Mean Localization Error Research Articles

CrowdLoc: Robust image indoor localization with edge-assisted crowdsensing

Image indoor localization, which is advantageous in infrastructure-less deployment and high positioning accuracy among various indoor localization schemes, is usually deployed in a lightweight end (shooting picture) and heavyweight server (searching image for location in the database) manner. Such a scheme is suffered from two major drawbacks: (1) The uploading and searching of the shooting pictures cause a severe burden to the server. (2) The image map will become defected as time elapse, such as missing features in initialization maps and continuous changes in the scene. To keep the image map up-to-date, a labor-intensive site survey is required with dedicated devices. The frequent updating will further exhaust the limited resources in the server. These two drawbacks have greatly hindered the image-based indoor localization from large-scale deployment. To this end, we study how to use crowdsourcing data to mitigate map defects and optimize location computation and map storage through edge architecture. In this paper, we propose CrowdLoc, an edge-assisted image localization architecture with lightweight multi-view localization on clients and crowdsourcing map-cache on the edge. Our method is mainly innovative in three parts. Firstly, CrowdLoc provides a lightweight multi-view localization on the client-side and the low-confidence localization can be offloaded to the edge server to use the crowdsourcing map for more accurate calculation. Secondly, CrowdLoc uses a map defect perception algorithm to recognize the update of the scene during user localization and update the crowdsourcing map-cache automatically on the edge side. Thirdly, in order to improve cache utilization, we propose a cache elimination strategy that runs periodically on the edge side. It can classify cache data according to the frequency of cache hits on a regular basis, remove invalidation data, and persist long-term updated data to the client map to reduce clients’ computing offload. We evaluate the performance of our method based on the real data in the lobby of an experimental building with a map missing rate of 20% and a map update rate of 40%. The results show that the mean localization error of CrowdLoc is 1.54 meters, which is 59.4% lower than that of multi-view localization without crowdsourcing maps, and only 0.56 meters higher than localization in non-defective maps.

Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization.

The advancement of smartphones with multiple built-in sensors facilitates the development of crowdsourcing-based indoor map construction and localization. This paper proposes a crowdsourcing-based indoor semantic map construction and localization method using graph optimization. Using waypoints, semantic landmarks, and Wi-Fi landmarks as nodes and the relevance between waypoints and landmarks (i.e., waypoint–waypoint, waypoint–semantic, waypoint–Wi-Fi, semantic–semantic, and Wi-Fi–Wi-Fi) as edges, the optimization graph is constructed. Initializing the venue map is the single-track semantic map with the highest quality, as determined by a proposed map quality evaluation function. The aligned venue and candidate maps are optimized while satisfying the constraints, with the candidate map exhibiting the highest degree of similarity to the venue map. The lightweight venue map is then updated in terms of waypoint and landmark attributes, as well as the relationship between waypoints and landmarks. To determine a pedestrian’s location on a venue map, similarities between a local map and a venue map are evaluated. Experiments conducted in an office building and shopping mall scenes demonstrate that crowdsourcing-based venue maps are superior to single-track semantic maps. Additionally, the landmark matching-based localization method can achieve a mean localization error of less than 0.5 m on the venue map, compared to 0.6 m in a single-track semantic map.

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

The increasing use of 3-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform manual landmarking. In this retrospective diagnostic study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set (n = 160) and a test set (n = 38). The reference data consisted of 33 landmarks, manually localized once by 1 operator(n = 178) or twice by 3 operators (n = 20, test set only). After inference on the test set, 1 CT scan showed “very low” confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements, and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3 mm, while success detection rates for 2.0, 2.5, and 3.0 mm were 90.4%, 93.6%, and 95.4%, respectively. Mean errors were −0.3 ± 1.3° and −0.1 ± 0.7 mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland–Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

Positioning and Tracking of Multiple Humans Moving in Small Rooms Based on a One-Transmitter-Two-Receiver UWB Radar Configuration.

