With the concept of sustainability gaining popularity, low-carbon tourism has been widely considered. In this paper, a multicriteria group decision making (MCGDM) process based on an uncertain environment is proposed to study the evaluation problem of low-carbon scenic spots (LSSs). In order to minimize the influence of subjective and objective factors, the traditional Vlse Kriterjumska Optimizacija I Kompromisno Resenje (VIKOR) method is expanded, using the improved best and worst method (IBWM) and Bayes approximation method, based on Dempster-Shafer Theory (B-DST). First, in order to make the evaluation process more professional, a number of evaluation criteria are established as effective systems, followed by the use of triangular intuitionistic fuzzy numbers (TIFNs) to evaluate alternatives of LSSs. Next, according to the evaluation results, the weights of the criteria are determined by the IBWM method, and the weights of the expert panels (Eps) are determined by B-DST. Finally, a weighted averaging algorithm of TIFN is used to integrate the above results to expand the traditional VIKOR and obtain the optimal LSS. The applicability of this method is proven by example calculation. The main conclusions are as follows: tourist facilities and the eco-environment are the two most important factors influencing the choice of LSSs. Meanwhile, the roles of management and participant attitudes in LSS evaluations cannot be ignored. Low-carbon tourism was first proposed during the 2009 World Economic Forum “towards low-carbon tourism” . As a meHowever, academic research on LSSs has lagged behind. Therefore, it is necessary to understand the connotation of LSS and establish an evaluation system as quickly as possible. This would provide not only a scientific basis for macro carbon emission reduction decisions, but also theoretical guidance for future LSS criteria verification, emission reduction project cooperation, and the establishment of an emission compensation system. At present, most published studies have focused on qualitative analyses, while few quantitative analyses have been published. Moreover, studies of scenic spots have only focused on the construction of a system of criteria ,5,6.In this paper, the evaluation of LSSs is studied by combining TIFNs, IBWM, B-DST, and the expanded VIKOR method , Network Analytic Hierarchy Process (ANP), Decision Experiment and Evaluation Experiment (DEMATEL), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), Linear Programming (LP), and the Data Envelope-Analysis method (DEA). In 1998, Opricovic proposed a VIKOR method which is applicable to MCDM technology, emphasizing the selection and ordering of alternative sets of conflict criteria ,27. It rIn order to comprehensively evaluate LSSs, a complete evaluation criteria system should be established. Therefore, the realization of LSS was mainly carried out from four aspects: eco-environment, tourism facilities, management system, and participant attitudes. The specific criteria system is shown in LSS is composed of natural resource-type scenic spots, such as wetlands and forests. There are three main aspects to the evaluation for scenic resources: biological environment, water environment and air quality. The unique low-carbon landscape in the scenic area is the premise for the development of low-carbon tourism, and it is also the key to attracting tourists . The typ(1) Refers to the sum of all plant communities in a certain area. This criterion is usually used to reflect the green status in the scenic area.(2) The quality of the ecological environment in the scenic spot is mainly reflected in the maintenance of species diversity in the scenic area.(3) Refers to the surface water environmental quality standard of the People’s Republic of China (GB3838-2002); surface waters includes rivers, lakes, canals, channels, reservoirs, and so on.(4) The drainage of scenic spots must meet the national standard (GB8978-88) comprehensive sewage discharge standard.(5) Simplifies the concentration of several air pollutants that are routinely monitored into a single conceptual index value form.(6) The air negative ion concentration is used as the basic observation index, and the air quality is evaluated by the unipolar and the air ion evaluation coefficientsTo build a LSS, the facilities inside the scenic spot should have low-carbon and environmental protection effects, mainly including two aspects: transportation facilities and waste disposal facilities. These two points play an important role in the construction for LSSs, and are also important criteria for evaluating the carbon level in the area . In the (1) Tourist attractions mainly choose green transportation modes such as battery cars and mountain bikes. At the same time, motor vehicles are prohibited from entering, reducing pollution and reducing carbon emissions from scenic traffic.(2) Road construction in the scenic area should select materials that are in harmony with the natural environment, reduce the proportion of hardened roads, and set up animal passages and reminder signs.(3) The road construction for scenic spots needs to increase the greening rate.(4) For the guidance information in the scenic area, original materials such as stones, wood, and pebbles can be selected.(5) The scenic ecological parking lot generally uses shrub as the isolation line and should minimize the area of the hardened parking lot.(6) Set up sorting bins in the scenic spot, and recycle and treat the recyclable garbage and non-recyclable garbage separately.(7) The disposal of solid waste should be treated in such a way that it minimizes the impact on the environment.Management level is a key factor by which to determine the quality of LSSs. Generally, management levels are divided into hard and soft management. Hard management mainly refers to some hard criteria such as management target compliance rates, policy support, implementation intensity, travel complaints and feedback mechanisms, low-carbon operating systems, and supervision agencies . Softwar(1) National policy support will help to stimulate scenic spots in accelerating the promotion of low-carbon tourism and forming a cyclical model of the entire industrial chain.(2) Reference to the people-oriented concept, i.e., one which is in line with the development needs of LSSs, and, at the same time, which is better able to protect the interests of travelers.(3) The water and air quality of scenic spots are vital, and the corresponding detection mechanisms must be established to sensitively detect environmental changes.(4) Install a device that calculates the carbon emissions of tourists. Furthermore, and a tree field should be set up for visitors. By purchasing planting seedlings, the “offset to carbon activities” of tourism can be achieved.(5) Tourism products in the scenic spot can be more localized agricultural products, and tourism packaging is as ecological as possible, thereby avoiding the waste of resources caused by excessive packaging.(6) In order to maintain the ecological environment and maintain the low-carbon effect, the scenic spot must invest a certain amount of its resources every year into maintenance.The participants in low-carbon tourism include tourists and local residents. Tourists are the ultimate consumers of low-carbon tourism products . While v(1) The attitude and participation of local residents toward low-carbon and environmental protection have an impact on the low-carbon operation of the scenic spot.(2) Through the dissemination of low-carbon knowledge, all employees in the scenic area will influence tourists regarding their own low-carbon environmental behavior.(3) Advocate low-carbon tourism and green tourism. Publicize and introduce low-carbon knowledge in public information materials.A novel MCDM method is introduced in this paper which combines TIFN, extended BWM, B-DST, and the extended VIKOR approach. Among these, TIFN is the basic evaluation language; extended BWM and combining evidence theory with Bayes approximation are mainly used to calculate the criteria and group decision makers, respectively. Extended VIKOR is the main research method.The fuzzy set theory proposed by Zadeh aims to simulate the ambiguity or inaccuracy of human cognitive processes . A fuzzyDefinition 1.Let As shown in andrespectively represent the maximum membership degree and the minimum non-membership degree, such that they satisfy the condition:,andDefinition 2.The basic operation process of TIFN is described by Wan in detail .Definition 3.For the TIFN, the weighted averaging algorithm (WAA) is defined as Equation (3) (The proof of Equation (3) is provided in Definition 4.Ifis a set of TIFN. The weighted probability meanis calculated as follows:Definition 5.The similarity measurement betweenand is calculated using the normalized Hamming distance and Euclidean distance, which are used to measure the shortest distance between two fuzzy numbers, as shown in Equations (5) and (6).The above two distance of TIFNs formulas have the following properties:(1)(2)(3) Ifis any TIFN, then(The proof of property (3) is provided in Definition 6.In TIFN, language terms can be effectively converted into TIFNs by transforming scales. Suppose that there are Both Phase 1. Determining the weight of the criteria (BWM-Entropy Weight Method).BWM was proposed by Rezaei in 2015 . This meStep 1. Determine the criteria system.The system is the basis for evaluating alternatives; suppose there is Step 2. Determine the best and worst criteria.In this step, the best and worst criteria are determined by the Eps, whereby Step 3. Compare the best and worst criteria with other criteria.This step is divided into two parts: one is that the language variables in Step 4. The mathematical model will be established to calculate the target weight.The aim is to calculate the optimal weights of criteria in this step. For the mathematical programming models, the optimal absolute difference is expressed as Next, supposed that:(1) When (2) When (3) When (4) When The target weight value As for the minimum consistency The consistency ratio can be calculated by Step 5. The entropy weight method is used to determine the objective weight. The specific process and the releated Equations (15) and (16) are shown in Step 6. The entropy weight method is used to modify BWM.In order to combine the BWM and the entropy weight methods, the entropy value variable is introduced and the final weight value Where (1) The final determined weight should be between the weights determined by the two methods.(2) The entropy value Phase 2. B-DST is used to determine the weights of Eps.B-DST is an extension of DST, which can be used to deal with the uncertainty existing in things. It decomposes the complex large evidence into simple, small pieces of evidence in a certain way. After the relevant processing of small evidence, it uses combination rules to synthesize the processing results, and finally, obtains the solution to the problem ,49. In tFor the TIFN multiattribute group decision matrix Step 1. Determine the attribute weighted evidence body.In the previous phase, the final criteria attribute weight modified by the entropy weight method to BWM is Step 2. Calculate the Bayes approximation function value of the attribute weighted evidence body.By considering the credibility of the criteria in the weighted evidence body, the uncertainty information provided by the criterion evidence with a low level of credibility will be reduced, and the uncertainty information provided by the uncertain criterion element will be increased, thereby reducing the influence of the criterion evidence body with low levels of credibility on the whole decision result.The Bayes approximation function value Step 3. Determine the weight of comprehensive evidence.According to the Bayes approximate formula of evidence theory, the attribution weighted evidence body Step 4. Calculate the distance and similarity between the sets of evidence.Let evidence set Similarity Step 5. Calculate the weight of Eps.If the similarity between the evidence is relatively large, it can be considered that the degree of mutual support between the evidence is relatively high, that is, the evidences are mutually supportive. In general, the higher the degree to which an evidence is supported by other evidence, the more credible that evidence. Let Let the relative confidence Phase 3 Determine the ranking of suppliers and select the best LSSs.VIKOR is an optimal compromise solution sequencing method