This paper proposes a method to detect humans in the image that is an important issue for many applications, such as video surveillance in smart home and driving assistance systems. A kind of local feature called the histogram of oriented gradients (HOGs) has been widely used in describing the human appearance and its effectiveness has been proven in the literature. A learning framework called boosting is adopted to select a set of classifiers based on HOGs for human detection. However, in the case of a complex background or noise effect, the use of HOGs results in the problem of false detection. To alleviate this, the proposed method imposes a classifier based on weighted contour templates to the boosting framework. The way to combine the global contour templates with local HOGs is by adjusting the bias of a support vector machine (SVM) for the local classifier. The method proposed for feature combination is referred to as biased boosting. For covering the human appearance in various poses, an expectation maximization algorithm is used which is a kind of iterative algorithm is used to construct a set of representative weighted contour templates instead of manual annotation. The encoding of different weights to the contour points gives the templates more discriminative power in matching. The experiments provided exhibit the superiority of the proposed method in detection accuracy. Detecting humans is an important topic in many applications, such as intelligent surveillance and intelligent transportation systems (ITSs) and has received considerable attention. However, vision-based human detection is still challenging due to factors including varied illumination conditions, complex backgrounds, various types of clothes, the occlusion effect, and a broad range of human poses and views. Compared to stereo vision, a monocular solution demands less computation and eases the calibration process. Therefore, we present an approach for detecting humans based on monocular vision. Since the camera is mounted on a moving platform, the background is not static so that the background subtraction approaches widely used for identifying the regions of human candidates are inapplicable in our work. The most common method of human detection in the literature is to use the sliding window strategy which formulates the detection problem as binary classification one. This scans an image pyramid by a fixed-sized window and bounding boxes around humans are then determined from the use of non-maximum suppression process.To mitigate the difficulty from intra-class variance, shape is a kind of effective feature in representing the human appearance and determining the existence of a human in a single window or not. A comprehensive survey of the use of the shape feature can found in . In the In this category, a detection window is divided into thousands of patches, each of which is described by local features. The modeling of the human shape using a local patch descriptor is either through feature concatenation ,13 or feThe proposed method mainly consists of training and detection phases. Given a training dataset consisting of positive (human) and negative (non-human) ones, the first step of the training phase is to generate a set of weighted contour templates by an expectation maximization (EM) algorithm. Then, we perform template matching to obtain a matching score for further distinguishing human from non-human through a thresholding strategy. In order to alleviate the problem generally faced by the HOGs-based approach, the classification result from the template-based classifier is imposed to the HOGs-based boosting framework by adjusting the bias of the support vector machine (SVM) hyper-plane at each boosting round. We refer to this framework that systematically integrates the global contour and local HOGs features as biased boosting. At the detection stage, the template-based classifier is firstly applied to determine the presence of a human. If the answer is yes, a set of learned positive-biased weak classifiers that have the bias in favor of positive label is taken for further voting to check the existence of a human in a scanning window. Otherwise, negative-biased weak classifiers are used for voting.The remainder of the paper is organized as follows. In Using a binary contour template to describe the human shape is popular in the literature. To improve the discriminative ability of contour templates, we impose importance(s) on the contour point(s) instead of considering them as equally weighted in the literature. The construction of the weighted contour templates is through the use of EM .kth contour point in y composed of a set of edge points and y and is defined as the distance from the pixel p to its closest edge point in y.A weighted contour human template  of (DT)  of the bith sample and By formulating the template construction problem as the maximum likelihood one, an algorithm called (EM)  is adoptTherefore, a latent random variable ith training image The first term For notation simplicity, let m, the E-Step is to calculate the possibilities of all training samples derived from each weighted contour template at the current stage as the definition of After introducing the EM framework for formulating the problem of weighted template construction, we further elaborate the implementation issues in this section. Initially, an incremental clustering similar to  is firstThe update of template The last step is to determine the weight (importance) The larger Algorithm 1: Algorithm for Weighted Template ConstructionInput: A set of training samples Output: A set of weighted templates Apply the distance transform to all training samples.Y+ to generate a set of Take the samples in positive set Set all template weights to repeatE-Step: Form log expected functionCalculate Form the Q-function defined in (7) M-Step: Update the parameters as follows to maximize the Q-function.for(1)Update (2)Determine the number of contour points (3)Assign a weight end for m ← m+1 UntilIn this section, we describe how to learn a classifier based on a set of weighted contour templates and analyze the performance improvement in imposing the weight to every contour point. The dataset used consists of 924 positive subjects from the MIT CBCL dataset  and 3342HOGs proposed by  are an eto Zhu’s  boostingFirst of all, we briefly describe Zhu’s boosting framework for the learning of a human detector in this section. Let The weight The integration of the global contour with local HOGs features is thus by adjusting the bias of the SVM classifier at each round In short, the weak classifier Thus, the mis-classification And each sample weight is updated as:Finally, we obtain the human detector consisting of Algorithm 2: Biased Boosting AlgorithmInput: A set of training samples Output:Initialize the positive sample weight to forj = 1 to M(1)Find the classifier (2)Estimate two bias values (3)Calculate the error rate (4)(1)Update (2)Determine the number of contour points (3)Assign a weight Update the sample weight end for m so as to maximally reduce the error rate on the weighted training set. To conform to this, a strategy for searching appropriate bias values is proposed and described as follows. We adjust The main concept of boosting is to choose a weak classifier at each round In this section, we describe how to determine the existence of the human of a scanning window To validate the effectiveness of the proposed method called WTM-Boost, we implement three methods proposed in  (HOG-SVMThe cost constant for training the SVM as the weak classifier for a block is 1.0 and the kernel function is Gaussian radius basis one. The number of weak classifiers used in boosting framework is 40 in all methods. The white rectangles in For performance validation, we use three popular human datasets including MIT CBCL, INRIA, and CVC in our experiment. The statistics of images from the three datasets for training and testing is listed in To further validate this point, we replace the training samples from the MIT CBCL dataset with those from the INRIA and CVC datasets, respectively, for experiments I and II, to construct the templates for matching in the TM-Boost method. The resulting templates are for global classifier learning followed by boosting the human detector, as mentioned. The ROC curves of the human detector learned using TM-Boost for INRIA and CVC datasets are shown in The main contribution of our work lies in two aspects. Firstly, we propose a method based on the EM algorithm to automatically construct a set of representatively weighted contour templates By formulating the problem of template construction as a maximum likelihood one, the contour template as well as contour point weight are determined in the M-Step according to the estimated likelihood probabilities of all training samples in the E-Step. The assignment of different weights to the contour points gives the constructed templates more discriminative power.Secondly, we systematically integrate the global contour and local HOGs features in the proposed biased boosting framework. The determination of bias values, respectively, for those with contours similar to and different from the pedestrian templates, is by finding the values minimizing the error rate. By comparing the other two approaches, the experimental results exhibit that the trained pedestrian detector increases the detection rate and reduces the false positive rate as well. Given the effectiveness and power of deep learning, the use of deep learning is the trend in the detection area ,34. One


Telemedicine consultations using real-time videoconferencing has the potential to improve access and quality of care, avoid patient travels, and reduce health care costs.The aim of this study was to examine the cost-effectiveness of an orthopedic videoconferencing service between the University Hospital of North Norway and a regional medical center in a remote community located 148 km away.An economic evaluation based on a randomized controlled trial of 389 patients (559 consultations) referred to the hospital for an orthopedic outpatient consultation was conducted. The intervention group (199 patients) was randomized to receive video-assisted remote orthopedic consultations (302 consultations), while the control group (190 patients) received standard care in outpatient consultation at the hospital (257 consultations). A societal perspective was adopted for calculating costs. Health outcomes were measured as quality-adjusted life years (QALYs) gained. Resource use and health outcomes were collected alongside the trial at baseline and at 12 months follow-up using questionnaires, patient charts, and consultation records. These were valued using externally collected data on unit costs and QALY weights. An extended sensitivity analysis was conducted to address the robustness of the results.This study showed that using videoconferencing for orthopedic consultations in the remote clinic costs less than standard outpatient consultations at the specialist hospital, as long as the total number of patient consultations exceeds 151 per year. For a total workload of 300 consultations per year, the annual cost savings amounted to €18,616. If costs were calculated from a health sector perspective, rather than a societal perspective, the number of consultations needed to break even was 183.This study showed that providing video-assisted orthopedic consultations to a remote clinic in Northern Norway, rather than having patients travel to the specialist hospital for consultations, is cost-effective from both a societal and health sector perspective. This conclusion holds as long as the activity exceeds 151 and 183 patient consultations per year, respectively.ClinicalTrials.gov NCT00616837; https://clinicaltrials.gov/ct2/show/NCT00616837 (Archived by WebCite at http://www.webcitation.org/762dZPoKX) Similar to many other countries’ publicly funded national health services, a key principle in Norway is that people should have equal access for equal need irrespective of their income or region of residence ,2. Thus,Musculoskeletal injuries are the most common causes of disability and chronic pain. Surgery for orthopedic conditions is witnessing some of the greatest growth rates in developed nations across the world . DecentrDecentralized services using outreach clinics or modern information and communication technologies have the potential to improve access, avoid patient travels, and reduce health sector costs. One such technology is telemedicine consultations using real-time videoconferencing. Today, the use of telemedicine to facilitate treatment and care over a distance has been investigated in