Journal articles: 'Error localisation'

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Wang, Mei, Nan Duan, Zou Zhou, Fei Zheng, Hongbing Qiu, Xiaopeng Li, and Guoli Zhang. "Indoor PDR Positioning Assisted by Acoustic Source Localization, and Pedestrian Movement Behavior Recognition, Using a Dual-Microphone Smartphone." Wireless Communications and Mobile Computing 2021 (July 8, 2021): 1–16. http://dx.doi.org/10.1155/2021/9981802.

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In recent years, the public’s demand for location services has increased significantly. As outdoor positioning has matured, indoor positioning has become a focus area for researchers. Various indoor positioning methods have emerged. Pedestrian dead reckoning (PDR) has become a research hotspot since it does not require a positioning infrastructure. An integral equation is used in PDR positioning; thus, errors accumulate during long-term operation. To eliminate the accumulated errors in PDR localisation, this paper proposes a PDR localisation system applied to complex scenarios with multiple buildings and large areas. The system is based on the pedestrian movement behavior recognition algorithm proposed in this paper, which recognises the behavior of pedestrians for each gait and improves the stride length estimation for PDR localisation based on the recognition results to reduce the accumulation of errors in the PDR localisation algorithm itself. At the same time, the system uses self-researched hardware to modify the audio equipment used for broadcasting within the indoor environment, to locate the acoustic source through a Hamming distance-based localisation algorithm, and to correct the estimated acoustic source estimated location based on the known source location in order to eliminate the accumulated error in PDR localisation. Through analysis and experimental verification, the recognition accuracy of pedestrian movement behavior recognition proposed in this paper reaches 95% and the acoustic source localisation accuracy of 0.32 m during movement, thus, producing an excellent effect on eliminating the cumulative error of PDR localisation.

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Buehner, Mark, and Anna Shlyaeva. "Scale-dependent background-error covariance localisation." Tellus A: Dynamic Meteorology and Oceanography 67, no. 1 (December 2015): 28027. http://dx.doi.org/10.3402/tellusa.v67.28027.

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Górak, Rafał, and Marcin Luckner. "Automatic Detection of Missing Access Points in Indoor Positioning System †." Sensors 18, no. 11 (October 23, 2018): 3595. http://dx.doi.org/10.3390/s18113595.

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The paper presents a Wi-Fi-based indoor localisation system. It consists of two main parts, the localisation model and an Access Points (APs) detection module. The system uses a received signal strength (RSS) gathered by multiple mobile terminals to detect which AP should be included in the localisation model and whether the model needs to be updated (rebuilt). The rebuilding of the localisation model prevents the localisation system from a significant loss of accuracy. The proposed automatic detection of missing APs has a universal character and it can be applied to any Wi-Fi localisation model which was created using the fingerprinting method. The paper considers the localisation model based on the Random Forest algorithm. The system was tested on data collected inside a multi-floor academic building. The proposed implementation reduced the mean horizontal error by 5.5 m and the classification error for the floor’s prediction by 0.26 in case of a serious malfunction of a Wi-Fi infrastructure. Several simulations were performed, taking into account different occupancy scenarios as well as different numbers of missing APs. The simulations proved that the system correctly detects missing and present APs in the Wi-Fi infrastructure.

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LINDERHOLT, A., and T. ABRAHAMSSON. "PARAMETER IDENTIFIABILITY IN FINITE ELEMENT MODEL ERROR LOCALISATION." Mechanical Systems and Signal Processing 17, no. 3 (May 2003): 579–88. http://dx.doi.org/10.1006/mssp.2002.1522.

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Neuland, Renata, Mathias Mantelli, Bernardo Hummes, Luc Jaulin, Renan Maffei, Edson Prestes, and Mariana Kolberg. "Robust Hybrid Interval-Probabilistic Approach for the Kidnapped Robot Problem." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 02 (April 2021): 313–31. http://dx.doi.org/10.1142/s0218488521500141.

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For a mobile robot to operate in its environment it is crucial to determine its position with respect to an external reference frame using noisy sensor readings. A scenario in which the robot is moved to another position during its operation without being told, known as the kidnapped robot problem, complicates global localisation. In addition to that, sensor malfunction and external influences of the environment can cause unexpected errors, called outliers, that negatively affect the localisation process. This paper proposes a method based on the fusion of a particle filter with bounded-error localisation, which is able to deal with outliers in the measurement data. The application of our algorithm to solve the kidnapped robot problem using simulated data shows an improvement over conventional probabilistic filtering methods.

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Yang, Jingdong, Jinghui Yang, and Zesu Cai. "An efficient approach to pose tracking based on odometric error modelling for mobile robots." Robotica 33, no. 6 (April 1, 2014): 1231–49. http://dx.doi.org/10.1017/s0263574714000654.

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SUMMARYOdometric error modelling for mobile robots is the basis of pose tracking. Without bounds the odometric accumulative error decreases localisation precision after long-range movement, which is often not capable of being compensated for in real time. Therefore, an efficient approach to odometric error modelling is proposed in regard to different drive type mobile robots. This method presents a hypothesis that the motion path approximates a circular arc. The approximate functional expressions between the control input of odometry and non-systematic error as well as systematic error derived from odometric error propagation law. Further an efficient algorithm of pose tracking is proposed for mobile robots, which is able to compensate for the non-systematic and systematic error in real time. These experiments denote that the odometric error modelling reduces the accumulative error of odometry efficiently and improves the specific localisation process significantly during autonomous navigation.

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De Vos, Maarten, Lieven De Lathauwer, Bart Vanrumste, Sabine Van Huffel, and W. Van Paesschen. "Canonical Decomposition of Ictal Scalp EEG and Accurate Source Localisation: Principles and Simulation Study." Computational Intelligence and Neuroscience 2007 (2007): 1–10. http://dx.doi.org/10.1155/2007/58253.

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Long-term electroencephalographic (EEG) recordings are important in the presurgical evaluation of refractory partial epilepsy for the delineation of the ictal onset zones. In this paper, we introduce a new concept for an automatic, fast, and objective localisation of the ictal onset zone in ictal EEG recordings. Canonical decomposition of ictal EEG decomposes the EEG in atoms. One or more atoms are related to the seizure activity. A single dipole was then fitted to model the potential distribution of each epileptic atom. In this study, we performed a simulation study in order to estimate the dipole localisation error. Ictal dipole localisation was very accurate, even at low signal-to-noise ratios, was not affected by seizure activity frequency or frequency changes, and was minimally affected by the waveform and depth of the ictal onset zone location. Ictal dipole localisation error using 21 electrodes was around 10.0 mm and improved more than tenfold in the range of 0.5–1.0 mm using 148 channels. In conclusion, our simulation study of canonical decomposition of ictal scalp EEG allowed a robust and accurate localisation of the ictal onset zone.

