Piezoelectric ultrasonic transducers have the potential to operate as both a sensor and as an actuator of ultrasonic waves. Currently, manufactured transducers operate effectively over narrow bandwidths as a result of their regular structures which incorporate a single length scale. To increase the operational bandwidth of these devices, consideration has been given in the literature to the implementation of designs which contain a range of length scales. In this paper, a mathematical model of a novel Sierpinski tetrix fractal-inspired transducer for sensor applications is presented. To accompany the growing body of research based on fractal-inspired transducers, this paper offers the first sensor design based on a three-dimensional fractal. The three-dimensional model reduces to an effective one-dimensional model by allowing for a number of assumptions of the propagating wave in the fractal lattice. The reception sensitivity of the sensor is investigated. Comparisons of reception force response (RFR) are performed between this novel design along with a previously investigated Sierpinski gasket-inspired device and standard Euclidean design. The results indicate that the proposed device surpasses traditional design sensors. Ultrasonic transducers have the ability to generate and detect ultrasonic waves. Ultrasonic sensors, that is, ultrasonic transducers operating in reception mode, convert mechanical energy into electrical energy through the contact of sound waves resulting in the production of an electrical signal ,2,3. UltFractals are complex geometrical objects which exhibit structural similarity across magnification levels in addition to being constructed from a range of length scales. To assist the construction of devices that operate efficiently over a larger range of frequencies, it is possible to consider the implementation of fractal-like structures into the design of new ultrasonic transducers ,9,10,11.In this paper, the Green function renormalization method is utilised to analyse the potential performance of a three-dimensional fractal-inspired ultrasound sensor. An expression for the reception force response (RFR) is derived and plotted against the operating frequency. This profile is then examined for the purpose of comparing against the standard (Euclidean) and another previously investigated fractal-inspired design. The results are indicative of an improvement in the RFR bandwidth in comparison to the Euclidean transducer and comparable to the previously investigated Sierpinski gasket-inspired transducer.The investigation of the Sierpinski tetrix fractal-inspired sensor begins in The self-similar structure used in this paper to imitate the complex geometry found in natural ultrasonic sensors is the Sierpinski tetrix. The Sierpinski tetrix can be classified as the three-dimensional equivalent of the Sierpinski gasket on account of its construction ,13,14. TA lattice counterpart of the Sierpinski tetrix will be used to investigate the propagation of an ultrasonic wave. The lattice represents the vibrations of the piezoelectric material of the Sierpinski tetrix. The interaction between the electrical and mechanical behaviour of the lattice vertices is decribed by  and detaph, G(n) ,16,17; snth generation graph has A, B, C and D are attached to the input/output vertices to accommodate the boundary conditions and to make the coordination number consistent, with l), remains fixed throughout the construction process. Thus, the edge length between adjacent vertices will decrease as the generation level is increased. The recursive relationship of the graphs can be described in terms of their adjacency matrixv blocks are equal to v blocks equal For the lattice structure, the i gasket . The lentherwise ,16,18. TThe movement of a wave through the piezoelectric material can be described using the stress equation of motionIn these equations, ϕ, is expressed asAdditionally, a relationship between electric field and electric potential, The poling direction of the piezoelectric material is parallel to the direction of the irection ,20. Thisirection  has showConsequently, in Equation ,(9)Tij,Electric charges do not flow easily within the piezoelectric ceramic as a result of it being a good insulator. Thus, from Gauss’ law,Using Equations –8) and  and 8) aEquation  can be rθ, can be written asn, l is fundamental in the sensor design, since the value for l will affect the overall performance of the device.The one-dimensional equation is subsequently used to derive the boundary conditions and RFR. It is discretized and, by introducing the non-dimensionalized variable The derivation of the effective one-dimensional model was the technique for simplifying the analysis of wave propagation within the fractal-like lattice. Longitudinal waves can propagate in all three forms, solid, liquid and gases, while shear waves can only propagate through solids ,22,23. Tansducer . Neverthansducer ,24 for tansducer  producedIn previous research ,11,25, tA and three output vertices at vertices B, C and D.It is assumed that Sierpinski tetrix fractal-inspired sensor will have a single input vertex at vertex A, B, C and D [t relates to the Sierpinski tetrix model. The corresponding Green functions at the next fractal generation level, ecursion . While tTo obtain Solving Equations –19) for for19) fTo account for boundary conditions, Equation  is utiliSolving these equations simultaneously results inThe pivotal elements calculated in this section can now be utilised to derive the relevant operating sensor characteristic. This is presented in the following section.B, C and D  receive an external load. Expressions for the displacement in the load and backing material are given asL and B refer to the mechanical load and backing layer, respectively, p is the Laplace transform variable. These expressions for the displacement in the load and backing material were found by obtaining solutions to the governing wave equations. At the sensor boundaries, conditions of continuity of displacement and force are applied. Accounting for symmetries in the Green function matrix and by applying the conditions of continuity of displacement, providesu refer to the mechanical displacements at the specified vertices, and the subscripted letters are the mechanical displacements at the fictitious vertices. Furthermore, the force on each vertex is given by T is the one-dimensional stress variable. Thus, continuity of force results inQ is the electrical charge, ξ is the ratio of the cross-sectional area of each edge to its length. The elements of the boundary condition matrix The model for the Sierpinski tetrix inspired sensor will have mechanical loads at the output vertices and an electrical load at the input vertex. The configuration of the proposed device follows a similar arrangement as the Sierpinski gasket fractal-inspired transducer . The senEquation , are fouprovidesu1=uA=AB,ZBZTpAB,uB−u6−hQYEquations  and 36)36) will F is the force applied to the front face of the transducer  and The RFR is defined as the ratio of the output voltage to the force at the front face of the sensor. The expression for amplitude of the forward propagating wave is given byansducer , it follWith the relevant operating characteristic now derived, we can now determine the possible benefits of a sensor based on the design of the Sierpinski tetrix.Current manufacturing limitations do not allow for the construction of a piezoelectric Sierpinski tetrix at high generation levels at the dimensions required for an operational frequency in the sub-megahertz range. This is a result of the increasing complexity in the design as the generation level is increased . Therefore, in this section, a mathematical computer model is employed to test the performance of a Sierpinski tetrix fractal-inspired sensor at generation levels one to five. This is to account for manufacturing restrictions that would be present in higher fractal generation level devices. Furthermore, a comparison of the RFR between the Sierpinski tetrix and gasket devices, at fractal generation level five, and the currently used Euclidean device is presented. The three devices have been modelled on a lead zirconate titanate (PZT-5H) ceramic with the material parameters shown in ) family ,26; whera), bandwidth  for each fractal generation level was used before its conversion into decibels.Additional resonances in the RFR profiles are desired as they increase the operational bandwidth of the device. Single length scale devices can only operate at a single frequency, whilst designs with a range of length scales have the potential to operate over a wide range of frequencies. Thus, to raise the entire RFR output, a device that presents multiple resonances is preferred. The desirable resonant behaviour of the sensor is clearly achieved by increasing the fractal generation level. Furthermore, these resonances occur at lower frequencies. The added advantage of higher fractal generation level devices is the effect it has on the RFR bandwidth. In certain instances, increasing the fractal generation level improves the operational bandwidth at the resonant frequency significantly. For example, due to the additional resonances of the fifth generation level design, the RFR outperforms the first generation device at almost every possible frequency. Additionally, the value of the gain bandwidth product is much more encouraging, as there is a noticeable increase in this figure of merit as the fractal generation level is increased. In some instances, this figure of merit increased more than three-fold over the previous fractal generation level. The gain bandwidth product was calculated using Comparisons of RFR are illustrated in It was expected that the pre-fractal Sierpinski tetrix sensor would exhibit more resonances than the Sierpinski gasket pre-fractal device on account of its lattice structure covering more length scales. However, the results do not validate this initial assumption, since, in Comparisons between the Sierpinski tetrix fractal-inspired sensor and Euclidean sensor are much more encouraging. The effect of increasing the fractal generation level resulted with an improvement in all three metrics with the exception of generation level two. In addition, there was a decrease in regards to the operational bandwidth and gain bandwidth product when increasing the fractal generation level from four to five. However, there is concern over the Sierpinski tetrix device performance in comparison to the Sierpinski gasket device at the same fractal generation level. On the other hand, the tetrix device performs much more effectively than currently favoured designs.f and generation level n. Previous research [The convergence of the fractal generation level for the RFR is presented in this section. This was achieved by computing the absolute value of the difference between successive fractal generations for up to 50 generation levels. That is,research ,11 has aThis paper investigated the RFR of a Sierpinski tetrix fractal-inspired transducer for the use of sensor applications. The Green function renormalization technique was utilised as a method for obtaining the sensor operating characteristic and was modelled on a PZT-5H ceramic. The Sierpinski tetrix fractal-like sensor was examined by constructing the lattice counterparts, a method previously performed for the Sierpinski gasket ,11 and SAs a result of current manufacturing procedures, the construction of pre-fractal sensors would be restricted to low fractal generation levels. This is a consequence of the reduction in the size of the length scales as the generation level is increased. In this paper, the first five fractal generation levels have been modelled. Additionally, fractal generation level fifteen has been included to determine what effect