The development of indoor positioning solutions using smartphones is a growing activity with an enormous potential for everyday life and professional applications. The research activities on this topic concentrate on the development of new positioning solutions that are tested in specific environments under their own evaluation metrics. To explore the real positioning quality of smartphone-based solutions and their capabilities for seamlessly adapting to different scenarios, it is needed to find fair evaluation frameworks. The design of competitions using extensive pre-recorded datasets is a valid way to generate open data for comparing the different solutions created by research teams. In this paper, we discuss the details of the 2017 IPIN indoor localization competition, the different datasets created, the teams participating in the event, and the results they obtained. We compare these results with other competition-based approaches (Microsoft and Perf-loc) and on-line evaluation web sites. The lessons learned by organising these competitions and the benefits for the community are addressed along the paper. Our analysis paves the way for future developments on the standardization of evaluations and for creating a widely-adopted benchmark strategy for researchers and companies in the field. Indoor positioning, location and navigation are hot research topics whose market is expected to growth at a 42% annual rate in the next five years, with an expected worth of 41 billion USD by 2022 and 58 billion USD by 2023 ,7, amongIn contrast to outdoor positioning, where the majority of systems and applications rely on GNSS, many technologies have emerged to provide positioning indoors. They are based on: Wireless Communication Technologies   is the v2 and 18 APs) and a warehouse (826.32 m2 and 70 Bluetooth beacons). Although both works present valid results, the differences in covered area, density and distribution of APs, number of reference fingerprints, and so on, make quantitative comparisons among them difficult.An illustrative example of the evaluation diversity problem can be found in two works presented at the 2017 IPIN conference ,39. The Since 2015, the IPIN Conference holds an indoor positioning competition . ThroughNowadays, providing databases with pre-recorded data is becoming more popular in the scientific literature, since it provides a way for third-party researchers to validate the positioning method proposed by the authors or provide alternative methods. In our opinion, sharing positioning data fosters the development of new localization algorithms.Databases can be introduced by publishing a data descriptor paper e.g., ,33,41) o,41 o33,4The creation of the ISC  of IPIN  was annoThe competition organization, the dedicated sessions and the proliferation of standards, such as ISO/IEC 18305:2016 , demonstTo sum up, the main aim of this paper is threefold: showing the results of the competition, demonstrating that all competitions are useful for the research community and discussing about improving the evaluation of indoor positioning systems.The rest of this paper is organized as follows: The indoor positioning community is concerned about the evaluation problem, as demonstrated by the proliferation of many different initiatives to promote the evaluation of indoor positioning systems.Several specific initiatives for evaluating indoor positioning systems have been proposed. First, operative indoor positioning prototypes are being evaluated through competitions, which might include tracks to cover different contexts. Those competitions not only evaluate working systems, but, in some on-line cases, also the ability for deploying a proper infrastructure in a short period of time. Second, open evaluation benchmarking testbeds and web platforms are emerging to provide a large variety of evaluation setups for indoor positioning systems. Third, many researchers are providing the collected data used in their experiments in public/general-purpose repositories or in personal/institutional websites.In general, the indoor community is adopting more open and transparent evaluation procedures which enable further comprehensive comparative works. The adoption of open evaluation approaches should be seen as a unique opportunity to share different complementary ideas, increase the motivation of the indoor community to improve the accuracy of current systems and, above all, consolidate the indoor positioning as an emerging topic. Since the indoor positioning topics are diverse, they are expected to have multiple open alternatives for open evaluation.The Microsoft Indoor Localization Competition at the International Conference on Information Processing in Sensor Networks (IPSN) is one of the most popular indoor localization competitions ,45,46. IThe EvAAL initiative ,48,49 orPerfLoc  is an onThe Geo IoT World conference also provided an indoor location testbed  where soIn addition to the competitions, platforms allowing the comparison of different solutions have great importance.The EVARILOS benchmarking platform (EBP) , with a The IndoorLoc Platform  is a pubAlthough the IndoorLoc Platform facilitates the access to reference databases, there are other databases available online for download.It has become a trend to publish the databases in personal/research group websites ,60 or inAs commonly done in machine learning, the evaluation of an indoor positioning system using a wide variety of datasets represents its general accuracy better since many evaluation contexts are considered. In addition, the availability of datasets allows comprehensive comparative studies that provides researchers with a better overview of all works related to, for instance, an indoor positioning technology. Unfortunately, there is not a well-established consensus about how to collect (strategies to generate the dataset) and provide this data (file format) yet.The Evaluating Ambient Assisted Living initiative (EvAAL) aims at establishing benchmarks and evaluation metrics for comparing Ambient Assisted Living solutions and it has organised international competitions on indoor localization and indoor activity recognition since 2011.Track 1: Smartphone-based (on-site)Track 2: Pedestrian Dead Reckoning Positioning (on-site)Track 3: Smartphone-based (off-site)Track 4: PDR for warehouse picking (off-site)In 2017, EvAAL members applied their experience to the organization of IPIN 2017 Competition, which was held during the IPIN Conference . The competition was composed of four tracks:This paper focuses on the organization, results and lessons learned in the 2017 IPIN competition’s Smartphone-based (off-site) track, which might be considered as a continuation of the Smartphone-based track from the previous IPIN competition. The following subsections will describe the main aspects of this database off-site competition.An off-site competition approach where the organizers provided the required data for calibration, validation and evaluation. It was forbidden for competitors to survey the evaluation scenarios by themselves. This constraint guaranteed that all competitors had the same data for participating in the competition. All competitors were notified about the existence of public-access databases that were collected for the previous 2016 IPIN Competition .GetSensorData v2, which is available at [The data were provided as logfiles recorded by a dedicated Android application (lable at  and whiclable at ). A logfA natural movement is assumed in most captured data. Some special movements were also considered: turns, moving backward/laterally at certain points and changing floors through stairs. The user speed was approximately constant while recording the data with stops at some positions. It is important to note that the data were collected by a human, and not a controlled constant speed device, which makes the data closer to a natural movement.None of the collected dataset included artificial phone holding. The smartphone was either stable in front of actor’s face or chest, which is a typical position for reading or typing on the phone, or with the arm downwards while holding the phone with a hand. In only very few cases, the phone was near the actor’s ear simulating a phone call. This unpredicted variability is closer to reality.Each logfile covered a trajectory in one of the evaluation buildings and it was recorded by an actor using a smartphone. The provided data are diverse because they consist of multiple trajectories taken at three different buildings. A total of eight people participated in the database creation and 10 devices were used.The scenarios remained unaltered during the collection process. Only one of them changed, because BLE beacons had been previously deployed in a small area for testing purposes. The information about the location of those beacons was provided to competitors.The main features of the 2017 IPIN Competition - Smartphone-based (off-site) track were:Bluetooth Low Energy data were available.Position of BLE beacons and a few Wi-Fi APs was provided.In a few cases, the actor simulated a phone call while data were being captured, to add a natural motion.More raw-data per building were provided.With respect to the 2016 IPIN off-site competition , the comThe competition environment comprised a total of three buildings: CAR , UJIUB  and UJITI , all of which were also part of the 2016 IPIN Competition.Raw geo-referenced data were provided as a set of logfiles for training and validation purposes, which corresponds to a stream of sensor data registered sequentially. Each line in the file corresponds to a register, a single sensor reading, stored as plain text. The register fields are separated by semicolons and contain the values provided by the sensor, the timestamps and a header that identifies the type of sensor. The register named POSI does not correspond to any sensor and it provides the ground truth location provided by the users. The sampling rate of each kind of sensor depends on the Android device and operating system version.Three types of datasets were available to competitors: training, validation and test. There was no substantial difference between them regarding the sensor’s registers. Only the training and validation logfiles contained the POSI registers with the geo-referenced positions of the reference points. The position was provided as latitude and longitude (WGS84 format) and the identifiers for floor and building.All datasets were simultaneously published on April 2017, i.e., five months before the deadline that the competitors had for submitting their location estimation results. Similarly, Regarding the ground truth data, which were provided by POSI lines in training and validation logfiles, some competitors suggested to have more reference data and shorten the period between two consecutive POSI entries in the logfiles. However, the data collection, which included marking each reference point, was not an easy task.The organizers included a tricky transition between floors in one of the logfiles. This transition (stairs area) was not included in the training and validation logfiles and was considered a good way to test the robustness of competing IPS when facing unexpected events.The floor penalty was applied whenever a competing system misidentified the floor. However, in floor transitions (stairs areas), a system may provide an estimation very close to the evaluation point and yet be prone to miss the floor. Some competitors indicated that future competitions should take into account these special cases. The competition organizers are considering to integrate a more realistic error distance metric  to calcuOnce the