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Shi, Hongchi, Xiaoli Li, Yi Shang, and Dianfu Ma. "Error analysis of quantised RSSI based sensor network localisation." International Journal of Wireless and Mobile Computing 4, no. 1 (2010): 31. http://dx.doi.org/10.1504/ijwmc.2010.030973.

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Sabra, Adham, and Wai-Keung Fung. "A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms." Sensors 20, no. 19 (September 25, 2020): 5496. http://dx.doi.org/10.3390/s20195496.

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This article proposes a holistic localisation framework for underwater robotic swarms to dynamically fuse multiple position estimates of an autonomous underwater vehicle while using fuzzy decision support system. A number of underwater localisation methods have been proposed in the literature for wireless sensor networks. The proposed navigation framework harnesses the established localisation methods in order to provide navigation aids in the absence of acoustic exteroceptive sensors navigation aid (i.e., ultra-short base line) and it can be extended to accommodate newly developed localisation methods by expanding the fuzzy rule base. Simplicity, flexibility, and scalability are the main three advantages that are inherent in the proposed localisation framework when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. A physics-based simulation platform that considers environment’s hydrodynamics, industrial grade inertial measurement unit, and underwater acoustic communications characteristics is implemented in order to validate the proposed localisation framework on a swarm size of 150 autonomous underwater vehicles. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation by 16.53% and 35.17%, respectively, when compared to the Extended Kalman Filter based localisation with round-robin scheduling.

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Friebel, Björn, Michael Schweins, Nils Dreyer, and Thomas Kürner. "Simulation of GPS localisation based on ray tracing." Advances in Radio Science 19 (December 17, 2021): 85–92. http://dx.doi.org/10.5194/ars-19-85-2021.

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Abstract. In recent years, many simulation tools emerged to model the communication of connected vehicles. Thereby, the focus was put on channel modelling, applications or protocols while the localisation due to satellite navigation systems was treated as perfect. The effect of inaccurate positioning, however, was neglected so far. This paper presents an approach to extend an existing simulation framework for radio networks to estimate the localisation accuracy by navigation systems like GPS, GLONASS or Galileo. Therefore the error due multipath components is calculated by ray optical path loss predictions (ray tracing) considering 3D building data together with a well-established model for the ionospheric error.

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Jose, Diniya, and Shoney Sebastian. "Taylor series method in TDOA approach for indoor positioning system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 3927. http://dx.doi.org/10.11591/ijece.v9i5.pp3927-3933.

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Localisation technologies have always remained in the limelight of positioning-science as researchers have ever shown keen interest to know the exact positions of things. Ultrasonic sensors are mainly used for localisation of mobile robots since they provide high accuracy. This paper presents Taylor Series Method in Time Difference of Arrival approach using ultrasonic sensors.Signals are send from the sensors periodically.The time difference of arrival of signals from the ultrasonic sensors is used by the receiver unit to estimate the location of the mobile unit. The equations formed by using Time Difference of Approach are solved using Taylor Series Method. Taylor Series Method provides a more accurate result since they give less error compared to other methods and they ignore the measurement errors.

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James, Jithin, Graham Clarke, Robins Mathew, Brian Mulkeen, and Nikolaos Papakostas. "On Reducing the Localisation Error of Modern Mobile Robotic Platforms." Procedia CIRP 112 (2022): 168–73. http://dx.doi.org/10.1016/j.procir.2022.09.067.

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Sterken, Yvonne, Alexander Toet, and Yen-Lee Yap. "Factors Limiting Large-Scale Localisation." Perception 23, no. 6 (June 1994): 709–26. http://dx.doi.org/10.1068/p230709.

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The mechanisms mediating relative spatial localisation in the visual system are still unclear. There is a growing amount of evidence that this capability is not merely limited by the processing of the front-end visual system. Models of localisation should, therefore, include higher-level processing stages. A careful study of the sources of error in localisation tasks may further our understanding of the nature of these processes. A study is reported in which the possible role of higher-order processing in relative spatial localisation is explicitly addressed. For this purpose the error sources of threshold performance were investigated for two similar relative-spatial-localisation tasks: two-dot separation discrimination and two-dot orientation discrimination. Fovea-centred stimuli with large dot separations were used. The front-end processing for these stimuli is probably identical in both tasks. Hence, differential effects of the variation of the experimental parameters on threshold performance for both tasks may reveal the characteristics of the higher-level processing involved. The effects of dot separation, stimulus orientation, and experimental procedure (single-stimulus binary forced choice versus two-alternative forced choice) on threshold performance for both tasks are reported. The results show that thresholds for both tasks increase proportionally with dot separation. However, separation-discrimination thresholds are always significantly higher than orientation-discrimination thresholds. Thresholds for separation discrimination are independent of stimulus orientation. In contrast, orientation-discrimination thresholds show an oblique effect: thresholds are consistently lower for horizontal stimuli. Both tasks also show a different dependency of threshold behaviour on the experimental procedure. For a horizontal stimulus orientation, separation discrimination is better with an explicit (physical) reference standard, whereas orientation discrimination is better with an implicit referent. These differential effects cannot be explained by any of the known characteristics of the front-end visual system. They suggest that large-scale spatial-localisation performance is probably limited at a processing level at which spatial relations are explicitly represented.

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Kolakowski, Marcin. "Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning." Sensors 21, no. 18 (September 18, 2021): 6270. http://dx.doi.org/10.3390/s21186270.

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This paper describes an automated method for the calibration of RSS-fingerprinting-based positioning systems. The method assumes using a robotic platform to gather fingerprints in the system environment and using them for training machine learning models. The obtained models are used for positioning purposes during the system operation. The presented calibration method covers all steps of the system calibration, from mapping the system environment using a GraphSLAM based algorithm to training models for radio map calibration. The study analyses four different models: fitting a log-distance path loss model, Gaussian Process Regression, Artificial Neural Network and Random Forest Regression. The proposed method was tested in a BLE-based indoor localisation system set up in a fully furnished apartment. The results have shown that the tested models allow for localisation with accuracy comparable to those reported in the literature. In the case of the Neural Network regression, the median error of robot positioning was 0.87 m. The median of trajectory error in a walking person localisation scenario was 0.4 m.