based on ideal solutions which determines the positive ideal solution and negative ideal solution of the decision matrix, and then sorts the solution according to the proximity between the attribute evaluation value of each alternative and the ideal solution. It can consider both group utility maximization and individual regret minimization, and fully reflect the subjective preference of decision makers. On the basis of the above studies and the classical VIKOR, this paper proposes an extension method of TIFNs multiattribute group decision making to select the optimal LSS. The main steps are as follows:Step 1. Calculate the attribute weight vector of comprehensive criteria.According to the research on criteria attribute weights Step 2. Construct the comprehensive decision matrix.According to the TIF-WAA operator in Definition 3, the following Equations (31) and (32) are used to integrate a single decision matrix Step3. Determine the positive and negative ideal solutions, where the positive ideal solution is Step 4. Calculate the group utility values, the individual regret values for each alternative, and the approximation between the alternative solution and the ideal solution.The Hamming distance of TIFN in Definition 4 is used in Equations (35) and (36) to calculate the above group utility value Calculate the approximation between the alternative solution and the ideal solution Step 5. The evaluation results are obtained by sorting the alternatives.According to In this section, five typical LSSs in area J are evaluated. Firstly, the background information of the five alternatives is given. Secondly, the proposed method was used to evaluate the five LSSs and determine the sorting order. Finally, the influence of different situations on the evaluation results was obtained through sensitivity and comparative analyses.In the context of global climate change, it is the common aspiration of mankind to reduce greenhouse gas emissions and achieve sustainable economic development. Low-carbon economy has become a trend of sustainable development for the global economy. As an important part of the global economy, tourism has an unshirkable responsibility to respond to the call for low-carbon development. As the carrier of tourism, the construction for LSSs is of incomparable significance. In recent years, various types of LSSs have been springing up, but a series of problems such as ununified selection subjects and incomplete construction methods have become increasingly prominent. Therefore, systematically evaluating LSSs is a problem that needs to be solved by academia. Based on the objective reality, and taking LSSs as the research object, this paper proposes a new MCDM to effectively solve the problem of LSS evaluation. There were three Eps Phase 1. Determine the weight of the criteria.After consultation, the Eps determined the optimal criterion The consistency index Phase 2. Calculate the weights of Eps.According to Equations (19)–(22), the Eps comprehensive weighted body The weight Phase 3. Determine the ranking and select the best LSS.By combining the weights obtained by phase1 and phase2 through Equation (30), the final weights for the 22 criteria can be obtained as shown in The method proposed in this paper can consider the maximum value of group utility, the minimum value of majority regret, and the minimum value of individual regret. In addition, the weights proportion of scoring Eps are taken into account effectively through Bayesian distribution. The decision system chooses the decision mechanism Due to the different selection of In this section, the impact of the group utility maximization coefficient In this paper, BWM is combined with the entropy weight method to obtain criteria weights. In order to demonstrate the scientific nature of the research results, this paper considers the comparison between the single BWM method and the single entropy weight method to calculate the weights. BWM is a very effective MCDM for determining criteria weights; it simplifies the operation process effectively by pairwise comparisons to calculate the required results. Entropy, as a measure of information uncertainty, can be used to determine the entropy weight of the criteria. The criteria weights are mainly determined according to the information contained in the evaluation value of the criteria, so as to avoid the influence of subjective factors. By comparison, the results obtained by the two methods are less different from those obtained by the research method in this paper. Therefore, the optimal multicriteria decision method of TIFNs proposed in this paper can effectively solve the MCDM problem. In comparison, the method in this paper considers both objective weight and subjective weight, and combines them effectively to make the evaluation results more universal. The evaluation results of the two methods are shown in This paper proposes a new MCGDM method by which to study evaluation methods for LSSs. IBWM and B-DST are used to improve the traditional VIKOR method. Considering the uncertainty of the decision-making environment, TIFN was chosen as the evaluation language. Finally, the following conclusions are obtained.(1) According to the comprehensive weight in (2) The operation of the scenic area is guided by low-carbon ideas. Scientific planning for tourist attractions is an important aspect of construction. The low-carbon concept should be put into every aspect of the operation of scenic spots, including strengthening the education of scenic area management personnel and service personnel. At the same time, scenic area managers should actively exchange and cooperate with foreign scenic spots to learn from their experience.(3) LSSs should actively strengthen environmental protection education for tourists. Tourists are the main body of tourism activities, and their behavior has a direct impact on the carbon emissions of scenic spots. Therefore, it is necessary to actively strengthen environmental protection education for tourists.The development of sustainable tourism and eco-tourism is a hot topic for experts and scholars at home and abroad. However, there are few research papers on low-carbon tourism. The method proposed in this paper can help select the best LSS and fill the academic gap in the LSS evaluation field. Then, in the context of global warming, this paper studies low-carbon evaluation criteria systems for tourist attractions. It is not uncommon for evaluation criteria systems to be applied in tourism, but this paper innovatively applies an evaluation system to the low-carbon evaluation of scenic spots. However, this research has some shortcomings. First, in the research process, the selection of the expert group members was relatively limited, and their views were relatively concentrated, which may have led to insufficient broadness of thought, i.e., making it too simple to reach consensus on issues. Second, the evaluation of alternative LSSs in this paper is expressed by TIFNs, and the evaluation value may be real numbers or other forms in practical application. Therefore, the MCGDM problem could be better studied when the decision information is of mixed language type.


In this article, we demonstrate the noise reduction and signal to noise ratio (SNR) enhancement in Brillouin optical time-domain analyzers (BOTDA). The results show that, although the main noise contribution comes from the Brillouin interaction itself, a simple low pass filtering on the detected radio frequency (RF) signal reduces remarkably the noise level of the BOTDA traces. The corresponding SNR enhancement depends on the employed cut-off frequency of the low pass filter. Due to the enhancement of the SNR, a mitigation of the standard deviation error of the Brillouin frequency shift (BFS) has been demonstrated. However, RF filters with low cut-off frequency could lead to distortions on the trace signals and therefore detection errors on a non-uniform BFS. The trade-off between the noise reduction and the signal distortion as well as an optimal cut-off frequency are discussed in detail. Distributed sensing applications such as the Brillouin optical time-domain analysis (BOTDA) have attracted immense interest in recent years in fields like health monitoring of large structures in oil and gas pipelines, railways and high voltage transmission lines ,2, high The accuracy of the BFS estimation is the key performance of the BOTDA sensing. The estimation error in the experiment can be statistically determined by the standard deviation of the measured BFS after a large number of consecutive measurements. It depends on the full-width at half maximum (FWHM) of the BGS stem SNR . TherefoMany methods have been proposed to increase the SNR in BOTDA sensors. The SNR can be enhanced by increasing the number of averages of the data traces, however, this comes at the cost of an increased measurement time. Other methods like differential lse pair , Raman alse pair , self-helse pair , opticallse pair , waveletlse pair  and 2D alse pair  have beelse pair ,20, are ir [Bsys , RF low n itself ,23. Thisz is given byT is the pulse width.For a CW-SBS interaction, the complex BGS profile is given by 1)GB(ω)ractions ,26,27,28GB(ω)ractractions . Provideractions ,30,31, tT and peak pulse power In BOTDA sensors, a pulse train is launched into the fiber as the pump signal with a pulse width In an additive white Gaussian noise channel (AWGN), the noise power is directly proportional to the bandwidth. Thus, noise reduction and SNR enhancement can be performed by removing (filtering) noisy spectral components out of the detected RF spectrum of the probe signal. Let T is the pulse width. The probe and pump power are set to −14 dBm and 18 dBm, respectively. In order to study the effect of the RF filtering, random noise is added to the traces. For a fair comparison, the noise level on all the traces in the simulation are the same. The spectra of the traces are obtained by applying a fast FT to the time domain traces. The filtering effect is simulated by multiplying the spectra with the transfer function of a RF low pass filter. The filtered time domain traces are retrieved by applying inverse fast FT to the filtered spectrum. The SBS linewidth is 50 MHz, while the assumed BFS is 10.615 GHz and the total fiber length is 10 km. The simulation is carried out based on the simple model presented in The ideal spectrum in The measurement accuracy, which plays a vital role in the evaluation of sensor performance, symbolizes the uncertainty of the measurand (temperature or strain). It is characterized by the BFS estimation error in a BOTDA system and highly dependent on the probe SNR. As the reconstructed BGSs in First, we study the effect of the RF filtering on the time domain traces in regard to noise reduction, Next, the effect of the RF filtering on the BOTDA accuracy was evaluated. At last, the figure of merit (FoM)  as a criTo further asses the performance of BOTDA with RF filtering, a strain point measurement was conducted. As illustrated in B and the rising time Under the criteria that the step response of the filter should not distort the temporal response of the sensing system, the optimal RF filter bandwidth can also be theoretically estimated. For a BOTDA system with 10 m spatial resolution achieved by 100 ns pump pulse, the best noise performance with the lowest trace distortion is optimized with a filter rising time of 00≈33 ns . Since tSince the bandwidth of the digitizer is narrower than the detector in the experiment, the system bandwidth n itself ,23. Instn itself  and thusIn this article, we have presented RF filtering on the detected signal to mitigate the noise level and SNR enhancement in BOTDA sensors. We have theoretically and experimentally demonstrated that the noise level can be reduced by low pass filters, and consequently, the measured standard deviation error of the BFS is reduced. The noise reduction level depends on the employed cut-off frequency of the low pass filter. However, especially for low bandwidths, the noise suppression is not as high as theoretically possible for a pure AWGN channel. This clearly shows the influence of the gain dependent SBS noise on BOTDA sensors, which requires further in detail investigation. The results also indicate that RF filters with too low cut-off frequency lead to a distortion of the trace signals and therefore detection errors for a non-uniform BFS. Instead of an RF filter, a cost effective detector with optimal bandwidth can be used.