datasets were actually used in the competition, some issues were found:The competition organizers appreciate the honest feedback from competitors and will work on balancing the competitors suggestions and competition challenges.After processing the evaluation logfiles (test subsets), participants could perform up to three different submissions of indoor location estimates. The estimates had to be provided at a pace of 0.5 s, starting from the first timestamp available in each logfile. The estimations had to adhere to the following format: timestamp, latitude, longitude, floor, and building.Each submission was evaluated considering the estimates provided for the seven evaluation logfiles. The base positioning error was defined as the geometric distance between the two-dimensional (latitude and longitude) real position as recorded by competition organizers and the estimated position provided by the competing IPS. This base error is commonly referred as two-dimensional positioning error or X-Y error. Furthermore, we took into account floor and building mis-identifications during the trajectories. In this case, 15 and 50 m penalties were added to the geometric error if the IPS had not correctly estimated the floor and building, respectively. Thus, the positioning error is defined in this paper as the two-dimensional positioning error plus the penalties terms. The final metric used for the competition results was the third quartile (75% percentile) of this joint positioning error.Of the three alternative sets of estimations submitted by the competing teams to the tests, only the one with the best results was considered for the ranking of competitors.The UMinho team: Adriano Moreira, Maria João Nicolau, António Costa, Filipe Meneses. University of Minho and Centro de Computação Gráfica, Guimarães, Portugal.The AraraDS team: Tomás Lungenstrass, Joaquín Farina, Juan Pablo Morales. AraraDS, Santiago, Chile.The Yai team: Wei-Chung Lu, Wen-Chen Lu, Ho-Ti Cheng, Shi-Shen Yang, Shih-Hau Fang, Ying-Ren Chien and Yu Tsao. Yuan Ze University, National Ilan University, Academia Sinica Research Center for Information Technology Innovation, Taiwan.The HFTS team: Stefan Knauth. Stuttgart University of Applied Sciences, Stuttgart, Germany .This section highlights the main features of the competing teams and references their works in the IPIN 2017 proceedings:The approach used by the UMinho team on the 2017 edition of the competition is a refinement of the solution used in the previous year competition . The corAraraIPS is Arara’s proprietary indoor positioning technology. This startup has rapidly grown during its four years of existence to become the largest Wi-Fi network administrator in Chile. Arara is engaged in developing advanced knowledge solutions and producing high-quality technology to address modern business and industry challenges. AraraIPS has been one of the central issues in their research agenda for the last year. In spite of being still in an early development stage it is already a functional and promising indoor positioning system.AraraIPS’ approach to indoor positioning has four distinctive characteristics: it is based on a cartographic paradigm (fingerprinting), it uses a discretization of the predicted floor/building, it is measurement-agnostic , and it exploits measurement history. In the following paragraphs, these features will be shown to get a better understanding of how AraraIPS works.Fingerprinting has become a relevant technique in indoor positioning approaches. Traditional positioning methods are generally based upon one such principle as trilateration or triangulation, in which sufficient geometric information (with respect to reference landmarks) singles out the position of the object to be located. These methods are based on underlying hypotheses  which usually do not hold in the dynamic, cluttered context of indoor spaces. Even though cartographic approaches still need to take care of changing environment, they can adapt to obstruction and other conditions that naturally occur in indoor spaces and thus avoid the pitfalls of traditional positioning techniques. From Arara’s point of view, this is a crucial property for a successful positioning system.Arara’s system also relies on the discretization of the underlying indoor space. In practice, this means that a graph is built from the map of a venue, in which nodes are possible locations where the tracked device can be found and edges connect neighboring nodes. This effectively turns the positioning problem into a classification one, in which the prediction is one of finitely many possible locations. It has the further advantage of ruling out inaccessible locations  and thus not complicating the prediction task unnecessarily. An example of a generated graph is shown in Fingerprinting also has the additional advantage of not needing any a priori knowledge about the nature of the measured quantities (provided that they are effectively vector fields in the indoor space). Arara’s system was expected to be as scalable and flexible as possible, and this was taken as a feature of the model. This is not a property that provides improved performance, but a desirable commercial feature for AraraIPS to be able to adapt fully and simply to varying operating conditions. Therefore, the system uses a very general probabilistic model for measurements taken at a given location, which is common to all mapped quantities.To make the system scalable, which is a fundamental concern, it does not rely on the installation and maintenance of additional hardware. The Arara’s team considers that the available information  under typical operating conditions in most buildings is not discriminative enough by itself to yield a sufficiently precise prediction. Hence, the solution proposed  is to enrich the information available to the prediction module by taking into account the history of measurements, exploiting the fact that measurements taken close in time will be strongly correlated. Thus, it is expected to narrow in the prediction on the true position, with greater and greater confidence as time goes by. The mathematical formulation of this is enclosed in what the Arara’s team called “random walk model”. The output of the algorithm is thus a probability distribution over all possible nodes.For the competition, the fingerprinting method was adapted to use the available data. The predictions submitted are almost solely based on Wi-Fi information . This was mostly because the system is currently under development and reliable PDR techniques have not yet been implemented/developed. One of the submissions of Arara’s team included an experimental feature using future information to improve present predictions with worse, inconsistent results. More work is still needed to look deeper into it to understand what is actually going on.Determination of the correct building;determination of the correct floor; andestimation of the latitude and longitude coordinates.An increasing growth of indoor localization solutions has been witnessed during the past few years. Although many different positioning approaches exist, the YAI team uses the traditional fingerprinting approach to address indoor positioning in the IPIN competition. Positioning was divided into three main stages parts:First, the YAI team uses the GPS data to determine the correct building. Due to the buildings distribution, this stage should not require any other advanced method in order to determine the correct building.Second, the Wi-Fi data were used to determine the floor differences. Wi-Fi fingerprints were labelled with the floor numbers. Then, the corresponding access point and its RSS value are used to calculate the Euclidean Distance between the training fingerprints and the testing fingerprints. The minimum distance in the RSSI space provided the predicted floor number. Pressure data was used to revise the final prediction of floor.if the distance between the two consecutive points is less than 5 m, the accelerometer and magnetometer data are used to predict the trajectory of the walking person; andif the distance between the two consecutive points is more than 5 m, Wi-Fi Fingerprinting approach is used to determine the coordinates.Third, data from Wi-Fi, Accelerometer and Magnetometer were used to determine the coordinates (longitude and latitude). This algorithm consists of two differentiated fine-grained positioning systems:The members of the YAI team found that using accelerometer and magnetometer data provided accurate results but the mean error increased remarkably as the distance between two points became larger. Thus, the threshold value was set to 5 m in order to switch to the fingerprinting-based positioning system.In the Wi-Fi fingerprinting based approach, the YAI team labelled the Wi-Fi data with coordinates. Then, the corresponding access point and its RSS value were used to calculate the Euclidean Distance between the training point and the testing point. Finally, the minimum distance provided the predicted coordinates.The competing system introduced by the YAI team was tested on the validation logfiles. The building identification was The HFTS team combined PDR/Wi-Fi algorithms  and emplHeading estimation is performed by combining gyroscope and magnetic information: the gyroscope is able to detect heading changes quite accurately on a short timescale, but will drift in the long-term. Therefore, the algorithm stabilizes the gyroscope heading with the compass heading. Compass heading is subject to strong local magnetic perturbations but shows no drift on a long-term scale. The estimated heading is calculated by summing gyroscope heading changes immediately, but relaxing back to magnetic heading with a configurable constant.The particle filter contains a constant number of particles. Besides the position, a particle state also comprises individual step length and heading offset values. The filter is updated each time a step is detected: All particles are moved according to the estimated heading, individually modified by the particle specific offsets. Each time a particle collides with, for example, a wall, it is replaced by a new one. For collision detection, the provided floor plans are used. New particles are seeded at the position of an existing particle, but with an own step length and heading offset value.A particular feature of the algorithm is its backtracking capability. By recursively tracking back the history of a particle, a continuous track can be estimated for each individual particle, allowing a posteriori elimination of failed particles. This leads to a significant accuracy increase for cases where the position information is not needed in real time.The HFTS 2017 competition algorithm includes fusion of PDR and Wi-Fi results: To keep the concept of continuous particle movement, i.e., no sudden displacement of particle positions, the Wi-Fi information is used to adjust the particle movement parameters which are heading and step length. This is described by the following equations: Starting at the position Rt of a pasOn stairs, the step length changes considerably and is dependent on the stairs stride. It is typically about half of the normal step length. This is taken into account by marking stair areas with a dedicated color in the floor maps. The collision check also includes a stair check based on the map marking. The particle filter then adjusts the step length accordingly to a predefined value, for the corresponding particles. The stair information is also used in the handling of floor change processing: Floor changes may only occur when passing stairs. At the moment when a floor change is detected, all