almost all clinical specialties -7. SeverThere exist few randomized controlled trials (RCTs) evaluating telemedicine used in orthopedic outpatient clinics , and eveIn this study, we report the results from an economic evaluation. The primary objective of this study was to examine the cost-effectiveness of the telemedicine service, compared with standard in-person consultations at the hospital from a societal perspective. The secondary objective was to assess the robustness of the results by conducting sensitivity analysis. The costs included were health care costs, patient costs, and time costs measured as production loss. Health outcomes were measured as quality-adjusted life years (QALYs) gained.The economic study was based on a prospective RCT. This trial was conducted to assess if remote consultations using videoconferencing in orthopedic consultation was as safe and effective as in usual in-person care. Patients were recruited from the 4 northernmost municipalities in Troms County . All parThe remote consultations were performed through real-time videoconferencing. Both the remote center and the hospital used the Cisco TelePresence System and the Norwegian Health Network for secure data transmission . The orthopedic surgeons had some initial training and technical assistance in the beginning of the trial. Registered nurses were operating the service at the remote center. Before the trial, 2 nurses from the remote clinic received intensive training both to operate the technical equipment and to assist in treating the patients locally. They attended casting courses and were trained in clinical examination techniques.The patients were scheduled for an appointment at the local center by the surgeon at the hospital. The orthopedic surgeons (3 consultants and 2 experienced registrars) were randomly selected to conduct the video-consultations if they were available at the specific time. The surgeon made the videoconference call to the remote center.The patients showed up and were welcomed by one of the trained nurses who set up the videoconferencing at the remote site. The nurses assisted during the consultation and performed physical tasks, for example, changed a cast or removed stitches. No physician was present during the video consultations at the remote site. An existing digital X-ray lab served by a local radiographer was available at the remote clinic. Digital X-rays were, if needed, available and shown to the patients at the time of the consultation.In the control group, patients received standard consultations at the hospital outpatient clinic. In 32% of the standard consultations, the orthopedic surgeons needed assistance from a nurse .In both the standard and the video consultation alternatives, the usual mandatory registration and documentation in patients’ medical records were carried out by the orthopedic surgeon. This involves the conclusion of the consultation, agreement between surgeon and patient regarding any follow-up appointments, prescriptions, referrals for operation, further investigations, physiotherapy, and an application for orthopedic aid if needed. The average number of consultations per patient was 1.5 (range 1-6). For more details of the trial method, see Buvik et al .The economic evaluation consisted of trial-based analyses following the guidelines for health economic evaluation ,28. The Data on costs and QALYs gained were collected alongside the trial at baseline and at 12 months follow-up using questionnaires, patient charts, and consultation records. These were valued using externally collected data on unit costs and utilities. To increase generalizability and make the cost-effectiveness result useful for decision making, the resources used in the trial were valued using equipment prices, unit costs, travel fares, and salaries from 2017/2018. An extended sensitivity analysis was conducted to address the robustness of the results.Three types of costs were included: (1) costs associated with implementing and running the telemedicine service in clinical practice, (2) travel costs, and (3) production losses.The implementation costs included the costs related to the investment in videoconferencing equipment  at the remote center and the hospital, and an extra computer and printer at the remote center. The remote center already had a broadband connection for other purposes. The computer at the remote center provided the nurse access to the patients’ hospital records, and the printer was used to give patients a paper copy of the records on request. In addition, costs related to running the service at the remote center were estimated. This included a registered nurse in a 20% position. Other costs associated with setting up the service included initial training sessions and travel costs related to these activities, line rent, and rent associated with the extra space needed at the local center. No extra technical support was needed as they used existing resources at the hospital. The costs estimated were only those that differed between telemedicine and hospital consultations, that is, the incremental costs. The time costs for the orthopedic surgeons were the same for both consultation forms . The conTravel costs were collected directly from the patients during the trial. Data on traveling time, distance, and mode of transport to the consultation were collected using a questionnaire that was handed to the patient directly after each consultation. Main occupation, if they were on sick leave, and the need of overnight stay were also included. Additionally, Google map was used to estimate the travel distance from the patients’ home to the consultation site either at the remote center or at the hospital (shortest and fastest). The orthopedic surgeon decided if the patient needed a companion or extra transportation on health-related grounds, reported the patients’ main occupation, and if they needed sick leave. The travel costs were calculated using regulations and official travel fare rates by the Norwegian Patient Travel Agency in 2018 .Production losses were estimated for patients in full- or part-time employment who had to take time off from work to attend the orthopedic consultations. Part-time employment was set to 50%. Time costs for the patients who were unemployed or on sick leave benefits were not included. If the information about working status was missing from the self-reported questionnaires, the orthopedic surgeons’ registration forms were used. Official Norwegian average wages were used to value absence from work to estimate the production losses.Only 3 of the 199 patients who were offered a video consultation at the remote center had a new consultation at the hospital, because of their need for a face-to-face consultation to carry out examination that is not possible over the video link. The cost of these second consultations was also included in the analysis. Based on the resources available at the hospital and the experiences from the trial, we assumed that for 300 patient consultations annually, 5 would need a second consultation.Health outcomes were measured in QALYs gained, a composite measure incorporating both quantity and quality-of-life impacts of treatment . As a pat tests, chi-square tests, or generalized estimating equations (GEEs). GEEs were used with an exchangeable covariance structure to control for dependence between 2 or more repeated consultations for some participants. All statistical analyses were performed using STATA version 14.0 .Results are presented as means (SDs) or numbers (percentages). Differences between the groups were analyzed using 2 sample One-way sensitivity analyses were conducted to assess the robustness of the results. Parameters have been varied one at a time to assess the effect on the cost-effectiveness and to determine breakeven values. We recalculated the cost analysis in 3 separate scenarios: one included a less costly Skype for Business solution, the second assumed a shorter distance to the main hospital, and a third scenario includes the expenditure to the hospital excluding patients’ own travel expenditures and production losses. The number of consultations needed to break even was calculated for all scenarios.The costs of setting up the teleorthopedic service are presented in P=.31). In addition, the need for travel companions was the same in both groups . The time spent on traveling was 6 times higher for patients traveling to the hospital. Patients in the telemedicine group saved an average 7 hours and 40 minutes on traveling  . These pIn most of the published literature, a physician  has been present at the remote site together with the patient ,11,33,34Whether to include production losses measured as time off work is controversial . The patOther studies have reported a reduction in the number of referrals to the specialist because of a learning effect and included this as part of the cost savings ,40. In tA third option to prevent the patient from traveling long distances is to have the specialist travel from the hospital to the remote location. However, because of a lack of orthopedic specialists at the central hospital in this region, the opportunity costs of their travel time would be too high. However, in other institutional contexts, this might be another alternative to consider.In this study, we demonstrated that significantly less public transport such as a bus or boat was used in the remote consultation group . This caThe main strength of this study is that the costs and effects have been collected alongside an RCT. The estimated travel costs included in the analysis are based on the actual journeys undertaken by the participants in the trial. The time and travel cost calculation was based on actual travel distances, modes of transportation, how many in need of a companion, time spent on traveling, and the working status of each patient. Official travel fares reimbursed from the Norwegian Patient Travel Agency were used as unit costs. Some of the patients’ journeys were organized by the Norwegian Patient Travel Agency (most of the taxi trips and flights). The patients had to apply for reimbursement for additional expenses.Official travel fares reimbursed by the Norwegian Patient Travel Agency were used to calculate the travel costs. If the patients chose to travel by a more expensive alternative than the travel agency’s reimbursement , the patients had to pay for the difference themselves. This makes the true travel costs for the patients potentially higher than estimated in this study. It is also possible that the actual travel costs for the health care sector are lower than calculated. Some patients did not apply for travel refund, either because they forgot or simply because they found it too troublesome to fill out the forms. One study from Norway demonstrated that 26% of the patients and 70% of the accompaniers did not apply for a travel refund .Another limitation of this study is that production loss for the persons accompanying the patients on travels was not included. The main reason for this was the lack of information about their working status, and it was considered important to avoid overestimating the benefits of the service. About 28% of the patients needed a travel companion. If these time costs were included, it would have made the teleorthopedic service even more cost-effective.Training costs have been included as a one-time cost at the startup of the teleorthopedic service. However, training should be included as an ongoing part of the service to promote and sustain use. Training is necessary because of staff turnover, particularly at the hospital. Casting courses and training in clinical examination techniques must also be arranged if there is a change in the nursing staff locally. Telemedicine services are often sensitive to changes in key personnel . SuccessOne challenge for economic evaluations of telemedicine services is generalizability. High diversity in terms of objectives, technology, application, and context might limit the generalizability of specific evaluations to other settings . The locTo make this study relevant outside of the current institutional setting, we have emphasized a transparent calculation of all cost and effectiveness items, based on 2 costing perspectives. The same methodology can then be generalized, however, based on local prices.This study showed that providing video-assisted orthopedic consultations at a remote clinic, rather than having patients travel to a centrally located hospital for consultations is cost saving. This conclusion holds from both a societal and a health sector perspective and as long as the activity exceeds 151 and 183 patient consultations per year, respectively.


