Metrology and Measurement Systems

The paper presents a method of measuring the angle of rotation and twist using a tilted fibre Bragg grating

(TFBG) periodic structure with a tilt angle of 6◦, written into a single-mode optical fibre. It has been shown

that the rotation of the sensor by 180◦ causes a change in the transmission coefficient from 0.5 to 0.84 at

a wavelength of 1541.2 nm. As a result of measurements it was determined that the highest sensitivity can

be obtained for angles from 30◦ to 70◦ in relation to the basic orientation. The change in the transmission

spectrum occurs for cladding modes that change their intensity with the change in the polarization of light

propagating through the grating. The same structure can also be used to measure the twist angle. The

possibility of obtaining a TFBG twist by 200◦ over a length of 10 mm has been proved. This makes it

possible to monitor both the angle of rotation and the twist of an optical fibre with the fabricated TFBG.

The micro-Particle Image Velocimetry (micro-PIV) was used to measure flow velocities in micro-channels

in two passive micromixers: a microfluidic Venturi mixer and a microfluidic spiral mixer, both preceded

by standard “Y” micromixers. The micro-devices were made of borosilicate glass, with micro-engineering

techniques dedicated to micro-PIV measurements. The obtained velocity profiles show differences in the

flow structure in both cases. The micro-PIV enables understanding the micro-flow phenomena and can help

to increase reproducibility of micromixers in mass production.

The paper deals with the preparation and measurement of an experimental polymer graphite cathode that

seems to be a promising and cheap source of electrons utilizing cold field-emission in high- and ultra-high

vacuum. Polymer graphite seems to be a proper material as it contains a large amount of hybridized carbon

with a low degree of surface oxidation and silicon monoxide (SiO). Within the frame of this work, a special

experimental method of tip preparation has been designed and tuned. This method is based on ion milling

inside a dual-beam electron microscope enabling to obtain ultra-sharp tips of a diameter smaller than 100 nm

with a predefined opening angle. The charge transport within experimental samples is evaluated based on

results provided by the noise spectroscopy of the total emission current in the time and frequency domains.

Reliable monitoring for detection of damage in epicyclic gearboxes is a serious concern for all industries

in which these gearboxes operate in a harsh environment and in variable operational conditions. In this

paper, autonomous multidimensional novelty detection algorithms are used to estimate the gearbox’ health

state based on vectors of features calculated from the vibration signal. The authors examine various feature

vectors, various sources of data and many different damage scenarios in order to compare novel detection

algorithms based on three different principles of operation: a distance in the feature space, a probability

distribution, and an ANN (artificial neural network)-based model reconstruction approach. In order to compensate

for non-deterministic results of training of neural networks, which may lead to different network

performance, the ensemble technique is used to combine responses from several networks. The methods are

tested in a series of practical experiments involving implanting a damage in industrial epicyclic gearboxes,

and acquisition of data at variable speed conditions.

The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag

networks is one of the important issues in the field of RFID, which affects the reading performance of

RFID multi-tag networks. In this paper, a novel method for 3D coordinate measurement of RFID multitag

networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of

RFID multi-tag networks from different angles. The iterative threshold segmentation and the morphological

filtering method are used to process the images. The template matching method is respectively used to

determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag. After that, the

3D coordinate of each tag is obtained. Finally, a back-propagation (BP) neural network is used to model

the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance.

The BP neural network can predict the reading distances of unknown tag groups and find out the optimal

distribution structure of the tag groups corresponding to the maximum reading distance. In the future work,

the corresponding in-depth research on the neural network to adjust the distribution of tags will be done.

The phase jitter enables to assess quality of signals transmitted in a bi-directional, long-distance fibre optic

link dedicated for dissemination of the time and frequency signals. In the paper, we are considering

measurements of jitter using a phase detector the detected frequency signal and the reference signal are

supplied to. To cover the wideband jitter spectrum the detected signal frequency is divided and – because of

the aliasing process – higher spectral components are shifted down. We are also examining the influence of

a residual jitter that occurs in the reference signal generated by filtering the jitter occurring in the same signal,

whose phase fluctuations we intend to measure. Then, we are discussing the evaluation results, which

were obtained by using the target fibre optic time and frequency transfer system.