particles not situated on a marked stair area are removed from the particle filter. This increases the convergence of the particle filter. The scheme could also be used to handle elevators or escalators.The particle filter may also get empty i.e., all particles are discarded. This may for example happen when the number of particles is too small, the heading estimation has a high error or the floor plan is not correct, i.e., people can walk through walls. A possible approach to solve the issue could be to seed new particles at random positions for example around the known Wi-Fi position. However, the reseeding of new particles at new positions cuts the link to the particles history, thereby disabling the particular backtracking feature of the algorithm.The chosen solution is to suspend the collision detection for a short time in cases where all particles would collide. The confinement of the particle cloud is then performed by the Wi-Fi position: Particles for which the distance to the Wi-Fi position exceeds a configurable value are discarded.PDR Offset cancellation is not only map-based but also Wi-Fi based: The particle movement process of the particle filter includes a Wi-Fi based heading offset and step length correction. Depending on parameter setting, this may lead to ignoring Wi-Fi, mixing Wi-Fi and inertial sensor information or completely adjust to Wi-Fi positions.The particle filter uses Wi-Fi results as a bound to remove outliers: By keeping an estimated Wi-Fi position, particles exceeding a configurable distance from the Wi-Fi position are removed from the particle filter, thus keeping the particle cloud confined to a certain radius.The PDR algorithm considers floor areas on which a level change may occur, e.g., stairs and elevators. These areas are marked manually on the floor map. This allows step length adjustment for stairs and, in combination with Wi-Fi floor change detection, particle filter “flushing”, e.g., removal of particles which are not on a floor change area.In cases where the particle filter loose all particles, the map requirement is withdrawn, e.g., particles may cross walls. They are still confined to some extend by the Wi-Fi position.In summary, the algorithm exhibits the following particular features:This section presents the track 3 IPIN 2017 competition results and, after that, those results are compared with the results of other competitions.The competition results for the total 505 evaluation points are shown in The winner of this edition was the UMinho team (Score: 3.48 m) followed closely by AraraDS team (Score: 3.53 m). The difference between their scores was just 5 cm, which demonstrates the quality of both competing systems regarding the competition metric. The Yai and HFTS teams were third and fourth in the competition rank, respectively. The CDF shown in Although the ranks provided in the IPIN competition correspond to the third quartile of the positioning error, we consider that more details are needed to understand why a comprehensive evaluation of indoor positioning systems (including different scenarios and contexts) is useful.All logfiles and Average. The All logfiles metric corresponds to the mean positioning error or the floor detection rate for the 505 evaluation points as a whole. The Average metric line, considers the average of the mean results shown for the seven logfiles. In all these metrics, all logfiles correspond to the best set of results out of the three files allowed to be presented by the competitors.The detailed extended evaluation results are shown in all logfiles and average) slightly differ from the competition metric since they are based on the average of the positioning error and hit detection rate instead of the third quartile. In addition, both aggregation alternatives are different to each other as the numerical results in the table reflects. This difference is because the former aggregation alternative averages all reference points with equal weight and the later corresponds to the average of the averaged accuracy of each logfile. In this second approach (average) the weight among all points is different as not all logfiles contain the same amount of reference points, so that shorter experiments have more weight.The results provided by the two aggregation alternatives ; and the HFTS provided the lowest mean positioning error in the logfiles collected at the UJITI building (logfiles 6 and 7). This fact demonstrates that comprehensive evaluation procedures are useful, not only to obtain a winning system but also to show the complexity of developing an indoor positioning system that provides the best results in all contexts and scenarios.According to the individual CDF plots, it can bee seen that the CDFs for logfiles 1, 3, 4 and 5  resembleLogfiles 1 and 2 correspond to CAR building. The CDFs show that the UMinho and AraraDS teams are the bests for logfile 1, and the AraraDS and HFTS teams are the best ones for logfile 2. Although both evaluation logfiles were collected almost simultaneously by two independent actors following a similar trajectory, logfile 2 was collected by means of a “new” device  which might have negatively affected a few competing systems .Logfiles 3, 4 and 5 correspond to the UJIUB building and the three CDFs are similar. It seems that the results provided in logfile 5 are slightly worse than in the other two logfiles collected in the same building (logfiles 3 and 4).Logfiles 6 and 7 correspond to two independent trajectories in the UJITI building. The CDFs show that the HFTS team was the best team in this building (with a mean positioning error lower than 2 m in both cases).The individual results and CDFs show that the results are not homogeneous and they depend on the scenario and context. The UMinho team won the competition, but the AraraDS and HFTS were the best in three individual logfiles using the competition metric (the third quartile). The accuracy does not only depend on the scenario, but also on the evaluation context: particular disturbances present in the environment, device used for positioning or, even, actor interaction as the results of logfile 5 show.Finally, It is worth noticing that the most challenging scenario in the 2016 edition was the UAH building according to the winner’s results; this fact could make us reconsider the re-inclusion of this building or the inclusion of challenging huge scenarios such as shopping malls in the evaluation process. The main problem of including this kind of scenarios is the extraordinary time needed to obtain a dense enough surveying.At first sight, the organisers have detected that the results are more coherent in 2017 than in 2016 edition. Two teams provided very competitive results with a difference of just 5 cm in the competition metric, but the other two teams also provided good accuracy. The difference between the winner and the last ranked team is ≈1 m in 2017. In 2016, the difference of the winner with respect to the runner-up team was much higher (≈1.5 m), and one competing team provided a very large positioning error (higher than 40 m). It seems that providing a large set of training and validation data has benefited all competitors at the expense of a comprehensive data log procedure from the organization team.Competitors in the current competition that also participated in previous editions were able to evaluate their systems over the time and track the implemented modifications. Moreover, having the historical data is useful for the indoor research community because a real comparative study of working systems is shown in real-life scenarios.The results provided in the Microsoft (MS-IPSN) and IPIN Competitions are shown in The context must be considered when comparing the results provided by the competition winners. The technological solution that won the MS-ISPN 2017 competition provided the lowest mean positioning error in the table (3 cm) and it was the best solution in its competition track. Although the positioning error is very low, it cannot be directly compared to the winners of the other competitions since the competitions do not share the same requirements. The winner of the MS-ISPN 2017 competition was based on LIDAR, which is not allowed in smartphone and PDR based applications and competition tracks. In addition, we cannot state that the winner of the IPIN 2017 Competition Track 3 is better than the winner of the IPIN 2017 Track 1 (Smartphone-based competitions), because the evaluation scenarios and rules were different in both tracks. In particular, the evaluation scenario of the IPIN Tracks 1 and 2 was very challenging in 2017, and the competitors of IPIN 2017 Tracks 1 and 2 had to provide the position estimations in real-time.Another important difference among the competitions is how the tracks (or categories) are defined. In the IPIN competition, the tracks are device oriented (smartphone or PDR mainly). In addition, the IPIN competition provides different tracks for on-site and off-site competitions, even if the same base device is used. In the MS-ISPN competition, a black-box evaluation is adopted and the systems are evaluated with independence of the technology employed for localization. In 2014 and 2015, the MS-ISPN competition provided two competition categories according to the infrastructure required for positioning (infrastructure-free or infrastructure-based). In 2016 and 2017, the MS-ISPN categories were 2D and 3D positioning. In general, the existing competitions cover different aspects of categorising the indoor positioning in order to consider the wide diversity of indoor positioning systems. Thus, the importance of having all those diverse initiatives to compare indoor positioning systems.The appropriate context is also relevant for the features shown in First, the Microsoft competition aimed at a real-time evaluation of working competing systems, where the evaluator stood for a few seconds at pre-defined test points. Competitors had a strict time slot for their system evaluation (15–20 min) and they were able to sense the environment and deploy any hardware the day before. Due to time and logistic constraints, the number of evaluation points was between 15 and 20 and the evaluation scenario comprised only a few rooms and corridors. Furthermore, the categories established are not oriented to a particular technology. Systems using visible light, ultrasounds, IMU or Radio-frequency based systems, among others have competed under the same category. Smartphone based systems have also participated in this competition, being a Wi-Fi fingerprinting-based system the winner of the infrastructure-free category in 2014.Second, the IPIN Competition (Tracks 1 and 2) also aimed at a real-time evaluation in multi-floor environments, where an actor followed a trajectory defined by a sequence of test points. The evaluation trajectory covered the IPIN conference venue and its access was not restricted (it was not a closed test environment). Since the evaluation path was long and covered different floors  and buildings (only in 2015 edition), the evaluation of competitors was done in parallel. In IPIN Tracks 1 and 2, hardware deployment in the evaluation area was forbidden. Due to time constraints, the number of evaluation points was about 60 and the evaluation length trajectory was about 500–600 m. Track 1 has been devoted to smartphone-based positioning, whereas foot-mounted devices have been used in Track 2 since 2015.Third, the IPIN Competition (Track 3) aimed at the off-site evaluation of smartphone-based solutions in complex multi-floor environments. Since the competition was run off-line and off-site, there was not strict time restrictions to