Detecting unresolved targets is very important for radars in their target tracking phase. For wideband radars, the unresolved target detection algorithm should be fast and adaptive to different bandwidths. To meet the requirements, a detection algorithm for wideband monopulse radars is proposed, which can detect unresolved targets for each range profile sampling points. The algorithm introduces the Gaussian mixture model and uses a priori information to achieve high performance while keeping a low computational load, adaptive to different bandwidths. A comparison between the proposed algorithm and the latest unresolved target detection algorithm Joint Multiple Bin Processing Generalized Likelihood Ratio Test (JMBP GLRT) is carried out by simulation. On Rayleigh distributed echoes, the detection probability of the proposed algorithm is at most 0.5456 higher than the JMBP GLRT for different signal-to-noise ratios (SNRs), while the computation time of the proposed algorithm is no more than two 10,000ths of the JMBP GLRT computation time. On bimodal distributed echoes, the detection probability of the proposed algorithm is at most 0.7933 higher than the JMBP GLRT for different angular separations of two unresolved targets, while the computation time of the proposed algorithm is no more than one 10,000th of the JMBP GLRT computation time. To evaluate the performance of the proposed algorithm in a real wideband radar, an experiment on field test measured data was carried out, in which the proposed algorithm was compared with Blair GLRT. The results show that the proposed algorithm produces a higher detection probability and lower false alarm rate, and completes detections on a range profile within 0.22 ms. For wideband monopulse radars, the unresolved targets may cause error in the target tracking phase. To detect the presence of unresolved targets, researchers ,2 studieThe Gaussian Mixture Model (GMM) is introduced to model the probability density function (PDF) of the echo monopulse ratio. Theoretically, the GMM can fit any form of PDF. Taking the advantage of the GMM, the algorithm can fit the PDF of an echo monopulse ratio by processing measured data instead of assuming. Therefore, the proposed algorithm is more adaptive to radars of different resolution than the existing algorithms.The detection result of the previous detection period is used to improve detection performance. Because the relative movement between targets is continuous, the detection result of the previous detection period can be used as a priori information for the current detection. Inspired by the PDAF–BD (Probabilistic Data Association Filter-Bayesian Detector) algorithm , we estaThe studies above all assume that the amplitude of target echo is Rayleigh- or Rician-distributed. The assumptions above only hold when there are a large number of scattering centers within resolution bins . As radar resolution improves, the number of scattering centers within resolution bins decreases sharply and the above assumptions can no longer hold. Based on field test measured data, Du Lan ,14 analyIn this paper, In wideband monopulse radars, the matched filtered echoes of unresolved targets are within the same resolution bin and are sampled with period hoes are ,17(1)sinsq(jΔt)di(j)=∑s=ge glint , phase cge glint , Dopplerge glint .jth sample at time The observed SNR of the In the typical existing algorithms ,4,5, theeratures , so we cFor extended targets, the purpose of the proposed algorithm is to detect the presence of unresolved scattering centers for each range profile sample. Therefore, we define the events as follows:The Bayesian detector can be written asThe proposed algorithm treats a group of unresolved scattering centers as a kind of special target named “u-target” in this paper. It is reasonable to believe that a sample closer to the predicted position of a u-target is more likely to be the sample of the u-target. Therefore, we set up tracking of the u-targets and calculate the a priori possibility -targets . To simp(1) Calculate (2) Calculate the covariance of the estimation errorThen the estimation error of the u-target position at time (3) Detect the sample of the u-target and data associationObviously, samples that are closer to the predicted position of the u-target The a priori probabilities The Bayesian detector can then be rewritten asAfter the test of all the samples of the range profile, a set of samples (4) Calculate the Kalman gain(5) Target state estimation(6) Update error covarianceIf the tracking of the u-target is not established, the a priori probability cannot be calculated, it is reasonable to assume The flowchart of GBD algorithm is shown in The GBD algorithm has two work modes, mode 1 is functional when the tracking of u-targets is not established. In this mode, only the GMM is used to detect samples of u-targets. Mode 2 is functional when the tracking of u-targets is established. In this mode, both GMM and a priori information are used. The establishment and deletion of tracks is designed according to a specific tracking scenario and is performed after the detection of all samples in every radar processing period.In this part, a series of simulations are carried out to compare GBD with the JMBP GLRT, and then an experiment based on measured data is carried out to evaluate the performance of GBD in a real wideband monopulse radar. The simulation is based on a typical scenario shown in In this simulation, the echoes are Rayleigh-distributed. For comparison, the definition of scattering center parameters is the same as those for JMBP GLRT , that isIn The amplitude of echoes may not remain Rayleigh-distributed in different resolutions, so detection algorithms should be adaptive to echoes of different distributions while keeping low computational load. In this simulation, the performance of GBD and JMBP GLRT on echoes of different distributions are compared.In this simulation, the resolution of the radar is high, and the echoes of them are bimodally distributed. We model the echoes by a mixture of two Gaussians according to the results in . In the For each In The JMBP GLRT is developed assuming only two targets are unresolved, but in real scenarios, there are probably more than two targets unresolved. In this section, we carry out a simulation to test the performance of GBD when there are more than two targets unresolved. For scenario 1, in which two targets are unresolved, target 2 and the fourth scattering center of target 1 is located in the fourth bin, and the parameters are As the unresolved target effect is the very factor that affects radar performance, the weaker unresolved target effect, caused by closely spaced unresolved targets for example, may result in the degradation of GBD performance, but its harmful effect to the radar will also degrade. That means the degradation of GBD performance caused by the weaker unresolved target effect may not necessarily degrade the radar tracking performance.In this section, the performance of GBD is evaluated by field test measured data. The data collection experiment is shown in In the experiment, the radar processing period is the coherent processing interval (CPI), which means the u-target detection and target angle estimation is performed in every CPI. The target angle is estimated by a centroid methodIn which According to the experiment record, the u-target is formed within the time interval of CPI = 7000–9000. It can be seen in To evaluate the performance of GBD on the measured data, the data above is used as the test data. For GBD, another 1000 ship echoes and 1000 unresolved target echoes sampled in the same scenario are used to train the ship GMM model and the unresolved targets GMM model, respectively, with the number of GMM components It can be clearly seen in the figure that during period CPI = 7000–9000, It can be seen from the table that in the time interval CPI = 7000–9000 where the u-target exists, the In the experiment, the average computation time of GBD in one CPI is Blair GLRT is a typical algorithm applicable to wideband radar. The experiment compared GBD with Blair GLRT on the basis of an LFM radar. As the signal models of the two algorithms are baseband signal models, the modulation of the radar signal has no major influence on the difference in performance of the two algorithms. If the two algorithms are applied to pseudo-noise sequence phase-coded radars, GBD will also have a higher detection probability and lower false alarm rate than Blair GLRT because GBD uses monopulse ratio and tracking information, while Blair GLRT only uses monopulse ratio. GBD also avoids the risk of model mismatch which Blair GLRT suffers in wideband radars. But the computation load of GBD is higher than Blair GLRT because the tracking function and GMM likelihood calculation of GBD takes more computation than the Neyman–Person test of Blair GLRT. The experiment results show that the MATLAB version of GBD takes approximately 0.2 ms to complete detections on a range profile, a time length already comparable to data acquisition periods. In our coding experience, a C language version will run much faster than a MATLAB version, and therefore, the computation time of GBD can be further reduced, meaning GBD has the potential to meet the computation time requirement of real-time processing.In this paper, we proposed an algorithm named GBD that detects unresolved targets for wideband monopulse radars. The proposed algorithm models target echo by GMM to be adaptive to different radar bandwidths and use a priori information to achieve high performance while keeping low computational load. Simulation results show that GBD has better detection performance and lower computational load than JMBP GLRT, and is adaptive to echoes of different distributions. The experiment on measured data proved that GBD can correctly detect samples of u-targets and performs better than Blair GLRT in a real wideband monopulse radar. The GBD algorithm can be applied to wideband monopulse radars of different resolutions, and has the potential to meet the computation time requirement of real-time processing. For wideband radars, a practical unresolved target detection algorithm should be adaptive to different bandwidths while keeping a low computational load, and this paper introduced our work to meet those requirements. The next goal is to further explore the idea of this paper by optimizing the signal model and tracking algorithm to handle more complicated real scenarios.