In this paper, we study the connectivity of cognitive radio ad-hoc networks (CRAHNs) where primary users (PUs) and secondary users (SUs) are randomly distributed in a given area following a homogeneous Poisson process. Moreover, for the sake of more realistic CRAHNs, contrary to previous works in the literature, we consider the case that primary network is comprised of multiple communication pairs which are spatial-temporal distributed in the network area. We also take into consideration the differences in transmission range and interference range of both PUs and SUs. The connectivity of such CRAHN is studied from three viewpoints. First, we mathematically analyze the probability of isolated secondary transmitter and secondary receiver. Second, we derive the approximation expression of the link probability between two adjacent SUs. Third, we investigate the path connectivity between two arbitrary SUs by using the simulation analysis approach. The correctness of our mathematical expressions is confirmed by comparing analytical results with simulation results. The results in this paper provide insights into how multiple communication pairs in primary network affect the connectivity of secondary network, which can be useful guidelines for the design of CRAHNs. In recent years, technological advances together with the demand for efficient and flexible networks have led to the development of wireless ad-hoc networks (AHNs). In such networks, mobile devices can communicate with each other in a peer-to-peer fashion with no need for any base station or pre-existing network infrastructure. AHNs have been mostly limited in their operations in the 900 MHz and the 2.4 GHz industrial, scientific, and medical (ISM) bands. With the fast increase in the number of wireless devices, these frequency bands are getting congested. At the same time, many other licensed frequency bands allocated through static polices are used only in bounded geographical area or over a specific period of time. The Federal Communication Commission (FCC) estimated that the average utilization of licensed frequency band varies between 15–18%  where unConnectivity is an important property of ad-hoc wireless networks. It is one of the main factors that influence the network performance. Connectivity in traditional wireless AHNs has been intensively studied in the literature. The authors in  investigToday, the connectivity of CRAHN has raised increasing awareness of researchers. Investigating on the connectivity of CRAHNs is a challenging issue compared with that of AHNs due to the influence of varying spectrum availability caused by the random locations and activeness of PUs. The reachability between two SUs depends not only on the distance between them but also on the availability of communication channels. Specifically, the communication links among SUs in secondary network are time-space varying due to the dynamic of spectrum opportunities. In , the autWe should note that all aforementioned works consider CRAHNs where PUs were not clearly classified as transmitters or receivers and the differences in transmission range and interference range were not mentioned. In addition, the primary networks comprising of multiple pairs of primary transmitters and receivers was not considered. Motivated by these issues, in this paper we mathematically study the influence of multiple communication pairs of primary network on the connectivity of secondary network. The following is a summary of the main contributions in this paper.In contrast to previous works in the literature which analyzed the connectivity of secondary network without explicitly specifying the transmitting and receiving roles of PUs in communication process, we consider more practical CRAHN where primary network comprises of multiple spatial-temporal one-hop communication pairs of primary transmitter—primary receiver such as in licensed cellular networks, and then analyze the connectivity of secondary network from this perspective.We derive the mathematical models of the isolation probability of SU and the link probability of two adjacent SUs, taking into account the differences in transmission range and interference range of both PUs and SUs. The path connection between two arbitrary SUs is also studied.We show that network size and the number of PU pairs determine the minimum value of SU’s isolation probability and the maximum value of path connectivity between two arbitrary SUs. Moreover, the distance between two adjacent nodes insignificantly affects the link probability between them.The rest of this paper is organized as follows. In We consider CRAHNs where PUs and SUs share a licensed spectrum band and are distributed according to homogeneous Poisson point process  in a finq in a specific finite subarea A, e.g., the coverage area of node, follows a Poisson distribution, i.e.,The number of nodes The primary network is modeled as a random graph where primary transmitters and primary receivers are distributed according to two-dimensional Poisson point process of density The ON state represents the interval in which the primary transmitter is active and the OFF state depicts the interval in which the primary transmitter is inactive. Similar to ,14, we a theorem , it can The secondary network is also modeled as a random graph where secondary users, acting as both secondary transmitters and secondary receivers, are distributed according to two-dimensional Poisson point process of density Due to the coexistence of primary network and secondary network in CRAHNs, there exists a spectrum opportunity from the secondary transmitter SUrI as in .Using the system model in In this subsection, we study the probability of isolated SU, i.e., the probability that a specific SU does not have available licensed frequency bands and any neighboring nodes to communicate with. Since every SU in secondary network can be either transmitter or receiver, the probability of isolated secondary user can be classified as the probability of isolated secondary transmitters and the probability of isolated secondary receivers.From the perspective of secondary transmitter, it is considered as an isolated node if one of the following cases happens:(i) There is at least one active primary receiver inside its interference range refer to a.m primary receivers within the interference area According to , the prom primary users are not active. By assigning In order to have spectrum opportunity for secondary user, it is required that all these g x=0 in , we obtam primary users are inactive isSince the operations of primary receivers are mutually independent, the probability that all these Therefore, the probability that no active primary receiver within the interference area of secondary transmitter is obtained by multiplying two probabilities given in  and 5),,5), i.e.(ii) There are no active primary receivers inside its interference range but there are also no secondary receivers inside its transmission range refer to b.From , the proThen, by combining  with 8)8), the p(iii) There are no active primary receivers inside its interference range refer to c.Since this event relates to several spatial dependence problems, deriving the mathematical expression is very challenging. Thus, the probability of isolated secondary transmitter given in  is approit is within the interference range of at least one active primary transmitter or (ii) it is not interfered by any active primary transmitter but it is not in the transmission range of any secondary transmitters, or (iii) it is not interfered by any active primary transmitter but all its neighboring secondary transmitters are blocked. Similarly, the probability of isolated secondary receiver can be approximately expressed asFor secondary receiver, the node is considered as isolated node if (i) From the viewpoint of secondary transmitter, it is allowed to transmit packet if there are no active primary receivers inside its interference area rding to , the proSimilar to the case of secondary transmitter, the probability that secondary receiver SUA wireless communication link between two adjacent nodes, i.e., the secondary transmitter SUk-hop path consists of the following phases: (i) source node S sends packet to intermediate node IFrom the link connectivity between two adjacent SUs presented in The mathematical analysis of hop count distribution between two arbitrary nodes still remains an open research issue because deriving the analytical models of hop count between two arbitrary SUs and the spatial dependent connection of wireless links on the path is very challenging. Hence, the path connectivity between two random nodes is often studied by using simulation ,8. SimilIn this section, we present the numerical results of the probability of isolated SU, the link probability between two adjacent SUs, and the path connectivity between two arbitrary SUs. We verify the mathematical models of isolation probability and link probability given in a, is varied from 1000 m to 2000 m. From a, and the number of SUs, a = 1000 m and a = 1500 m are very similar .To quantitatively study the isolation probability of SUs and verify the correctness of  and 11)11) in Sea = 1000 m. More particularly, as a = 1000 m, 1500 m, and 2000 m, respectively. In both a = 1500 m and a = 2000 m) because the probability that all neighbors of a SU are blocked by active PUs is alleviated. In the case of small network size, there are gaps between the analysis results and simulation results. However, the gaps are not very big and from the perspective of low complexity of closed-form mathematical model, this tolerance is acceptable.The isolation probability of SU as a function of the number of PU-Tx-PU-Rx pairs is depicted in Next, we will study the possibility of having a direct communication link between two adjacent SUs in CRAHNs. a, and the number of PU-Tx-PU-Rx pairs, l, is much smaller than transmission range, l gets higher, this effect is relieved. In addition, as observed from a and a.ned from  in SectiWe are now interested in examining the path connectivity between two arbitrary nodes. a. From a = 1000 m is always higher than those of a = 1500 m and 2000 m. This is because when the number of SUs is small, larger network areas result in remarkably low path connectivity between two random SUs due to the following combined effects: (i) lower node density increases the possibility that a SU has no neighbors, and (ii) longer path in terms of hop count makes the path establishment more difficult. As more SUs are added into the networks, path connectivity of each setting of network size a increases rapidly, reaches the maximum value and then keeps constant. The maximum value of path connectivity when a = 2000 m is a = 1500 m (a = 1000 m (a. The results in a = 2000 m, a = 1500 m, and a = 1000 m, respectively. The reason is that when the number of PU-Tx-PU-Rx pairs increases, the remaining network area for communication among SUs is greatly reduced.This paper is motivated by the fact that the connectivity properties of CRAHN should be investigated in the case that primary network is comprised of multiple communication pairs and the transmission range and interference range of both PUs and SUs are different. We have studied the connectivity of CRAHNs in three different viewpoints: the viewpoint of a single node, wireless link, and multi-hop path. More specifically, we mathematically analyze the probability of isolated secondary transmitter and secondary receiver, the link connectivity between two adjacent SUs, and study the path connectivity between two arbitrary SUs by using the simulation approach. The major results observed in this paper can be summarized as follows.For a given network size, the isolation probability of SU, The distance between two adjacent SUs does not noticeably influence the link probability a while the stable maximum value of path probability is proportional to a.In contrast to the isolation probability of SU, the path probability Based on the analysis framework in this paper, further studies of the connectivity of CRAHN with multi-pair primary network for different wireless channel models and antenna models and under the impacts of other factors such as latency, packet congestion can be carried out.