higher fractal generation levels have on the performance of the sensor. The point of convergence of the sensors performance was also studied. From this analysis, it was shown that the RFR converged by fractal generation level Computer models are a valuable resource to predict the performance of hypothetical designs as they help minimise the associated time and costs involved in manufacturing new ultrasonic sensors. Additionally, they assist in determining the most effective configuration of new designs. Thus, to assess the performance of the Sierpinski tetrix fractal-inspired sensor, a computer model was utilised to plot the RFR as a function of the operating frequency. This was then compared to a previously investigated Sierpinski gasket fractal-inspired transducer, in reception mode as well as the conventional 1–3 composite sensor. As expected, the tetrix device experienced more resonances at a wider range of frequencies than the standard sensor. Furthermore, an increase in the fractal generation level led to a more resonant device with the presence of higher frequency resonances. Thus, fractal-inspired sensors demonstrate suitability for a various number of purposes. For the Sierpinski tetrix pre-fractal, an increase in fractal generation level was mostly followed by an increase in the device’s RFR amplitude, bandwidth and gain bandwidth product. For fractal generation level five, these figures of merit were all reduced in comparison to the previously investigated Sierpinski gasket-inspired sensor. However, in regards to device performance over traditional designs, the analysis presented in this paper indicates a substantial increase in each of these figure of merits. These results do therefore suggest the possibility that a Sierpinski tetrix fractal-inspired sensor could be suitable in ultrasonic design to enhance current transducer performance in reception mode.This paper investigated the theoretical performance of a Sierpinski tetrix fractal-inspired sensor by utilising the Green function renormalization method. Given the simplified nature of the model, it could be beneficial to investigate this structure using finite element analysis akin to that done on the Sierpinski gasket pre-fractal in ,24. Util


In this paper, we describe a new low-cost and portable electronic nose instrument, the Multisensory Odor Olfactory System MOOSY4. This prototype is based on only four metal oxide semiconductor (MOS) gas sensors suitable for IoT technology. The system architecture consists of four stages: data acquisition, data storage, data processing, and user interfacing. The designed eNose was tested with experiment for detection of volatile components in water pollution, as a dimethyl disulphide or dimethyl diselenide or sulphur. Therefore, the results provide evidence that odor information can be recognized with around 86% efficiency, detecting smells unwanted in the water and improving the quality control in bottled water factories. At present, the number of sensor devices connected to the Internet of Things (IoT), referring to different object types of embedded devices with internet connection (low-power and low-cost devices), is rapidly growing to support new and more daring applications thanwas predicted many years ago . The IoT2 gas sensors for automotive applications. Other sensors are commonly used for healthcare applications to record Electrocardiography (ECG), Electroencephalogram (EEG) and Electromyogram (EMG) data [Current research on sensors and the IoT shows that odor sensors are onlystarting to be integrated in the same range as other types of sensors. With regard toenvironmental sensors, these are mainly used to measure temperature, humidity and displacement , as wellMG) data . For indMG) data ,7.An eNose instrument is able to simulate the human nose , replicaDifferent types of eNose have been developed extensively by researchers in recent years for various purposes, and  summarizTraditional eNoses with large amounts of sensors, from 8 to 32 or more, raisedifficulties in terms of integrating themin the embedded system for IoT applications (oriented to low-power and low-cost applications), because the computingand electrical power consumption demanded bythe system is high. Furthermore, feature parameter extraction and classification plays an important role in the performance of electronic noses. Therefore, it is important to optimize the sensor array, the extracted feature subset and classifications algorithms on eNose systems to be integrated widely in IoT.In this paper, we present an electronic nose with only four gas sensors for sulphur-based VOC water pollution detection. The sensors are chosen from the low-power Figaro family TGS26; this feature makes the electronic nose suitable for the IoT with mains. Applications and eNose are integrated together into a smooth and coherent system. eNose users can run an experiment attheir own place, and the results can be easily visualized by a comprehensible user interface. The results are stored in the user’s profile inthe cloud, and other users can access the results.The rest of this paper is organized as follows: The multi-sensory odor olfactory system used to choose the best sensors has 32 metal oxide semiconductors (MOS) sensors (MOOSY32). In MOOSY32, TGS2610-c00 and TGS2610-d00 areused for LP gas, TGS2611 for methane, TGS2620 for alcohol, solvents and vapor, with an accuracy of 500–10,000 ppm and TGS2600 for air contaminants with an accuracy of 1–30 ppm. The last one, TGS2600, has a poor accuracy in comparison to the others, but it can detect a wide range of air contaminants.It is very common to use electronic chemical sensors with partial or no specificity. This situation purports to have different sensitivities against different substances. This study has tried to identify the sensors that offer greatest signals, thus reducing the dimensions and the cost of the system. This approach to minimizing eNose has tried to reduce the current sophisticated desktop systems, such as MOOSY32 with 32 gas sensors for glucose detection in humans , toa simThe functional principles and the architecture of the MOOSY4 eNose can be easily summarized in A Beaglebone Black with 24-bit ADC and internal memory of 4GB multimedia card (eMMC) is used. According to the characteristics of the sensors and their response sensitivity, asa first step we check some samples  in the MWe built an expansion board with connections for gas sensors, temperature and humidity sensor and the LMP90100 AD converter from Texas Instruments was selected. The ADC has 24-bit resolution, low-noise programmable gain and an SPI serial interface. The SHT21Q2009 temperature and humidity sensor from Sensirion was used. Additionally, a voltage reference was necessary, for which the ADR3440 from Analog Devices was selected. A 10 mL screw-top vial of transparent glass from Scharlab was used for the sample. The air was introduced using two kinds of needles, and the experiments were performed by preheating samples to 40 °C. The system cleanedthe chamber for 20 s, and thenwaited for 15 s before opening the duct with gas from the sample. The sample gas was passedfor 135 s. The airflow of the sample was closed, following which a new cycle of measurement was started.R is the resistance of the sensor, 0R is the nominal resistance of the sensor, T is the temperature of the chamber, and RH is the relative humidity.For each experiment, account was taken of the current valuesforhumidity and temperature. In our case, we used algorithms to compensate for deviations, using a mathematical model to obtain the equations from the graphics of the data sheet of manufacturer . Three eThe feature selection wasperformed through a correlation-based approach: Correlation based Feature Selection (CFS). CFS is a filter algorithm that ranks feature subsets according to a correlation-based evaluation function. The goal of the evaluation function is find subsets that contain features that are highly correlated with the class and uncorrelated with each other. Irrelevant features should be ignored, and redundant features should be screened out . In thisThe sensor responses were analyzed usingmultivariable classification analysis with WEKAsoftware, using different algorithms. The algorithms applied were the Nearest Neighbor algorithm (NNA) and Multilayer Perception (MLP). The technique of the ‘Nearest Neighbor’ was used to classify new instances, the similarity function to calculate the similarity between the training instance and the instances of the data set ,35. MultThe classification analysis consists in the organization of data in classes, using given class labels to order the objects in the data collection. Classification approaches normally use a training set in whichall objects are already associated with known class labels. The classification algorithms learn from the training set to build a model. The model is used to classify new objects . The artWe tested the MOOSY4 eNose developed in an application for Sulphur-based VOCs in bottled water. Bottled water companies confront major problems controlling water pollution, mainly because of undesirable components release from the ground in connection with heavy rain. Bacteria and other microbes in water can make the water toxic, or make it smell bad. Water contamination is usually tested by growing bacteria in the lab or by gas chromatography or mass spectroscopy. In our work, we used gas sensors to smell water. Sometimes, bottled water with different smells, such as rotten eggs, wet cloth, butane or rubber, showed the presence of volatile dimethyl selenides and dimethyl sulphides, whose concentrations ranged, respectively, from 4 to 20 ng/L and from 1 to 63 ng/L .There are a number of different types of Figaro gas sensors. We very carefully selected four gas sensors forour specific application. Two classification algorithms were used in this work: the nearest neighbor algorithm (NNA), which is based on scoring the distances between two instances according to the similarities of its attributes, and the neural network Multilayer Perceptron (MLP), based on the use of an activation function based on the number hidden layers. Experiment results showed that CFS and MLP or NNA quickly identifies the different substances. The results are illustrated in the confusion matrix, which represents the accuracy of the solution of the classification problem. It allows the visualization of the performance of an algorithm. Each column of the matrix represents the instances in a predicted class, while each row represents the instances in the actual class. The ideal result is to have all the samples end up on the diagonal cells of the matrix .The classification features utilized for this study were: the maximum value of the rising part of the curve (vA), and the rising slope curve (slopeB) about For this application, we were interested in the discrimination of commercial water, (d) in In this work, we have developed an electronic nose with a four metal oxide gas sensors and a modular platform based on Internet of Things technology. That can be integrated into IoT Technology with the help of a Beaglebone Black. The four gas sensors are chosen depending on application. The MOOSY4 with the processing method used was able to distinguish water contaminants using commercial Figaro sensors, series TGS-26xx.The results showed that something changed in the aromatic pattern of water when it containedcontaminants, such a dimethyl sulphides or dimethyl selenides, and that the pattern variation wasdifferent on the different contaminants. The results showed that it is possible to distinguish sulphur-based VOC water pollution in the drinking water with up to 86% accuracy for correct identifications using the features slopeB and vA with the Multilayer Perception classification algorithm.