This paper presents a novel sideslip angle estimator based on the pseudo-multi-sensor fusion method. The

kinematics-based and dynamics-based sideslip angle estimators are designed for sideslip angle estimation.

Also, considering the influence of ill-conditioned matrix and model uncertainty, a novel sideslip angle estimator

is proposed based on the wheel speed coupling relationship using a modified recursive least squares

algorithm. In order to integrate the advantages of above three sideslip angle estimators, drawing lessons

from the multisensory information fusion technology, a novel thinking of sideslip angle estimator design is

presented through information fusion of pseudo-multi-sensors. Simulations and experiments were carried

out, and effectiveness of the proposed estimation method was verified.

The paper shows a study on the relationship between noise measures and sound quality (SQ) features that

are related to annoyance caused by the traffic noise. First, a methodology to perform analyses related to

the traffic noise annoyance is described including references to parameters of the assessment of road noise

sources. Next, the measurement setup, location and results are presented along with the derived sound quality

features. Then, statistical analyses are performed to compare the measurement results and sound quality

features. The included conclusions are focused on showing that the obtained loudness values, regardless of

the used system, are similar in a statistical sense. Contrarily, sharpness, roughness and fluctuation strength

values differ for the tools employed.

Two low-cost methods of estimating the road surface condition are presented in the paper, the first one

based on the use of accelerometers and the other on the analysis of images acquired from cameras installed

in a vehicle. In the first method, miniature positioning and accelerometer sensors are used for evaluation of

the road surface roughness. The device designed for installation in vehicles is composed of a GPS receiver

and a multi-axis accelerometer. The measurement data were collected from recorded ride sessions taken

place on diversified road surface roughness conditions and at varied vehicle speeds on each of examined

road sections. The data were gathered for various vehicle body types and afterwards successful attempts

were made in constructing the road surface classification employing the created algorithm. In turn, in the

video method, a set of algorithms processing images from a depth camera and RGB cameras were created.

A representative sample of the material to be analysed was obtained and a neural network model for classification

of road defects was trained. The research has shown high effectiveness of applying the digital image

processing to rejection of images of undamaged surface, exceeding 80%. Average effectiveness of identification

of road defects amounted to 70%. The paper presents the methods of collecting and processing the

data related to surface damage as well as the results of analyses and conclusions.

This paper presents a portable exhaled breath analyser, developed to detect selected diseases. The set-up

employs resistive gas sensors: commercial MEMS sensors and prototype gas sensors made of WO3 gas

sensing layers doped with various metal ingredients. The set-up can modulate the gas sensors by applying

UV light to induce physical changes of the gas sensing layers. The sensors are placed in a tiny gas

chamber of a volume of about 22 ml. Breath samples can be either injected or blown into the gas chamber

when an additional pump is used to select the last breath phase. DC resistance and resistance fluctuations

of selected sensors using separate channels are recorded by an external data acquisition board. Low-noise

amplifiers with a selected gain were used together with a necessary bias circuit. The set-up monitors other

atmospheric parameters interacting with the responses of resistive gas sensors (humidity, temperature, atmospheric

pressure). The recorded data may be further analysed to determine optimal detection methods.

Beamforming is an advanced signal processing technique used in sensor arrays for directional signal transmission

or reception. The paper deals with a system based on an ultrasound transmitter and an array of

receivers, to determine the distance to an obstacle by measuring the time of flight and – using the phase

beamforming technique to process the output signals of receivers for finding the direction from which the

reflected signal is received – locates the obstacle. The embedded beam-former interacts with a PID-based

line follower robot to improve performance of the line follower navigation algorithm by detecting and

avoiding obstacles. The PID (proportional-integral-derivative) algorithm is also typically used to control

industrial processes. It calculates the difference between a measured value and a desired set of points, then

attempts to minimize the error by adjusting the output. The overall navigation system combines a PID-based

trajectory follower with a spatial-temporal filter (beamformer) that uses the output of an array of sensors to

extract signals received from an obstacle in a particular direction in order to guide an autonomous vehicle

or a robot along a safe path.