Supercell thunderstorms can form extremely dangerous and destructive tornadoes. While high fidelity supercell simulations have increased the understanding of supercell mechanics to help determine how and when tornadoes form, there is a lack of targeted, in situ measurements taken aboveground in supercells to validate these simulations. Pseudo-Lagrangian drifters (PLDs) are atmospheric probes that can be used to attain thermodynamic measurements in areas that are difficult or dangerous to access, such as from within supercells. Of particular interest in understanding tornadogenesis is the rear-flank downdraft (RFD). However, strong outflow winds behind the rear-flank gust front (RFGF) make the RFD particularly difficult to access with balloon-borne sensors launched from the ground. A specific type of PLD, an air-launched drifter (ALD) that is released from unmanned aircraft systems (UAS), can be used to access RFD inflows, present at higher altitudes. Results from initial tests of ALDs are shown, along with results from a ground-released PLD test during a supercell intercept in the Oklahoma Panhandle on 12 June 2018. In characterization tests performed at the 2018 International Society for Atmospheric Research using Remotely piloted Aircraft (ISARRA) flight week, it was found that the ALD sensor system performs reasonably well against industry standards. However, improvements will be made to increase the aspiration of the sensor. Tornadoes are one of the most common and destructive forms of extreme weather in the United States. In 2017 alone, the United States experienced 1429 confirmed tornadoes . HoweverRemote sensing systems such as weather radar do not provide thermodynamic data in the RFD. In situ measurements at the surface have been provided by mobile mesonets , portions of the storm have been sampled that were otherwise inaccessible. UAS have allowed for temperature, pressure, and humidity measurements to be taken around and within vertical portions of supercells . FacilitHowever, operating sUAS within supercell environments necessitates the ability to fly in dangerous conditions where rain, hail, and strong aerodynamic forces are present. These conditions risk the safety and operations of the aircraft, as well as the ability to conduct accurate measurements of the supercell. Therefore, direct measurements of the more severe portions of the supercell from sUAS are dangerous and ill-advised. It can be surmised that other methods for sampling above-ground portions of supercells are needed.This paper is organized as follows. Pseudo-Lagrangian drifters have been used for decades to sample Earth’s atmosphere . A “trueEarly atmospheric pseudo-Lagrangian drifters (PLDs) took the form of large, helium-filled, superpressure balloons that functioned as high-altitude observation platforms . Unlike During the Rivers of Vorticity in Supercells (RiVorS) project in May 2017, Markowski et al.  used groThe rear-flank downdraft (RFD) is one of the regions of the supercell targeted for in situ measurements to study tornadogenesis . The RFDOutflows following the RFD gust front make ground-release of PLDs impractical as balloons will be carried away from the storm. Consequently, the RFD is a severely undersampled portion of supercell storms. However, supercell models indicate that air is entrained into the RFD at some altitudes above the ground. We expect PLDs to be entrained into the RFD if released at the appropriate altitude and distance from the storm. The motivation for the development of the air-launched drifter deployment system is to release PLDs to target supercell regions that cannot be accessed by ground release. Throughout this paper, the term PLD will be used to refer to this type of atmospheric measurement system in general, specifically those that are ground released, as in The balloon envelope for the air-launched drifter (ALD) was comprised of 0.03 mm-thick polyethylene foil, a durable, lightweight material with properties that minimize the loss of helium lift gas through effusion; see The sensor payload, displayed in The primary sensor onboard the microsonde, the MS8607 PTH sensor from TE Connectivity, was chosen as it combines high accuracy, low power consumption, and a compact design . The firThe CAM-M8Q GPS module from ublox is an omni-directional antenna module that can provide reception of up to 3 global navigation satellite systems (GNSS)  at a given time . The GPSThe microsonde was powered by a 1.5-V AAA battery. At room temperature, this battery was able to power the microsonde for approximately nine hours with decreased times expected at lower temperatures. A step-up synchronous DC/DC converter was used to adjust the battery output voltage of 1.5 to 3.0–3.6 V used to power the board.While it is economically beneficial to recover and reuse the microsondes after deployment, it cannot be assumed that this will be possible. Therefore, onboard data storage is impractical and would take up a large portion of the limited mass budget available. Instead, the data were relayed to a ground station through the RFM95 HopeRF radio module . Signal The Mistral, a small unmanned aircraft system (UAS) developed by IRISS, outfitted for ALD release had the capability of performing a fill and release of three ALDs. Once all of the desired ALDs were released, the Mistral could travel to a location away from the storm to allow for safer control and conditions during landing. For in-flight deployment, it is necessary to carry the helium required to fill the ALD balloons onboard the UAS. A compact, high-pressure tank was used that had a volume of 1.5 L and had the ability to be pressurized to approximately 31 MPa. The helium provided by this tank was enough to fill three ALD balloons.The helium tank was placed in the fuselage, forward of the center of gravity. Tubing connected the helium tank to the unfilled ALD balloons, which were located aft of the center of gravity. The ALD balloons were folded up along stabilizing rods that ran along the length of the aircraft towards the tail boom. Folds allowed the ALD balloons to fill up in a safe and controlled manner such that interference with control surfaces of the Mistral was minimized. The ALD balloons were connected to the helium tank through a quick disconnect connector. A servo rotated to press the connection between the tank and ALD balloons together such that the balloon fill process was initiated. Over a 60-s period, the ALD balloons were inflated behind the center of gravity of the aircraft. Positioning the ALDs aft of the center of gravity decreased the pitch induced on the aircraft as the balloon filled. Once the balloon reached the desired fill point, a release command was sent from the ground station. Once this happened, the servo rotated further to release the connector from its mount. The drag on the balloon was such that the ALD was pulled free and ascended away from the Mistral. This assembly is shown in A FAA blanket certificate of authorization (COA) enabled the Mistral to fly up to an altitude of 400 ft. (120 m) AGL at class-G airspace anywhere in the United States. Other COAs that cover more than 500,000 sq. mi.  can can17) cquations –(17). FoFrom the results, it appeared that the ALDs were most likely to be entrained when they were released within 5 km of the rear-flank gust front. This can be seen in In Simulation results provided additional groundwork for performing similar analysis in the field. To assist targeting locations of ALD release, it should be possible to run these same simulations using radar data from nearby stations. Doppler radars use pulse pair motion to calculate precipitation motion. From this, an estimate of the wind field can be estimated. For use in the field, this could be used as an input to the simulations. Results could determine whether or not a given supercell is feasible for ALD release to target the RFD or other regions of the supercell. Furthermore, once feasibility is determined, results could aide pilots and scientists in establishing the ideal storm-relative locations of ALD release.During the 2018 deployment of the Severe-storm Targeted Observation and Robotic Monitoring (STORM) project, a PLD was ground released into a supercell near Gate, Oklahoma. The PLD path through the supercell can be seen in Characterization tests of the microsondes were carried out during the Lower Atmospheric Process Studies at Elevation—a Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE), in Alamosa, Colorado, during the week of 14 July 2018.For the first test, PLDs were ground released next to a Vaisala RS92-SGP attached to a National Severe Storms Laboratory (NSSL) weather balloon, as shown in To get a better idea of the microsonde performance, a microsonde was flown next to a Vaisala RS92 under the wing of a Talon fixed-wing, foam-body sUAS. Results from the comparison between the microsonde and RS92 are given in The air-launched drifter (ALD) system was designed to place pseudo-Lagrangian drifters (PLDs) into the rear-flank downdraft (RFD) of tornadic supercells to perform thermodynamic measurements of the RFD. PLDs offer a unique solution to sample the RFD safely and remotely. While ground-released pseudo-Lagrangian drifters have had promising success in measuring portions of the forward-flank downdraft, it is believed that the airborne Lagrangian drifter system would offer the freedom to sample more parts of the storm, including the RFD.Simulations were done using high-resolution simulated supercell data to determine the feasibility and o guide the concept of operations for an ALD system. It was found that by releasing the ALD south of the storm, that they were most likely to become entrained into the RFD. These simulations are important in the understanding of supercell structures; however, it will also take field experience to understand fully how to successfully entrain the ALDs in the RFD. Furthermore, these simulations assumed that the supercell was in a steady-state condition over the 120-min period for which the trajectories of the ALDs were simulated. It is an ongoing point of research to determine if the effects of adding the time-dependent component to the supercell simulation would result in a large enough change of the trajectories to warrant the added computational cost of running such large datasets.The ALD system has been flown on the Mistral in a number of promising test flights. As of August 2018, one successful release of an ALD balloon in flight had been achieved. With further testing, it is believed that dependable ALD release will be possible by the end of the year for deployments in the spring of 2019.