The azimuth multichannel imaging scheme with the large receive antenna divided into multiple sub-apertures usually leads to azimuth non-uniform sampling, and echoes from all azimuth channels should be reconstructed based on the signal model before conventional SAR imaging. Unfortunately, the multichannel signal model of a moving target is different from that of a fixed target. This paper analyzes the multichannel signal model of the moving target and the effect of the target velocity on azimuth multichannel reconstruction. Based on the multichannel signal mode of the moving target, a new multichannel signal reconstruction algorithm is proposed. Furthermore, the slant range velocity is estimated by computing signal energy distribution. Simulation results on point targets validate the proposed approach. Geometric resolution and swath width are two of most important parameters for spaceborne synthetic aperture radar (SAR) systems. However, they pose different pulse repetition frequency (PRF) requirements . In the d target ,4.Compared with the multichannel signal model of the fixed target, the signal model of the moving target is obviously different. If the azimuth multichannel raw data of moving targets are still handled by the conventional azimuth multichannel reconstruction algorithms, the Doppler spectra of moving targets can’t be well recovered, which results in defocused moving targets and ghost targets. The defocusing problem for moving targets is mainly due to the changed relative velocity that cannot be well resolved by an auto-focusing approach similar to the single channel case ,6,7,8,9.In this paper, the azimuth multichannel signal model of the moving target is described, and imaging results of the azimuth multichannel raw data of moving targets handled by the conventional approach ,4 for thThis paper is arranged as follows: in In this section, the signal mode of a moving target in an azimuth multichannel SAR system for HRWS imaging is described. In the following, the focus is turned to the azimuth signal component of the raw data. Compared with the multichannel impulse response in azimuth of a fixed target, the multichannel phase mismatch phenomenon is analyzed.tv and the SAR platform velocity is tv. As shown in rv and av, respectively.n separated by A is a complex constant, j, respectively. The multichannel impulse response in azimuth is described as:For a given receive sub-aperture r is much larger than Due to Compared with the approximated multichannel impulse response of a fixed point target, the multichannel impulse response of a moving point target also evolves from the monostatic response but by a different relative velocity As a result, the multichannel impulse response function in azimuth could be rewritten as follows:According to the above analyzed signal mode of the moving target in the HRWS SAR system, if we still adopt conventional azimuth multichannel reconstruction filters and imaging algorithms to handle the multichannel raw data for moving targets, the moving target would be defocused and ghost targets would occur. With the simulation parameters listed in Assuming that the slant range velocity of the target is 15 m/s, with the simulation parameters listed in However, different phase shifts Due to As a result, the multichannel response Consequently, similar to the fixed scene case in the HRWS SAR , the aziAfterwards, the aliased Doppler spectra of all individual are combined together to recover the whole Doppler spectrum of the moving target.From (15), it can be seen that the along track velocity A new slant range velocity estimation approach based on the energy distribution of the reconstructed azimuth multichannel signal in the Doppler domain. For a fixed target in the azimuth multichannel case, all energy of the received echoes in each channel are distributed on a band of PRF around a center Doppler frequency, and most of energy would be distributed on a band of According to the phenomenon as shown in To validate the proposed multichannel processing approach for moving targets, simulation experiments on point targets are carried out in this section. Simulation parameters are listed in Assumed that the slant range velocity is 10 m/s, Azimuth multichannel with a large receive antenna divided into multiple sub-apertures will be adopted in future spaceborne SAR missions for the HRWS imaging capacity. However, the multichannel signal model of a moving target is quite different from that of a fixed target. If the conventional azimuth reconstruction algorithm is to handle the raw data of moving targets, multiple ghost targets would occur due to the azimuth multichannel imbalance caused by the slant range velocity of moving target. In this paper, an azimuth multichannel imaging approach for moving targets is proposed. The key points of the proposed approach are the moving target slant range velocity estimation and azimuth multichannel reconstruction based on the multichannel signal model of the moving target. Simulation results of point targets validate the proposed azimuth multichannel reconstruction approach for moving targets in high resolution wide swath SAR. Furthermore, for the case of moving targets on the ground, clutter suppression is required and moving targets are detected. For the case for ship monitoring at low sea states, clutter suppression is even not required. Afterwards, azimuth lines of interest lines for moving targets are picked out to avoid the degraded SAR image of stationary scatters, and then the selected raw data is handled by the proposed algorithm. Therefore, the proposed algorithm is carried out to handle the raw data of moving ships on sea.


The well-known rubber hand paradigm induces an illusion by having participants feel the touch applied to a fake hand. In parallel, the kinesthetic mirror illusion elicits illusions of movement by moving the reflection of a participant's arm. Experimental manipulation of sensory inputs leads to emergence of these multisensory illusions. There are strong conceptual similarities between these two illusions, suggesting that they rely on the same neurophysiological mechanisms, but this relationship has never been investigated. Studies indicate that participants differ in their sensitivity to these illusions, which provides a possibility for studying the relationship between these two illusions. We tested 36 healthy participants to confirm that there exist reliable individual differences in sensitivity to the two illusions and that participants sensitive to one illusion are also sensitive to the other. The results revealed that illusion sensitivity was very stable across trials and that individual differences in sensitivity to the kinesthetic mirror illusion were highly related to individual differences in sensitivity to the rubber hand illusion. Overall, these results support the idea that these two illusions may be both linked to a transitory modification of body schema, wherein the most sensitive people have the most malleable body schema. Optical illusions may be the first example of illusions that spring to mind. Yet other types of illusions are at least as impressive. Take, for instance, bodily illusions based on manipulations of the body schema. The body schema is an internal and dynamic representation of the body, of the relative positions of body parts, and of their relative metrics –4. It isThe well-known rubber hand illusion is a prominent example of body schema manipulation . In thiOther examples of illusions affecting the body schema can be obtained using mirrors. Mirror illusions were first used as a tool to reduce phantom limb pain after amputation . The autIn healthy subjects, the same mirror configuration can also be used to induce multiple motor and perceptual responses on the arm hidden behind the mirror . For exaAlthough the rubber hand illusion and the kinesthetic mirror illusion use different materials, they seem to work in very similar ways. It is well established that both illusions can be explained as resulting of the integration of conflicting visual and somatosensory inputs , 21. In One possibility of investigating the relationship between these two illusions would be to study individual differences. The body schema is not a universal, innate representation; instead, it is subject to variability. For example, development of the body schema depends on the functioning multisensory integration. Multisensory foundations of the body schema only reach an adult state at 10 to 11 years of age, which indicates intraindividual variability .Illusory feelings affecting the body schema are consistently variable and depend on the participant; in particular there are individual differences in sensitivity to the kinesthetic mirror illusion and the rubber hand illusion. For instance, in the context of experiments performed in our own lab e.g., , 20, 27), 2720, 2In summary, both the rubber hand illusion and the kinesthetic mirror illusion conceptually appear to rely on the same mechanism of body schema manipulation. There are also individual differences in the sensitivity to these illusions, which have been attributed to individual differences in the malleability of the body schema. These individual differences provide an adequate basis to determine whether the rubber hand illusion and the kinesthetic mirror illusion do rely on the same mechanism: if malleability of the body schema is the source of the illusion in both paradigms, participants who are more sensitive to the rubber hand illusion should also be more sensitive to the kinesthetic mirror illusion.The main purpose of the present study was to test this hypothesis. A sample of nonclinical participants completed both the rubber hand and the kinesthetic mirror illusion paradigms, and their sensitivity to both illusions was assessed. To ensure that differences between participants reflected stable individual differences in illusion sensitivity, rather than random variation, illusion strength was assessed at two different time points based on three different measures for each illusion. Participants also completed control conditions for each illusion to ensure that illusory responses were specific to body schema manipulation. Lastly, the convergence between sensitivity to the rubber hand and kinesthetic mirror illusions was assessed. The two measures were expected to correlate, compatible with the hypothesis of a common mechanism of body schema manipulation.Additionally, some studies have shown a relationship between sensitivity to body schema illusions and eating disorders , 29, 30,2) was 21.45 (SD = 2.87).Thirty-six undergraduates in psychology  completed the experiment. Only women were included in the sample to avoid gender effects on body perception . AlAll participants provided written informed consent prior to completing the study. The experiment was performed in accordance with the declaration of Helsinki. Each