In this paper, we propose an averaging pixel current adjustment technique for reducing fixed pattern noise (FPN) in the bolometer-type uncooled infrared image sensor. The averaging pixel current adjustment technique is composed of active pixel, reference pixel, and calibration circuit. Polysilicon resistors were used in each active pixel and reference pixel. Resistance deviation among active pixels integrated with the same resistance value cause FPN. The principle of the averaging pixel current adjustment technique for removing FPN is based on the subtraction of dark current of the active pixel from the dark current of the reference pixel. The subtracted current is converted into the voltage, which contains pixel calibration information. The calibration circuit is used to adjust the calibration current. After calibration, the nano-ampere current is output with small deviation. The proposed averaging pixel current adjustment technique is implemented by a chip composed of a pixel array, a calibration circuit, average current generators, and readout circuits. The chip was fabricated using a standard 0.35 μm CMOS process and its performance was evaluated. The bolometer-type uncooled infrared image sensor has recently been researched in various fields such as defense, medical care, and non-destructive testing ,2,3. UncResistance deviation among the resistors integrated in the active pixel and reference pixel causes serious fixed pattern noise (FPN). In a readout circuit and a signal processing circuit integrated using a standard CMOS process, a method for compensating for these deviations is necessary in order to obtain an efficient infrared image. Various methods have been researched to solve the problem of the deviation between resistors integrated in the active pixel and reference pixel ,17,18.In this research, in order to compensate the resistance deviation occurring among the pixels of the bolometer-type uncooled infrared image sensor, the averaging pixel current adjustment technique was used, which reduces the dark current by averaging the currents generated in the bolometers of reference pixels in column readout circuits. For verifying the proposed averaging pixel current adjustment technique, the output nodes of each reference pixel are all tied. As a result, the output current of the reference pixel in which the light receiving portion is blocked to avoid being affected by the incident light is averaged. The bolometer resistor in the active pixels and reference pixels has a dark current component. When the current from each active pixel array is subtracted from the current from the reference pixel, the dark current of the active pixel can be removed. The averaging pixel current adjustment technique consists of a reference pixel, active pixel, calibration circuit, and readout circuit. The entire chip based on the averaging pixel current adjustment technique was fabricated using a 0.35 μm standard CMOS process and its characteristics were analyzed.The deviation information is converted to voltage and digital codes through a current–voltage converter and an analog–digital converter (ADC), respectively. This deviation information causes the FPN. The In order to obtain the deviation information based on the resistance of the active pixel using the proposed averaging pixel current adjustment technique, the resistance of the reference pixel must be fixed without any deviation. However, not only the resistor of the active pixel has a process deviation, but also the resistor of the reference pixel has a process deviation. In the case of the signal processing circuit of the bolometer-type uncooled infrared image sensor, it is important that the signal electrons generated by the infrared radiation or dark current of each pixel obtain the same output voltage for a certain integration time by the integrator. In order to verify the averaging pixel current adjustment technique, we compared the output current of the un-calibrated pixel and the integrator output voltage generated by the output current of the calibrated pixel according to the integration time.Z-axis, is the output voltage of the integrator. A 3D graph of before and after the proposed averaging pixel current adjustment technique of the entire array was applied is presented in In this paper, we proposed an averaging pixel current adjustment technique for reducing FPN in the bolometer-type uncooled infrared image sensor. In order to reduce the resistance deviation causing the FPN among pixels of the bolometer-type uncooled infrared image sensor, an averaging pixel current adjustment technique was successfully implemented using a standard 0.35 μm CMOS process. According to the experimental results, it was confirmed that the standard deviation of the output voltage according to the resistance of the active pixel was reduced by about 34% when the active pixel output voltages before and after the calibration were compared. Therefore, it was concluded that the output offset current according to the deviation of the bolometer resistance of each pixel was reduced by controlling the output code of the ADC using the proposed technique. We expect the proposed averaging pixel current adjustment technique to be effectively applied to the bolometer-type uncooled infrared image sensor for reducing FPN.


This article introduces a new approach in the field of network optimization based on Transmission Optimization Metric (TOM), which is aimed at improving traffic flow continuity and increasing the chances for traffic flow sustainability in a way that helps to minimize inter-packet gaps. The work is mainly focused on harsh transmission conditions in narrow-band networks. Finally, the presented approach has impact on better resource allocation as fewer attempts are necessary for successful completion of a transmission. A significant part of the article deals with parameterization of coefficients used by the TOM optimization method. Examples of analysis for several topologies of narrow-band wireless networks based on CSMA/CA and TDMA protocols are used to demonstrate various issues related to proper setting of parameters. The introduced TOM metric has the potential to become a standard for optimization, for example, in sensor networks that are characterized by the specific nature of data traffic. The complexity of the design of telecommunication devices and systems grows with demands for improving their parameters and quality. The design of wireless devices using complex modulation and transmission techniques is no longer just the matter of exact mathematical and physical apparatus. In the case of Wireless Sensor Networks (WSN) or Internet of Things (IoT) networks, the problematics is even more complicated because very limited computational and power resources have to be taken into account ,2. In paThe performing of a simulation itself is not sufficient for obtaining the feedback on the behavior of the communication system–it must be supplemented by a detailed analysis and subsequent evaluation of the data. Then the evaluation results can be used as feedback when optimizing or modifying the simulated communication system.This article introduces a new Transmission Optimization Metric (TOM) based on inter-packet gaps or packet observation time differences. The main objective of this article is to introduce the TOM metric and its benefits, to show specific setup issues and their impact on the outcomes.The evaluation system is applied to simulation results of TCP/IP communication protocol running in a Point-to-Multipoint narrowband wireless network based on CSMA/CA and TDMA with channel width up to 25 kHz, 4-CPFSK fixed modulation and 3/4 FEC. The maximum speed achieved on real equipment in point-to-point mode is approximately 15 kbit/s in half-duplex mode. Approximately 7% difference in throughput is assumed in the simulation model of CSMA/CA system . The simulation model is implemented in a discrete OMNeT++ [The evaluation of communication networks simulations is a very complex task. Methods convenient for network traffic optimization lie in several areas.At first we can focus on methods based on ITU-T and IETF recommendations. For optimization purposes, the recommendations from the range of ITU-T Y.1500-1599  ,8,9,10 aIn addition to the routine statistical evaluation concerning the frequency of transmitted packets, determining the throughput, delay or loss, several other parameters can be obtained. In the field of communication systems using the TCP/IP family of protocols, we are talking mainly about the fairness analysis of the individual TCP connections by using some of the fairness index options, such as epsilon fairness , proportAnother way is to determine the success rate of the individual transmissions. A typical representative is the set of evaluation metrics CER/TER . This meDepending on the particular type of communication used, other parameters can be evaluated, mostly those related to the internal function of the respective communication protocol or the whole system. Most of these parameters require that the evaluation is performed on a captured data block. Continuous (per packet) analysis significantly reduces the accuracy and credibility of the obtained information. This method of analysis uses principles of link parameters acquisition used in communication protocols SCTP  and RSTPThe monitored parameters  in narrow-band wireless networks are very sensitive to the current load of transmission path. Even a small change in the sequence of packets or their holding may significantly influence the transmission parameters or even lead to breakdown of the connection. In broadband networks, this phenomenon is not very noticeable: it occurs mainly due to a sustained load of the transmission channel by a limit data traffic.The sequence of packets and the delays between their arrivals are affected by several factors. If we ignore intentional use of QoS control mechanisms  then another important factor influencing these parameters consists in the used access mechanism of the second-layer (L2) protocols. CSMA-based mechanisms show more efficient bandwidth use, but at higher load (more than 28%) there occurs rapid deterioration of the monitored parameters and more frequent disruption of the established TCP connections . TDMA coThe number of packets passing through an IP connection between two consecutive packets