This work demonstrates a neuromechanical model of rat hindlimb locomotion undergoing nominal walking with perturbations. In the animal, two types of responses to perturbations are observed: resetting and non-resetting deletions. This suggests that the animal locomotor system contains a memory-like organization. To model this phenomenon, we built a synthetic nervous system that uses separate rhythm generator and pattern formation layers to activate antagonistic muscle pairs about each joint in the sagittal plane. Our model replicates the resetting and non-resetting deletions observed in the animal. In addition, in the intact  rat walking simulation, we observe slower recovery after perturbation, which is different from the deafferented animal experiment. These results demonstrate that our model is a biologically feasible description of some of the neural circuits in the mammalian spinal cord that control locomotion, and the difference between our simulation and fictive motion shows the importance of sensory feedback on motor output. This model also demonstrates how the pattern formation network can activate muscle synergies in a coordinated way to produce stable walking, which motivates the use of more complex synergies activating more muscles in the legs for three-dimensional limb motion. Neural circuits in the spinal cord, called central pattern generators (CPGs), generate neural oscillations and control rhythmic movements. These CPGs are responsible for repetitive motions such as walking ,2, swimmPreviously, we developed a synthetic nervous system (SNS) that applies the neurobiological hypothesis of CPGs as a primary component of the controller for walking. We successfully showed that the reduced pattern generating circuit is sufficient for performing forward walking in a rat model and a dog robot ,9. HowevRecent animal and neural modeling experiments have demonstrated there may be two separate structures in locomotor spinal circuits. Studies of the fictive locomotor activity of decerebrate cats ,12,13,14Based on these studies, Rybak and colleagues ,18,19,20Researchers working in parallel have done some similar work. Markin’s group developed a neuromechanical model of cats that includes a two-layer CPG and a musculoskeletal model of cat hind limbs that generates motion-dependent afferent input to the neural model . HoweverThe method in which the pattern formation layer activates the multitude of muscles in the limb during walking is still unknown; however, a leading theory involves the use of muscle synergies ,22,23,24The research described in this paper applies the two-layer CPG hypothesis to a neuromechanical model of the rat hindlimb during locomotion. Here, we aim to test whether the rat model simulation could match existing experimental data by separating the control of motoneuron timing and magnitude. To reach this goal, we expanded our previous model to include a new, leg-level rhythm generator that helps coordinate the timing of the pattern generating circuits. We demonstrate the success of this expansion in several ways. First, we compare the neural behavior of the CPG of the deafferented neuron model with that of the decerebrate cat fictive locomotion experiments. After reproducing the animal behavior with our simplified neural circuits, we apply this structure to the intact, neuromechanical rat model, and measure the impact of sensory feedback in the simulation. In order to examine whether muscle synergies could reduce the complexity of the circuit design, two pattern formation groups are used to activate the six muscles in the hindlimb, one activating hip muscles while the other co-activates knee and ankle muscles. Our results show that the two-layer CPG successfully replicates non-resetting deletions and produces stable walking with a simple muscle synergy. This two-layer CPG model of locomotion demonstrates complex interdependencies of mechanics, sensory feedback, and rhythm generation during phase resetting and non-phase resetting deletions of locomotor rhythms.Our previous SNS for rat hindlimb locomotion used isolated subnetworks to control individual limb joints . It succIn the previous, single layer model, the CPG synapsed directly onto the motoneurons. As sensory feedback shapes CPG behavior and period, this structure is sufficient to produce steady coordinated walking. However, any perturbation to the CPG system results in simultaneous changes both in step timing and muscle force magnitude, which does not allow for controlling force generation and rhythm separately. To demonstrate this, we applied an excitatory stimulus to an extensor neuron of the half-center to simulate a sudden perturbation B. To eliE stands for a constant reference voltage . A), slope (S) and reversal potential (E) are constant parameters, specific to The conceptual neural circuit for a single limb A builds All the modeling and simulation was performed in Animatlab . The bioThe new neural network is composed of a three-level hierarchy A. The toThe pattern formation layer reshapes the swing–stance timing signals received from the rhythm generator. According to Rybak’s model ,14,18,19Before implementing the two-layer CPG structure into the full neuromechanical rat model, it was first tested in isolation to see if the deafferented model could reproduce results from the decerebrate cat  and mousIn the first test, the rhythm generator undergoes external stimulation. The stimulus is applied to both extensor and flexor neurons of the rhythm generator to simulate a descending command from the midbrain locomotor region (MLR). The same current is applied to both the extensor neuron and flexor neuron, so the extensor and flexor period are identical. The second set of tests examines the influence of external currents on the pattern formation layer. For this test we applied two stimuli to the model, one stimulus is shorter than one step cycle and the other is longer than one step cycle. This test investigates how the two-layer CPG reacts to external currents and if it reproduces the findings on the decerebrate cat that we mentioned in the Introduction section.The result of external stimulus to the rhythm generators demonstrates that rhythmic periods can be modulated with minimal influence on the motoneuron activation amplitudes. When a positive electrical current is applied to both rhythm generator neurons . We have chosen rats as the experimental subject because their size makes them excellent for studying full body kinematics in front of our three-dimensional X-ray acquisition system. Moreover, they are easy to handle and train, and are robust against infections and heal quickly after experimental procedures.We also performed animal experiments to collect rat motion data. The preparations are female The motion data was collected through X-ray videography of rat walking on treadmills . The X-rTo test the hypothesis that the SNS with the addition of the rhythm generator is capable of different modes of sensory control and non-resetting deletions, we ran tests to investigate if the full neuromechanical model can replicate the observed animal behaviors. To better observe the rhythm maintenance and phase shifting, the network was tuned to make the rat produce hopping motions in simulation, in which the behavior and neuron activities of the left and right hindlimbs are mirrored. To eliminate other influences, we applied each stimulus into the network with the same intensity and activation timing.The first experiment tests for non-resetting deletions. By inhibiting the extensor neuron of the pattern formation layer . In addition, the rhythm generator is affected by afferent feedback for the following few cycles, which results in some phase shifting. However, the influence of the feedback is reduced by the intrinsic rhythmogenic properties of the rhythm generator, and thus the step cycle rhythm recovers. Experimental results showing longer deletions in the pattern formation layer are presented in the To better understand this rhythm behavior, we inspected the neuron activities of the left hindlimb. As shown in t = 2 s, top plot, in black), and the walking rhythm is permanently altered. This switches the gait from hopping to alternating stepping, despite there being no neural connections between the hindlimbs. Another interesting result of this change in gait is that the range of motion of the knee joint changes as well due to mechanical feedback within the system.Inhibiting the extensor neuron of the rhythm generator layer  while the hip joint motion follows its own pattern. Frame 4 to frame 5 in In our previous neuromechanical model, all three joints were independently coordinated by feedback to match the desired timing between stance and swing phase . CompariWe tuned the intact rat model to produce steady alternating stepping with an appropriate tonic noise on the left hindlimb. Simulation data collected include joint kinematics and neural activity from all neurons. Data was processed using MATLAB. The cycle period and step start time were calculated using the same method used in the deafferented model. Joint motions in each cycle were manipulated by interpolation and the mean function was used to get characteristic kinematic data. The comparison of animal joint motions with simulation characteristic kinematic data  shows soIn this work, we present improvements to our previous neuromechanical model for rat locomotion  for the Resetting and non-resetting deletion experiments revealed that our improved neural model is capable of handling different sensory control tasks by separately adjusting the step cycle timing and motoneuron activation as known to occur in animals. This is in agreement with the results of the biomechanical cat model proposed by Markin and colleagues ,22 and tThe inclusion of the rhythm generator circuit expands our previous SNS to a two-layer CPG in which the top layer controls timing, and the second layer controls muscle force. The network can now handle different modes of sensory control to more effectively manage gait control. First, timing signals from other hindlimb can impact the rhythm generation (timing) layer, while force signals from within the hindlimb can be used to impact the pattern formation (magnitude) layer. Second, descending commands from the CNS can be used to interface with the rhythm generation layer, changing the rhythmic timing to change gaits without affecting muscle force generation. Descending commands from the vestibular system, however, may impact the pattern formation layer, to change the ground reaction force. Additional experiments