participant was tested individually in a quiet room. They completed both the kinesthetic mirror illusion task and the rubber hand illusion task; details are provided in the next sections. The participant completed two sessions of the kinesthetic mirror illusion task (KMI-1 and KMI-2) and two sessions of the rubber hand illusion task (RHI-1 and RHI-2) to confirm that there were stable individual differences in time. The two illusions were interleaved and order was counterbalanced across participants, so that half of the participants completed KMI-1, RHI-1, KMI-2, and RHI-2, and the other half completed RHI-1, KMI-1, RHI-2, and KMI-2. The participant performed control conditions for both illusion tasks to confirm that responses of feeling the illusions were not due to social desirability. Each session of the kinesthetic mirror illusion task included four trials in the illusion condition and two trials in control conditions; each session of the rubber hand illusion task included two trials in the illusion condition and two trials in a control condition. The number of trials was doubled in the illusion condition of the kinesthetic mirror illusion, given the short duration of trials in this illusion (18 sec) when compared to the rubber hand illusion (90 sec). It must be mentioned that results were comparable when analyzing only the first two trials to equalize the number of trials across all conditions. Illusion and control conditions were performed in random order. At the end of the procedure, the participant completed two questionnaires related to eating disorders and their height and weight were measured to allow for computation of their BMI. not present) to 6 , and a total score is computed as the sum of all answers. Possible scores range from 34 to 204, with higher scores indicating more concern about body shape. We used the French validation of the BSQ [The Body Shape Questionnaire (BSQ) is a one-dimensional, 34-items self-report questionnaire that assesses the frequency of concerns about body shape over the preceding four weeks [ the BSQ . drive for thinness, bulimia, and body dissatisfaction. The other eight subscales measure psychological traits characteristic of patients with eating disorders . Responses are rated on a Likert scale from 1 (never) to 6 . A total score is then computed as the sum of all answers, with higher scores indicating more pathological traits. We used the French validation of the questionnaire [The Eating Disorders Inventory-2 (EDI-II) is a self-report questionnaire with 91 items and 11 subscales measuring symptoms and psychological traits commonly associated with eating disorders . Three sionnaire .The participant sat in front of a large, custom-built box . In addition to this illusion condition, the participant completed two control conditions: a control condition without displacement of the left arm, where the participant saw the reflection of their static left arm in the mirror, and a control condition without a mirror, where the participant's left arm was flexed but the mirror was replaced by an opaque board. No illusion was expected in either control condition.Three measures were collected in each trial and averaged over all trials in a session (KMI-1 and KMI-2 sessions): proprioceptive drift, onset latency, and subjective speed of the illusion. (1) Before and after each trial, the participant was required to estimate the angular position of their right hidden forearm relative to the sagittal axis . This measure reflected proprioceptive drift. (2) During each trial, the participant was required to immediately report to the experimenter if she felt that her hidden, stationary right arm started moving. The delay between the beginning of the trial and the moment the participant felt the illusion (in seconds) was recorded by the experimenter using a stop watch. This delay reflected onset latency of the kinesthetic illusion. If the participant did not report feeling the illusion at any point, onset latency was recorded as the total duration of the trial 18 seconds). (3) At the end of each trial, the participant was required to rate the perceived speed of the displacement of their right forearm during the trial, on a scale ranging from 0 to 20 with steps of 1. The answer 0 corresponded to feeling no displacement at all, 10 corresponded to feeling a displacement with velocity equal to that of the passively moved left forearm, and 20 corresponded to feeling a displacement with velocity equal to twice that of the passively moved left forearm. This measure reflected subjective speed of the kinesthetic illusion . The latency z-score was reversed to yield a measure on the same scale as the two other indices . The three z-scores were then averaged to yield the composite index of the kinesthetic mirror illusion.To summarize the illusion strength with a single value reflecting individual differences as precisely as possible, a composite illusion strength index was also computed by aggregating the values obtained for proprioceptive drift, onset latency and subjective speed. The three values were first standardized . They were instructed to always keep looking at the rubber hand.Each trial lasted 90 seconds. In the illusion condition, the experimenter synchronously stroked the rubber hand and the participant's right hand with two soft brushes. The strokes were performed at the same time, at the same frequency, and on the same location for both the fake hand and the real hand. Strokes always went from the top of the hand, just above the knuckle, toward the fingertip, with a constant frequency of approximately one stroke per two seconds. In this condition, the participant was expected to experience the rubber hand illusion . In the control condition, the strokes were asynchronous: the fake hand and the real hand were touched at different times in different locations. No illusion was expected in this condition. embodiment questionnaire [As in the kinesthetic mirror illusion, three measures were collected in each trial and averaged over all trials in a session (RHI-1 and RHI-2 sessions): proprioceptive drift, onset latency, and subjective intensity of the illusion. (1) Before and after each trial, the participant was required to estimate the location of their hidden right index finger . For thionnaire , 38: (i)ionnaire ). The fihttps://doi.org/10.1155/2017/6937328. Descriptive statistics are displayed in The raw data are available in Supplementary Material available online at For the kinesthetic mirror illusion task, none of the 36 participants reported any kinesthetic illusion in the two control conditions (conditions without arm displacement and without mirror vision), confirming that the illusion only appeared when looking at the reflection of the left arm being passively moved.Z = 4.23, p < .001), for onset latency , and for subjective intensity , confirming the presence of the rubber hand illusion in the illusion condition.For the rubber hand illusion, the difference between illusion strength measures in the experimental condition (synchronous strokes) and the control condition (asynchronous strokes) was tested using Wilcoxon's signed-rank test. Illusion strength was significantly greater in the illusion condition for proprioceptive drift , subjective speed , proprioceptive drift , and the composite illusion strength index .Test-retest stability of illusion strength measures was assessed by computing their correlation over the two sessions of trials using Spearman's rho. For the kinesthetic mirror illusion, the analysis carried out high positive and significant correlations between the first session and the second session for onset latency , subjective rating , and the composite illusion strength index  and still acceptable for proprioceptive drift . In short, participants demonstrated stable individual differences in their sensitivity to the kinesthetic mirror and to the rubber hand illusions. Given the very high test-retest reliability of illusion strength indices, subsequent analyses were performed by averaging scores collected in the two sessions of trials.For the rubber hand illusion, the correlation between first session and second session was also very high for onset latency , indicating that the relationship was not due to similarities in measurement processes. The strong relationship between the strength of the two illusions was summarized by the significant correlation between the two composite illusion strength indices, ρ = .70, p < .001; this relationship is depicted in As predicted, there was also a relationship between the strength of the kinesthetic mirror illusion and the strength of the rubber hand illusion. Measures of the same type were especially correlated across the two illusions, with all correlations greater than ρ = .11, p = .541) and in the illusion condition of the kinesthetic mirror illusion . In other words, the participants' responses were related to the two conditions eliciting a manipulation of the body schema, but they were not related to a condition involving no manipulation of the body schema.To confirm that this relationship between indices was specific to a manipulation of the body schema, we tested whether responses in the illusion conditions were related to responses in the control condition of the rubber hand illusion . The composite index in the control condition of the rubber hand illusion was unrelated to the composite illusion strength index in the illusion condition of the rubber hand illusion (n = 36) elicited even lower relationships between illusion strength and the questionnaires, with only two correlations remaining significant. In other words, the data suggested weak evidence of a relationship between body schema malleability and the risk of eating disorders, even in the younger participants.To test the hypothesis of a relationship between sensitivity to embodiment illusions and traits related to eating disorders, we assessed bivariate correlations between the two composite illusion indices, the BSQ and the EDI-II. All analyses were restricted to the 30 younger women in the sample  to avoid confounding effects of age on the risk of developing eating disorders. The results are detailed in The main objective of the present study was to test the link between two bodily illusions tasks, supported by manipulation of sensory inputs and multimodal integration, namely, the rubber hand illusion task and the kinesthetic mirror illusion task. Of secondary interest, we aimed to test whether traits related to eating disorders in a subclinical population were linked to sensitivity to these illusions.Participants demonstrated reliable individual differences in illusion sensitivity, as evidenced by the temporal stability of illusion strength across sessions and by the convergence between different measures of illusion strength within the same illusion task. As predicted, individual differences in sensitivity to the