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Tissier, Ann-Sophie, Jean-Michel Brankart, Charles-Emmanuel Testut, Giovanni Ruggiero, Emmanuel Cosme, and Pierre Brasseur. "A multiscale ocean data assimilation approach combining spatial and spectral localisation." Ocean Science 15, no. 2 (April 26, 2019): 443–57. http://dx.doi.org/10.5194/os-15-443-2019.

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Abstract. Ocean data assimilation systems encompass a wide range of scales that are difficult to control simultaneously using partial observation networks. All scales are not observable by all observation systems, which is not easily taken into account in current ocean operational systems. The main reason for this difficulty is that the error covariance matrices are usually assumed to be local (e.g. using a localisation algorithm in ensemble data assimilation systems), so that the large-scale patterns are removed from the error statistics. To better exploit the observational information available for all scales in the assimilation systems of the Copernicus Marine Environment Monitoring Service, we investigate a new method to introduce scale separation in the assimilation scheme. The method is based on a spectral transformation of the assimilation problem and consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The target is to improve the observational update of the large-scale components of the signal by an explicit observational constraint applied directly on the large scales and to restrict the use of spatial localisation to the small-scale components of the signal. To evaluate our method, twin experiments are carried out with synthetic altimetry observations (simulating the Jason tracks), assimilated in a 1/4∘ model configuration of the North Atlantic and the Nordic Seas. Results show that the transformation to the spectral domain and the spectral localisation provides consistent ensemble estimates of the state of the system (in the spectral domain or after backward transformation to the spatial domain). Combined with spatial localisation for the residual scales, the new scheme is able to provide a reliable ensemble update for all scales, with improved accuracy for the large scale; and the performance of the system can be checked explicitly and separately for all scales in the assimilation system.

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Zhong, Lijia, Dejun Li, Mingwei Lin, Ri Lin, and Canjun Yang. "A Fast Binocular Localisation Method for AUV Docking." Sensors 19, no. 7 (April 11, 2019): 1735. http://dx.doi.org/10.3390/s19071735.

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Docking technology plays a critical role in realising the long-time operation of autonomous underwater vehicles (AUVs). In this study, a binocular localisation method for AUV docking is presented. An adaptively weighted OTSU method is developed for feature extraction. The foreground object is extracted precisely without mixing or missing lamps, which is independent of the position of the AUV relative to the station. Moreover, this extraction process is more precise compared to other segmentation methods with a low computational load. The mass centre of each lamp on the binary image is used as matching feature for binocular vision. Using this fast feature matching method, the operation frequency of the binocular localisation method exceeds 10 Hz. Meanwhile, a relative pose estimation method is suggested for instances when the two cameras cannot capture all the lamps. The localisation accuracy of the distance in the heading direction as measured by the proposed binocular vision algorithm was tested at fixed points underwater. A simulation experiment using a ship model has been conducted in a laboratory pool to evaluate the feasibility of the algorithm. The test result demonstrates that the average localisation error is approximately 5 cm and the average relative location error is approximately 2% in the range of 3.6 m. As such, the ship model was successfully guided to the docking station for different lateral deviations.

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Lladó, Pedro, Petteri Hyvärinen, and Ville Pulkki. "Auditory model-based estimation of the effect of head-worn devices on frontal horizontal localisation." Acta Acustica 6 (2022): 1. http://dx.doi.org/10.1051/aacus/2021056.

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Auditory localisation accuracy may be degraded when a head-worn device (HWD), such as a helmet or hearing protector, is used. A computational method is proposed in this study for estimating how horizontal plane localisation is impaired by a HWD through distortions of interaural cues. Head-related impulse responses (HRIRs) of different HWDs were measured with a KEMAR and a binaural auditory model was used to compute interaural cues from HRIR-convolved noise bursts. A shallow neural network (NN) was trained with data from a subjective listening experiment, where horizontal plane localisation was assessed while wearing different HWDs. Interaural cues were used as features to estimate perceived direction and position uncertainty (standard deviation) of a sound source in the horizontal plane with the NN. The NN predicted the position uncertainty of localisation among subjects for a given HWD with an average estimation error of 1°. The obtained results suggest that it is possible to predict the degradation of localisation ability for specific HWDs in the frontal horizontal plane using the method.

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Górak, Rafał, Marcin Luckner, Michał Okulewicz, Joanna Porter-Sobieraj, and Piotr Wawrzyniak. "Indoor Localisation Based on GSM Signals: Multistorey Building Study." Mobile Information Systems 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/2719576.

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Among the accurate indoor localisation systems that are using WiFi, Bluetooth, or infrared technologies, the ones that are based on the GSM rely on a stable external infrastructure that can be used even in an emergency. This paper presents an accurate GSM indoor localisation system that achieves a median error of 4.39 metres in horizontal coordinates and up to 64 percent accuracy in floor prediction (for 84 percent of cases the floor prediction is mistaken by not more than a single floor). The test and reference measurements were made inside a six-floor academic building, with an irregular shape, whose dimensions are around 50 metres by 70 metres. The localisation algorithm uses GSM signal readings from the 7 strongest cells available in the GSM standard (or fewer, if fewer than 7 are available). We estimate the location by a three-step method. Firstly, we propose a point localisation solution (i.e., localisation based on only one measurement). Then, by applying the central tendency filters and the Multilayer Perceptron, we build a localisation system that uses a sequence of estimations of current and past locations. We also discuss major accuracy factors such as the number of observed signals or the types of spaces in the building.

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Ros, Montserrat, Joshua Boom, Gavin de Hosson, and Matthew D'Souza. "Indoor Localisation Using a Context-Aware Dynamic Position Tracking Model." International Journal of Navigation and Observation 2012 (February 13, 2012): 1–12. http://dx.doi.org/10.1155/2012/293048.

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Indoor wireless localisation is a widely sought feature for use in logistics, health, and social networking applications. Low-powered localisation will become important for the next generation of pervasive media applications that operate on mobile platforms. We present an inexpensive and robust context-aware tracking system that can track the position of users in an indoor environment, using a wireless smart meter network. Our context-aware tracking system combines wireless trilateration with a dynamic position tracking model and a probability density map to estimate indoor positions. The localisation network consisted of power meter nodes placed at known positions in a building. The power meter nodes are tracked by mobile nodes which are carried by users to localise their position. We conducted an extensive trial of the context-aware tracking system and performed a comparison analysis with existing localisation techniques. The context-aware tracking system was able to localise a person's indoor position with an average error of 1.21 m.