are needed to explore how different types of descending commands from the brain to the CPG can make walking control more robust.As stated in the Introduction, there are several differences between our model and Makin’s work. Markin’s computational circuits  are consAfter implementing a knee–ankle synergy, our SNS successfully produced steady hopping  and alteKinematics between the simulation and those obtained by experimental data with the animal do not match well because of some limitations and approximations of the model. However, when making the simplifications necessary for neuromechanical simulations, there will always be difficulty in matching experimental data. For example, in the work by Markin et al. , they tuIt is noteworthy that the presented work is useful for understanding the underlying coordination within the neural system, and not in understanding the specific activation of muscles during walking. Given enough complexity of a model, its kinematics and muscle activations can always be tuned to produce some specific motions and values. For instance, different species of quadrupeds demonstrate different kinematics, despite anatomical similarities at the skeletal, muscular, and neural levels. Despite their different walking patterns, animals can still adapt their locomotion and gait. Such flexibility suggests that feed-forward muscle activation, descending commands, and proprioceptive feedback are all used to shape motor output, motivating the continued study of hypothetical circuits that may facilitate such control. This specific work demonstrates how the nervous system can serve multiple purposes at the same time; in our case, maintaining proper interlimb coordination while maintaining stability in individual stepping limbs.The limitations of model simplification lead to the question: Will those flaws be solved by including more details in the biomechanical model? The rat has 38 muscles in each of its hindlimbs to control seven degrees of freedom. Even though rats have many more muscles in their legs than appear necessary for locomotion, those “redundant” muscles probably perform functions that are not yet understood. We expect that simplified biomechanical models will fail to reproduce or reduce the efficiency of some animal postures and movements. Therefore, future work will include adding more muscles including biarticular muscles . Such moFuture work could also extend the network with additional structures or different connectivity in the pattern formation layer to establish time-varying synergies inside the neural network. This process could lead to insights in to how to design time-varying synergies for more complicated muscular systems, motivating the use of a more sophisticated full muscle structure in future models. In the model by Hart and Giszter , temporaThe results of the work by Markin and colleagues ,22 has rExtending purely neurological models to include biomechanical components with sensory feedback enables studying additional questions regarding locomotion. For instance, one can investigate the influence of different kinds of disturbances on locomotion. In the present study, we only tested how this biomechanical model reacts to one kind of stimulus. There are many other types of stimuli that can be tested in a more complete neuromechanical model of the rat, such as stepping over obstacles, walking along an incline or decline, and stepping into a hole in the ground (postponed ground touch). There exist studies of how rats ,35 and c


The paper reports on the solution, principles, and application results related to a system for diagnosing main transformers in power plants via the radiofrequency method. The subject of the diagnostics is the occurrence of partial discharge activity in transformers. The technical solution of the system is characterized in the introductory section of the article. There then follows a description of the operating principle and the implemented novel advanced methods for signal detection and source localization. The results obtained from practical application of the system within the diagnostics of high-power transformers are presented as well. Because ambient electromagnetic disturbance was recognized as a major issue during the system development, novel detection methods were proposed, implemented, and verified. The principal approach utilizes an external radiofrequency sensor to detect outer impulse disturbance and to eliminate disturbance-triggered acquisitions, and it also ensures direct real-time visualization of the desired impulse signals. The ability of weak signal detection was verified via artificial impulse signal injection into the transformer. The developed detection methods were completed with localization techniques for signal source estimation. The desired impulse signal was detected and localized during full operation of the main transformer, despite the presence of strong electromagnetic interference. Reliable production and distribution of electrical energy are important preconditions for sustainable development within industry and, generally, human society. At present, the energy is produced mainly at central facilities (power plants), where it is also transformed and subsequently transported to the consumer. The production and distribution chain comprises various blocks with specific functions, and the safety and reliability of each of the blocks is intensively monitored.One of the key components within the chain is a main transformer (generator transformer) in a power plant. This device transforms the voltage level on the output of a turbine-driven generator  to a level suitable for long-distance electricity transmission .The transformer carries significant electrical power ranging from tens to hundreds of megawatts, and it thus assumes considerable dimensions . Since tThe insulation installation embodies a demanding portion of the transformer assemblage procedure. The insulation has to withstand long operation under high electric field intensities. As working insulation plays an important role in a transformer’s reliability and safety, its condition needs to be observed in order to prevent the development of serious partial discharge (PD) activity. The partial discharge is the effect where an electrical discharge bridges over only a part of the insulation between electrodes with different potentials and a certain amount of an electric charge is transported . In the 1 pC and 106 pC.The outer manifestations of PD activity commonly consist in voltage transients on the power lines of an electrical device. Quantitative characterization of the PD is impeded by the fact that it occurs inside devices and is not directly accessible for observation and magnitude measurement. Thus, magnitude quantification is often expressed by the quantity referred to as apparent charge. This value embodies the amount of a charge in Coulombs which, when injected into the device, causes the same magnitude of voltage transients as the PD itself. The PD apparent charge in a high-power transformer typically ranges between 10Due to the seriousness of potential consequences, PD activity is intensively monitored. Various detection and diagnostic methods have been developed over time. The detection techniques exploit relevant side effects, including chemical decomposition of the medium; electrical transients in the power lines; and emission of acoustic signals and radiation in the radiofrequency and optical regions of the electromagnetic spectrum.One of the central, classic approaches lies in chemical analysis of PD-generated gases dissolved in transformer oil . The disThe latest trend in the field is to prefer detection methods that, in addition to online PD detection, ensure also relevant localization. An intensively studied technique rests in the ultra-high frequency procedure for detecting and localizing partial discharges (PD UHF), which provides some unique advantages, as described below . InteresDespite its exclusive features, the UHF method can nevertheless suffer from electromagnetic interference (EMI) when used in a real environment; therefore, significant effort has been made to investigate shielding effects in transformer vessels . A novelThe introduction and evolution of the PD UHF method were allowed by the current rapid progress in high-frequency and high-speed electronics and instrumentation. The technique detects PDs via sensing their electromagnetic emissions. The main spectral content of a PD-radiated electromagnetic (EM) signal lies between hundreds of MHz and units of GHz (UHF band) . Since tEM waves propagate inside the transformer vessel with relatively low attenuation in cases of a free path in the oil medium; however, a wave can propagate at a certain (but somewhat higher) attenuation also through the transformer winding . Thus, iA typical technical solution utilizing the PD UHF method relies on a sensor unit or a set of these; high-frequency circuits for signal conditioning; and a recording device and a computer for signal processing and PD events evaluation . All steThe signals at a sensor output are recorded and stored or directly passed on for follow-up processing. An important consequent step consists in signal analysis to detect the PD-related impulse. In real-field employment of the UHF method, it is often necessary to distinguish between a PD impulse signal and a possible interfering signal . Such a An advantageous capability of the PD UHF method lies in estimation of the PD source spatial location in the transformer vessel by means of triangulation. Such a procedure exploits mutual time relations of the acquired signals. Since the exact PD occurrence time is unknown, the time of arrival (TOA) parameters of the signals cannot be evaluated; instead, the time difference of arrival (TDOA) parameters can be calculated and consequently used for the localization. The TDOA represents the time intervals between particular impulse signal detections in each channel of the recording device. Generally, for the three-dimensional (3D) TDOA spatial localization, four suitably positioned sensors are required . FurtherSeveral methods are available for the estimation of TDOA parameters. Because of the high propagation velocity of the EM wave in the medium and the relatively short distances, it is essential to estimate the parameters with sub-nanosecond precision. The classic approach determines the time of threshold crossing by the PD signal; this procedure is not immune to noise added to the signal. The method exploiting the energy accumulation curve (or its modifications) can yield better results in