The paper aims to propose a sequence of steps that will allow multi-person tracking with a single UWB radar equipped with the minimal antenna array needed for trilateration. Its localization accuracy is admittedly limited, but on the other hand, thoughtfully chosen placement of antennas can increase the detectability of several humans moving in their immediate vicinity and additionally decrease the computational complexity of the signal processing methods. It is shown that the UWB radar measuring with high rate and fine range resolution in conjunction with properly tuned processing parameters can continually track people even in the case when their radar echoes are crossing or merging. Emphasis is given to the simplified method of the time-of-arrival (TOA) estimation and association and the novel method needed for antenna height compensation. The performance of the proposed human tracking framework is evaluated for the experimental scenario with three people moving closely in a small room. A quantitative analysis of the estimated target tracks confirms the benefits of suggested high antenna placement and application of new signal processing methods in the form of decreasing the mean localization error and increasing the frequency of correct target position estimations.

Three-dimensional localization algorithm of mobile nodes based on received signal strength indicator-angle of arrival and least-squares support-vector regression

Node localization is one of the key technologies in the wireless sensor network research field, which is crucial to the high-accuracy localization of mobile nodes, but the positioning error of traditional algorithms such as received signal strength indicator and angle of arrival is more than 4 m, which has almost no practical value. For example, the localization accuracy of the localization algorithm based on received signal strength indicator will be reduced sharply when affected by signal reflection, multipath propagation, and other interference factors. To solve the problem, a three-dimensional localization algorithm of mobile nodes was proposed in this article based on received signal strength indicator–angle of arrival and least-squares support-vector regression, which fused the ranging information of received signal strength indicator algorithm and the angle of arrival algorithm and optimized the estimated distance of unknown nodes. Next, the mobile node model and least-squares support-vector regression modeling mechanism were built according to the hop count of the shortest distance between nodes. Finally, the unknown mobile nodes were localized based on least-squares support-vector regression modeling. The experimental results showed that compared with the localization algorithms without optimized ranging information or least-squares support-vector regression modeling, the algorithm proposed in this study exhibited significantly improved stability, a reduced mean localization error by more than 50%, and increased localization accuracy.

Map-based localization for intelligent vehicles from bi-sensor data fusion

Accurate localization is a challenging task for intelligent vehicles (IVs). Map-based localization methods are promising as they have no cumulative errors and high accuracy. Most methods are based on a single sensor, such as a camera or laser rangefinder (LRF). However, these sensors have disadvantages. Thus, this study proposes a map-based localization method from bi-sensor data fusion. We proposed a method using a camera, LRF, differential Global Positioning System, Inertial Navigation System to generate a multi-scale map. The multi-map consists of scenario nodes, and each node encodes scenario features, road 3D structure, and trajectories. Based on the multi-scale map, we propose an accurate localization method that includes coarse, node-level, and metric localizations. A bitopological localization model is set up for coarse localization. In this model, we use previous feature matching results to predict the current position. The features include image and light detection and ranging (LIDAR) data, and each of them set up one topological model. The two models make up a bitopological localization model. In node-level localization, we extract the LIDAR and image features and match them with the multi-scale map. The closest node is found by K-nearest neighbors from multiple feature spaces. In this step, we keep the image feature extraction in mind and propose a novel LIDAR feature extraction method called the LIDAR-image feature. In metric localization, we solve the perspective n-points problem between the current position and the closest node to compute the pose of IVs. In the experiments, our method has been tested on open road and campus. Each route covers different times and weather conditions. The proposed method can achieve less than 0.20 m mean localization errors. Compared with existing map-based methods, our method utilizes bi-sensors to improve localization performance.