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Su, Ching‐Yao, and Jar‐Ferr Yang. "A robust two‐stage face recognition system with localisation error compensation." IET Computer Vision 8, no. 6 (December 2014): 690–700. http://dx.doi.org/10.1049/iet-cvi.2013.0281.

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Zheng, Jianying, Yu e. Sun, Yan Huang, Yiming Wang, and Yang Xiao. "Error analysis of range-based localisation algorithms in wireless sensor networks." International Journal of Sensor Networks 12, no. 2 (2012): 78. http://dx.doi.org/10.1504/ijsnet.2012.050074.

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Sivakumar, S., R. Venkatesan, and M. Karthiga. "Evolutionary approaches for minimising error in localisation of wireless sensor networks." International Journal of Sensor Networks 17, no. 1 (2015): 17. http://dx.doi.org/10.1504/ijsnet.2015.067587.

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Tholen, Christoph, Tarek A. El-Mihoub, Lars Nolle, and Oliver Zielinski. "Artificial Intelligence Search Strategies for Autonomous Underwater Vehicles Applied for Submarine Groundwater Discharge Site Investigation." Journal of Marine Science and Engineering 10, no. 1 (December 22, 2021): 7. http://dx.doi.org/10.3390/jmse10010007.

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In this study, a set of different search strategies for locating submarine groundwater discharge (SGD) are investigated. This set includes pre-defined path planning (PPP), adapted random walk (RW), particle swarm optimisation (PSO), inertia Levy-flight (ILF), self-organising-migration-algorithm (SOMA), and bumblebee search algorithm (BB). The influences of self-localisation and communication errors and limited travel distance of the autonomous underwater vehicles (AUVs) on the performance of the proposed algorithms are investigated. This study shows that the proposed search strategies could not outperform the classic search heuristic based on full coverage path planning if all AUVs followed the same search strategy. In this study, the influence of self-localisation and communication errors was investigated. The simulations showed that, based on the median error of the search runs, the performance of SOMA was in the same order of magnitude regardless the strength of the localisation error. Furthermore, it was shown that the performance of BB was highly affected by increasing localisation errors. From the simulations, it was revealed that all the algorithms, except for PSO and SOMA, were unaffected by disturbed communications. Here, the best performance was shown by PPP, followed by BB, SOMA, ILF, PSO, and RW. Furthermore, the influence of the limited travel distances of the AUVs on the search performance was evaluated. It was shown that all the algorithms, except for PSO, were affected by the shorter maximum travel distances of the AUVs. The performance of PPP increased with increasing maximum travel distances. However, for maximum travel distances > 1800 m the median error appeared constant. The effect of shorter travel distances on SOMA was smaller than on PPP. For maximum travel distances < 1200 m, SOMA outperformed all other strategies. In addition, it can be observed that only BB showed better performances for shorter travel distances than for longer ones. On the other hand, with different search strategies for each AUV, the search performance of the whole swarm can be improved by incorporating population-based search strategies such as PSO and SOMA within the PPP scheme. The best performance was achieved for the combination of two AUVs following PPP, while the third AUV utilised PSO. The best fitness of this combination was 15.9. This fitness was 26.4% better than the performance of PPP, which was 20.4 on average. In addition, a novel mechanism for dynamically selecting a search strategy for an AUV is proposed. This mechanism is based on fuzzy logic. This dynamic approach is able to perform at least as well as PPP and SOMA for different travel distances of AUVs. However, due to the better adaptation to the current situation, the overall performance, calculated based on the fitness achieved for different maximum travel distances, the proposed dynamic search strategy selection performed 32.8% better than PPP and 34.0% better than SOMA.

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Yang, Manman, Leijian Yu, Cuebong Wong, Carmelo Mineo, Erfu Yang, Iain Bomphray, and Ruoyu Huang. "A cooperative mobile robot and manipulator system (Co-MRMS) for transport and lay-up of fibre plies in modern composite material manufacture." International Journal of Advanced Manufacturing Technology 119, no. 1-2 (November 23, 2021): 1249–65. http://dx.doi.org/10.1007/s00170-021-08342-2.

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AbstractComposite materials are widely used in industry due to their light weight and specific performance. Currently, composite manufacturing mainly relies on manual labour and individual skills, especially in transport and lay-up processes, which are time consuming and prone to errors. As part of a preliminary investigation into the feasibility of deploying autonomous robotics for composite manufacturing, this paper presents a case study that investigates a cooperative mobile robot and manipulator system (Co-MRMS) for material transport and composite lay-up, which mainly comprises a mobile robot, a fixed-base manipulator and a machine vision sub-system. In the proposed system, marker-based and Fourier transform-based machine vision approaches are used to achieve high accuracy capability in localisation and fibre orientation detection respectively. Moreover, a particle-based approach is adopted to model material deformation during manipulation within robotic simulations. As a case study, a vacuum suction-based end-effector model is developed to deal with sagging effects and to quickly evaluate different gripper designs, comprising of an array of multiple suction cups. Comprehensive simulations and physical experiments, conducted with a 6-DOF serial manipulator and a two-wheeled differential drive mobile robot, demonstrate the efficient interaction and high performance of the Co-MRMS for autonomous material transportation, material localisation, fibre orientation detection and grasping of deformable material. Additionally, the experimental results verify that the presented machine vision approach achieves high accuracy in localisation (the root mean square error is 4.04 mm) and fibre orientation detection (the root mean square error is 1.84∘) and enables dealing with uncertainties such as the shape and size of fibre plies.

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García Daza, Iván, Mónica Rentero, Carlota Salinas Maldonado, Ruben Izquierdo Gonzalo, Noelia Hernández Parra, Augusto Ballardini, and David Fernandez Llorca. "Fail-Aware LIDAR-Based Odometry for Autonomous Vehicles." Sensors 20, no. 15 (July 23, 2020): 4097. http://dx.doi.org/10.3390/s20154097.