meet. In the on-line competitions, the organizers have usually had one day to evaluate all competing teams during the conference. With an off-site competition, the competition can be done in advance and the results can be presented during the conference. The competition timings allows the collection of longer evaluation trajectories—with an accumulated length of ≈4.4 km and ≈3.2 km in 2016 and 2017, respectively—that include many evaluation points—more than 500 in both editions.In real-time on-site competitions, it might not feasible to evaluate each competitor in a extremely long trajectory or in a very large scenario. On the one hand, the timing and logistic restrictions might be a severe issue. In current on-site competitions, more than 20 teams are evaluated the same day. In addition, competitors can deploy  hardware in some competitions , whose costs might be prohibitive in very large scenarios. On the other hand, the probability of having a wide variety of unexpected errors might increase during the evaluation in long evaluation trajectories, such as application hangs, battery drain, or actor’s fatigue, among many others.The off-site competitions, such as IPIN Track 3, offer the possibility of evaluating the positioning accuracy in long tracks with a high number of reference points. This evaluation procedure assures that all competing teams have at their disposal the same data and information about the environment at calibration and evaluation stages. Moreover, diversity is introduced in the evaluation paths since data are gathered in different buildings, by different people and with different devices (smartphones in this case). However, having a very large evaluation path with diversity is not a synonym of being a better evaluation procedure. The evaluation procedure used in the off-site competition cannot take into consideration the ability of the competing teams to successfully deploy their localization infrastructure in the environment for positioning purposes. In addition, the off-site competition cannot take into consideration the ability of the competing teams to perform an optimal survey of the environment and successfully calibrate an indoor positioning system for the evaluation area. Finally, the real-time component is not present in the off-site competitions, where the competitors have the opportunity of post-processing the position estimated before its final submission. In addition, the competing systems in on-site competitions must be robust because if they fail during the evaluation window, they might not have another chance to compete.We consider that the strategies followed in real-time on-site competitions are suitable for evaluating indoor positioning systems. Off-site competitions can be considered a complementary evaluation strategy, where a more comprehensive evaluation  can be done. However, the “comprehensive” evaluation of the positioning error in off-site competitions is done at the expense of not considering some hidden evaluation aspects, for example, the availability of the indoor positioning system, ability to successfully adapt or customize the competing system to a particular scenario in a few hours, and ability of optimally surveying the environment, among other features.In this paper, we have shown the results obtained by the competitors in Track 3 of the IPIN 2017 competition, regarding off-site evaluation of smartphone-based solutions. Results have been compared with previous (2016) competition and with other competitions.Provide more detailed data without gaps .Try to minimize unexpected events like missing stairs areas in training and validation log files.Code the floor transitions with a special floor code  to minimise the unfair large positioning errors near areas close to floor transitions.The competitors provided to the organizing team many suggestions to improve the competition, being the most representative ones:Registration of datasets. The registration of data in the logfiles for the competition is done in a continuous way with minor stops. Some competitors, especially those that mainly rely on fingerprinting, felt uncomfortable with this procedure because they prefer a static registration, or at least a very slow continuous motion (0.2 m/s), in order to have more stable Wi-Fi fingerprints and more samples. They also would like a dense survey with many points, and no testing experiments outside of the surveyed area. We understand this point of view, but we also consider the current relevance of the generation of fingerprinting solutions from incomplete or low sampled scenarios, surveyed by actors in motion . Next editions could include some sites with a high density of points taken at static positions, just to observe how some systems can benefit from it, but we consider that we should keep the core of the current registration approach.Metric fairness. Regarding the metric used in the IPIN competition, which is based on the third quartile plus some penalties generated by floor or building identification mistakes, some competitors suggested that the weights for those penalties were too large . The usage of those penalties emphasizes that a positioning service must pay high importance to guaranteeing a correct floor indication. For some applications, a wrong floor indication can be very adverse for the location service provided. For example, a mistake in a floor number can make the user to lose valuable time when navigating in a mall from one section to another, and, most importantly, could lower the user interest in that service since it is perceived as misleading. The 15 m penalty is even a small penalty considering the time needed to correct our route from a failure due to a floor mistake. The competition organizers are considering in further editions to determine the positioning error using a more realistic distance, which computes the walkable distance between the estimated and real position.After a positive discussion with competitors, the organizers detected two major concerns about the competition.To improve the future editions of the off-site smartphone-based competition, the aspects analysed in this paper must be reinforced, maintained, promoted or changed. The main goal of the competition is to provide a forum and a evaluation procedure that embrace all competitors while keeping challenging competition conditions that mimic realistic and diverse environments.Databases are important for comprehensive comparisons. The creation of different databases, in different buildings, with different actors, different smartphones, and with a high degree of diversity is, in general, the only way we can analyse the performance of systems with enough detail. We have seen from our analysis that some teams created algorithms whose behavior was better in a certain environment, or even for test occurrences, than others. Our feeling is that the typical evaluation procedure for IPS found in the literature, where a team selects a particular building and compares its method with a ground truth or with another approach, is prone to excessive fine-tuning of algorithms, and insufficient to conclude that the proposed positioning approach is robust and accurate enough for real-life uncontrolled scenarios. Competitions are a good way to collect experimental data of diverse sites for the creation of banks of databases against with to compare.Competition motivates competitors from Academia. Research involved in non-commercial applications does not commonly have the pressure of providing the best accurate working systems in every possible scenario. Once the details of their base system have been published , the resources are dedicated to explore novel aspects and features of indoor localization. However, an annual competition that involves challenging participants from academia or industry forces every competitor to be highly competitive and to develop accurate working systems, ad not just prototypes. This fact boosts their motivation to improve and refine their systems to levels that are not usually considered in traditional R & D works.Competition integrates Academia and Industry. Competitions can be seen as showrooms where both worlds, Academia and Industry, show their developments, advances and interesting findings. Although the main aim of a competition is to compete and win, synergies between the competitors can be consolidated after the competition. In fact, informal contacts between competitors were detected after presenting the competition results. Creating a forum, to discuss about indoor positioning and establish new contacts, was one of the competition pillars.Competitions must be diverse. Although this paper presents the results of experiences of the 2017 off-site smartphone-based competition, there are other interesting competitions and evaluation initiatives available which consider different base positioning technologies , real-time evaluation, joint off-line and on-line evaluation, among many other evaluation features. We consider that diversity in competitions is important, because it allows a huge indoor positioning community to be represented.Competitions need to cover multiple scenarios and contexts. The results of Track 3 from IPIN 2017 Competition show an interesting finding: none of the competing systems provided the best accuracy  in all the evaluation logfiles. This finding demonstrates that the accuracy of a positioning system depends on the scenario and context. Therefore, to have a comprehensive evaluation, as many as possible scenarios and contexts have to been considered in the evaluation set-up, thus avoiding winners that provide high accuracy for specific settings but perform poorly under other scenarios and contexts. However, considering multiple scenarios and contexts might not be feasible in on-site competitions, because the time constraints are very tight. Even for off-site competitions, the effort of considering multiple scenarios and contexts is huge, because the time required for planning the data collection, gathering the raw data and performing post-processing is considerable.Competitions need continuity. Although all competitions are important, the ones with continuity are particularly relevant. Continuity is an essential feature in order to track the competition evolution and to keep the motivation of competitors. Annual competitions, such as the IPIN and MS competitions, enable competitors to participate each year. The teams from the University of Minho and the Technical University of Stuttgart have participated in all three editions of this competition and they have received valuable feedback to refine their systems.The general conclusions reached after the competition are as follows.Finally, one of the most important findings of this paper is that the evaluation procedure of indoor positioning systems should be diverse and include different evaluation contexts and scenarios. Finding a golden method encompassing in all scenarios and contexts was not easy in the 2017 IPIN competition  according to the detailed results presented in this paper: each competing system stands out in a particular context or scenario in the 2017 competition. Although the UMinho team provided the best competition scores, the other competing systems also provided interesting partial results. Although diversity in evaluation is desirable, it is not possible under some circumstances. It is worth mentioning that a trade-off between diversity and time constraints has to be met in on-site competitions, which is less strict in off-site competitions. In general, all competitions have had a positive impact on the evaluation of indoor positioning systems and the positioning research field.