Geomagnetic Positioning-Aided Wi-Fi FTM Localization Algorithm for NLOS Environments

WiFi-based indoor localization using the fine time measurement (FTM) protocol has become a popular technique. However, in harsh Non-Line-of-Sight (NLoS) environments, WiFi FTM positioning (WFP) suffers from poor performance. In this letter, a novel WiFi FTM localization method with the assistance of geomagnetic positioning (GP) is proposed. To ensure the accuracy of GP, an enhanced mind evolutionary algorithm (EMEA) is designed. A fine-grained WiFi position estimation method using the overlapping searching area (OSA) and the coincident points selection strategy is proposed. Experimental results show that the EMEA-based GP improves the localization performance of WFP in NLOS environments, the mean localization error (ME) and root-mean-square error (RMSE) of the GP-aided WFP are 1.82 m and 2.08 m, respectively. Compared with the classic WFP using the weighted least square (WLS) method, the ME and RMSE are reduced by 51.7% and 52.4%, respectively.

Localizing wild chimpanzees with passive acoustics.

Localizing wildlife contributes in multiple ways to species conservation. Data on animal locations can reveal elements of social behavior, habitat use, population dynamics, and be useful in calculating population density. Acoustic localization systems (ALS) are a non‐invasive method widely used in the marine sciences but not well established and rarely employed for terrestrial species.We deployed an acoustic array in a mountainous environment with heterogeneous vegetation, comprised of four custom‐built GPS synchronized acoustic sensors at about 500 m intervals in Issa Valley, western Tanzania, covering an area of nearly 2 km2. Our goal was to assess the precision and error of the estimated locations by conducting playback tests, but also by comparing the estimated locations of wild chimpanzee calls with their true locations obtained in parallel during follows of individual chimpanzees. We assessed the factors influencing localization error, such as wind speed and temperature, which fluctuate during the day and are known to affect sound transmission.We localized 282 playback sounds and found that the mean localization error was 27 ± 21.8 m. Localization was less prone to error and more precise during early mornings (6:30 h) compared to other periods. We further localized 22 wild chimpanzee loud calls within 52 m of the location of a researcher closely following the calling individuals.We demonstrate that acoustic localization is a powerful tool for chimpanzee monitoring, with multiple behavioral and conservation applications. Its applicability in studying social dynamics and revealing density estimation among many others, especially but not exclusively for loud calling species, provides an efficient way of monitoring populations and informing conservation plans to mediate species loss.

Age Differences in Speech Perception in Noise and Sound Localization in Individuals With Subjective Normal Hearing.

Hearing loss in old age, which often goes untreated, has far-reaching consequences. Furthermore, reduction of cognitive abilities and dementia can also occur, which also affects quality of life. The aim of this study was to investigate the hearing performance of seniors without hearing complaints with respect to speech perception in noise and the ability to localize sounds. Results were tested for correlations with age and cognitive performance. The study included 40 subjects aged between 60 and 90 years (mean age: 69.3 years) with not self-reported hearing problems. The subjects were screened for dementia. Audiological tests included pure-tone audiometry and speech perception in two types of background noise (continuous and amplitude-modulated noise) which was either co-located or spatially separated (multi-source noise field, MSNF) from the target speech. Sound localization ability was assessed and hearing performance was self-evaluated by a questionnaire. Speech in noise and sound localization was compared with young normal hearing adults. Although considering themselves as hearing normal, 17 subjects had at least a mild hearing loss. There was a significant negative correlation between hearing loss and dementia screening (DemTect) score. Speech perception in noise decreased significantly with age. There were significant negative correlations between speech perception in noise and DemTect score for both spatial configurations. Mean SRTs obtained in the co-located noise condition with amplitude-modulated noise were on average 3.1 dB better than with continuous noise. This gap-listening effect was severely diminished compared to a younger normal hearing subject group. In continuous noise, spatial separation of speech and noise led to better SRTs compared to the co-located masker condition. SRTs in MSNF deteriorated in modulated noise compared to continuous noise by 2.6 dB. Highest impact of age was found for speech perception scores using noise stimuli with temporal modulation in binaural test conditions. Mean localization error was in the range of young adults. Mean amount of front/back confusions was 11.5% higher than for young adults. Speech perception tests in the presence of temporally modulated noise can serve as a screening method for early detection of hearing disorders in older adults. This allows for early prescription of hearing aids.