kinesthetic mirror illusion were also highly related to individual differences in sensitivity to the rubber hand illusion. These results are—to our knowledge—the first to evidence a relationship between the two illusions and support the possibility that they share a common mechanism, as suggested by several authors , 21, 25.As mentioned in the introduction, there are several conceptual reasons to believe that this common mechanism is a transitory manipulation of the body schema. In the case of the rubber hand illusion, several arguments suggest that it is directly caused by modulation of the body schema. For example, the illusory feeling of touch is associated with activity in multiple sensory areas, including the ventral premotor cortex but also intraparietal cortices and the cerebellum . As showAt first glance, the effects of the mirror paradigm on kinesthesia (sense of movement), through manipulation of multisensory integration, may seem to be exclusively of visual origin , insteadIf both the rubber hand illusion and the kinesthetic mirror illusion can be interpreted in terms of body schema manipulation, individual differences in sensitivity to these illusions may be driven by individual differences in malleability of the body schema. In other words, sensitive participants would have a more malleable body schema when compared to nonsensitive participants. This hypothesis, first suggested to explain the extra sensitivity of patients with eating disorders to the rubber hand illusion , is comp bulimia, ineffectiveness, and maturity fears. The bulimia subscale is directly related to eating disorders and represents the tendency of participants to engage in behaviors such as binge eating and self-induced vomiting. The relationship between bulimic behaviors and sensitivity to the rubber hand illusion is in line with the results of prior studies [ drive for thinness and body dissatisfaction subscales does not provide support for the hypothesis of a systematic link between illusion sensitivity and eating disorders. The ineffectiveness and maturity fears subscales reflect two psychological dimensions often associated with eating disorders; their relationship with illusion sensitivity is less straightforward to interpret but also hints at possible correlates between malleability of the body schema and psychological traits. Although these preliminary findings would obviously need to be replicated and extended in future studies, they suggest that sensitivity to embodiment illusions might constitute a marker of vulnerability to body misperception and ultimately to eating disorders, opening a promising line of research. This possibility would need to be explored in a larger sample and in participants with clinical eating disorders.A secondary purpose of the present study was to test whether participants at risk of developing eating disorders would be more sensitive to both illusions. Indeed, one interesting consequence of the relationship between sensitivity to body schema illusions and eating disorders is that nonclinical participants with a malleable body schema might share common psychological traits with patients suffering from eating disorders. Sensitivity to embodiment illusions was unrelated to concerns about body shape as measured with the BSQ, but as predicted, significant correlations emerged with three subscales of the Eating Disorders Inventory-II: studies , 30, altA final point of discussion concerns the relationships between the various measures of illusion strength. In the case of the rubber hand illusion, measures of proprioceptive drift (change in perceived position of the hidden hand) and subjective intensity (change in perceived limb ownership) are used interchangeably to measure illusion strength. However, some recent studies have shown dissociations between these two indices –46. WherEchoes of these dissociations appeared in our own results. Overall, proprioceptive drift demonstrated the weakest correlations with other measures. In the case of the rubber hand illusion, proprioceptive drift was not significantly correlated with the other two measures of illusion strength, compatible with dissociations observed in the literature –46. TakeThe present study is the first to show that there exist reliable individual differences in sensitivity to the kinesthetic mirror illusion and the rubber hand illusion and that participants sensitive to one illusion are also sensitive to the other. Overall, these results strongly support the idea that the two body schema illusions share the same mechanism. One possibility is that the two illusions represent a transitory modification of body schema, wherein the most sensitive people would have the most malleable body schema. Body schema illusions might constitute a marker of vulnerability to body malleability, to body misperception, and ultimately to eating disorders, opening a promising line of research.Raw data for both the kinesthetic mirror illusion and the rubber hand illusion.


A secondary dataset was generated from the Euldph-λ semi-automatic Algorithm, (ESA) developed to automatically computes various depths to the magnetic anomalies using a primary data set from gridded aeromagnetic data obtained in the study area. Euler Deconvolution techniques, (EDT), was adopted in the identification and definition of the magnetic anomaly source rocks in the study area. The aim is to use the straightforward technique to pinpoint magnetic anomalies at a depth which substantiate mineralization potential of the area. The ESA was integrated with the imaging function of Oasis Montaj 2014 source parameter from Geosoft® Inc. From the data, it could be summarized that similar tectonic processes during the deformation and metamorphic activities, the subsurface structures of the study area produce corresponding trending form. Specifications TableValue of the data•Depths computation in geophysical prospection is always the best estimator in the identification and definition of the subsurface magnetic anomaly source rocks with least errors.•The application of data enhancement techniques offered the opportunity of high accuracy in subsurface minerals prospection.•Studies of this nature are limited in the area, hence, the data offered a better understanding of the subsurface structures emplaced in this area to the geoscientific community.1The high-resolution aeromagnetic data was gridded, using the minimum curvature algorithm of Oasis Montaj 2014 version at a sampling interval of 100 m on a Universal Transverse Mercator (UTM) projection. The gridded data was divided into East-West and North-South geomagnetic cross-section profile lines, as shown in Each sub-grid data was de-trended to eliminate the mean and the trends by taking out the first order surface that best fit the data using the least-squares method. Edges of the sub-grids are narrowed by the applications of a 10-point cosine bell to damp discontinuities at the edges. The zeros were appended beyond the grid edges to obtain sub-grids of 559 by 559 data sampling points which reduced the data to 312,481 secondary data points.A two-dimensional Discrete Fourier transform, (DFT) was used by the Oasis Montaj software from Geosoft® Inc to estimate each sub-grid. The two-dimensional power spectrum was calculated and simplified to a one-dimensional radial spectrum by averaging values within concentric rings about the spectral origin and normalizing with respect to its value at r = 0 The depth to the base of the magnetic crust was then determined with the applications of the ESA technique using the average depth to the deepest magnetic horizon and the position of the spectral peak along a profile. The primary aeromagnetic raw datasets and the results from the structural index analysis are as presented in the Supplementary files.2The primary data exploration involved the initial data processing methods and preparations through the applications of the necessary DFT filtering techniques of data reduction and the applications of the ESA technique to generate data of the depths to the magnetic anomaly sources in the study area. This stage was carried out to identify the best relevant variables, i.e., the depths and Structural Index (SI), values that define the nature of the anomalies. The stage involves the choice of simple shape models that represent the anomalies.The aeromagnetic data processing was transformed from space domain to frequency domain with the applications of DFT filters. This method made the computation of continuous discrete potential field data less stressful as the speed with which the data was processed is faster than the old method of applying Taylor׳s series In magnetic method of geophysical prospecting, the aeromagnetic data is used to prospect for both magnetic minerals  and none magnetic minerals  directly. The method involves tracing of ore-bearing formations and geological features such as faults /fractured , or rockTo deviate from the known trial-and-error or indirect determination of source parameters in magnetic data interpretations, the inverse methods involve direct determinations of the magnetic source parameters from the measured data. In magnetic data interpretations, each anomaly has an infinite number of permissible sources. The interpreter needs to narrow down these infinite number of permissible sources to some smaller number by way of constraining the parameters of the source rocks. Even though, there are only two parameter sets that govern the shape of any magnetic anomaly, i.e., the shape of the causative body and the magnetic materials distributions within the body. In magnetic data analysis, one of these two must be fixed or kept constant whilst the other varied. Simple models are constantly used to estimate magnetic source parameters, that is, the depth, strike direction, inclination and declination angles, etc. However, simple pole and dipole approximation are commonly applicable in aeromagnetic anomalies associated with subsurface mineral exploration. Most airborne magnetic interpretations use a vertical prism for basement depth estimation