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Autonomous driving systems are set to become a reality in transport systems and, so, maximum acceptance is being sought among users. Currently, the most advanced architectures require driver intervention when functional system failures or critical sensor operations take place, presenting problems related to driver state, distractions, fatigue, and other factors that prevent safe control. Therefore, this work presents a redundant, accurate, robust, and scalable LiDAR odometry system with fail-aware system features that can allow other systems to perform a safe stop manoeuvre without driver mediation. All odometry systems have drift error, making it difficult to use them for localisation tasks over extended periods. For this reason, the paper presents an accurate LiDAR odometry system with a fail-aware indicator. This indicator estimates a time window in which the system manages the localisation tasks appropriately. The odometry error is minimised by applying a dynamic 6-DoF model and fusing measures based on the Iterative Closest Points (ICP), environment feature extraction, and Singular Value Decomposition (SVD) methods. The obtained results are promising for two reasons: First, in the KITTI odometry data set, the ranking achieved by the proposed method is twelfth, considering only LiDAR-based methods, where its translation and rotation errors are 1.00 % and 0.0041 deg/m, respectively. Second, the encouraging results of the fail-aware indicator demonstrate the safety of the proposed LiDAR odometry system. The results depict that, in order to achieve an accurate odometry system, complex models and measurement fusion techniques must be used to improve its behaviour. Furthermore, if an odometry system is to be used for redundant localisation features, it must integrate a fail-aware indicator for use in a safe manner.

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Luckner, Marcin, and Rafał Górak. "Automatic Detection of Changes in Signal Strength Characteristics in a Wi-Fi Network for an Indoor Localisation System." Sensors 20, no. 7 (March 25, 2020): 1828. http://dx.doi.org/10.3390/s20071828.

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This paper faces the issue of changing the received signal strength (RSS) from an observed access point (AP). Such a change can reduce the Quality of Service (QoS) of a Wi-Fi-based Indoor Localisation System. We have proposed a dynamic system based on an estimator of RSS using the readings from other APs. Using an optimal threshold, the algorithm recognises an AP that has changed its characteristics. Next, the system rebuilds the localisation model excluding the changed AP to keep QoS. For the tests, we simulated a change in the analysed Wi-Fi network by replacing the measured RSS by an RSS obtained from the same AP model that lies in another place inside the same multi-floor building. The algorithm was evaluated in simulations of an isolated single-floor building, a single-floor building and a multi-floor building. The mean increase of the localisation error obtained by the system varies from 0.25 to 0.61 m after the RSS changes, whereas the error increase without using the system is between 1.21 and 1.98 m. The system can be applied to any service based on a Wi-Fi network for various kinds of changes like a reconfiguration of the network, a local malfunction or ageing of the infrastructure.

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Papliatseyeu, Andrei, Venet Osmani, and Oscar Mayora. "Indoor Positioning Using FM Radio." International Journal of Handheld Computing Research 1, no. 3 (July 2010): 19–31. http://dx.doi.org/10.4018/jhcr.2010070102.

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This paper presents an indoor positioning system based on FM radio. The system is built on commercially available short-range FM transmitters. This is the first experimental study of FM performance for indoor localisation. FM radio possesses a number of features, which make it distinct from other localisation technologies. Despite the low cost and off-the-shelf components, this FM positioning system reaches a high performance, comparable to other positioning technologies such as Wi-Fi. The authors’ experiments have yielded a median accuracy of 1.0 m and in 95% of cases the error is below 5 m.

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Mamun, Md Abdulla Al, David Vera Anaya, Fan Wu, and Mehmet Rasit Yuce. "Landmark-Assisted Compensation of User’s Body Shadowing on RSSI for Improved Indoor Localisation with Chest-Mounted Wearable Device." Sensors 21, no. 16 (August 10, 2021): 5405. http://dx.doi.org/10.3390/s21165405.

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Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance triggered by various factors from the indoors that influence the link quality of radio signals. Localisation using body-mounted wearable devices introduces an additional source of error when calculating the RSSI, leading to the deterioration of localisation performance. The broad aim of this study is to mitigate the user’s body shadowing effect on RSSI to improve localisation accuracy. Firstly, this study examines the effect of the user’s body on RSSI. Then, an angle estimation method is proposed by leveraging the concept of landmark. For precise identification of landmarks, an inertial measurement unit (IMU)-aided decision tree-based motion mode classifier is implemented. After that, a compensation model is proposed to correct the RSSI. Finally, the unknown location is estimated using the nearest neighbour method. Results demonstrated that the proposed system can significantly improve the localisation accuracy, where a median localisation accuracy of 1.46 m is achieved after compensating the body effect, which is 2.68 m before the compensation using the classical K-nearest neighbour method. Moreover, the proposed system noticeably outperformed others when comparing its performance with two other related works. The median accuracy is further improved to 0.74 m by applying a proposed weighted K-nearest neighbour algorithm.

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McLoughlin, Benjamin, Harry Pointon, John McLoughlin, Andy Shaw, and Frederic Bezombes. "Uncertainty Characterisation of Mobile Robot Localisation Techniques using Optical Surveying Grade Instruments." Sensors 18, no. 7 (July 13, 2018): 2274. http://dx.doi.org/10.3390/s18072274.

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Recent developments in localisation systems for autonomous robotic technology have been a driving factor in the deployment of robots in a wide variety of environments. Estimating sensor measurement noise is an essential factor when producing uncertainty models for state-of-the-art robotic positioning systems. In this paper, a surveying grade optical instrument in the form of a Trimble S7 Robotic Total Station is utilised to dynamically characterise the error of positioning sensors of a ground based unmanned robot. The error characteristics are used as inputs into the construction of a Localisation Extended Kalman Filter which fuses Pozyx Ultra-wideband range measurements with odometry to obtain an optimal position estimation, all whilst using the path generated from the remote tracking feature of the Robotic Total Station as a ground truth metric. Experiments show that the proposed method yields an improved positional estimation compared to the Pozyx systems’ native firmware algorithm as well as producing a smoother trajectory.

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Zhang, Q., and G. Lallement. "Dominant error localisation in a finite element model of a mechanical structure." Mechanical Systems and Signal Processing 1, no. 2 (April 1987): 141–49. http://dx.doi.org/10.1016/0888-3270(87)90067-7.

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Kotiyal, Vaibhav, Abhilash Singh, Sandeep Sharma, Jaiprakash Nagar, and Cheng-Chi Lee. "ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks." Sensors 21, no. 11 (May 21, 2021): 3576. http://dx.doi.org/10.3390/s21113576.

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Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5–0.8 m. In addition, the proposed algorithm also shows an 80% decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.

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Jiang, Wuyang, Ling Pei, Changqing Xu, Liang Chen, and Wenxian Yu. "Two-stage Localisation Scheme Using a Small-scale Linear Microphone Array for Indoor Environments." Journal of Navigation 68, no. 5 (February 23, 2015): 915–36. http://dx.doi.org/10.1017/s0373463315000107.