Indoor Localization Using Mind Evolutionary Algorithm-Based Geomagnetic Positioning and Smartphone IMU Sensors

With the pervasiveness and ubiquitous distribution of the magnetic field in indoor environments, indoor localization using magnetic positioning (MP) has attracted considerable attention. This work concentrates on the MP and pedestrian dead reckoning (PDR) method, and constructs a fusion system for smartphones using MP and PDR based on the extended Kalman filter (EKF). The mind evolutionary algorithm (MEA) is introduced to search for the optimal magnetic position based on a heuristic searching strategy, which uses the similartaxis and dissimilation for the evolutionary operation. In the PDR module, the acceleration characteristics of different walking patterns are analyzed and the corresponding features are extracted. The enhanced genetic algorithm-based extreme learning machine (EGA-ELM) is adopted to train these features and address the gait recognition problem of different walking patterns. Finally, to obtain a lightweight and high-precision fusion method, MEA-based MP is integrated with PDR based on the EKF. Extensive experiments are conducted to evaluate the proposed methods. The testing results showed that MEA-based MP can obtain a location error within 2.3 m and steps can be recognized with a mean accuracy of 95% when different users participate in testing. The positioning results after fusion with PDR reveal that the mean location error and root-mean-square error (RMSE) are 1.25 m and 1.53 m respectively, which outperforms the MP, PDR, MP and PDR fusion methods using improved particle filter (IPF) and genetic particle filter (GPF).

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics.

Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimization algorithms.

A Defect Localization Approach Based on Improved Areal Coordinates and Machine Learning

The defects are usually generated during the structural materials subjected to external loads. Elucidating the position distribution of defects using acoustic emission (AE) technique provides the basis for investigating the failure mechanism and prevention of materials and estimating the location of the potentially dangerous sources. However, the location accuracy is heavily affected by both limitation of localization area and reliance on the premeasured wave velocity. Here, we propose a novel AE source localization approach based on generalized areal coordinates and a machine learning algorithmic model. A total of 14641 AE source location simulation cases are carried out to validate the proposed method. The simulation results indicate that even under various measurement error conditions the AE sources could be effectively located. Moreover, the feasibility of the proposed approach is experimentally verified on the AE source localization system. The experiment results show that the mean localization error of 3.64 mm and the standard deviation of 2.61 mm are obtained, which are 67.55% and 75.46% higher than those of the traditional method.

Multiclass Permanent Magnets Superstructure for Indoor Localization Using Artificial Intelligence

Smartphones have become a popular tool for indoor localization and position estimation of users. Existing solutions mainly employ Wi-Fi, radio-frequency identification (RFID), and magnetic sensing techniques to track movements in crowded venues. These are highly sensitive to magnetic clutters and depend on local ambient magnetic fields, which frequently degrade their performance. Also, these techniques often require pre-known mapping surveys of the area, or the presence of active beacons, which are not always available. We embed small-volume and large-moment magnets in pre-known locations and arrange them in specific geometric constellations that create magnetic superstructure patterns of supervised magnetic signatures. These signatures constitute an unambiguous magnetic environment with respect to the moving sensor carrier. The localization algorithm learns the unique patterns of the scattered magnets during training and detects them from the ongoing streaming of data during localization. Our contribution is twofold. First, we deploy passive permanent magnets that do not require a power supply, in contrast to active magnetic transmitters. Second, we perform localization based on smartphone motion rather than on static positioning of the magnetometer. In our previous study, we considered a single superstructure pattern. Here, we present an extended version of that algorithm for multisuperstructure localization, which covers a broader localization area of the user. Experimental results demonstrate localization accuracy of 95% with a mean localization error (MLE) of less than 1 m using artificial intelligence (AI).