within the monitored IP connection; see illustration of the principle in The time between the arrivals of two consecutive packets within a single IP connection; see illustration of the principle in We define the term inter-packet gaps analysis (IPGA) as the basic approach to evaluation of gaps qbetween the arrivals of packets within one or several TCP/IP connections. The analysis can be performed for both connection-oriented (TCP) and connectionless (UDP) transmissions. The observed inter-packet gaps metrics can be described in two ways:The inter-packet gap directly affects a variety of transmission path parameters; in the first place, however, the overall delay, including its fluctuations. With connection-oriented protocols (TCP), it influences the transmission rate and round-trip time (RTT). For connectionless protocols (UDP), the arrival times of the individual packets are affected, and thus the quality of service (QoS). In particular, multimedia applications are very sensitive to this fluctuation . This isN represents the number of all packets in all flows, and k is the number of processed packets.We developed the new Transmission Optimization Metric (TOM) to evaluate benefits of optimization in narrow-band networks. The TOM metric is not bound to any specific area of network optimization. It can reflect changes made to all layers of TCP/IP communication stack. It relies on the specified inter-packet gap boundary (packet- or time-based), classifying the packets into two groups. The group of packets that exceed the required boundary  exceeding the allowed boundary.The TOM optimization methodology is based on the classification of inter-packet gaps in the measured or simulated network and their subsequent evaluation according to the given criteria that have to be defined with regard to the requirement to improve sustainability or to respect the specific request flow continuity for each connection during transmission in a narrow-band network. The transmission system is subsequently optimized in order to avoid the occurrence of L2 to L4 protocol timeouts, or to adhere to strictly given requirements for application-level message delivery .The first step is to set up measurement scenarios that define the extent of the performed analysis in relation to the required reliability of the results. Within each scenario, the critical parameters are the duration of measurement, the number of iterations and the default inter-packet gap for the TOM metric evaluation. It is also possible to set the desired size of confidence interval (CI).Then follows the actual measurement according to the defined scenarios and their parameters. When multiple repetitions are scheduled, change of random number generator (RNG) seed is performed.The IPGA analysis is performed with the obtained data, providing inter-packet gaps as its output.Calculation according to the TOM metric is applied to the inter-packet gaps.From the obtained results, the confidence interval  is calcuIf there are any further measurements not yet performed, they are carried out according to items 2–5 with a new network and measurement setup (including RNG seed setup).At the end, the results are exported and evaluated.The fundamental optimization algorithm is shown in The presented metric reflects the ratio of packets exceeding the defined boundary. That results in outcomes which only depend on the boundary setup. In this case we have a metric that is able to help in optimizing the traffic continuity by minimizing the maximum packet delays.We verified (performing from 20 to 30 measurements with the same settings and different RNG seed setu—the results are represented by boxplot diagrams) that the TOM metric is capable of reflecting network conditions in a wide range of network setups . On one hand, the TOM metric performs as expected under variety of network conditions; on the other hand with specific setup, when compared to a metric based on JFI, the results show that the TOM metric is capable of delivering almost the same results as the metric based on JFI, as shown in When compared to JFI-based metrics, the main difference consists in the fact that TOM metric depends only on packets exceeding the defined boundary, and the distribution of packets among streams is not important. With TOM, the network is optimized in a way that minimizes the number up packet gaps exceeding the defined boundary. This has direct impact on traffic flow continuity, resulting in increased sustainability of communication.The TOM outputs are strongly dependent on the setting of the inter-packet gaps boundary in both the packet and time domains–see the decision functions If the information about lower-layer protocols and their configurations is available, it is possible to use it for parameterization. Typically, the information about settings of different protocol timers can be used for setup and optimization. In the case of communication optimization in narrow-band networks, the boundary values of parameters in the time domain can reach hundreds of seconds, but they should be less than the total expiration time for the TCP connection.The situation is even more difficult when we need to push optimization further to improve also communication delays of the optimized traffic flows. Given the much stricter requirement for setting the boundary values (compared to the requirement for connection sustainability), it is necessary to perform the configuration much more carefully.The real problem, however, occurs when the TOM metric should be used purely to minimize the inter-packet gaps. If the only criterion is to reach the minimum, the question arises about the initial setting of the metric. During the basic testing we chose a procedure that relies on one set of measurements, which is considered the initial state; then we compare it to the results of measurements with modified parameters and decide whether or not the observed criterion has been improved. And right here we are encountering the problem of how to set the initial TOM boundary values so that the pros and cons of each setting modification can be evaluated during the whole measurement with the different configurations.The curve The curve The curve The shape of the curve The shape of curves The actual use and verification of the proposed TOM methodology is demonstrated in several real examples (scenarios). The samples used were selected with respect to the specific features to reveal issues related to TOM boundary settings. We prepared several scenarios such as simple DumbBell topology with shared channel and FIFO queue, CSMA/CA and TDMA transmission systems to outline the mentioned issues. As simulation environment we used OMNeT++ with INET framework.The aim of the following examples is to demonstrate the way how the TOM metric can be used, but mainly to illustrate the outcomes of specific TOM boundary value setting.In this scenario, a simple DumbBell  communicioned in ,31.If we take a closer look at the values of the TOM metric, which correspond to the values of the 0.95 and 0.99 quantiles, it is clear from the graph that if we choose a boundary gap of approximately 5 s (0.95 quantile), then it appears that this criterion best meets a network where the error rate is approximately 5 × The scenario with a narrow-band network using CSMA/CA protocol is based on a real implementation of this protocol into the simulation environment. Configuration of the simulated network is derived from a real-life configuration of narrow-band modems operating in the frequency band between 140 and 960 MHz using channel width of 25 kHz . The CSMA/CA protocol is very efficient by allocating of network resources to individual communicating nodes, but also highly susceptible to congestion, which consequently leads to a significant impact on or even the breakdown of the active connections. This is a field for further optimization.On one hand, the following example demonstrates how the TOM metric can be used to optimize narrow-band communication systems. On the other hand, it shows typical obstacles in TOM boundaries selection. The As can be seen from However, as shown in a detailed view of the individual optimization curves for quantiles 0.96 , 0.99 F and 0.99As for the TOM metric values for the 0.96 quantile , the optFor the 0.99 quantile , the optFor the 0.999 quantile , the optThe scenario uses the TDMA access method for communication among devices in a narrow-band network. Round-robin algorithm was used for resources allocation to the communication parties. For the sake of simplicity, the network structure was chosen similarly as in the DumbBell scenario, including the maximum throughput of 19 kbit/s. The delay is given by the used L2 algorithm and by the number of connected clients. The network setup allows direct comparison of simulation results between DumbBell and TDMA scenarios. The simulation was also parameterized using physical channel error rate. As each node connection was parametrized via different level of BER, we introduced special parameter called BER penalty in the measurements, being used to increase the global probability of bit errors in whole network  and real implementations of these communication systems and industrial network applications (CSMA/CA and TDMA scenarios).The performed analyses show the contribution of the TOM-based methodology for improving the properties of optimized data streams in narrow-band communication networks. The analyses also revealed one of the major disadvantages of these optimization procedures, which is a relatively difficult parameterization.The results show that if we push hard on lowering the TOM boundary, then the TOM maximum shifts to different areas of the presented graphs; that means we optimize the continuity of data stream at the expense of increased error rate. This occurs in situation when we encounter packet gaps caused by transmission control mechanisms. Therefore we have to be aware of such issues and set the optimization boundaries carefully.Further research and development will now be undertaken in the direction of automation of boundaries selection. Eventually we want to obtain an analyzing system based on the TOM methodology, the parameterization and operation of which would be fully automatic; this will open new deployment capabilities in the form of real-time analysis performed directly in the respective communication devices.