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The small-scale linear microphone arrays that are widely found in smartphones could be used to locate a sound source for indoor environments. After the Time Differences Of Arrival (TDOAs) in microphone pairs are estimated, a TDOA-based hybrid localisation scheme is proposed for a small-scale linear microphone array. The scheme contains two stages: the initialisation stage using the Levenberg-Marquardt (LM) algorithm, and the refining solution stage using the Weighted Least-Square (WLS) algorithm or the Multi-Dimensional Scaling-based (MDS) algorithm. Simulations and field tests show that the proposed indoor localisation scheme outperforms the existing schemes, and it can achieve an average error of 0·32 metres in an 8 m by 5 m area.

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Blocher, Caroline, Filippo Pecci, and Ivan Stoianov. "Prior Assumptions for Leak Localisation in Water Distribution Networks with Uncertainties." Water Resources Management 35, no. 15 (October 26, 2021): 5105–18. http://dx.doi.org/10.1007/s11269-021-02988-z.

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AbstractHydraulic model-based leak (burst) localisation in water distribution networks is a challenging problem due to a limited number of hydraulic measurements, a wide range of leak properties, and model and data uncertainties. In this study, prior assumptions are investigated to improve the leak localisation in the presence of uncertainties. For example, $$\ell _2$$ ℓ 2 -regularisation relies on the assumption that the Euclidean norm of the leak coefficient vector should be minimised. This approach is compared with a method based on the sensitivity matrix, which assumes the existence of only a single leak. The results show that while the sensitivity matrix method often yields a better leak location estimate in single leak scenarios, the $$\ell _2$$ ℓ 2 -regularisation successfully identifies a search area for pinpointing the accurate leak location. Furthermore, it is shown that the additional error introduced by a quadratic approximation of the Hazen-Williams formula for the solution of the localisation problem is negligible given the uncertainties in Hazen-Williams resistance coefficients in operational water network models.

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Cvetkova, Jelizaveta, Agnella Craig, Theresa O’Donovan, and Laura Mullaney. "Set-up variation in palliative radiotherapy: one versus three skin localisation marks." Journal of Radiotherapy in Practice 19, no. 3 (November 8, 2019): 206–9. http://dx.doi.org/10.1017/s1460396919000748.

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AbstractBackground:Accuracy and reproducibility of the patient’s position is crucial for successful delivery of radiotherapy (RT). Data on palliative patients’ set-up uncertainties are sparse. The aim of this study was to calculate set-up errors observed for palliative patients positioned using one skin mark (Group 1) versus three skin marks (Group 2) and to assess the accuracy of both approaches.Methods:Displacements in the left–right (L–R) and superior–inferior (S–I) directions were retrospectively analysed for 175 sites treated with a course of fractionated palliative RT. Population mean, systematic and random errors were calculated in both directions for patients positioned with one and three skin marks. Frequency of deviations was also examined for both groups.Results:The population mean, systematic and random errors for Group 1 and 2 for the L–R direction were 0·0, 4·4, 4·8 and 0·4, 3·1 and 3·3 mm, respectively, and in the S–I direction: 0·1, 3·4, 4·2 and 1·2, 2·7 and 3·3 mm, respectively. Frequency of images within the clinical tolerance of 5 mm was 47·1% for Group 1 and 65·9% for Group 2.Conclusion:Three skin marks are recommended for patients receiving a fractionated course of palliative RT, as it reduces set-up error, reduces the number of gross displacements (>10 mm) and increases the number of displacements within the clinically acceptable tolerance of 5 mm.

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Yu, Yue, Ruizhi Chen, Zuoya Liu, Guangyi Guo, Feng Ye, and Liang Chen. "Wi-Fi Fine Time Measurement: Data Analysis and Processing for Indoor Localisation." Journal of Navigation 73, no. 5 (May 4, 2020): 1106–28. http://dx.doi.org/10.1017/s0373463320000193.

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Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Wi-Fi based indoor localisation has become attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which can be used for Wi-Fi ranging between intelligent terminal and Wi-Fi access point. This paper introduces a framework of Wi-Fi FTM data acquisition and processing that can be used for indoor localisation. We analyse the main factors that affect the accuracy of Wi-Fi ranging and propose a calibration, filtering and modelling algorithm that can effectively reduce the ranging error caused by clock deviation, non-line-of-sight (NLOS) and multipath propagation. Experimental results show that the proposed calibration and filtering method is able to achieve metre-level ranging accuracy in case of line-of-sight by using large bandwidth. Estimation results also show that the proposed Wi-Fi ranging model provides an accurate ranging performance in NLOS and multipath contained indoor environment; the final positioning error is less than 2·2 m with a stable output frequency of 3 Hz.

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Acharya, Debaditya, Sesa Singha Roy, Kourosh Khoshelham, and Stephan Winter. "A Recurrent Deep Network for Estimating the Pose of Real Indoor Images from Synthetic Image Sequences." Sensors 20, no. 19 (September 25, 2020): 5492. http://dx.doi.org/10.3390/s20195492.

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Recently, deep convolutional neural networks (CNN) have become popular for indoor visual localisation, where the networks learn to regress the camera pose from images directly. However, these approaches perform a 3D image-based reconstruction of the indoor spaces beforehand to determine camera poses, which is a challenge for large indoor spaces. Synthetic images derived from 3D indoor models have been used to eliminate the requirement of 3D reconstruction. A limitation of the approach is the low accuracy that occurs as a result of estimating the pose of each image frame independently. In this article, a visual localisation approach is proposed that exploits the spatio-temporal information from synthetic image sequences to improve localisation accuracy. A deep Bayesian recurrent CNN is fine-tuned using synthetic image sequences obtained from a building information model (BIM) to regress the pose of real image sequences. The results of the experiments indicate that the proposed approach estimates a smoother trajectory with smaller inter-frame error as compared to existing methods. The achievable accuracy with the proposed approach is 1.6 m, which is an improvement of approximately thirty per cent compared to the existing approaches. A Keras implementation can be found in our Github repository.

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Liu, Yangyang, Baowang Lian, Lin Zhang, and Taoyun Zhou. "Gaussian Message Passing-Based Cooperative Localisation with Bootstrap Percolation Scheme in Dense Networks." Journal of Navigation 72, no. 5 (May 2, 2019): 1275–96. http://dx.doi.org/10.1017/s0373463319000171.