With ever-increasing amounts of sequence data available in both the primary literature and sequence repositories, there is a bottleneck in annotating molecular function to a sequence. This article describes the biocuration process and methods used in the structure-function linkage database (SFLD) to help address some of the challenges. We discuss how the hierarchy within the SFLD allows us to infer detailed functional properties for functionally diverse enzyme superfamilies in which all members are homologous, conserve an aspect of their chemical function and have associated conserved structural features that enable the chemistry. Also presented is the Enzyme Structure-Function Ontology (ESFO), which has been designed to capture the relationships between enzyme sequence, structure and function that underlie the SFLD and is used to guide the biocuration processes within the SFLD.Database URL:http://sfld.rbvi.ucsf.edu/ Thus, obtaining a well-annotated sequence dataset is almost impossible due to the speed at which new data are being generated. We need new strategies to divide and conquer the sequence space of the protein universe, sorting it into groups such as superfamilies (SFs) and families that can be more effectively tackled. Existing resources that include UniProtKB, Genbank (th September 2016) and this covers approximately 82% of proteins in UniProtKB; i.e. almost one in five proteins in the sequenced universe have no predicted family membership, as they are different enough from any known proteins that their functions cannot be predicted via homology and annotation transfer. Further, a considerable number of proteins are assigned as domains of unknown function (DUFs), it is thought that approximately 20% of all families in Pfam are DUFs . With the near-exponential growth in the number of protein sequences available  aims to catalogue the specific sequence and structural attributes reflected in SFs of enzymes, with a key focus on functionally diverse SFs  in the extended set. It is publicly available at  Dataset  for eval Dataset .This article describes the anatomy of an SF within the SFLD and the framework for analysis employed in going from a set of evolutionarily related proteins to a well-curated SFLD SF. We discuss some of the challenges for digitisation of the biochemical information into computer-readable formats. We also describe the use of sequence similarity networks (SSNs) as a primary curation tool and the roles of networks in enhancing data utilisation and making function prediction and dissemination to users more efficient and information-rich.Traditional enzyme classification efforts commonly take a sequence-centric approach In order to analyse an SF in a systematic and largely automated manner, we need to be able to represent the data in a computer-readable format. For the SFLD, this process is guided by an ontology and schema, the Enzyme Structure-Function Ontology (ESFO). The ESFO formalises the relationships between sequence and function using the hierarchy of evolutionary relationships  and Family), which allows for a greater level of detail than is provided by most other biological ontologies. Whilst the ESFO accurately captures the conceptual architecture of the SFLD, a more detailed level of functional description is provided by the MACiE database , which uThe EFD is the portion of an SF sequence that is responsible for the annotated function and includes all of the domains required for that function. This is a critical concept for associating functional properties with their sequences and structures, as it accounts for the fact that not all proteins are composed of a single domain on a single chain in a monomer.  Many SRelated to the concept of the EFD, the ‘SF Domain’ (SFD) is defined as the domain that performs the conserved chemistry using functional residue types conserved across all members of the SF. Using a protein from the Radical SAM SF, At its simplest, a protein sequence is isofunctional with an EFD that covers the full length of the protein sequence. This is not always the case, as a single peptide can have multiple functions  and 4. TAs introduced above, the enzyme structure-function relationships captured in the ESFO are based on the original hierarchical classification scheme of the SFLD , which dAs shown in As illustrated in Whilst it is technically possible to create an MSA of all the many thousand (or more) sequences that make up SFLD SFs, this level of detail is difficult for human curators to work with or understand. Further, the larger the MSA, the more complex the HMM may become, especially if there are longer sequences being added, which can significantly add to the time it takes to perform computational analyses with the HMMs. Automated methods of producing MSAs are also not foolproof , and wheThe gathering threshold (GA) represents the score at which a sequence is defined as belonging to a functional group . These aThis approach is also useful for addressing misannotation. Historically, misannotation has been a significant issue in annotation transfer and remains a vexing challenge for biological databases . Use of With the continuing explosion of sequence data, SFLD SFs now represent tens of thousands of sequences, raising significant issues for data management and scalable curation procedures. To meet this challenge, new methods have been required to support in-depth curation of SFLD structure-function relationships. We have developed PSNs to summarize mapping of functional features in SFs to the context of sequence similarity. A PSN is the general term used to describe similarity networks created using protein features, including SSNs  and structural similarity networks  studies, assay data, catalytic residues, mutation studies, structure characterization or mechanistic studies.Resources external to the SFLD. Major resources include UniProtKB, Panther, Pfam, InterPro, TigrFams, CATH and SCOP. Sequence-associated metadata and genomic context are two main types of data obtained from external resources. Sequence-associated metadata include source species, molecular function (from GenBank ( GenBank  and Swis GenBank ) that ca GenBank , IMG , a single optimal clustering solution often does not track with the complex structure-function relationships. Thus, the primary method used in curation of the Core SFLD SFs is manual and involves examining the network of the SF at increasingly stringent E-values (as computed by BLAST) until a level of clustering deemed appropriate for separating subgroups with relevant shared features is obtained .http://sfld.rbvi.ucsf.edu/django/superfamily/29/), the optimal separation for the first-level subgroups was determined primarily by the multi-domain architectures present, and chosen by sampling different E-value threshold cut-offs to identify the level of similarity at which enzymes of a specific domain architecture could be visually distinguished from other architectures.Every SF is different with respect to the E-value threshold at which similarity clusters track with functional patterns. For some, an E-value threshold cut-off at which separation is informative may be related to the complex multi-domain architecture of its members, while for others, that same significance threshold may distinguish different chemical reactions or patterns of speciation. Curators use several different types of annotation information  together in order to best define subgroups and families. For example, in the case of the Radical SAM SF  are warranted or supported by the data. Reflecting the importance of this information for curators, our results indicate that the huge majority of SF sequences in the SFLD are experimentally uncharacterized and often have very limited automated annotation associated with them  file containing all the annotations available in the SFLD for each sequence at that level in the hierarchy.A multiple sequence alignment (MSA) in Stockholm format (upon which the HMMs are based) with the functional  residues highlighted. When a user queries the database with a sequence, or is looking at a specific level of the hierarchy with a sequence in mind, there is also an option to align the sequence to the MSA. With or without a query sequence included, the MSAs can be viewed at the website as in , saved aRepresentative and one-sequence-per node SSNs with the associated attributes annotated onto nodes in the networks, containing information and metadata like that used in the curation process described in Section 3. The annotations include all the data provided in the tsv file; in the case of the representative networks, these data are provided as a summary, typically as a list and (where appropriate) a dominant annotation value. Currently, SSNs are packaged into a .zip file that contains the network in XGMML format along with an explanatory README file and a length distribution histogram for each SSN.At each level, , the following information is available for download:At the family level only, the SFLD includes an active site image (if a 3-D structure is available), and the corresponding Chimera session can be downloaded for interactive manipulation. Together, this information is intended to help users ask the key question, ‘What is the function of my protein?’The SF concept, as defined by the SFLD, addresses annotation challenges through identification of conserved features of enzyme function  at different levels of grouping. Mapping these features to sequence and structural similarities at the SF, subgroup, and family levels offers both a broad context for functional inference of unknowns as well as detailed information about experimentally characterized proteins most similar to unknowns of interest. In particular, the use of PSNs offers a powerful and intuitive framework for both biocuration and designing experiments to elucidate the functions of unknowns.via a dissociative mechanism, but is now known to proceed via a covalent mechanism . Even ohttp://biocuration.org) and other interactions among specialized protein resources (Whilst it is clear that experimentally guided annotation will continue to fall further and further behind and that we can no longer expect to achieve detailed and high-confidence annotation of a significant proportion of the protein universe, sampling of the data through the use of reference proteomes, and the coordination of resources in consortia such as InterPro  offer poesources , 61 willSupplementary DataClick here for additional data file.