A Narrow Beam Steering Antenna Array for Indoor Positioning Systems Based on Wireless Sensor Network

This paper proposes the 2.45 <i>GHz</i> planar Beam Steering Antenna Array for Indoor Positioning Systems. The antenna array consists of a phase shifter based on a 4 &#x00D7; 4 Butler matrix, and an array of four dipole Yagi antennas. By choosing one of four beams using a switching controller, the main beam of the antenna can steer in four directions from +37&#x00B0;, &#x2013;12&#x00B0;, +12&#x00B0; to &#x2013;36&#x00B0;. The antenna has a narrow fan beam with a beamwidth in the azimuth plane of from +21.5&#x00B0; to +24.5&#x00B0; and in the elevation plane of approximately 90&#x00B0; which effectively minimizes multipath signals. These advantages are suitable for positioning systems to increase location accuracy. The antenna achieves a peak gain of from 9.1 to 9.8 <i>dBi</i> in four directions, a wide band of 400 <i>MHz</i>. The fully electronically steerable antenna prototype has been designed using CST software, simulated based on the finite element method, and fabricated on the RO4003C substrate as well as measured. The antenna is implemented in the Indoor Positioning System using three different location methods, including trilateration, triangulation, and fingerprinting, to highlight the antenna&#x2019;s advantages in improving location accuracy with the ratio between the mean location error and area of 0.051, 0.033, and 0.023 respectively.

Real-Time Short-Range Human Posture Estimation Using mmWave Radars and Neural Networks

Millimetre-wave (mmWave) radar is increasing in popularity for human activity recognition, due to its advantages of high resolution, non-intrusive nature and suitability for various environments. In this paper, we present a novel human posture estimation system using mmWave radars. The system detects people with arbitrary postures in indoor environments at close distances (within two metres), and estimates the posture by localising the key joints. We use two mmWave radars to capture the scene and a neural network model to estimate the posture. The neural network model consists of a part detector that estimates the subject’s joint positions, and a spatial model that learns the correlation between the joints. A temporal correlation step is introduced to further refine the estimate when in real-time operation. The system can provide an accurate posture estimate of the person in real-time at 20 fps, with a mean localisation error of 12.2 cm and an average precision of 71.3%.

Measurement of Lead Localization Accuracy Based on Magnetic Resonance Imaging.

Post-implantation localization of deep brain stimulation (DBS) lead based on a magnetic resonance (MR) image is widely used. Existing localization methods use artifact center method or template registration method, which may lead to a considerable deviation of > 2 mm, and result in severe side effects or even surgical failure. Accurate measurement of lead position can instantly inform surgeons of the imprecise implantation. This study aimed to identify the influencing factors in DBS lead post-implantation localization approach, analyze their influence, and describe a localization approach that uses the individual template method to reduce the deviation. We verified that reconstructing direction should be parallel or perpendicular to lead direction, instead of the magnetic field. Besides, we used simplified relationship between magnetic field angle and deviation error to correct the localization results. The mean localization error can be reduced after correction and favors the feasibility of direct localization of DBS lead using MR images. We also discussed influence of in vivo noise on localization frequency and the possibility of using only MR images to localize the contacts.

Abstract 10236: Prospective Assessment of ECG-Image-based Real-Time Individual Virtual-Heart Automatic Localization (RIVAL) for Scar-Related Ventricular Tachycardia (VT)