We report on the development and testing of a proof of principle water temperature measurement system deployed on an unmanned aerial system (UAS), for field measurements of thermal discharges into water. The primary elements of the system include a quad-copter UAS to which has been integrated, for the first time, both a thermal imaging infrared (IR) camera and an immersible probe that can be dipped below the water surface to obtain vertical water temperature profiles. The IR camera is used to take images of the overall water surface to geo-locate the plume, while the immersible probe provides quantitative temperature depth profiles at specific locations. The full system has been tested including the navigation of the UAS, its ability to safely carry the sensor payload, and the performance of both the IR camera and the temperature probe. Finally, the UAS sensor system was successfully deployed in a pilot field study at a coal burning power plant, and obtained images and temperature profiles of the thermal effluent. Many industrial processes generate waste heat as a byproduct. A typical method for dissipating the heat is known as once through water-cooling, where water is drawn in from a nearby water source , passed through a heat exchanger, and discharged back into the water source. This process produces a thermal discharge into the water, creating a thermal plume which can be several degrees Kelvin warmer than the original water temperature. The impact of these plumes on the environment was a factor in the development of regulations for waste heat discharges by the Environmental Protection Agency (EPA). In 1972, the Federal Water Pollution Control Act of 1948 became what is currently referred to as the Clean Water Act (CWA). The CWA revisions made it unlawful to discharge any pollutant from a point source into navigable US waters without a permit [To address the aforementioned effluent regulations, power plant operators or environmental consultant companies periodically characterize the effluent thermal plumes and examine their impact. Currently, the typical procedure is to pilot small boats, e.g., with two-person crews, through the discharge area. The boats tow thermistors behind them to determine the extent of the plume and establish locations for full-depth temperature readings. At desired locations, full-depth readings are taken by dropping specialized sensors  sensors) downward through the water column and recording the data as a function of depth. Data collection with such methods can take a day or more to complete and can be time and labor intensive. More fundamentally, because some of the discharges are near moving water or tidal areas, the plume can change shape, location, and temperature during the measurement period, thereby giving results that may be time dependent. Practical impediments, such as the lack of boat ramps at suitable locations, can also complicate the use of a boat. The use of autonomous boats or submarines may be of interest in the future but these technologies are still under development , do notThe limitations of the existing methods along with the rapid emergence (and regulatory acceptance) of unmanned aerial systems (UAS) are opening the possibility of new measurement paradigms. A UAS water temperature measurement system was conceptually developed by Alden Research Laboratory, Inc. . Working with a team of Colorado State University mechanical engineering students and their faculty advisor, the concept for field measurements of thermal discharges into water was developed into an operational prototype system. The UAS platforms have the potential to access thermal plumes with temperature sensors without requiring nearby shoreline or boat access. Furthermore, the UAS platforms can be equipped with various imaging modalities, and with global positions systems (GPS), such that data can be geo-referenced. Potential advantages of the UAS approach include the ability to make measurements more quickly (lower cost), with better spatial and/or temporal resolution, with greater reliability , and in a manner that provides access to more diverse bodies of water and flow types . Where boat access is readily available, the UAS data can augment the understanding of the boat collected data with a surface image of the plume .The present contribution represents the first simultaneous integration of both an infrared (IR) camera and an immersible temperature probe to a UAS, thereby providing a powerful new capability for measuring and visualizing the temperature of water plumes over relatively large spatial scales (~100 m–kms). Several other groups are developing UAS systems for water resource monitoring  but witThe present contribution summarizes the development, validation, and testing of a UAS based temperature imaging system for thermal plumes. The layout of the remainder of the paper is as follows. The overall design concept is shown in The system employs the Matrix-I quadcopter from Turbo Ace as a cost-effective aerial platform for proof of concept testing. Favorable characteristics include the ability to safely carry the sensor payload mass (~1.5 kg) with an “all up”  mass of about 4.5 kg, thereby allowing the “small UAS” designation by the Federal Aviation Administration (capped at 25 kg (55 pounds)). The Matrix-I has flight parameters consistent with plume mapping requirements, in particular, navigational accuracy better than ±1 m and a range of greater than 300 m. The endurance depends on the payload configuration and choice of batteries. The current system employs a 6 cell, 8000 mAh, 25.2 V LiPo battery with a mass of about 1.1 kg which allows up to 30 min of endurance under optimal flight conditions. Battery sets can be rotated between flights to allow time for drained batteries to be charged while continuing operation with fully charged batteries. The Matrix-I is a four-bladed platform with a carbon fiber frame and high performance propellers (38 cm length) powered by brushless motors. Advanced multi-rotor algorithms offer better performance and reliability than generic hobby quad-copter algorithms and increase motor efficiency. Custom sensor mounting components were 3-D printed from acrylonitrile butadiene styrene (ABS) plastic.The aircraft can be autonomously controlled with waypoint navigation, including landing and takeoff, via a Pixhawk flight controller. Missions are pre-programmed and the flight status is monitored from the ground using the Mission Planner software. The UAS also includes the ability to use a first-person-view (FPV) system to monitor the flight from the ground station as seen from the UAS. To ensure safe operation, all autopilot flight plans were configured such that the UAS platform never passed directly above people or within 10 m of structures. Additionally, the system was configured to initiate an automated return-to-launch in the event that the drone lost contact to the controller or the battery reached a minimum voltage.An IR camera is mounted on the UAS to perform thermal imaging of the water surface as a means to visualize the overall size and contours of targeted thermal plumes. The use of thermal imaging cameras, based on uncooled (non-cryogenic) IR arrays, for quantitative temperature determination is a relatively mature technology  with comA custom made immersible probe is used to measure temperature depth profiles (relative to the water surface) at desired individual locations to map the thermal plume. The probe can be dipped into the water at multiple locations during a single UAS flight. Temperature measurements are made with a thermistor based probe  that was selected due to its combination of size, mass, and ease of use in the water environment. To determine the depth of the temperature probe (relative to the water surface), pressure measurements are recorded with a transducer  and hydrostatic pressure relations are used. A micro-controller (Arduino) samples data from the temperature and pressure sensors at a rate of 2 Hz and the data is stored to a memory card (microSD) for post-flight retrieval. The temperature and pressure sensors were individually tested in dedicated experiments, described in For safety reasons, the UAS flight controller is set to limit the flight path such that the UAS has a minimum altitude of 2.7 m above the water surface. Given the uncertainty in flight position  of ~1 m, this results in limiting the actual UAS altitude to a minimum of ~1.7 m (~5 feet). The corresponding probe immersions can then be to maximum depths in the range of ~3–4 m. This depth limit is sufficient for the desired profiles in this work, but longer cord lengths could be used if needed. Note that despite the uncertainty in controlled UAS flight, the immersive probe measures its position relative to the water surface much more precisely .This section contains the results of UAS platform testing, sensor testing, and demonstrative results of the full proof of principle system operating at a power plant.The majority of flight testing was done at Christman Field which is an unused air field at Colorado State University in Fort Collins, CO, USA. A series of flight tests were performed examining basic UAS operations and procedures for taking off and landing, flight maneuvers, altitude changes to dip the immersible sensor, and autonomous waypoint navigation. Flight testing was used to determine the sensitivity of the UAS to changes in the center of gravity (CG) of the system. The onboard flight controller uses multiple accelerometers for precision flight adjustments. The system performs best when the overall center of gravity coincides with the location of the flight controller. Gusty winds (>~10 m/s for the present system) are an obvious risk for stable flight as they can cause the flight controller to overcompensate pitch-and-roll feedback. The system is particularly vulnerable during landing where a wind gust coinciding with touchdown can trigger an undesired response from the onboard flight controller. Other mounting schemes and/or more sophisticated control schemes can be considered if UAS control becomes problematic due to the change in CG position caused by the umbilical and immersible probe [Infrared camera thermal imaging systems are generally designed for the measurement of solid objects or terrestrial surfaces, not liquid surfaces . Initial testing of the IR camera verified that the camera could capture temperature variations of 1 K (by imaging some known scenes separately measured with thermocouples). The thermal camera was mounted and flown on the UAS to examine the field of view and the effects of the resolution and shutter speed settings. Test flights for the thermal camera were performed by mounting it to the UAS, as well as in separate flights where it was mounted to a fixed wing aircraft  flying over the discharge reservoir at the Rawhide Energy Station near Wellington, CO, USA. The field of view (on the water surface) attainable by the camera is defined by the flight altitude and angular extent of the image, where the latter is determined by the lens and camera optical design. For typical altitudes of ~120 m, the IR camera yielded image dimensions of ~100 m × 80 m based on the view angles of 44 and 36 degrees, with an image resolution of 333 × 256 pixels. This field of view is generally too small to image a full thermal plume, such that multiple images should be “stitched” (combined) together to achieve the desired larger view. In this work, we have manually combined the images using a commercial software package (Photoshop), though more sophisticate stitching combined with geo-tagged images could be used in the future. Testing confirmed that the camera can be used to provide qualitative images of the thermal