Real-time monitoring of deformation of large structure parts is of great significance and the deformation

of such structure parts is often accompanied with the change of curvature. The curvature can be obtained

by measuring changes of strain, surface curve and modal displacement of the structure. However, many

factors are faced with difficulty in measurement and low sensitivity at a small deformation level. In order

to measure curvature in an effective way, a novel fibre Bragg grating (FBG) curvature sensor is proposed,

which aims at removing the deficiencies of traditional methods in low precision and narrow adjusting. The

sensor combines two FBGs with a specific structure of stainless steel elastomer. The elastomer can transfer

the strain of the structure part to the FBG and then the FBG measures the strain to obtain the curvature.

The performed simulation and experiment show that the sensor can effectively amplify the strain to the

FBG through the unique structure of the elastomer, and the accuracy of the sensor used in the experiment is

increased by 14% compared with that of the FBG used for direct measurement.

The results of surface texture measurements obtained with the stylus equipment, white light interferometer

and confocal profilometer of the same samples were compared. Machined isotropic and anisotropic surfaces,

of symmetric and asymmetric ordinate distribution were measured. Forms were removed using polynomials.

Sampling intervals and measuring areas during computations of parameters were the same. Discrepancies

between the results obtained with various methods were observed and discussed. It was found that errors of

surface texture measurement with the optical methods depend on the type of surface topography.

The advance of MEMS-based inertial sensors successfully expands their applications to small unmanned

aerial vehicles (UAV), thus resulting in the challenge of reliable and accurate in-flight alignment for airborne

MEMS-based inertial navigation system (INS). In order to strengthen the rapid response capability

for UAVs, this paper proposes a robust in-flight alignment scheme for airborne MEMS-INS aided by global

navigation satellite system (GNSS). Aggravated by noisy MEMS sensors and complicated flight dynamics,

a rotation-vector-based attitude determination method is devised to tackle the in-flight coarse alignment

problem, and the technique of innovation-based robust Kalman filtering is used to handle the adverse impacts

of measurement outliers in GNSS solutions. The results of flight test have indicated that the proposed

alignment approach can accomplish accurate and reliable in-flight alignment in cases of measurement outliers,

which has a significant performance improvement compared with its traditional counterparts.

Understanding the factors that influence the quality of unmanned aerial vehicle (UAV)-based products is

a scientifically ongoing and relevant topic. Our research focused on the impact of the interior orientation

parameters (IOPs) on the positional accuracy of points in a calibration field, identified and measured in an

orthophoto and a point cloud. We established a calibration field consisting of 20 materialized points and

10 detailed points measured with high accuracy. Surveying missions with a fixed-wing UAV were carried

out in three series. Several image blocks that differed in flight direction (along, across), flight altitude

(70 m, 120 m), and IOPs (known or unknown values in the image-block adjustment) were composed. The

analysis of the various scenarios indicated that fixed IOPs, computed from a good geometric composition,

can especially improve vertical accuracy in comparison with self-calibration; an image block composed

from two perpendicular flight directions can yield better results than an image block composed from a single

flight direction.

Conventionally, the filtering technique for attitude estimation is performed using gyros or attitude dynamics

models. In order to extend the application range of an attitude filter, this paper proposes a quaternionbased

filtering framework for gyroless attitude estimation without an attitude dynamics model. The attitude

estimation system is established based on a quaternion kinematic equation and vector observation models.

The angular velocity in the system is determined through observation vectors from attitude sensors and the

statistical properties of the angular velocity error are analysed. A Kalman filter is applied to estimate the

attitude error such that the effect from the angular velocity error is compensated with its statistical properties

at each sampling moment. A numerical simulation example is presented to illustrate the performance of the

proposed algorithm.