The analytical chemist involved in industrial trace analysis is frequently confronted with many varied problems. Programs to produce acceptable data can be divided into two classes depending upon the level of quality assurance that is required. Frequently, unvalidated analytical procedures can be used to generate data for screening programs or process control. On the other hand, validated methods with rigorous quality assurance guidelines are absolutely necessary for work involving product specifications, industrial hygiene, or regulatory matters.When the analyst is asked to develop a trace analytical method, he must determine the end-use of the data. There are many parameters which must be factored into the analytical method. The analyst would like to build into the method the highest achievable sensitivity, accuracy, and reliability; and the customer wants the lowest cost and fastest analysis time possible. We can see then, from a practical viewpoint, the method development begins with a series of compromises. When implemented properly, these compromises can improve the overall quality of the method.N equals the number of measurements necessary, Z equals the standard normal variate based upon the level of confidence, σ equals the standard deviation of the method, and E equals the tolerable error in the estimate of the mean. Obviously, for constant values of Z and E, N is proportional to σ2. Any changes in an analysis method that increase σ will result in an exponentially larger increase in N. When a high degree of accuracy is required, an analytical method with a small σ will have a smaller value of N and require fewer measurements. In fact, by building the highest possible degree of reliability into a method, it may be possible to actually decrease the amount of time necessary to run N analyses. The method reliability can be improved by eliminating or at least recognizing factors which can lead to either high or low results.The American Chemical Society has published “Principles of Environmental Analysis”  to aid in designing analytical measurements on environmental samples. The relationship between the number of samples necessary to obtain data within a defined acceptable error, and the standard deviation of the method, is shown by the equation:Isotopically-labeled internal standards can be contaminated with native analyte or with other labeled impurities which could potentially lead to high results. Prior to the use of any internal standard in a sample analysis, we perform an extensive series of GC-MS analyses to verify chemical identity, isotopic purity, chemical purity, and absence of analyte contamination. To date, ~40% of purchased internal standards have failed to meet the criteria for use in our method.Positive reagent blanks can originate from poor equipment cleaning, environmental contamination of sample extracts, or from contamination of chemicals or adsorbents used in the sample preparation. We have imposed a rigorous quality assurance program which involves analysis of a reagent blank with every set of three samples. A positive reagent blank can result in rejection of data and reanalysis of samples after the source of contamination is identified and eliminated.Interferences in sample extracts are minimized by application of identification criteria which include multiple liquid chromatographic separations and gas chromatography-mass spectrometry separation and measurement.Matrix effects can cause high results by changing the mass spectrometer response. The use of an analyte specific clean-up is useful in minimizing this problem. Identification of the species producing the effect and application of specific clean-up steps can eliminate problems.High results can be caused by contaminated internal standards or reagents, interferences in the sample, or matrix effects caused by co-extractives in the sample extract. We have taken precautions to minimize the potential for these problems in our method for the determination of chlorinated dibenzo-p-dioxins (CDDs) and dibenzofurans (CDFs) by addressing each area.Extraction efficiencies can be improved by dissolving the sample matrix whenever possible and determining the optimum solvent for analyte extraction. When dissolution of the matrix is not practical, an exhaustive extraction procedure with an optimum solvent should be used.Loss or degradation of the analyte can be controlled by determining the recoveries of all analytes through each clean-up step. An understanding of the chemical and physical properties of the analyte will not only improve recoveries, but also can allow optimization of each clean-up step.Matrix effects leading to low results generally are caused by exceeding the capacity of the clean-up procedure for either analytes, related compounds, or coextractive species. This “overloading” can change the chromatographic retention of the analyte. In general, these effects can be minimized by decreasing the sample size or using a high-capacity pretreatment step to remove the coextractives.Low results can result from incomplete analyte extraction, loss or degradation of the analyte during sample clean-up, or matrix effects. These problems can be eliminated by a careful evaluation of each of the steps employed in the sample preparation.57, 2717 (1985)]. By implementing the practices described above, laboratory 2 avoided generating either unaccountably high or low results , method 2 can actually generate data with a specified precision in the shortest time.The apparent drawback of the laboratory 2 method is the relatively long analysis time per sample. However, when the standard deviation of the recoveries for each laboratory is calculated and used in the equation to determine the relative time to analyze


The varying trend of a moving vehicle’s angles provides much important intelligence for an unattended ground sensor (UGS) monitoring system. The present study investigates the capabilities of a small-aperture microphone array (SAMA) based system to identify the number and moving direction of vehicles travelling on a previously established route. In this paper, a SAMA-based acoustic monitoring system, including the system hardware architecture and algorithm mechanism, is designed as a single node sensor for the application of UGS. The algorithm is built on the varying trend of a vehicle’s bearing angles around the closest point of approach (CPA). We demonstrate the effectiveness of our proposed method with our designed SAMA-based monitoring system in various experimental sites. The experimental results in harsh conditions validate the usefulness of our proposed UGS monitoring system. The motion parameters of vehicles are important intelligence for a unattended ground sensor (UGS) system and Intelligent Transport System (ITS) ,3,4,5,6.The microphone array (MA) sensor provides low cost, low power consumption, and non-line-of-sight measurement, which is widely used for acquiring military intelligence of intruding targets ,3,6,12. The MA-based detection and classification of moving targets in UGS have received much attention ,16,17,18The counting of vehicles and the estimation of their moving direction with small aperture and lower sampling rate are a knotty problem. Another challenge for the SAMA sensor in the wild environment is the wind-generated noise. The sound of the vehicle in the real-world environment has free-field characteristics, and the wind noise has noise-field characteristics . It is wIn this paper, a SAMA-based system for counting vehicles and estimating their moving direction is provided and its aperture is only 4 cm. We defined a decision zone (DZ) near to the closest point of approach (CPA) to identify the number and moving direction of vehicles. Since the direction of arrival (DOA) estimation error around the CPA is relatively small, we can achieve higher estimation accuracy. The interference of wind noise in the real-world environment is reduced through the estimation of the useful frequency bands by spatial coherence.This paper is organized as follows. In general, the uniform array can provide balanced space for circuit design and the uniform circular array (UCA) has the same resolution in all directions. The vehicle signal occupies the frequency bands from 100 Hz to 3000 Hz . The apecrophone .The block diagram of the prototype SAMA system is depicted in As shown in f denotes the frequency of interest. The complex cross power spectrum defined in Equation . Carter [T and the time delay D.Here, . Carter  gives anT = 125 ms (1024 sampling points), D = 8.31 × 10−5 s (array aperture of 4 cm). In our case, An improved multiple signal classification (MUSIC) algorithm is employed to DOA estimation associated with spatial coherence to discriminate between the wind noise and the acoustic signal of a vehicle. The algorithm first tests the spatial coherence for each frequency bin, then identifies the useful frequency bands for wind noise robust DOA estimation. Details of identifying spatial coherence and selecting useful frequency bands for DOA estimation are discussed in .In addition, inspired by , we desiIn this section, we describe the number and moving direction estimation method of vehicles travelling on a previously established route with the calculated DOA. Through statistical analysis, the varying trend of vehicle’s bearing angles can provide the number information of vehicles, as shown in Angles that fall into the DZ more than or equal to three times represent a vehicle that was detected passing through a SAMA sensor. Subsequently, the number of vehicles is increased by 1 and the moving direction can be obtained by checking the varying trend of the angles. The vehicle is approaching from the left direction if the angles are gradually decreasing, and conversely from the right direction. At this stage, the detection of a vehicle and its moving direction is finished. After those operations, we skip eight frames to avoid the repeated count of the same vehicle because, in such a short time, another vehicle will not appear.v) is uniform, then its DOA satisfies the inverse tangent law as Equation θ=arSince counting and moving direction estimation are based on the same vehicle, we should not analyze either of them individually. The estimation of moving direction is executed immediately once a vehicle is counted.DOA estimation is easily disturbed by other interference signals when the target is far away from the CPA, and the DOA estimation error is positively related to the distance from the CPA. The proposed method, based on the DZ, tightly surrounds the CPA which makes the method more effective.Ground vehicles are moving targets of focal interest to UGS. Therefore, these three types of vehicles are employed in our experiment and part of their specifications is listed in The noise emitted by a vehicle at low and medium speeds is composed of tyre/road noise and mechanically originated noise . ConsideIn this section, the experimental data are cropped and a total of 306 min useful acoustic samples from different sites are analyzed. The average counting accuracy of three types of vehicles is shown in Since counting and moving direction estimation are based on the same vehicle, a one-to-one relationship exists between them. Through our statistics, the moving directions of all correctly counted vehicles are correctly estimated (accuracy: 100%). It is worth emphasizing that the falsely counted vehicles are not involved in the calculation. Considering the decision mechanism, judging the increasing or decreasing trend of angles in the vicinity of the CPA, the result is reliable. Because of the 100% estimation accuracy of the moving direction, the overall performance of the system is dominated by counting accuracy.Considering the application in the field environment, in contrast to ITS, vehicle speed is slow and vehicle flow is limited. Therefore, we can achieve satisfactory results of vehicle counting and moving direction estimation with small aperture and lower sampling rate. However, the ITS has the characteristics of faster vehicle speed, traffic intensity and multi-lane interference. Consequently, it needs large aperture arrays, synchronous acquisition and a very high sampling rate. Those demands lead to a high power consumption and make the UGS system difficult to implement. Hence, we did not compare the performance of the proposed method with methods in ,2. CertaThe proposed method realizes the counting and moving direction estimation issue through the proposed algorithm with only 4 cm aperture array. The method also introduces spatial coherence for wind noise suppression and IAR to overcome the interference from unrelated targets. In the situation of different types of closely spaced vehicles, however, the method failed because the acoustic signals are dominated by very noisy vehicles. Then, the problem of separating signals in multiple target scenarios needs to be solved, which will be considered in future work.In this paper, we proposed a SAMA-based single node acoustic monitoring system with only 4 cm aperture. The proposed method includes vehicle counting and motion direction estimation. The method obtains the required intelligence by analysing the varying trend of a moving vehicle’s angles within the vicinity of the CPA. Spatial coherence was assessed to select frequency bands for wind noise suppression and DOA estimation. We applied our proposed system to four different experimental environments, and assessed the accuracy of vehicle counting and motion direction estimation. The experimental results in harsh conditions confirmed the availability of our proposed UGS monitoring system.