this respect ,26. OtheOne of the often mentioned benefits of the PD UHF detection technique is the potentially high sensitivity, the reason being that the UHF signal detection is performed in the metal transformer vessel. The vessel should provide sufficient shielding against external electromagnetic disturbance of various origins. Such a view is based on two presumptions: First, the transformer vessel provides sufficient shielding and does not contain weak points that allow interference penetration; second, interference sources are absent from the inside of the vessel itself and connected armatures. Both of the presumptions, however, are difficult to fulfil completely in a real case. During our research and development of the PD UHF sensors system for transformer diagnostics, we registered violations of the two conditions specified above. Such adverse state then did not allow us to detect weak PD simulating signals. In order to overcome this issue, we designed, implemented, and verified a novel solution for eliminating the disturbance impact on weak impulse signal detection. The approach exploits full signal waveform acquisition as well as processing and visualization combined with discrimination of acquisitions disturbed by the interference. The developed detection method utilizes localization techniques for signal source estimation, all adopted for specific configuration of the sensor system.The development of the PD UHF diagnostic system was initiated by the need of periodic long-term operational monitoring of high-power transformers with respect to partial discharge activity. Another incentive was to introduce a tool facilitating research into innovations within PD UHF detection, PD signal analysis, and PD localization.The target devices for the diagnostics consisted in eight three-phase main transformers installed in the Dukovany nuclear power plant, the Czech Republic b. The uns11 parameter.Other problems of interest included limited length and inner diameter of the inlet. This aspect then constrained the design and dimensions of the antennas. An investigation into selected types of antennas was performed yield the most suitable option. One of the central demands consisted in securing a greater length to improve the sensitivity, mainly at lower frequencies; another requirement was a sufficient sensitivity in broader spectral range in order to be able to receive signals in the UHF band. The best compromise properties for the given restrictions were found in the monopole conical antenna shown in The overall setup of the PD UHF diagnostic system comprises the main unit, five sensor units, and a set of signal transmission and control cables. The sensor unit consists of the above-mentioned receiving antenna, a UHF signal processing chain, power supply circuits, and a shielding case. The signal processing chain was developed on the basis of laboratory experiments with the detection of low-level impulse signals. A simplified schematic of the sensor unit’s inner structure is presented in Each sensor unit is embedded in a semi-opened metal casing and connected to the inlet via an EMI gasket (round configuration of the finger contacts). The gasket prevents the EMI penetration into the transformer vessel through the junction between the sensor case and the inlet. The implementation of a sensor unit is represented in Each of the assemblies is connected to the main unit via a pair of double-shielded coaxial cables facilitating the RF signal transmission, power supply, and transmission of the amplifier’s gain control signal.The components of the main unit  are embeThe block scheme of the main unit is shown in The connection between the main unit and each sensor setup is provided by a pair of 17 m long coaxial cables with double shielding to increase the EMI immunity. The cable pairs ensure transmission of the received UHF and gain control signals and also facilitate remote power supply of the sensors’ circuitry. Since the TDOA parameters of the acquired signals are computed after the impulse detection, great attention was paid to equal lengths of the UHF signal transmission cables. The differences in the lengths were minimized using the time domain reflectometry (TDR) technique during the fabrication of the cables. The signal propagation differences are below 10 ps, a value sufficient for the given application.The system’s software controls the signal amplification, acquisition, processing, and visualization. It exploits a multithread code and is optimized for the given hardware specification of the control computer to allow maximal utilization of the system resources. The software performs continuous visual monitoring of the signal waveforms and visualizes their phases in a diagram with respect to the phase of the net voltage, and it also calculates the signal source coordinates to visualize them in a 3D projection of the transformer vessel. The triggering of the signal acquisitions is based on crossing the adjusted threshold by any of the signals in the four channels of the digitizer. The signal gain of the sensors is automatically adjusted in order to obtain the full range of the digitizer’s inputs. The acquired signals can be visualized in real time and stored. After a successful signal acquisition, the time differences of arrivals (TDOA parameters) of the impulse signals are estimated. To achieve this goal, the energy accumulation curve (EAC) is derived for each signal . The EACThe third tab window of the software interface serves for the spatial localization of the signal source (the discharge event location). This task is computed using multiple acquisitions (multiple sets of TDOA parameters) in order to statistically improve the accuracy.Two localization methods are implemented in the relevant software module: one utilizing an analytical approach , with thBecause of the specific layout of the sensors with respect to the transformer geometry, it is not possible to unambiguously determine the position of the source. The computed source positions are represented by points on a circle. The line which connects the sensor positions passes perpendicularly through the center of the circle. This ambiguity, however, is not a serious restriction factor in the diagnostic procedure. The computed and visualized circle crosses the corresponding winding, and the signal source location can be estimated via examining the intersection.1, S2, S3, and S4, as shown in As mentioned above, two methods for the spatial localization of the signal source are implemented: an analytical method and a comparative technique. The analytical option uses relations for Cartesian coordinates derived from the set of hyperbolic equations formulated for the known sensor positions Sx, y, z represent the three unknown coordinates of the signal source; xSi, ySi, zSi  are the sensor coordinates; v denotes the propagation velocity of the signal; and t12, t13, t14 stand for the TDOA parameters of the acquired signals. Further, t0 (as the fourth unknown variable) is the time instant between the PD occurrence and the first signal arrival to the first sensor. Although the given spatial sensor  and substituted into the second and third equations of the set (2)The variable By further expansion of the quadratic terms, we gety asFrom the first equation of the set (4), we can derive the formula for x, y coordinates:Introducing (5) into (3), we get1-S2-S3; S1-S2-S4; S1-S3-S4). The resulting coordinates are obtained as the average values yielded from the three results for x and y. This approach improves the accuracy. The localization results are then visualized by means of either Bézier curves in the transformer side view or axonometric view. The analytical method allows very fast localization of the signal source; however, its accuracy is sensitive to that of TDOA parameters estimation. Because of this significant sensitivity, the outcomes are further filtered to remove the results showing the source position outside the vessel. An example of the analytical localization procedure is shown in The derivations of relations (1)–(7) are based on three sensors positions. Since we have four sensors in total, the source position is computed three times for the sensor combinations  is pre-calculated. The matrix contains the TDOA parameters for each point according to the equation set:With more consistent TDOA parameter estimation results, it is advantageous to use the comparative method, which combines the outcomes of the TDOA estimation for all signal acquisitions. The whole transformer volume is divided into a mesh of points with the spacing of 10 cm. Over this mesh, a four-dimensional matrix For simplification, the TDOA parameters are calculated for direct propagation paths between the actual point and the sensors, and they are then given as:Further, a parameter which defines the tolerance interval of comparison accuracy is introduced as the quantity with the unit of nanosecond. This parameter can be modified by the operator. The localization consists in comparing the currently estimated TDOAs from the signal acquisition with the matrix elements that contain pre-calculated TDOAs. If the result (the TDOA estimate) falls within the range given by the mesh point and the tolerance interval, the value of the target variable for the defined vicinity of the point is incremented. The defined vicinity for each point represents a cube with the side dimension of 50 cm, as shown in The functionality of the developed diagnostic sensor system was verified within experimental measurement on eight 300 MVA power transformers at the Dukovany power plant. The testing cycle was carried out under full operating conditions. The sensor units were installed in the inlets, and pertinent impulse signals on their outputs were sought. Since the transformers had been manufactured comparatively shortly before the tests, no observable PD-related discharge activity was expected; however, we observed intensive discharge activity in transformer 1. The recorded signals contained strong impulse-like waveforms. An example of the recorded signals is shown in Further investigation showed that the source of interference was situated in one of the high voltage bushings that feed the transformer from a turbo-generator; we then inferred that the source rests in discharge activity on the inner 22 kV line support. The bushing structure comprises a dilatation compensating junction a, which Similar effects were observed in transformers 2 to 5; transformer 6, however, exhibited different behavior, with all of the internal sensors indicating comparable signal