plume, allowing the determination of the plume contour. Examples of IR camera images are shown in R2 = 0.99993) and showed that depth could be measured to within ±7 cm.Tests were performed to confirm the performance of both the temperature and depth measurements from the immersible probe. For the former, temperature readings of a water bath from the immersible probe thermistor were compared against a reference thermocouple and were found to agree to within ±1 K, which is taken as the uncertainty (error bar) for temperature readings. The approach for determining the sensor depth is to measure the local pressure and then infer depth (relative to the water surface) using hydrostatic pressure relations, i.e., pressure increases by ~96 kPa/m of depth in fresh water. The sensor depth readings were calibrated (to 5 m depth) by immersing the sensor to varying known depths within a column of water. As shown in The full UAS sensor system was tested on 21 April 2016 during three separate flights at the Rawhide Energy Station (RES) in Wellington, CO, USA. The RES has a single 280 MW coal burning unit, four 65 MW single-cycle natural gas turbines and one 128 MW single cycle gas turbine. Testing comprised of flying a survey mission at an altitude of ~120 m and obtaining both IR images as well as subsurface water temperature profiles, by dipping the temperature sensor at critical points in the plume. Winds were relatively strong at ~6 m/s to 9 m/s, and the UAS flew successfully, with the payload, under these conditions.Imaging of the thermal plume with the IR camera was performed by ascending to a flight ceiling of ~120 m and making several passes over the water to image the extents of the plume. The left panel of The final aspect of UAS flight testing was to conduct three immersion dips to measure the subsurface water temperature profiles. Example temperature depth profiles from the immersible UAS probe are shown in The present research has developed a UAS sensor package for the study of thermal plumes in water, particularly for the examination of thermal effluents from power plants. Such approaches can potentially displace (or complement) the boat-based operations that are currently widely used for effluent characterizations for regulatory and environmental assessments. A quad-copter UAS has been modified to carry the sensors. With the appropriate consideration of the center of gravity location, stable flights could still be achieved with the modified configuration. An attractive feature of UAS operation is the ability to navigate via automated waypoints as demonstrated in the present research. The two main required sensors for the thermal characterization are an infrared thermal imaging camera, used for qualitative imaging of thermal gradients to reveal the overall plume location, and an immersible probe to record the temperature depth profiles at different fixed point locations in the plume. Both sensor subsystems were characterized in controlled tests and were then integrated to the UAS. The immersible probe hangs below the UAS, such that it can be dipped in/out of the water by lowering/raising the overall UAS, and is connected to the UAS via an umbilical cord. For safety reasons, the umbilical cord contains a breakaway mechanism allowing the probe (and the lower part of the cord) to disconnect and fall into the water in the event that the probe is snagged or cannot be retracted.The full system was flight tested in a proof of concept study at the Rawhide Energy Station including waypoint navigation, imaging of the thermal plume from the IR camera, and acquisition of the temperature depth profiles. The IR camera used in the present work provided a proof of concept demonstration but could ultimately be replaced with a more advanced unit allowing more capability for geotagging of the images, higher resolution, on board storage, and the use of automated image stitching software. The dip probe data demonstrates that the water temperature can be measured at the surface and below the surface using a UAS. Testing is required for each UAS system to understand the accuracy and time response of the temperature probe.The most significant shortcomings of the system are the limited flight duration and weather and regulatory challenges. The current configuration cannot operate in precipitation and is limited to 20–35 min of flight time , which can be limiting for sampling large plumes. However, these limitations can be largely overcome by using a larger UAS and continued improvements in battery technology. The fully loaded UAS is currently limited to flying with wind speeds less than ~10 m/s. The wind speed limitation is expected to improve if a larger UAS is used and as flight controller technology improves. The most significant wind limitation is during landing, where a gust of wind can adversely affect flight performance when the UAS is at low power near the ground. More advanced flight controllers would also benefit the performance in high winds and general stability, and one could integrate the sensor control and data storage functions into the flight controller itself (obviating the need for the Arduino currently used for this purpose).In the United States, the operation of UAS systems  is limited to 122 m (400 feet) above ground level by the Federal Aviation Administration. This limits the area which can be seen in a single image. A wide-angle camera lens or oblique view of the terrain can improve the coverage footprint; however, a wide-angle lens introduces distortion and oblique views are more challenging in terms of determining the position of the plume.


This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS  integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. The Global Positioning System (GPS) and inertial navigation system (INS) have complementary operational characteristics and the synergy of both systems has been widely explored ,2,3. TheTo better treat the nonlinearity, the unscented Kalman filter (UKF) ,6,7 has Under the Bayesian filtering framework, the CKF ,11,12 prThe spherical-radial cubature rule is composed of two different integrals, spherical and radial integrals. This is based on the spherical-radial transformation and generates an even number of equally weighted cubature points. However, these integrals are then numerically computed by the spherical cubature rule and the Gaussian quadrature rule, respectively. The performance can be improved in terms of estimation accuracy, numerical stability and computational costs ,12,13,14There are basically two main factors that influence the estimation performance. First, there is model uncertainty because the adopted system model may not satisfy the actual state transition process. Second, there exist parameter uncertainties in the system model. The fixed value of covariance matrix cannot reflect the actual characteristics of the system error and measurement error. For a nonlinear system, the nonlinearity approximating capability may not be satisfied during the high-dynamic vehicle maneuvers, no matter what kind of filter: the Taylor series-based EKF, the unscented transformation-based UKF, or the spherical-radial transformation-based CKF is used. Using a nonlinear filter with fixed values of parameters in the varying dynamic environment has a risk on divergence due to modeling errors. To overcome the problem, an adaptive mechanism that dynamically identifies modeling errors can be adopted.To deal with the system nonlinearity as well as the noise uncertainty, the fuzzy logic adaptive system (FLAS) is introduced ,17,18,19The remainder of this paper is organized as follows. In Kalman filtering has been recognized as one of the most powerful state estimation techniques. The purpose of the Kalman filter is to provide the estimation with minimum error variance. The UKF is a nonlinear version of the Kalman filter and is widely used for the navigation sensor fusion. The unscented Kalman filtering deals with the case governed by the nonlinear stochastic difference equations:ith row of the matrix square root. ith point; and ith point.The UKF was first proposed by Julier et al.  to addreThe sigma vectors are propagated through the nonlinear function to yield a set of transformed sigma points,The mean and covariance of As compared to the EKF’s linear approximation, the unscented transformation is accurate to the second order for any nonlinear function. A nonlinear Bayesian filter called the cubature Kalman filter (CKF) was presented by Arasaratnam and Haykin . Proposenonlinear function × Gaussian density. The CKF utilizes the property of the efficient numerical integration method known as spherical-radial cubature rule for those multi-dimensional integrals [ith cubature point.The Bayesian filter solution reduces to computing multi-dimensional integrals, whose integrands are of the form ntegrals . Based oith cubature point and ith column vector of the identity matrix The CKF algorithm involves the following stages: Firstly, it approximates the mean and variance of the probability distribution through a set of Under the assumption that the posterior density at time Like the UKF, the CKF is another type of nonlinear filtering approach without linearization of nonlinear model. The CKF and UKF are distinguished in several aspects: (1) there has been emphasis on spherical-radial integration rule of the CKF approach which has desirable numerical accuracy criterion than on the efficiency; (2) the approaches to perform the Cholesky factorization on the error covariance matrix as the first step of both the time and measurement updates in each time step; (3) the CKF follows directly from the spherical-radial cubature rule for numerically computing Gaussian-weighted integrals whose important property is that it does not entail any free parameters, whereas the UKF introduces a nonzero scaling parameter; and (4) the covariance matrix in the UKF is not always guaranteed to be positive definite, hence, the decomposition of the covariance matrix is unavailable. In view of numerical stability, the use of UT in the design of the UKF may marginally improve its performance at expense of a reduced numerical stability because the estimation error covariance matrix is not always guaranteed to be positive semidefinite. Compared to the CKF, which builds on the numerical-integration perspective of Gaussian filters, employs a third-degree spherical-radical cubature rule to compute Gaussian-weighted integrals numerically and is a relatively derivative-free nonlinear filter with improved performance over the UKF in terms of numerical stability. In addition, they are fundamentally different in sampling point set: for the sigma-point set, the stem at the center is highly significant as it carries more weight, whereas the cubature point set does not have a stem at the center and thus does not have the numerical instability problem of UKF. To avoid