LAIdf showed stability with an R2 of greater than 0.9 in three growing stages, and the stability is optimal in June. This study combines the spectral characteristics of the stress effect with the time characteristics, and confirms the potential of long-term remotely sensed data for improving the accuracy of crop heavy metal stress identification.The use of remote sensing technology to diagnose heavy metal stress in crops is of great significance for environmental protection and food security. However, in the natural farmland ecosystem, various stressors could have a similar influence on crop growth, therefore making heavy metal stress difficult to identify accurately, so this is still not a well resolved scientific problem and a hot topic in the field of agricultural remote sensing. This study proposes a method that uses Ensemble Empirical Mode Decomposition (EEMD) to obtain the heavy metal stress signal features on a long time scale. The method operates based on the Leaf Area Index (LAI) simulated by the Enhanced World Food Studies (WOFOST) model, assimilated with remotely sensed data. The following results were obtained: (i) the use of EEMD was effective in the extraction of heavy metal stress signals by eliminating the intra-annual and annual components; (ii) LAI Heavy metal pollution in farmland not only destroys the normal function of the soil and hinders crop growth, but also endangers human health through the food chain ,2,3. AccThe use of satellite remote sensing data to monitor a wide range of heavy metal pollution in farmland has become an important technical method . The curVarious factors may cause stress on crop growth in the complex farmland ecosystem. These environmental stress factors can be divided into volatile factors  and stable factors  for crops . For a gEcophysiological changes of crops under environmental stress can be characterized by damage to a series of photosynthetic apparatuses ,22. LeafCurrently, it is difficult to use remote sensing technology to directly estimate the LAI with long time resolution. The World Food Studies (WOFOST) model can achieve the dynamic simulation of LAI with a one-day time step through assimilating with remotely sensed information ,27,28. WThus, in this study, we extracted the rice heavy metal stress signal feature by using a decade of remotely sensed data. By focusing on the difference in stress effects at different time scales for various environmental stress types, the LAI time series simulated by the enhanced crop growth model (WOFOST) was decomposed to three kinds of signals through Ensemble empirical mode decomposition (EEMD). After getting rid of some other stress and crop innate growth signals, the remaining signal containing heavy metal stress information was extracted. Finally, we explored the features that can characterize the heavy metal stress accurately and evaluated its stability. These three analyses are the main objectives of this paper. This paper provides a reliable method using long time-series remote sensing data for accurate identification of heavy metal stress in crops, which could be further applied in the precise monitoring of heavy metal stress.The study area is located in northern Zhuzhou . The area is located in a humid subtropical monsoon climate zone with sufficient light intensity and with an annual average temperature of 15.5 °C–25 °C. The annual precipitation is 1250–1500 mm. This province hosts non-ferrous metals; rare metal mining and smelting operations are concentrated in the Xiangjiang River Basin. In addition, a significant amount of industrial waste water is discharged into the surrounding environment, causing severe heavy metal contamination of water and the soil. Two experimental sites adjacent to the Xiangjiang River, labeled A and B in http://glovis.usgs.gov/). These were used to retrieve the Normalized Difference Vegetation Index (NDVI), which is the best indicator of vegetation growth status and vegetation coverage. In 2010, however, cloud pollution and restricted space coverage resulted in few effective cloud-free Landsat images in the study area, multi-sensor observations of the HJ-1 CCD and Landsat 8 OLI have been used to compensate for the spatial deletion of LAI synthetic products [http://www.cresda.com/CN/) to supplement the missing images. The climatic conditions of Zhuzhou City during the crop growing period include a lot of cloudiness, rain, and fog, thus, the use of a single remote sensing data source will result in abundant missing data. Previous research reported that although Landsat 5 and Landsat 7 differ in calibration accuracy, the response rate difference of TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) is less than 0.02 . The stuproducts . We thusThe meteorological data applied in this study includes daily maximum and minimum air temperature and the daily actual hours of sunshine. The data for 2007 to 2013 were acquired from Zhuzhou weather station, and those for 2014 to 2016 were downloaded from the China Meteorological Data Sharing Service System. These data were transformed into solar radiation as a climatic input parameter for the enhanced WOFOST model ,34.In order to convert discontinuous remote sensing information into time-continuous crop information, the assimilation technique and the enhanced WOFOST model were combined to dynamically monitor the time continuity of the growth parameters. The WOFOST model is a process-based quantitative analysis model which can obtain the continuous growth parameters of the stressed crops. To do so, it quantitatively simulates the distribution of roots, stems, leaves, and storage organs (seeds), in addition to the daily growth status of crops in the growing process ,27,35. Td pixels a. The ind pixels b.To extract the heavy metal stress signal in our experiment, we used Ensemble Empirical Mode Decomposition EEMD, c, which There are studies illustrating that the LAI time series can be decomposed into three modes and a residual of different scales by means of EEMD, including intra-annual, annual, and inter-annual ,40,41. Td)  on behalf of some short-term stress. The steady change observed every year after removing the noise is the inherent trend, i.e., the annual mode (IMF5). The inter-annual mode and residual contain heavy metal stress (LAId) . Therefod were derived to show the slope of the curve, i.e., its rate of change  and the number of times the noise was added (NE) were used to determine the decomposition effect. The Nstd range is 0.01–0.4; we chose increments of 0.01, 0.05, 0.1, 0.2, and 0.3 for adding NE 50 and 100 times. The experiment revealed that regardless of the number of additions when Nstd was the same, the decomposition results were similar. Therefore, we set NE to 100 times.2) of the annual component among the ten years. The value with the highest correlation was chosen as the Nstd used in this study. 2 reached 0.927, and the relationship was at its closest. What’s more, the p value was less than 0.05 and the correlation was prominent. Therefore, 0.3 was selected as the most appropriate Nstd value for our decomposition.Because the annual trend of crop growth is essentially constant for ten years, the experiment was conducted using several prepared Nstd values according to a benchmark of the coefficient of determination  compositions, respectively. According to the analysis in the method, three kinds of signals were extracted; the signal of IMF1-4 in the LAI time series with high fluctuation frequency, the changes during the year, and interannual changes which were highly irregular. The intra-annual component is the green line in The rest of the signals are the interannual component and residual , July (booting stage), August (flowering stage), and September (ripening stage)). The range of average R2 values was from 0.268 to 0.360. The lower R2 demonstrated that they had low similarity, and thus that stability was not strong.The intra-annual component for the entire growth period of the decade is shown in The noise from other environmental factors that were present can cause long-term fluctuations in the signal, so the fluctuations for the years from 2008 to 2015 may be the result of noise. Conversely in 2007 and 2015 the signal frequency is lower compared to other years, and the vibration amplitude is larger. This probably results from transient stress, such as moisture, pests, and diseases, because the impact on rice from these factors relative to the environmental factors is more obvious. The conclusions drawn from the analysis are that the intra-annual component can be proven to be unstable signals containing transient stressors and noise.2 between LAI time series for healthy rice and the annual signal at both sites A and B. At site A, despite the abnormal trend in the years of 2016 and 2007, the average R2 reached 0.853; the average R2 at site B was 0.754 and the R2 in 2016 is also not high enough. In general, the LAI time series on healthy rice is relatively highly correlated with the annual signal. The results verified that the annual component was able to reveal the signal response to the rice’s inherent growth trend.d). To further exclude the interference from other low frequency signals and extract the stable heavy metal stress feature, the LAIdf time series was obtained through the first derivative processing to LAId. These LAIdf were compared in the four growing periods  reflect the dynamic situation of the LAIdf. dfs in different growth periods.We conducted a stability analysis from two aspects. On the one hand, the analysis of the Rdfs across the 10 years. The fluctuations of the LAIdfs for the decade were both minimal in June regardless of the site. In summary, it is concluded that the stability of trend of the LAIdf was most obvious in June. It is possible that the LAIdf was less sensitive in other periods, and the stability was thus not prominent.The results show the fluctuant magnitude of the LAIdfs for sites A and B over the entire growth phase are shown in dfs at site A reached the zero point in August, and site B reached zero in July. The results demonstrate that the overall dynamic of LAIdf throughout the growing season at site A was similar to that at site B: the trajectories appeared to grow first and then fall as a whole, and all showed a peak during July to August. Based on the above analysis, the LAIdf of ten years has an equivalent variation trend both in the whole growth period and in each of the growing stages. This indicates that LAIdf can be regarded as a stable feature to represent the rice heavy metal stress signal.The dynamics of LAIdf had a good stability in the four growth periods and for the full growth process. This was supported by the coefficient of determination, the standard deviation statistic and trajectory analysis for LAIdf, which indicated that the EEMD is a reliable means to extract the heavy metal stress signal.The EEMD method for extracting the heavy metal stress signals in rice based on long time series LAI was presented. The reasonable characteristics of heavy metal stress were produced using a first derivative operation. The LAIdf indicates that the change rate of LAId is maximal at this time only under heavy metal stress. LAId can be regarded as the reduced magnitude of the curve of the LAI time series in rice under heavy metal stress relative to non-stressed rice. In other words, the effect of stress on the crop growth. During the vigorous vegetative growth period, the growth center is the growth and development of vegetative organs. Studies have shown that heavy metal pollution in this period can cause serious damage to the chloroplast such as the dissolution of the membrane system of the chloroplast [df also reached its maximum. The peak of the LAIoroplast ,50,51,52df over the ten years, there are two years with abnormal readings in July. One reason for this is that, relative to the other three months, this period does not have a very clear stable trend. The trend in this stage also exhibits greater uncertainties. Moreover, the trend inconsistencies are most likely to result from sensor inconsistencies  or between-sensor inconsistencies  [df is not stable enough on the scale of the month; this can be further verified by selecting other indicators, which in the case of other conditions remain unchanged. All of these factors contribute to the broader uncertainties associated with the LAIdf. In general, even though the performance of LAIdf in July is not particularly desirable, the trend of change over the 10 years was relatively stable. The LAIdf can still be used as a stable feature to reveal the rice heavy metal stress.It could be argued that in contrast to the trajectory of LAIeometry) . The comThis study is based on the discrepancy between the stress features at different time scales; thus, we accessed the dataset from 2007–2016. The land use type of the study area during the 10 years showed little difference and the heavy metal stress in the study area is dominant status because of the potential hazards. Therefore, the extent