magnitudes. Examining the vicinity of the bushing junction by means of a commercial UHF probe b did notPreviously published papers often mention that the metal transformer vessels ensure convenient shielding to facilitate low-level PD UHF signal detection. However, when evaluating the results of the experimental measurement, we formulated a conclusion not proposed to the date, namely, that in certain types of transformers an outer disturbance can penetrate into the transformer’s vessel. This constitutes a serious problem since such a condition can impede successful detection of a weak PD electromagnetic signal. Further in this context, it is not always possible to provide convenient shielding, due to the high voltage safety and other structural measures.In order to overcome unexpected disturbance occurrences, we designed and verified a new method for detecting weak signals generated by partial discharges: the discrimination of disturbed acquisitions (DDA) technique. This method was verified via detecting an artificial impulse signal injected into the transformer vessel.As only new transformers with unmeasurable PD activity were available, we built an artificial impulse signal generator that simulates the PD electromagnetic radiation when connected to an antenna. The device utilizes rapid avalanche breakdown in the transistor structure and produces a negative polarity impulse with a frontal rise time below 300 ps and full width at the half of the maximum time of 700 ps. The peak value at the output equals 5.9 V. The impulse waveform is represented in In the standard operating mode of the sensor system, the artificial impulse signals were not detected, the reason being the presence of strong impulse disturbance. The detection was nevertheless successful after the DDA method had been implemented. The diagnostic system was equipped with an external sensor designed for detecting the outer EM impulse disturbance, The DDA method utilizes the multi-triggering ability of the built-in digitizer. The signal acquisitions can be triggered via signal threshold-crossing when this occur in any of the input channels. The external sensor is connected to an input channel instead of an internal sensor. Thus, only 3 internal sensors are used for the transformer’s internal signal detection; such an arrangement, however, suffices for the signal source localization. The external sensor is installed preferably near a location where the disturbance can penetrate (or leak away from) the transformer’s vessel. A convenient option is the service footbridge on the top of the transformer b. If plaSubsequently, the signal acquisition procedure begins. The signal gains of the internal sensors are set to a suitable level. The disturbing signal from the bushing (or the outer source) propagates inside the vessel towards the internal RF sensors and also outside, where it is detected by the external sensor to trigger the signal acquisition process. A strong disturbing impulse signal may overlap a weak PD  one in the internal sensor if they occur simultaneously; such a scenario, however, does not materialize regularly.Subsequently the signal gain of the internal sensor is raised to such level when also weak signal  trigger acquisitions. At this point we obtain two sets of signal acquisitions. One group of acquisitions are triggered by the disturbing signal by the signals from PD or artificial source. To pick the relevant acquisitions a sorting procedure is performed. The acquisitions triggered by the external sensor are discriminated from further data processing. These acquisitions were triggered by the disturbing signals. The rest are the acquisitions triggered by the low-level signals via internal sensors. The low-level signals are of internal origin and cannot penetrate to the external sensor with sufficient magnitude for triggering. These signals are further saved and processed for displaying or for computation of the source location.Using the implemented DDA method, the diagnostic sensor system can detect and display in real-time weak impulse signals generated in the transformer’s vessel under the presence of stronger impulse disturbance coming from other discharge activities outside the vessel. The demonstration of weak signal detection is presented below.In case of an external sensor configuration (utilized within the DDA mode), the external sensor is connected to the input of the third channel and installed close to the expected source of disturbance (the bushing dilatation junction). In order to demonstrate the performance of the DDA method, the above-described impulse generator was used for injecting an artificial impulse signal into the third inlet. The experimental setup is shown in The acquired waveforms are shown in Within the DDA mode, the sensor gains are adjusted either by an autonomous algorithm or manually. In the case of proper gains setting, we can observe the waveforms of the amplified artificial impulse signal in channels 1, 2, and 4 . ChannelThe acquired signals were consequently used for the experimental localization of the signal source. It has to be noted that the above-described measurement was conducted with the high voltage transformer under full operation. Very strong interference was present in the given environment. Despite such conditions, the weak EM impulse signal was successfully detected and localized.In transformers exhibiting extremely low PD activity , the DDA method leads to a stoppage of the signal visualization process as there is no signal to satisfy the prerequisites for visualization.The system software has implemented another tool for discharge activity evaluation that can be alternatively used for disturbance discrimination. This tool utilizes the fact that impulse signals which are not generated internally in the vessel  are characterized by significant temporal dispersion due to their complex propagation path from the bushings. Considering this, it is possible to separate signals with low temporal dispersion  from the set of all acquisitions. To do so, adjustable limits that the signal cannot exceed in the sense of magnitude and time duration are introduced. These limits define a banned area—red rectangles in the waveform graphs in Within the operation of this detection mode, the sensors’ amplifiers gain is set on high value in order to trigger the acquisitions by weak signals also. In this mode, the weak impulse signals  can be detected without the need of external sensor. However, the disadvantage consists in the need of manual user tuning of the banned area limits to successfully separate the desired signals.The PD UHF method embodies one of the most advanced approaches to the diagnostics of high voltage transformers as regards partial discharge activity monitoring. A significant advantage consists in the real-time detection and localization of partial discharges. Nevertheless, several precautions still have to be adopted. The electromagnetic signal radiated by the discharges is usually weak; thus, the effect of external disturbances must be considered. The field experiments clearly pointed to the fundamental importance of proper shielding as the PD signal level is considerably below the interference one. Common interpretations of the PD UHF method emphasize its excellent sensitivity arising from inherent shielding of the transformer’s vessel. In this context, however, we established that it is not sufficient to rely solely on the shielding properties, because in many cases sufficient additional shielding cannot be ensured due to various high voltage safety precautions and structural measures.Importantly, most of the transformers examined by our research team exhibited discharge activity in the feeding bushings, and such a problem is difficult to resolve, as already proposed above. In one of the investigated devices we also observed the coupling of interference from a distant source, concluding that the penetration paths might be sought in the bushing dilatation joints and the 400 kV bushing on the extra high voltage side of the transformer.To eliminate the deficiencies, we designed and verified a novel method for detecting the weak signals generated by partial discharges. The technique is combined with the real-time visualization of the acquired signals, allowing us to simultaneously discriminate the signals produced by spurious discharge activities and to observe the waveforms arriving from the transformer’s vessel. These signals can be of either PD or artificial origin .The Discrimination of Disturbed Acquisitions (DDA) technique was verified via detecting the weak artificial impulse signal injected into the transformer vessel. The DDA method utilizes the multi-triggering ability of the built-in digitizer as well as an external sensor to detect the outer disturbing signal. Within the DDA procedure, the acquisitions triggered by the external sensor are excluded from further processing as they have deteriorated through the action of the disturbance.Further, we verified the technique that discriminates the temporal dispersed signals; this is an alternative approach to signal separation, based on setting such time and amplitude limits that the signal cannot cross over.The verification of the DDA method proved the procedure’s capability of detecting weak artificial impulse signals and positively localizing their sources. Interestingly, the actual practical demonstration was carried out on transformers under full operation. Although interference was present in the testing environment, the weak artificial EM impulse signal was successfully detected and localized.The developed technique was completed with localization methods to estimate the signal source, and these methods were adapted to the sensor system configuration.The above-outlined principles, devices, and methods are implemented into the proposed diagnostic system, which finds application in periodic predictive diagnostic monitoring of main transformers in nuclear power plants.At present, the literature offers discussions analyzing disturbance and its influence on partial discharge UHF detection. Our paper, however, predominantly outlines the particulars of the specific condition where the disturbance source is comprised directly in the transformer’s armatures and may thus be unresolvable via conventional approaches. A unique aspect of the technique consists in the possibility of ensuring real-time signal waveform observation together with fast signal source localization. To our knowledge, no previously published work describes a corresponding transformer diagnostic procedure employed in real conditions.