the numerical instability problem, the CKF can effectively improve filtering stability ,12,13,14The fuzzy logic adaptive system is introduced to the CKF framework to enhance improvement for vehicular navigation in high-dynamic environments. There are many state estimation methods that were found to have practical applications for vehicle positioning and navigation in various dynamic environments, with a pursuit of estimation accuracy and adaptive capability. Although the cubature-based CKF solution solves the nonlinear approximation issue in a different way from UKF, it still has to meet the requirements of robust estimation and performance stability in high dynamic environments. In the design of CKF, tolerance to the uncertainty factors, including the noise uncertainty and system modeling inaccuracy, is not a high concern, which require the development of robust demand for CKF filtering scheme. The problem in this work is described as a strategy to adaptively adjust the weighting factor in CKF framework and achieve a balance between robustness and estimation performance.Fuzzy modeling is the method of describing the characteristics of a system using fuzzy rules, which are linguistic IF-THEN statements involving fuzzy sets, fuzzy logic, and fuzzy inference. There are two major types of fuzzy rules exist, namely, Mamdani fuzzy rules and Takagi-Sugeno (T-S) fuzzy rules. The designed FLAS utilizes a fuzzy inference system of Takagi-Sugeno type, which has special properties since it represents the nonlinear systems in the form of an interpolation between local linear models. A typical fuzzy system consists of three components: fuzzification, fuzzy reasoning (fuzzy inference), and fuzzy defuzzification, as shown in Model Rule THEN Output where k-th rule. For a zero-order model, the output level is a constant:Model Rule The T-S fuzzy model represents the conclusion by functions. A typical rule in the T-S fuzzy model has the form:Model Rule THEN Output where For the first-order model, the fuzzy rule can be expressed in the form:In the FLAS, the weighted average method of defuzzification to find the crisp output. The weighted average defuzzification method can be expressed as:In processing navigation states using the model-based filters as discussed in this paper, the time varying parameters are considered uncertainties to exist in the covariance matrices. The FLAS module in the CKF is employed to adapt the filter on-line. The T-S fuzzy model was used to directly estimate the variance and covariance components for the measurements and adapt the CKF. Generally, when the covariance is becoming large and is deviating from zero mean, the filter will be toward to the instability. To avoid filter divergence and improve the robustness, the fuzzy logic system is used to adapt the CKF by selecting the appropriate weighting factor The innovation information is a critical factor for the filter, including the CKF. For the filter to be optimal, the innovation is a zero-mean Gaussian white noise. Therefore, the performance of the CKF can be monitored using the value of the innovation information. It is defined as the discrepancy between actual measurements and predicted measurements:The DOD parameters for identifying the degree of change in vehicle dynamics need to be defined. Examples of possible approaches are given as follows. The innovation information at the present epoch is employed for timely reflect the change in vehicle dynamics. The DOD parameter In the FLAS, the DOD parameters are employed as the inputs for the fuzzy inference engines. By monitoring the DOD parameters, the FLAS is able to on-line tune the weighting factor according to the innovation information. In this work, the fuzzy logic used to perform adaptation involves two parameters, 1 and μ2 . For thiThe FLAS is used to identify the appropriate weighting factor so as to keep the innovation sequence toward to the zero-mean white sequence. Two DOD parameters based on the innovation are chosen as the input variables for timely reflection of the time-varying vehicle dynamics. The output is the weighting factor for tuning the process noise covariance matrix. Generally, when the covariance is becoming large and is deviating from zero mean, the filter will be toward to the instability. In such cases, the process noise covariance needs to be increased by applying a larger weighting factor for compensating the modeling error. When the innovation mean and covariance are small, the residual between actual and predicted measurements are small as well, meaning that the two measurements match adequately well.IF IF IF IF IF IF IF IF IF where k-th rule. Parameters In this paper, the first-order T-S fuzzy model has been employed. The membership functions (MFs) of input fuzzy variables as shown in Numerical simulations have been carried out to evaluate the performance of the FACKF approach in comparison with those of EKF, UKF and CKF approaches for the integrated navigation data fusion. The commercial software Satellite Navigation (SATNAV) Toolbox by GPSoft LLC was employed for generating the satellite positions and pseudoranges after the test trajectory has been defined. It is assumed that the differential GPS mode is used and only the multipath and receiver thermal noise are included.2. Let each of the white-noise spectral amplitudes that drive the random walk position states be Assume that the differential GPS (DGPS) mode is used and most of the errors can be corrected, but the multipath and receiver thermal noise cannot be eliminated. The measurement noise variances 2 for the accelerometers and For the first test, a simulated vehicle originates from the  m location in the ENU coordinate frame. A loosely-coupled GPS/INS integration configuration is used for this two dimensional case. The trajectory can be divided mainly into thirteen time intervals (or segments) according the dynamic characteristics, as indicated in The differential equations describing the two-dimensional inertial navigation state, where two accelerometers and one gyroscope are involved as follows:The second example for the three-dimensional navigation case is presented for further confirmation of the robustness and effectiveness of the proposed method. The data for INS error specifications are taken from Crista IMU specifications , as showThe three-dimensional vehicle trajectory for Scenario 2 is shown in The INS equations describing the three-dimensional inertial navigation state are:The error model employed for INS is a terrestrial INS psi angle error model :(46)δR˙A tightly-coupled GPS/INS integration configuration is used. The states and the measurements are related nonlinearly. The nonlinear pseudorange equation can be linearized by expanding Taylor’s series around the approximate  user position and neglecting the higher terms. If only the pseudorange observables are available, the linearized measurement equation based on The effectiveness of the proposed method for Scenario 2 is given by the results shown from (1)During the time intervals the vehicle is conducting maneuvering, i.e., circular motion, turn motion, constant acceleration, or variable acceleration, the model mismatch to the actual situation leads to increased errors, as can be seen from the solution obtained by the conventional EKF. The CKF is about to converge in the high dynamic regions where there still exist noticeably large errors for the UKF-based solutions.(2)Since the use of fixed value of covariance matrix cannot reflect the realistic vehicle dynamics, the performance of the nonlinear filters degrades due to such uncertainties on parameter values. To improve the performance of the CKF, a robust technique that the FLAS mechanism is incorporated for dynamically tuning the process noise covariance in the CKF framework. Remarkable improvement in estimation accuracy is obtained using the FACKF algorithm.(3)For the three-dimensional case, the estimation results of Euler angles based on the UKF are unreliable, while the CKF is able to provide acceptable results. When using the FACKF, the results are further improved.(4)The results from the two illustrative examples demonstrate that, by monitoring the innovation information based on DOD parameters, the FACKF possesses superior capability to detect the change in vehicle dynamics and adjust the scaling factor so as to prevent the divergence and remain better navigation accuracy. For the segments with sharp turns or abrupt maneuvers involved, the performance improvement becomes obvious.(5)The results show that through tuning of the process noise covariance, the FLAS helps to resolve the problem on noise uncertainty in the CKF for fusing the integrated navigation system data. Therefore, when a designer does not have sufficient information to develop the precise model, the proposed approach provides a useful alternative for designing the GPS/INS integration.Based on the results, several important remarks are given as follows:This paper has presented a sensor fusion method for the GPS/INS integrated navigation systems. The proposed approach is based on the combination of cubature Kalman filter (CKF) to treat the system nonlinearity, and fuzzy logic adaptive system (FLAS) to tune the covariance matrix of the process noise during the vehicle maneuvering.The proposed FLAS-assisted CKF enhances the robustness of CKF with better treatment to system models including statistical uncertainties (mainly for adjusting the covariance matrix of the process noise) through the FLAS. If the nonlinear filter does not perform satisfactorily well, the FLAS would provide an appropriate weighting factor to improve the navigation accuracy of the CKF. The FLAS adaptive mechanism provides adequate tuning of the weighting factor, which enables the system to achieve a balance between estimation accuracy and robustness. Based on the T-S fuzzy model, the proposed FACKF scheme timely performs effective detection of vehicle maneuvering motion and exhibits robustness against the divergence problem. The FACKF method is designed to overcome the possible degradation problems caused by modeling errors on noise uncertainty, so as to improve the navigation accuracy in highly dynamic segments without sacrificing precision in the other regions.To validate the effectiveness of the proposed method, two illustrative examples for the GPS/INS integration have been presented, one for the two-dimensional land vehicle navigation using the loosely-coupled configuration; the other for the three-dimensional navigation using the tightly-coupled configuration. Evaluation of navigation performance among various nonlinear filters, including EKF, UKF, CKF, and FACKF, has been presented. As an enhanced version of CKF, the FACKF possesses superior performance improvement in navigational accuracy and reveals very good potential as an alternative navigation state estimator for the GPS/INS navigation design.