Automatic and accurate mapping and modeling of underground infrastructure has become indispensable for several important tasks ranging from urban planning and construction to safety and hazard mitigation. However, this offers several technical and operational challenges. The aim of this work is to develop a portable automated mapping solution for the 3D mapping and modeling of underground pipe networks during renovation and installation work when the infrastructure is being laid down in open trenches. The system is used to scan the trench and then the 3D scans obtained from the system are registered together to form a 3D point cloud of the trench containing the pipe network using a modified global ICP (iterative closest point) method. In the 3D point cloud, pipe-like structures are segmented using fuzzy C-means clustering and then modeled using a nested MSAC (M-estimator SAmpling Consensus) algorithm. The proposed method is evaluated on real data pertaining to three different sites, containing several different types of pipes. We report an overall registration error of less than  The underground space in urban areas has played an important role in urban development over recent years. Underground utilities are critical infrastructure for all modern cities. They carry drinking water, storm water, sewage, natural gas, electric power, telecommunications, etc. In most cities, this underground infrastructure reflects the growth and history of the city.Mapping underground infrastructure has become indispensable for many important tasks pertaining to urban planning, safety and hazard mitigation, construction and maintenance, environmental impact, resource efficiency, etc. However, this offers several technical and operational challenges. The different infrastructure elements are usually placed at different depths resulting in lots of occlusions and overlap. The pipes and cables in the underground networks may well be conceptually simple, but they are narrow objects of different sizes, spanned over large geographical areas and made of multiple segments. For these very reasons the use of 3D technology from different scanning angles is more suited for such a task, as it allows a more accurate 3D mapping and modeling of the complex infrastructure. Several new regulations introduced recently in major cities impose a class-A geo-referencing/mapping  method is used to fit a plane to the neighbors to estimate the surface normal for each 3D point. The local surface curvature is then approximated using the residual of the plane fitting. Whereas a large residual indicates a curved surface, a small residual indicates a planar surface. A region growing method is then employed, in which points with the smallest residual are used as seed points, with neighboring points having residuals below a predefined threshold added to the current region based on the smoothness constraint. One of the shortcomings of this method is the computational complexity of the algorithm as k-NN needs to be computed for every point.Underground mapping of pipe networks have been a topic of interest in the past several years now. Most of the pipe detection and mapping is done over the ground using ground penetrating radar (GPR) such as presented in ,2,3. HowLiu et al.  presenteIn , an adapMasuda  proposedIn addition to LiDARs, some studies have also employed RGB-D cameras, however, to little success. Nahangi et al.  proposed and Gao  proposed and Gao , a technThe surface obtained after feature extraction needs to be represented and reconstructed. One of the most effective method is using implicit functions for this task ,25. ThisMost of these works either used single scans or an already registered point cloud, minimizing the role of registration, and they also focused more on a structured industrial environment. To the best of our knowledge, no work has directly used terrestrial LiDAR data to detect, map, and model pipes in open ditches in the outdoor environment. The unstructured environment poses several other challenges such as sparsity of point cloud, limited access and visibility, undue clutter especially due to vegetation and dirt/rubble, unforeseen occlusions, indiscriminating surfaces, etc. In this work, we use 3D data to detect, map, and model underground pipe networks during renovation and installation work when the infrastructure is being laid down/examined in open trenches, using a portable data acquisition system  of the VLP-16 sensor, the user walks around the ditch extending the sensor assembly over it to effectively capture/scan the pipe network inside as shown in The data is acquired and stored on the computer (laptop) which is placed in a backpack along with the portable battery. The total weight of the sensor assembly is about The rough orientation and position of the 3D scans help to provide an initial guess/alignment for multiple scan registration to form a single registered 3D point cloud, as explained in the next section.The rough alignment with the help of the AHRS and GPS data was not enough to form a coherent registered 3D point cloud. Thus, this initial information was used to finely register the acquired 3D scans to form a registered 3D point cloud using a modified global ICP technique as explained below.In the global ICP the different 3D scans are finely registered using a type of bundle adjustment method as presented in . The proIt is shown in  that theThe correspondences are established for each pair of overlapping regions in three distinct steps: the Selection, Matching, and Rejection steps.In the selection step, a subset of points is selected within the overlap region using maximum leverage sampling (MLS). This technique helps select those points, which are best suited for the estimation of the parameters. For this, the effect of each point on the parameter estimation, i.e., its leverage, is considered. The points with the maximum leverage, which correspond to the lowest redundancy, are selected. This strategy considers the coordinates and the normal vectors of the points.k-dimensional trees (k-d trees). As in the adjustment phase the point-to-plane distance is minimized for each correspondence, two associated points do not have to be identical in the object space, they only have to belong to the same (tangent) plane.The matching step establishes the correspondences. Each selected point from the selection step is paired to the nearest neighbor (the closest point) in the overlapping scans. The nearest neighbor search is efficiently realized using short for Plane roughness of corresponding points: In order to minimize the point-to-plane distance, the reliability of both normal vectors must be ensured. This condition is not met if the scanned object cannot be appropriately modeled by a plane, e.g., in the case of vegetation.Angle between the normal vectors of corresponding points: To ensure that two corresponding points belong to the same plane, the angle Point-to-plane distance between corresponding points: Apart from a few false correspondences, the a priori point-to-plane distances  median) . In thisThe rejection step aims to identify and subsequently reject unreliable or false correspondences. Based on our application and type of data, each correspondence is tested using the following criteria:These criteria ensure that only the most reliable correspondences are retained. As is evident from these criteria, most of the correspondences belonging to structured parts of the environment are used whereas unreliable correspondences belonging to unstructured environment such as vegetation are rejected. Even if some false correspondences still remain after these rejection criteria, a robust adjustment method used helps in their detection and removal.i-th correspondence. The signed point-to-plane distance i-th correspondence, and In the adjustment phase a robust least squares adjustment is performed to minimize the weighted sum of squared point-to-plane distances (which is defined as the perpendicular distance from one point to the tangent plane of the other point):i belongs to the scan pair k, then its weight is determined by:k.The weight of the correspondences We find that although we are mainly concerned with 3D points belonging to the ditch, the ditch points are not fully used in the registration phase as many of these are rejected due to the strict rejection criteria. This is why the choice of a longer range sensor such as VLP-16  of the 3D point clouds with respect to the plane  as shown in In order to extract pipes, we are only interested in the 3D points belonging to the ditch. So in order to extract the part of the point cloud belonging to the ditch, we apply certain semantic filters. The filters are based on distance, height, and profile. As we know that the scanning was done around the ditch itself, we only take into account the 3D points with horizontal and vertical distance of less than loyed in , we analOnce the 3D points belonging to the ditch are extracted, they are segmented/clustered to form potential objects. In such a non-structured environment, the clustering/segmentation task, especially if only based on 3D information, can be quite difficult. This type of 3D data is widely hampered due to jump edges , featureEvolved from hard C-means (HCM) , fuzzy CFuzzy clustering aims to minimize the following cost function as presented in :(4)Jm  to paramu  for each segment is estimated, the different segment lines are concatenated and second order polynomial estimation is used to obtain a smooth center line. The radius R of the curved cylinder pipe is considered as the mean of the radii of all the different segments.Even though the length of the cylinder pipe is equal to the length of the curve line estimated using the 3D points, the smooth center line is then extrapolated up to the full length of the ditch (limited by the length and width of the ditch) as the pipe curvature and cross-sectional diameter usually remain constant right through its length. This helps to obtain the complete pipe in the ditch and also helps cater for missing pipe segments due to lack of 3D points, occlusions, etc.Algorithm 1: A summary of the proposed pipe modeling method.In order to evaluate the proposed method, real data were obtained using the portable LiDAR acquisition system explained in For quantitative analyses ground truth was obtained, for each site, using a Leica P-20 total scan station . The scaX,Y, and Z axes in the registered 3D point cloud were analyzed and compared with those in the ground truth. The difference in the dimensions is considered primarily due to the registration errors. The different objects and features ‘n’ selected for the analysis mainly include large objects from the structured environment present around the ditch, and on the site, such as perpendicularity, length and height of nearby buildings and walls, dimensions of several windows, height and width of piping canals and lighting poles, etc. The dimensions of the same selected features from both the ground truth and the registered point cloud were then compared using the mean absolute error ratio  around the ditch area and so as a result the registration process was somewhat hampered due to the rejection criterion explained in This shows the importance of having more structured environment around the ditch area to improve the registration quality.The segmentation and classification results were evaluated at 3D point level. With the help of the available ground truth, the 3D points in the point cloud were manually labeled as belonging to pipes or otherwise. The results expressed in Like other metrics, MCC is calculated using the values/counts of the true positives , false positives, true negatives and false negatives. A coefficient value of +1 signifies a perfect prediction, 0 is no better than random prediction, and −1 implies total disagreement. The results, including an overall accuracy (ACC) and Matthews Correlation Coefficient (MCC) greater than X, Y, and Z axes, using a common point as a measurement reference. The results are evaluated using the standard deviation The segmented pipes are modeled as cylindrical tubes/pipes. Two main parameters of these modeled pipes are evaluated  and compared with the corresponding values obtained from the ground truth. The position of the center line was measured along the Equation  is used Z-axis due to a lack of 3D points belonging to the bottom part of the pipe, as it is usually occluded during scanning, and also some lower parts of the pipe were extracted out as part of ground points (see Z-axis. It is for this reason that the dimensions along the X-Y axis are used to estimate the pipe diameter rather than the height axis Z. The error in the diameter measurement is strongly affected by the segmentation errors, especially due to jump edges between two adjacent objects in the unstructured environment as shown in A higher standard deviation is observed in the case of the In this paper, we present a portable, automated mapping solution for the 3D mapping and modeling of underground pipe networks during renovation and installation work, when the infrastructure is being laid down in open trenches. The system is used to scan the trench and the 3D scans obtained from the system are registered together to form a 3D point cloud of the trench containing the pipe network using a modified global ICP method. The pipes are segmented in the resulting 3D point cloud using fuzzy C-means clustering and then modeled using a nested MSAC algorithm. The proposed method is evaluated on real data from three different sites, containing several different types of pipes. An overall registration error of less than entation .The evaluated results demonstrate the efficacy and the technical prowess of the proposed solution. This solution is also an important step in reducing operational costs and improving mapping accuracy of underground pipe networks.


