EEG Signals for Epileptic Patient Identification

According to the behavior of its neuronal connections, it is possible to determine if the brain suffers from abnormalities such as epilepsy. This disease produces seizures and alters the patient’s behavior and lifestyle. Neurologists employ the electroencephalogram (EEG) to diagnose the disease through brain signals. Neurologists visually analyze these signals, recognizing patterns, to identify some indication of brain disorder that allows for the epilepsy diagnosis. This article proposes a study, based on the Fourier analysis, through fast Fourier transformation and principal component analysis, to quantitatively identify patterns to diagnose and differentiate between healthy patients and those with the disease. Subsequently, principal component analysis can be used to classify patients, employing frequency bands as the signal features. Besides, it is made a classification comparison before and after using principal component analysis. The classification is performed via logistic regression, with a reduction from 5 to 4 dimensions, as well as from 8 to 7, achieving an improvement when there are 7 dimensions in the precision, recall, and F1 score metrics. The best results obtained, without PCA are: precision 0.560, recall 0.690, and F1 score 0.620; meanwhile, the best values obtained using PCA are: precision 0.734, recall 0.787, and F1 score 0.776.

Concrete anisotropy estimated from ultrasonic signal amplitudes

The anisotropy of concrete is an essential issue in the construction industry. In this study, for the first time, ultrasonic compression and shear wave signals have been investigated for the orthogonal directions of unreinforced concrete by means of fast Fourier transformation (FFT). For this purpose, cubic concrete samples were prepared in 12 designs of different strengths for ultrasound transmission measurements. The characteristic amplitudes at dominant frequencies were determined by the FFT of these signals. The FFT amplitude differences in the compression and the shear wave signals on the orthogonally oriented surfaces provide essential information about the presence and degree of anisotropy. According to linear regression analysis, the FFT amplitude anisotropies and the amplitude ratios of the compression and shear waves decreased significantly according to increasing concrete strength. In addition, it was found that the anisotropy and the ratio of the FFT amplitudes increased proportionally to the water/cement ratio, the porosity and the water content of the various concrete designs.

A Comparative Study of Signal Processing Methods for Contactless Geodetic Monitoring

Building structures are subject to various deformations caused by external and internal factors. Deformations are determined by various methods in the form of monitoring. It is very important to monitor the dynamic vibration response on bridge structures since these measurements allow us to identify any possible damage over time and take appropriate action. Our experiment, described in this article, is based on the use of non-contact methods, among which we used a geodetic instrument RTS (Robotic Total Station) and a seismograph to measure vibrations. The purpose and novelty of our work are reflected in the use of geodetic instruments to determine the dynamic response and synchronization of the obtained results. When using RTS technology, we increased data acquisition from 9 to 26 measurements per second. Comparative analysis of the measured signals was performed using FFT (Fast Fourier Transformation) and LSP (Lomb–Scargle Periodogram), based on LSSA (Least-Squares Spectral Analysis). The results showed us that when using the RTS geodetic instrument, it is possible to achieve frequency spectra comparable to those measured with a seismograph instrument. By increasing the number of measurements, the RTS method can be used to obtain more continuous data, which are essential for dynamic analyses.

Unsupervised deep learning based ego motion estimation with a downward facing camera

Knowing the robot's pose is a crucial prerequisite for mobile robot tasks such as collision avoidance or autonomous navigation. Using powerful predictive models to estimate transformations for visual odometry via downward facing cameras is an understudied area of research. This work proposes a novel approach based on deep learning for estimating ego motion with a downward looking camera. The network can be trained completely unsupervised and is not restricted to a specific motion model. We propose two neural network architectures based on the Early Fusion and Slow Fusion design principle: “EarlyBird” and “SlowBird”. Both networks share a Spatial Transformer layer for image warping and are trained with a modified structural similarity index (SSIM) loss function. Experiments carried out in simulation and for a real world differential drive robot show similar and partially better results of our proposed deep learning based approaches compared to a state-of-the-art method based on fast Fourier transformation.

Acoustic emission as a valuable technique used for monitoring polymer failures

In the paper, acoustic emission (AE) system was presented as a method that can be used to monitor polymer material failures. Samples fabricated of two aluminum profiles bonded together with a thick layer of cured epoxy resin were subjected to fracture tests. Epidian 53 epoxy resin cured with Z1 curing agent as well as Epidian 5 epoxy resin cured with PAC curing agent were selected as adhesives. Acoustic emission parameters were acquired during Double Cantilever Beam (DCB) tests. The frequencies of elastic waves released during failure were then analyzed using both Fast Fourier Transformation (FFT) and Wavelet Transformation (WT) for the two materials.

Rhodopsins build up the birefringent bodies of the dinoflagellate Oxyrrhis marina

The ultrastructure of the birefringent bodies of the dinoflagellate Oxyrrhis marina was investigated by transmission electron microscopy. Ultrathin sectioning revealed that the bodies consist of highly ordered and densely packed lamellae, which show a regular striation along their longitudinal axis. A lattice distance of 6.1 nm was measured for the densely packed lamellae by FFT (Fast Fourier Transformation) analysis. In addition, a rather faint and oblique running striation was registered. Lamellae sectioned rather oblique or almost close to the surface show a honeycombed structure with a periodicity of 7.2–7.8 nm. Freeze-fracture transmission electron microscopy revealed that the lamellae are composed of highly ordered, crystalline arrays of particles. Here, FFT analysis resulted in lattice distances of 7.0–7.6 nm. Freeze-fracture transmission electron microscopy further revealed that the bodies remained intact after cell rupture followed by ascending flotation of the membrane fractions on discontinuous sucrose gradients. The birefringent bodies most likely are formed by evaginations of membranes, which separate the cytoplasm from the food vacuoles. Distinct, slightly reddish-colored areas, which resembled the birefringent bodies with respect to size and morphology, were registered by bright field light microscopy within Oxyrrhis marina cells. An absorbance maximum at 540 nm was registered for these areas, indicating that they are composed of rhodopsins. This was finally proven by immuno-transmission electron microscopy, as antisera directed against the C-terminal amino acid sequences of the rhodopsins AEA49880 and ADY17806 intensely immunolabeled the birefringent bodies of Oxyrrhis marina.

Development of an FPGA-based realtime DAQ system for axion haloscope experiments

A real-time Data Acquisition (DAQ) system for the CULTASK axion haloscope experiment was constructed and tested. The CULTASK is an experiment to search for cosmic axions using resonant cavities, to detect photons from axion conversion through the inverse Primakoff effect in a few GHz frequency range in a very high magnetic field and at an ultra low temperature. The constructed DAQ system utilizes a Field Programmable Gate Array (FPGA) for data processing and Fast Fourier Transformation. This design along with a custom Ethernet packet designed for real-time data transfer enables 100% DAQ efficiency, which is the key feature compared with a commercial spectrum analyzer. This DAQ system is optimally designed for RF signal detection in the axion experiment, with 100 Hz frequency resolution and 500 kHz analysis window. The noise level of the DAQ system averaged over 100,000 measurements is around -111.7 dBm. From a pseudo-data analysis, an improvement of the signal-to-noise ratio due to repeating and averaging the measurements using this real-time DAQ system was confirmed.