Background: We developed a novel RIVAL system that consists of a CT-based computational simulation to identify VT circuits and an ECG-based automated approach to localize VT exit sites. Objective: Prospectively assess the ability of the RIVAL system to localize VT circuits and exits. Methods: Patients presenting for VT ablation were enrolled into the study. Pre-procedural cardiac CT was performed and used to conduct heart simulations for predicting VT circuits. The patient’s CT geometry with the predicted VT circuits was registered to the electroanatomic shell created during the procedure and imported into the RIVAL program. During the procedure, exit sites of induced VTs are localized in real-time using the 12-lead ECG onto the patient-specific CT surface. Predicted ablation regions obtained by combining RIVAL-predicted VT circuits with exit sites were analyzed offline to assess localization accuracy to the invasive VT ablation procedure. Results: Four patients with ischemic cardiomyopathy undergoing VT ablation had preprocedural cardiac CT. In P1, two VTs were induced during the procedure. The RIVAL predicted 2 VT circuits and achieved a mean localization error of 7.0mm for VT exit site prediction (Fig1). There was a spatial concordance between the predicted ablation areas and the clinical ablation regions. For P2, three VTs were induced during the VT ablation. A large area of scar was associated with 6 RIVAL-predicted VT circuits. The exit site localization accuracy could not be precisely quantitated because the VTs terminated with ablation at a mid-diastolic site. For P3, no VTs were inducible, however substrate modification (SM) was performed in the anterior-apical LV. The RIVAL predicted only one VT circuit located in the area where SM was performed. No VT was induced for P4 and the RIVAL did not predict any VT circuits. Conclusions: The RIVAL predicts the VT circuit and exit accurately, which may improve the precision of ablation therapies and procedure outcomes.

Smart antenna with circular polarization for high accuracy indoor localization system

This article presents a new switched beam antenna (SBA) for indoor localization applications. The SBA consists of seven circularly polarized (CP) element antennas using a novel feeding network. The attractive characteristics of the proposed CP antenna are high gain, wide bandwidth (BW) with the BW of 36.2% at the reflection coefficient less than −10 dB (4.6–6.7 GHz), the 3 dB axial ratio (AR) BW of 22.4% (5.4–6.7 GHz), wide 3 dB AR beamwidth as about 60 degrees, and maximum gain of 9 dBi within the total bandwidth. Experimental results show good agreement with simulated ones in terms of radiation pattern, AR, and reflection coefficient. We proposed a low-cost and simple RF-based indoor localization system based on a single anchor node equipped with this SBA. The system is demonstrated through experimental validations in a 6 m × 6.5 m × 3.5 m office room with a mean localization error of 0.72 m.

P.167 Application of the Anatomical Fiducials Framework to a Clinical Dataset of Patients with Parkinson’s Disease

Background: Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications. A point-based set of anatomical fiducials (AFIDs) was recently developed and validated to provide quantitative measures of image registration. We applied the AFIDs protocol to magnetic resonance images (MRIs) obtained from patients with Parkinson’s Disease (PD). Methods: Two expert and three novice raters placed AFIDs on MRIs of 39 PD patients. Localization and registration errors were calculated. To investigate for unique morphometric features, pairwise distances between AFIDs were calculated and compared to 30 controls who previously had AFIDs placed. Wilcoxon rank-sum tests with Bonferroni corrections were used. Results: 6240 AFIDs were placed with a mean localization error (±SD) of 1.57mm±1.16mm and mean registration error of 3.34mm±1.94mm. Out of the 496 pairwise distances, 40 were statistically significant (p&lt;0.05/496). PD patients had a decreased pairwise distance between the left temporal horn, brainstem and pineal gland. Conclusions: AFIDs can be successfully applied with millimetric accuracy in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence, facilitating accurate aggregation of imaging datasets and comparisons between various neurological conditions.

An Accurate WiFi Indoor Positioning Algorithm for Complex Pedestrian Environments

This paper proposes a precise WiFi fingerprinting indoor positioning algorithm for complex pedestrian environments. We transform the disturbed received signal strength (RSS) from the original space to latent space using the improved probabilistic linear discriminant analysis (PLDA). In the latent space, Bayes rule is used to calculate the posterior probability of the similarity between the test point and the reference points, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> reference points with the highest posterior probability are weighted to estimate the position. Actual on-site experiments involving three floors demonstrate that the mean localization error of the proposed algorithm is 1.38 m, which outperforms the Horus algorithm by 29% under the same test conditions. In addition, by studying the variability of mean value of RSS in different pedestrian environments, the fingerprint maps in different states of personnel movement are simulated. By using which, the average localization error of the proposed algorithm increases slightly to 1.63m, while the workload required during the offline training phase is significantly reduced.