Gadus sp.) and capelin  is important in many panarctic ecosystems. Since the early 2000s, the Northeast Arctic cod stock (G. morhua) in the Barents Sea has increased greatly, and the sea has been exceptionally warm. Such changes have potentially large effects on species distributions and overlap, which in turn could affect the strength of species interactions. Due to its high latitude location, the Barents Sea has strong seasonal variation in physical conditions and interactions. To study drivers of variation in cod-capelin overlap, we use data from two annual surveys run in winter and in autumn of 2004–2015. We first model winter and autumn spatial distributions of mature and immature cod and capelin. We then calculate overlap from model predictions on a grid with similar spatial resolution as the survey data. Our approach allowed us to interpret changes in overlap as species-specific effects of stock size and temperature, while accounting for sampling variation due to sampling time and depth. We found that during winter both species expanded their distribution in response to increased stock sizes, but how strongly and where the expansion occurred varied. The effect of temperature on distributions varied in space, and differed for cod and capelin and for different components of the two species. The results for autumn were clearer and more consistent. Both species expanded their distribution areas as their stock sizes increased. A positive effect of temperature was found in the north-eastern Barents Sea, where temperatures were lowest at the start of the study. Overlap increased and shifted north-eastwards during the study period and remained high despite a decline in the capelin stock. The increased overlap during autumn could mainly be attributed to the shift in cod distribution with increased cod stock biomass.The trophic link between cod ( Understanding the drivers of overlap is thus underlying any assessment of predation rate and natural mortality of a prey. In a fishery context, overlap has potentially important implications for management because of its influence on stock dynamics [Spatial association or dynamics . A strondynamics –4. Howevdynamics , are thedynamics , 7.Gadus sp.) and capelin  are abundant species forming an important predator-prey interaction [Gadus morhua; hereafter cod) and Barents Sea capelin  is key for the ecosystem dynamics. Cod is the main predator on post-larval capelin [In several shelf ecosystems in the panarctic region, cod  covering the south-central Barents Sea in the pre-spawning season of cod and capelin when both species undertake their spawning migration (ecosystem survey (2004 –) covering the whole shelf in the main feeding season   were estimated using standard methods for cod swept area calculation in the Barents Sea, that is, number caught at each trawl station divided by trawled area, assuming that the effective fishing width along the trawled transect is dependent on cod length [Cod densities .2/nmi2) were based on data collected during the ecosystem survey, the same data which is used to provide an absolute abundance estimate for the capelin stock assessment each year [Autumn capelin acoustic densities , and then calculated overlap from model predictions of local species densities (Quantifying overlap below). We chose to do this rather than calculate overlap directly from the raw data, since we wished to relate changes in overlap to species-specific responses to the environment. In addition, our model predictions included variables controlling for sampling variation that could have biased indices calculated on raw data [We first developed single species distribution models for cod and capelin for autumn and winter using Generalised additive models  , bottom depth, sun height, survey day, and year. The temperature variables were allocated to each trawl station from the nearest CTD measurement from the same survey. The bottom depth was that measured at the beginning of trawling. Sun height was included to account for sampling variation associated with diurnal vertical migrations, and was calculated from the day of the year, geographical position, and sampling time. Survey day was expressed relative to the earliest day of the season  across the study period when sampling took place, and was included to account for the quasi-synoptic coverage and inter-annual differences in timing of sampling in relation to the migrations of cod and capelin. The geographical coordinates iomasses  of each All data points containing missing values in any covariate were removed. To avoid large outliers in the covariates, we also limited the data to include bottom temperatures in the range -2 to +7.5°C, and depths of 50–500 m, which contained the bulk of observations in both seasons, leaving 3994 observations for analysis in winter, and 4644 observations in autumn. Calculation of variance inflation factors indicated that the correlations between covariates were not a cause for concern , exceptmgcv [ggplot2 [cowplot [itsadug [Since the Barents Sea contains many islands and complex coastlines, we applied the soap film smoother in the GAMs -1, where di is the Euclidian distance between the acoustic sampling points and the station [The response variables in the models were local cod and capelin densities. Due to the large amount of acoustic data and the application of the soap film smoother, convergence of the capelin models was problematic. We therefore chose to include only acoustic registrations adjacent to the bottom trawl stations, using distance weighted interpolation of the area backscatter (NASC) within a 15 nmi (28 km) radius with weights of the form  station . This dicomponent , we fitted separate distribution models with the untransformed species density D,t in position x,y in year t as the response, conditional on other environmental covariates, using a GAM with Tweedie distribution and the default log-link. The variance of the Tweedie distribution is related to the mean by a power function ,t is the error term whose variance is related to the mean according to candidate models describing the distribution of each component. These models included different combinations of the covariates in For each season and function :var(Y)= in mgcv . Firstlypacf  and variog  and maximisation of deviance explained after backwards elimination of non-significant predictors. The relationships between response and covariates were assessed by examining their robustness across model formulations, i.e., if the relationships were stable or varied, where the latter could indicate that the predictor captured residual variation in the model rather than a meaningful pattern. The models were visually inspected for residual correlation using the R-functions ry geoR, ). None ory geoR,  on the cry geoR,  to modelextended habitat models). These models were used for inference only, not for calculation of overlap.For cod, we also fit separate habitat models that included local capelin density as predictor (t. The grid resolution was the same as the standard inter-station distance of the survey with the coarsest station grid (the ecosystem survey). By using a standard grid, the seasons and years could be compared, despite variation in survey design and execution. Grid cells containing fewer than 5 (autumn) or 8 (winter) observations across the study period, as well as cells falling outside the sampled area in year t, were eliminated from the grid. In this way, we only predicted on locations where the models had been given a reasonable amount of data. The overlap O,t in position (grid cell) x,y in year t was then calculated for each combination of components, using the formula:To calculate overlap, the best candidate distribution model for each component was used to predict species density on a 35 x 35 nmi (65 x 65 km) regular grid of the study area with covariate values corresponding to the nearest observation from the central point of the grid cell in year Finally, the mean overlap across the grid and the extent of the overlap  were calculated for each year, season and cod-capelin component combination to get an overview of the temporal dimension of the overlap, i.e., the between-year variation in how well cod and capelin densities matched.For all models, the estimated Tweedie power parameters fell within the range 1.4–1.8, indicating that the compound Poisson-gamma distribution was a good fit for our data. The covariates generally contributed significantly to explaining species distributions, except for sun height in the capelin autumn models, and depth in the candidate models of both acoustic and trawl capelin in winter . The devThe estimated relationships between local densities and depth and temperature from the habitat models in autumn  are showCapelin was mainly restricted to the central-northern parts of the Barents Sea, with a core distribution area east of Svalbard . For capImmature cod occurred in areas with slightly shallower bottom depths compared to mature cod . Peak deFor all autumn models, the effect size of stock biomass was larger than that of temperature as judged by the difference in AIC between the habitat models and the candidate models with the respective spatially variant term .and highest temperatures, but not those in between  in winter included spatially variant effects of temperature . For acoIn winter, immature and mature cod were associated with similar depths as in autumn, that is between 150–200 m , while tCod was found throughout the study area, with density peaks of immature cod in the western- and easternmost areas  while maMaps of overlap by year and season for all cod-capelin component combinations can be found in Capelin was distributed in a comparatively smaller area than cod, mainly restricted to the central-northern parts of the Barents Sea . The maiCod had a wider distribution than capelin also in winter . ImmaturThis is the first study to explicitly estimate overlap between cod and capelin in the Barents Sea. While overlap does not on its own imply consumption, the spatial pattern of overlap tells us where cod and capelin are more likely to interact as predator and prey. We found that overlap varied with season; the main overlap areas were east of Svalbard in autumn, and south of Svalbard and along the Norwegian/Russian coasts in winter. In autumn, the overlap area shifted towards the north-east during the study period. This could be attributed to increased cod stock biomass, and to a lesser extent, increased capelin stock biomass and increased temperature in this area. The autumn overlap remained high after the capelin stock collapse at the end of our study period. The spatial pattern of overlap in winter reflected the disjunct distribution of capelin when matures migrate towards the southern coasts of the Barents Sea to spawn and immatures remain closer to the autumn distribution area (discussed below).The autumn survey has been designed to collect synoptic data on several trophic levels , while tIn the present study, a main aim was to investigate spatial match between cod and capelin densities. For this purpose, the overlap index was defined such that high values of overlap at any given location resulted from high density of both species. Moreover, in order to express seasonal and inter-annual variation in overlap at a comparable scale, we considered overlap relative to densities within—not across—each year and season . However, differences in overlap between seasons are expected due to the seasonal variation in behaviour of both species. In autumn, feeding has high priority for cod and capelin, and both species remain in the feeding areas throughout the survey. We found that the autumn overlap was concentrated to the east of Svalbard for both immature and mature cod, and the overlap area moved towards the northeast during the study. The capelin stock was in a state of collapse during the last year of our study period, but the estimated consumption of capelin by cod remained high . This isIn contrast, in winter, immature and mature individuals of both species differ in their spatial preferences. Immature capelin overwinter in the northernmost ice-free areas of the Barents Sea, whereas mature capelin separate from the rest of the stock to start their spawning migration to the southern coasts . ImmaturGadus macrocephalus) and capelin, the cod-capelin link is much weaker than in the Atlantic ecosystems. This weak link is the result of a cold pool that in some years keeps cod confined to the warmer waters on the southern shelf while capelin finds a refuge in the north [Prey availability to predators may be constrained by physiological adaptations to factors such as depth and/or temperature that differ from those of the prey . The prhe north . In the he north . Off Icehe north , 59. We he north , and refOur results from the winter regarding overlapping habitats and spatial refuges were less clear than the results from autumn. The across-space correlations between cod and capelin densities were weak or negative in some years, reflecting the complex spatial distribution of the capelin stock Figs . The denGenerally, the match between cod and capelin densities was low; the overlap never reached maximum value . Possibly, the spatial match is stronger at a different scale. A process should be observed at the smallest scale where a driving variable affects the outcome of the process . For thClupea harengus), shrimp  and polar cod (Boreogadus saida) as alternative prey. The only consistently positive relationship between both cod diet and cod distribution and prey density was found for capelin. The strongest candidate as important alternative prey during autumn is polar cod, which is found in the cold waters of the northern Barents Sea.Distribution of alternative prey could also influence the spatial distribution of cod, but was outside the scope of this paper. Johannesen et al.  studied A possible explanation for the weak spatial match is that it is not necessary for cod to track the highest densities of capelin. Considering that it takes several days for a cod to digest a stomach full of capelin in the cold waters of the Barents Sea , the timEffects of the physical environment and of species abundance on distributions have been difficult to disentangle in other cod-capelin systems , and thIn winter, influence of both temperature and stock biomass on the overlap was more variable than in autumn, since these factors either did not have strong effects on local species densities, had opposite effects on cod and capelin densities, or did not affect species densities in the main overlap areas. It is likely that the diverging behavioural motivation between immature and mature capelin, and the lack of a strong quantitative index of capelin density contribute to masking any clear signals in the winter data.Due to the difficulty of monitoring and estimating the capelin stock in winter immediately prior to the fishing season, the capelin stock prediction model used in the assessment simulates the stock six months into the future from the time of monitoring in autumn to terminated spawning. Predation by immature cod on mature capelin is explicitly modelled for the first three months of the year, while interactions between other cod-capelin components are ignored , 23. OurS1 Appendix(PDF)Click here for additional data file.S2 Appendix(HTML)Click here for additional data file.S3 Appendix(PDF)Click here for additional data file.S4 Appendix(PDF)Click here for additional data file.