of crop stress by heavy metals can be regarded as a constant factor. However, the retrieved LAI was calculated from the obtained rice pixels through supervised classification in the region, which can produce some error. In addition, the LAI values of the study area were averaged at each phase, and the number of rice samples in the past decade changed, which could have also had an effect on the results. The influencing factors of the enhanced WOFOST assimilation model are weather parameters and crop parameters. Our 10 years of meteorological data were obtained from a weather station. Owing to the small range of the study area, the variation in weather conditions did not lead to uncertainty, however, it is necessary to consider the regional distribution of climate data if this method is to be applied on a large regional scale . For theFor the obtained rice heavy metal stress signal features, the stability is still insufficient. We can further explore whether other indicators can better characterize heavy metal stress based on the proposed method. We can establish a comprehensive index of some indicators to improve the stability; and more accurately assess the rice characterization results from the differences in stress levels. The determination of a comprehensive evaluation index and the establishment of an evaluation model of heavy metal stress are topics for our future research. df that characterize the heavy metal stress in the whole growth period were similar, and were also consistent in each growth period. The stability of the LAIdf response to rice heavy metal stress can be best evaluated in June.This study describes a new concept of extracting stable heavy metal stress features and provides a research method for accurate remote sensing identification of heavy metal stress in crops. In this paper, we used the EEMD approach to extract rice heavy metal stress signals based on multi-year remote sensing data and excavated the varying features of heavy metal stress signals. The change trends of the LAIdf manifests as a stable heavy metal stress feature and well embodies the desired meaning: the influence of heavy metal stress on rice growth. This study provides a scientific basis for remote sensing monitoring of crop environmental stress. The EEMD method combined with the stress mechanism, stress parameters, and stress time effects can promote the accuracy of crop heavy metal stress monitoring.In summary, according to differences in temporal characteristics presented by all types of stress effects, the method of EEMD can effectively be used to identify rice heavy metal stress. The LAI


Many-body problem on the honeycomb lattice systems have been the subject of considerable experimental and theoretical interest. Here we investigate the phase transitions of the ionic Hubbard model on the honeycomb lattice with an alternate ionic potential for the half filling and hole doping cases by means of cellular dynamical mean field theory combining with continue time quantum Monte Carlo as an impurity solver. At half filling, as the increase of the interaction at a fixed ionic potential, we find the single particle gap decreases firstly, reaches a minimum at a critical interaction  The correlation effects in the honeycomb lattice systems have been extensively studied, which result in a number of exotic phenomena in both theory and experiment, such as the correlated electrons in the graphene134791011Recently, a new class of two dimensional materials The dynamical mean field theory (DMFT)343639In this paper, we study the phase transitions of the ionic Hubbard model on the honeycomb lattice as a function of the hole doping and temperature. We adopt the CDMFT combined with the continuous time quantum Monte Carlo method (CTQMC)41We consider the ionic Hubbard model on the honeycomb lattice (see inset in We begin with the tight-binding Hamiltonian with staggered potential on the honeycomb lattice, corresponding to that the interaction In this section, we summarize our main results of the ionic Hubbard model on the honeycomb lattice, deferring the details of how they were obtained to the following sections. The phase diagram as a function of interaction For the small hole doping case, the system goes a phase transition from paramagnetic metal to antiferromagnetic metal when changing the interaction In In this section, we firstly try to understand the correlation effects on the band insulator on the honeycomb lattice. We concentrate on the half-filling case In order to examine how the system evolves with the variation of the local interaction, we firstly calculate the four local quantities: staggered charge density The staggered magnetization and uniform magnetization are defined as The results for the staggered charge density In The local density of states provide more detailed information on the single particle properties. The spin-resolved single particle density of states are computed as follows In this section, we now turn to study the phase transitions of the hole doping ($. The single particle density of states Firstly, let us study the single particle density of states as a function of interaction for various hole doping. mi level . When , mponents . When , ifferent . In bothBesides the changes of the local density of states, the interaction will influence the momentum-resolved spectral density in the Fermi level raction  . In the  corners . In the roadened .In addition to the spectral properties, we also calculate four local quantities: the staggered charge density In this work, we have investigated the effect of on-site interaction and staggered ionic potential in a band insulator and doped band insulator on the honeycomb lattice based on the ionic Hubbard model. By means of the cellular dynamical mean field theory combing with continue time Monte Carlo method, we construct a phase diagram as a function of interaction and hole doping. At half filling, although the single particle spectral functions always posses a energy gap, the system shows a band insulator to Mott insulator transition at a critical interaction We get itinerant metals with spin density wave state which are an interesting class of materials where electrons show spin polarization or staggered spin polarization behavior. They have applications in spintronics as they can generate spin-polarized currents4445In order to study the ionic Hubbard model in honeycomb lattice which describes the correlation effects on the band insulator and the hole doped band insulator, the Cellular dynamical mean field theory are employed. The Cellular dynamical mean field theory is an extension of dynamical mean field theory, which is able to partially cure dynamical mean field theory’s spatial limitations. We replace the single site impurity by a cluster of impurities embedded in a self-consistent bath. The cluster-impurity problem embedded in a bath of free fermions can be written in a quadratic form, Within cellular dynamical mean field theory, the interacting lattice Green’s function in the cluster site basis is given by, The CDMFT iteration procedure is summary as follows. Given a cluster self-energy The weak coupling interaction expansion continuous time quantum Monte Carlo method is efficient method to treat the impurity model. The method employs same tricks, which used to derived Feynman perturbation theory, to stochastically generate the partition function


Transmembrane proteins are mostly nanochannels playing a highly important role in metabolism. Understanding their structures and functions is vital for revealing life processes. It is of fundamental interest to develop chemical devices to mimic biological channels. Structural DNA nanotechnology has been proven to be a promising method for the preparation of fine DNA nanochannels as a result of the excellent properties of DNA molecules. This review presents the development history and current situation of three different types of DNA nanochannel: tile-based nanotube, DNA origami nanochannel, and DNA bundle nanochannel. These nanostructures either perform functions independently or simulate other biomolecules; among them, the DNA nanochannel is one of the finest examples that could mimic the biological nanochannels in both structure and function. Biological nanochannels are widely distributed in nature; they play important roles in various biological processes, regulating the transport of materials and signals through biomembranes6. An accurate understanding of the structures and functions of the nanochannels would help us unveil the life processes and has important guiding significance for drug design, disease diagnosis, and manufacturing of bio-inspired devices. In this review, we will briefly introduce the development history and current situation of DNA nanochannels in three classifications: tile-based nanotube, DNA origami nanochannel, and DNA bundle nanochannel.DNA has been proven to be an excellent structural molecule besides being used for genetic information storage and transfer because the specificity of the base pairing allows target structures to be encoded in the base sequences of component oligonucleotides. After more than 30 years of development since Nadrian Seeman proposed that DNA could be used to construct junction structures in 19827 or triple crossover8. Then, these preformed tiles could assemble into periodic lattices through DNA hybridization. The DNA tiles could be assembled into a variety of shapes; moreover, they could be used to implement algorithmic self-assembly, making them a platform for DNA computing9. In 2004, two articles focusing on the DNA tile-based nanotube were published in theJournal of the American Chemical Society in the same issue11 ssue11 . Both of16. It has been widely used as a bottom-up approach for the assembly of versatile nanostructures. In 2012, Keyseret al. constructed a funnel-shaped three-dimensional DNA origami nanochannel, which could be inserted into the silicon nitride nanopores17. The designed shape enabled the structure to fit into the solid pore size  DNA backbone instead of the conventional phosphate DNA backbone to construct the nanochannel25. This way, the barrel featured a hydrophobic belt composed of 72 ethyl groups, which endowed the nanochannel with the ability of spanning the lipid membrane  tags were introduced to the similar six-duplex bundle nanochannel, which could anchor the channel into the lipid bilayer, via the Sonogashira reaction between an acetylene-TPP derivative and a deoxyuridine nucleoside27. Because the hydrophobicity of TPP is much stronger than that of the ethyl groups, only two TPP molecules were enough to insert the channel into the lipid membrane. In addition, the TPP possessed fluorescence emission, allowing the direct visualization of the anchored nanochannel once incubated with giant unilamellar vesicles. In 2016, Howorkaet al. reported a gating nanochannel based on a similar six-bundle nanochannel28. A lock DNA could hybridize with the channel to close it, and additional key DNA would replace the lock DNA from the channel and as a result the channel opened (et al. reported a nanochannel composed of only four DNA bundles, which were arranged on a square lattice29. In this arrangement, the diameter of the central channel in the middle of the four helices was only about 0.8 nm. Similarly, two cholesterols here were used to assist the channel to be inserted into the lipid membrane.Unlike DNA origami, which requires a long chain template, the DNA bundle nanochannel was very simple; it was formed by only a few oligonucleotides. This kind of nanochannel was composed of a bundle of six interconnected DNA duplexes, with an aperture of about 2 nm. The DNA bundle channel was inserted into the lipid membrane by decoration of the outer layer with hydrophobic residuesembrane . The aut opened . Once th31, which possessed different kinds of responsiveness, will be developed in the future.In this review, we have outlined the development history and current situation of the nanochannel based on the structural DNA nanotechnology. So far, three different types of DNA nanochannel have been reported: the tile-based nanotube, the DNA origami nanochannel, and the DNA bundle nanochannel. It has been proven that the DNA nanochannel was promising in the area of bio-mimic channel and molecule transport regulation of nanoscale. Furthermore, the DNA nanochannel could tune membrane fluidity and trigger a cytotoxic effect. However, this is an emerging field that in many respects is still in its infancy: one of the major challenges with the DNA nanochannels is to improve their stability for the purpose of avoiding the ionic leakage caused by fluctuations of the nanochannel in the membrane. In addition, at present, there is only one kind of responsiveness for the DNA nanochannel, and how to introduce more responsiveness into the nanochannel to better mimic the native protein channel is still a big challenge. We believe that more and more intelligent DNA nanochannels, inspired by the DNA-modified solid-state nanochannels