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In dense networks, the tremendous computational complexity and communication overhead of cooperative localisation are the two main bottlenecks that limit practical application. In this study, we introduce a bootstrap percolation scheme into Gaussian message passing-based cooperative localisation for precise positioning, aimed at reducing the system overhead. Considering the uncertainty information and geometric distribution of neighbours, an approximate collinear detection criterion is proposed to detect the possible flip ambiguities in cooperative localisation. According to the detection result and our connection constraint, agents are divided into three categories and are approximated by different distribution families. A message passing rule is designed to control the propagation direction from high precision to low precision, thereby mitigating potential error propagation. Additionally, a layer-by-layer positioning mechanism is established where the agents are located gradually. Analytical and simulation results indicate that when the ranging standard deviation is 0·2 m, 89·3% of the agent nodes can be located within 0·4 m using the proposed algorithm. Compared with the Hybrid Sum-Product Algorithm over A Wireless Network (H-SPAWN), this ratio is increased by 8·4% and the computational complexity is reduced by 72%.

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Jin, Zhenlu, Xuezhi Wang, Bill Moran, Quan Pan, and Chunhui Zhao. "Multi-Region Scene Matching Based Localisation for Autonomous Vision Navigation of UAVs." Journal of Navigation 69, no. 6 (April 18, 2016): 1215–33. http://dx.doi.org/10.1017/s0373463316000187.

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A multi-region scene matching-based localisation system for automated navigation of Unmanned Aerial Vehicles (UAV) is proposed. This system may serve as a backup navigation error correction system to support autonomous navigation in the absence of a global positioning system such as a Global Navigation Satellite System. Conceptually, the system computes the location of the UAV by comparing the sensed images taken by an on board optical camera with a library of pre-recorded geo-referenced images. Several challenging issues in building such a system are addressed, including the colour variability problem and elimination of time-varying details from the pairs of images. The overall algorithm is an iterative process involving four sub-processes: firstly, exact histogram matching is applied to sensed images to overcome the colour variability issues; secondly, regions are automatically extracted from the sensed image where landmarks are detected via their colour histograms; thirdly, these regions are matched against the library, while eliminating inconsistent regions between underlying image pairs in the registration process; and finally the location of the UAV is computed using an optimisation procedure which minimises the localisation error using affine transformations. Experimental results demonstrate the proposed system in terms of accuracy, robustness and computational efficiency.

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Moutinho, João, Diamantino Freitas, and Rui Esteves Araújo. "Indoor Global Localisation in Anchor-based Systems using Audio Signals." Journal of Navigation 69, no. 5 (February 16, 2016): 1024–40. http://dx.doi.org/10.1017/s0373463315001095.

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This paper presents a method that allows mobile devices to be globally self-localised in indoor localisation systems by transmitting to them data from position reference anchors. The objective is to establish a reliable one-way down-link communication through signals used in the localisation process in a typically strong fading and multipath channel environment. This is accomplished by using signal processing techniques, including coding and forward error correction, to transmit data using a specific transmission control protocol. Experimental results, using audio as the signal between anchors and the mobile device, demonstrate successful data transmission in realistic scenarios like a common noisy and reverberant room. Spread spectrum noise-like masked signals 4·9 dB below background noise were sufficient to attain correct data reception at four metres distance between a loudspeaker anchor and a mobile device's microphone.

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Li, Meng, and Ratnesh Kumar. "Automated test generation and error localisation for Simulink/Stateflow modelled systems using extended automata." IET Cyber-Physical Systems: Theory & Applications 1, no. 1 (December 1, 2016): 95–107. http://dx.doi.org/10.1049/iet-cps.2016.0024.

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Rataj, V., J. Galambošová, and M. Vašek. "Determining the guidance system accuracy with the support of a large GNSS dataset." Research in Agricultural Engineering 59, Special Issue (December 13, 2013): S65—S70. http://dx.doi.org/10.17221/60/2012-rae.

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Several methods are used presently to assess the accuracy of machinery guidance systems. However, these offer a limited number of records and are time and cost consuming. As the machinery is often equipped with a monitoring system for the management purposes, these data can be used. The aim of this work was to develop and verify a method to determine the accuracy of the machinery guidance systems based on a large dataset obtained from the machinery monitoring system. The proposed method uses the transformation of global navigation satellite systems (GNSS) data into a rectangular coordinate system SJTSK (National projection system – Krovak projection). Based on the geometry principle, the ideal line can be determined, and afterwards, the off-track error of each actual position can be calculated. After the verification of this method, it can be concluded that it brings benefits in terms of further use of the data from the monitoring systems, the estimation of the error based on a robust dataset, elimination of subjective and measurement method errors, as well as spatial localisation of the off-track errors at the field.

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Groves, Keir, Andrew West, Konrad Gornicki, Simon Watson, Joaquin Carrasco, and Barry Lennox. "MallARD: An Autonomous Aquatic Surface Vehicle for Inspection and Monitoring of Wet Nuclear Storage Facilities." Robotics 8, no. 2 (June 18, 2019): 47. http://dx.doi.org/10.3390/robotics8020047.

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Inspection and monitoring of wet nuclear storage facilities such as spent fuel pools or wet silos is performed for a variety of reasons, including nuclear security and characterisation of storage facilities prior to decommissioning. Until now such tasks have been performed by personnel or, if the risk to health is too high, avoided. Tasks are often repetitive, time-consuming and potentially dangerous, making them suited to being performed by an autonomous robot. Previous autonomous surface vehicles (ASVs) have been designed for operation in natural outdoor environments and lack the localisation and tracking accuracy necessary for operation in a wet nuclear storage facility. In this paper the environmental and operational conditions are analysed, applicable localisation technologies selected and a unique aquatic autonomous surface vehicle (ASV) is designed and constructed. The ASV developed is holonomic, uses a LiDAR for localisation and features a robust trajectory tracking controller. In a series of experiments the mean error between the present ASV’s planned path and the actual path is approximately 1 cm, which is two orders of magnitude lower than previous ASVs. As well as lab testing, the ASV has been used in two deployments, one of which was in an active spent fuel pool.

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Ostřížek, František, and Oldřich Trenz. "Data Measurements and Its Uses for the Autonomous System." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 66, no. 6 (2018): 1581–87. http://dx.doi.org/10.11118/actaun201866061581.

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This article describes the problematic area of UAV (Unmanned Aerial Vehicle). This device can be used to communicate with corporate IS and other devices, calculation of localisation, mapping of new places which covered with Wi‑Fi technology and many others purposes. Nowadays, trends in the solution of driving and maintenance describe precise localisation. The fundamental error of GPS while using it inside buildings is enormous, and for this reason, we were trying to use infrastructure based on 802.11 a/b/g/n. It takes a focus on the part of measuring of signals and the ability of Wi‑Fi signals processing. A limiting factor for this solution might be an environment with a weak or null signal and situations with a smaller amount of beacons. The primary aim of this article is providing an introduction to the problem of use drones and describe collected data for future use.