The introduction of local resolution has enormously helped the understanding of cryo-EM maps. Still, for any given pixel it is a global, aggregated value, that makes impossible the individual analysis of the contribution of the different projection directions. We introduce MonoDir, a fully automatic, parameter-free method that, starting only from the final cryo-EM map, decomposes local resolution into the different projection directions, providing a detailed level of analysis of the final map. Many applications of directional local resolution are possible, and we concentrate here on map quality and validation. It is important to analyse the local resolution of cryo-EM maps. Here the authors present MonoDir, a fully automatic and parameter free method for the directional local resolution analysis of cryo-EM maps that requires only the final map as input and they also propose indicators for assessing map quality. In contrast, local approaches determine this reliability voxel by voxel through different approaches as windowed FSC between two halves2, or the detection of structural features above the noise level4.Once a map is reconstructed, its resolution is estimated to avoid possible over-interpretations. Resolution can be determined in a global or in a local manner. The global one is a single parameter that determines the degree of reliability of the whole map; being the FSC the most common  locality by performing a complete “directional local resolution” analysis, other way to analyze the informational content of a cryo-EM map emerges, opening many new possibilities, from map validation and quality control to chain tracing, among others. This article focuses on some of the suggested quantitative indicators of map quality assessment. In all cases we want to note two important points: (1) that the single input to our method is the final cryo-EM map, without any knowledge of the original particles or their assigned projection directions, (2) that our approach rests on the strong mathematical framework of monogenic signals7, building on MonoRes4 (see Methods section).The need for local resolution approaches emerges as a consequence of multiple effects happening at the sample at the imaging and processing levels, such as: existence of sample heterogeneity, flexibility, wrong angular assignment of single particles, bad angular coverage of the projection sphere, existence of preferred directions or radiation damage among others. Local resolution maps may quantify the combined effect of all these limiting factors into the final cryoEM map; however, they are not able to isolate the effect of each factor alone. Recent works like the 3DFSCThe new algorithm, that we call MonoDir, calculates the local resolution along a set of directions in 3D (see Methods section). In essence, given a voxel location  and Lowest  local-Directional Resolutions Maps”. The highest resolution map informs about the highest resolution value, The next indicator that we have introduced is “the Average Directional Resolution” (ADR). It only requires the detection of the direction contributing to the highest resolution The ADR provides valuable information about the map quality and informs about the possible existence of preferred directions. However, to infer this information it is required the local resolution map to compare with, the ADR map by itself is not a local anisotropy metric. In a way, it is like a local resolution map weighted by the local anisotropy. Note that when the values Obviously, the ADR and the half interquartile range inform about the resolution anistropy, but they do not inform about which are the preferred directions. However, this piece of information can be very easily measured by introducing a modification of the otherwise classical plot of projection directions. The new plot will be referred to as polar plot and it represents the number of voxels that have their highest resolution along a specific direction. In other words, the polar plot represents a histogram of the highest local-directional resolution values on the projection sphere, where the number of counts along each direction is represented by the size of the dot, see an example in Fig. MonoDir computes the local-directional resolution map along a certain set of directions. However, it is also possible to analyze the local directional resolution along particular directions that may be related to critical characteristics of the map. In this context, it is specially interesting to analyze resolution along two directions connected to a polar decomposition of the map: the radial and tangential directions. In the case of the calculation of the radial local resolution, for each point of the map we calculate the direction of a line joining that point to the center of the map, and we calculate the local resolution along this direction, see Supplementary Fig. 9. The aim of this synthetic example is to illustrate in a detailed manner the information that MonoDir provides. Thus, the atomic model was first converted into an electron density map10 and a gallery of 500 random projections was obtained with known angular assignments; Gaussian noise with zero mean and standard deviation of 2 a.u. was then added. We performed two experiments with these data, the results are presented in Fig. We first tested MonoDir in a synthetic fully controlled setting, using the structural model of mentclass2pt{minimTo start with the ADR, the values were clearly different between the map reconstructed from all particles Fig. Finally, the plots of radial and tangential resolutions per radius are very informative of alignment errors and confirm our previously introduced expectations; to test this issue, the known angular assignment of particles and shift were randomized around the true direction; a Gaussian distribution with a standard deviation of 1.2 degrees was considered for the angular assignment and a standard deviation of 1 pixel for the shift. In this way, the plot corresponding to the case of not having errors in the assignment of projection directions Fig. 11 (EMDataBank: EMDB-6287) and the 80S Ribosome12 (EMDB-2275), which are shown side by side in Fig. 5  also indicates isotropy. On the contrary, the case of the ribosome is totally different, with a wide dispersion of ADR values. Then, polar plots for directional distribution of particles .We start with the case of two data sets, the 20S Proteasome5, for which two maps were deposited , the first one was strongly affected by the presence of preferred orientations, while this handicap was greatly minimized in the second map by tilting the EM grid. It is clear in Fig. 5 . Regarding the capacity of MonoDir to identify alignment errors, we have presented clear examples in which the slope of radial plots of both tangential and radial can be qualitatively related to the existence of higher of lower angular errors, providing a unique guidance tool that did not exist in the field before. Furthermore, in Supplementary Information we have analyzed more in-depth this effect, proving that the approximation to a linear behavior between alignment error and tangential and radial resolution slopes indeed hold, although more work is need to fully characterize this effect, probably analyzing scaling terms not yet taken into account, so that ultimately we could provide with a quantitative description of this relationship.We have also shown how the use of local-directional measurements provide new information about the quality of the reconstructed map, identifying angular alignment errors and the existence of preferred directions and resolution anisotropy; moreover, this information is extracted from the reconstructed map, without the need to access the original set of images or any other information related to them  estimation is significantly higher than noise estimation at any given frequency. A mask allows to discriminate between noise and protein. To measure local-directional resolution maps and, therefore, anisotropy, a directional filter must be previously applied.The root of Our goal was to filter directionally the electron density map and then to calculate the local resolution of the directional-filtered map obtaining the so-called local-directional resolution map. In Supplementary Figures a scheme of the measurement process is shown. The directional filter is defined by determining the Fourier Transform of the volume and masking the Fourier Transform with a cone that only keeps the frequencies inside it. The cone axis is arranged along the direction in which the local-directional resolution is calculated, and the cone angle is set by the user (in our examples we used 30 degrees). By computing the inverse Fourier Transform, a directional filtered map is obtained.MonoRes4. MonoRes computes the local amplitudes of noise and signal. Unfortunately, despite using smooth filters in Fourier space, the directional filtering introduced some ringing, in particular around the borders and in the plane perpendicular to the measured direction. This ringing might modify the noise statistics. To avoid that, the original MonoRes algorithm was modified. These modifications can be found in the Supplementary Information.We then measured the local resolution of these directionally-filtered maps using The process of measuring local-directional resolution is repeated until we have covered the full projection sphere. In our examples, we computed 81 directions with an angular sampling of 15 degrees. Once all directions were analyzed, the local-directional resolution information is combined to produce the different outputs .Supplementary Information