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Rajan, Vishnu Arun Kumar Thumatty, Arjun Nagendran, Abbas Dehghani-Sanij, and Robert C. Richardson. "Tether monitoring for entanglement detection, disentanglement and localisation of autonomous robots." Robotica 34, no. 3 (July 3, 2014): 527–48. http://dx.doi.org/10.1017/s0263574714001623.

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SUMMARYTethered mobile robots are ideal for electrically noisy environments and for time-consuming tasks that require robust data communication and uninterrupted power delivery. However, tethers may become entangled in cluttered environments, leading to immobilisation and consequent mission failure. This work addresses real-time monitoring of tethers to detect tether entanglement, perform disentanglement through tether following and localise within line of sight. Experimental hardware is proposed to implement the tether monitoring techniques. Experiments are performed for single and dual mobile robots to search a target environment and entanglement detection is shown to be successful using quantitative metrics such as mean localization error.

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Ferreira, C., Manuel François, and Ronald Guillén. "Residual Stress Error Introduced by Truncated Patterns: Prediction and Correction." Materials Science Forum 490-491 (July 2005): 171–76. http://dx.doi.org/10.4028/www.scientific.net/msf.490-491.171.

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The truncation of diffraction patterns in residual stress determination is often observed for broadened peaks when the 2θ acquisition range is not wide enough. The loss of information effects induced can either be traduced by a bad estimation of the background line, for the methods including a background subtraction, or a restriction of the analysis area for the others. In that borderline case, the results obtained by all methods with theirs specific parameters, developed to estimate the peak localisation are rather distributed in a wide range of stress values. In this paper we propose to review and to test some of the most common methods for stress evaluation (parabola, middle of chord, centred centroïd, asymmetrical pseudo-Voigt fitting). A separate study is made concerning error introduced on the 2θ peak position and on the final stress value estimated. For the parabola method, an analytical expression including some approximations such as the peak shape and its full width at half maximum is then given for the prediction and the correction of these errors. This study is sponsored by PSA PEUGEOT-CITROËN, RENAULT and SNECMA.

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Manser-Smith, Kelda, Daniele Romano, Luigi Tamè, and Matthew R. Longo. "Fingers hold spatial information that toes do not." Quarterly Journal of Experimental Psychology 74, no. 1 (October 4, 2020): 95–105. http://dx.doi.org/10.1177/1747021820960094.

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Fingers have preferential associations with relative spatial locations. Tactile localisation is faster when the fingers are in these locations, such as when the index finger is in a relatively higher spatial position, and the thumb in a relatively lower position. However, it is unclear whether these associations are related to hands specifically, or are a more general characteristic of limbs. The present study therefore investigated whether toes have similar spatial associations. If these associations reflect the statistics of natural limb usage, very different patterns of association would be expected for the fingers and toes, given their different functional roles in daily behaviour. We measured reaction time (RT) and error rates of responses to tactile stimuli applied to the middle finger/toe or thumb/big toe, when they were positioned in a relative upper or lower location. We replicated the finding that fingers have preferential associations that facilitates localisation—RT and error rate were lower when the index finger was in the top position, and the thumb in the bottom position. We found that toes do not hold the same spatial information, though it remains unclear whether toes hold different spatial information or none at all. These results demonstrate spatial information held by the fingers is stronger and more reliable than for the toes, so is not a general characteristic of limbs, but possibly related to hand use.

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Marx, Christian. "The western coast of Africa in Ptolemy'sGeographyand the location of his prime meridian." History of Geo- and Space Sciences 7, no. 1 (March 11, 2016): 27–52. http://dx.doi.org/10.5194/hgss-7-27-2016.

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Abstract. A controversial question concerning ancient geography is the location of the prime meridian which underlies the position data in Ptolemy's Geography and runs through the Fortunate Islands. An answer to this question is derived by means of a localisation of the places given by Ptolemy at the African western coast, i.e. in Mauritania Tingitana and Libya Interior, whose modern identifications are often uncertain or unknown. The origination of Ptolemy's positions from the distance data of seafarings is considered. A comparison of his data with distances reported by Pliny yields a satisfactory agreement. The localisation of Ptolemy's places is based on distances derived from Ptolemy's coordinates and partly on further information by ancient authors. Through it previous identifications are confirmed and new identifications are found. It shows that the Fortunate Islands correspond to several eastern islands of the Canary Islands. Explanations are given for the errors of Ptolemy's position data. A likely error by Ptolemy barely considered is his repetition of a part of Mauritania Tingitana in his description of Libya Interior. The existence of this repetition is confirmed by an adjustment of a transformation between the positions of the duplicated places and a statistical test of the arranged model. A comparison of longitudinal distances in different ancient sources reveals that the position of Ptolemy's prime meridian is based on distances given by Marinos and Eratosthenes.

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Durrant, Aiden, Georgios Leontidis, and Stefanos Kollias. "3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection." EPJ Nuclear Sciences & Technologies 5 (2019): 20. http://dx.doi.org/10.1051/epjn/2019047.

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With Europe's ageing fleet of nuclear reactors operating closer to their safety limits, the monitoring of such reactors through complex models has become of great interest to maintain a high level of availability and safety. Therefore, we propose an extended Deep Learning framework as part of the CORTEX Horizon 2020 EU project for the unfolding of reactor transfer functions from induced neutron noise sources. The unfolding allows for the identification and localisation of reactor core perturbation sources from neutron detector readings in Pressurised Water Reactors. A 3D Convolutional Neural Network (3D-CNN) and Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) have been presented, each to study the signals presented in frequency and time domain respectively. The proposed approach achieves state-of-the-art results with the classification of perturbation type in the frequency domain reaching 99.89% accuracy and localisation of the classified perturbation source being regressed to 0.2902 Mean Absolute Error (MAE).

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Puvitha, Harriet, Saravanan Palani, V. Vijayakumar, Logesh Ravi, and V. Subramaniyaswamy. "Investigation of multi-objective optimisation techniques to minimise the localisation error in wireless sensor networks." International Journal of Grid and Utility Computing 12, no. 1 (2021): 33. http://dx.doi.org/10.1504/ijguc.2021.112459.

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Sabokrou, M., M. Fathy, and M. Hoseini. "Video anomaly detection and localisation based on the sparsity and reconstruction error of auto‐encoder." Electronics Letters 52, no. 13 (June 2016): 1122–24. http://dx.doi.org/10.1049/el.2016.0440.

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Journal articles on the topic 'Error localisation'

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