Performance Analysis of Location Estimation Algorithm Using an Intelligent Coordination Scheme in RTLS

Automatic evidence quality prediction to support evidence-based decision making.

The face-to-face superimposition was experimentally performed to assess the reliability of the facial image comparison with the computer-assisted facial image identification system. For evaluating the match of the 3D and 2D facial images of the same person, the 3D facial image obtained from 25 examinees was compared to the 2D oblique facial image of the same examinees ten times, yielding 250 superimpositions. In the case of the different person, the 3D facial images of 25 examinees were each compared to the 2D facial images of other 24 examinees, yielding 600 superimpositions. The average distance obtained from the reciprocal point-to-point differences on sixteen anthropometrical points of the 3D and 2D images was used as a matching criterion. The results showed that the measuring system for the reciprocal point-to-point differences on the superimposition image was reproducible. The ranges of the average distance were 1.4-3.3 for the same person and 2.6-7.0 for a different person, respectively. The average distance and percentage error at the FP/FN crossover point were 3.1 and 4.2%. In this experiment, it was suggested that the facial image comparison using the reciprocal points matching was reliable when the threshold of the average distance was 2.5.

Node Localization technology is one of key technologies in wireless sensor network. DV-Hop localization algorithm is a kind of range-free algorithm. In this paper, an improved DV-Hop algorithm aiming to enhance localization accuracy is proposed. To enhance localization accuracy, average per-hop distance is replaced by corrected value of global average per-hop distance and global average per-hop error. When calculating hop distance, unknown nodes use corresponding average per-hop distance expression according to different hop value. Comparison with DV-Hop algorithm, simulation results show that the improved DV-Hop algorithm can reduce the localization error and enhance the accuracy of sensor nodes localization more effectively.

The precise and accurate performance of location estimation is a vital component of context-aware applications. Numerous mobile devices with built-in IEEE 802.11 Wi-Fi technology can be used to estimate a user's location through a wireless local area network (WLAN) in indoor environments in which fixed access points are deployed. This study deals with improving the common techniques of such positioning once the acquisition of the fingerprint database in offline phase is performed. The main idea is to propose a methodology that includes two layers of classification: a concurrent hierarchical partitioning of both signal and physical space in a way that signal patterns in each part of building have the highest similarity, and a precise and independent positioning in a given part. A procedure for combining the proposed classifier with either artificial neural network (ANN) or Bayesian probabilistic model is then introduced. We also consider an alternative strategy for ANN learning by including all raw observations. The average distance error was successfully reduced in the proposed methodology by 32 % compared to the simple approach. We concluded that the physical partitioning should also consider the signal behavior. Toosi location-aware mobile system was ultimately implemented, providing different services (e.g., friend finder and nearest point of interest) based on the proposed technique via WLAN. The system benefits from the high level of interaction provided by Asynchronous JavaScript and XML (AJAX) technology. It is capable of transferring locational data and GIS map services efficiently to the mobile terminal.

In this work, we propose P-Loc, a device-free indoor localization system based on power-line network within the building. P-Loc measures the electromagnetic (EM) coupling between a human body and existing power-lines, which are simultaneously used for electric power transmission. To avoid the impact of AC mains and noise from other electrical sources, we inject a signal into the ground-line to generate the occupant location fingerprint of a specific frequency. A 0.48m average error distance is obtained in the preliminary experiments.

Radio Frequency Identification (RFID) technology is one of emerging technologies in which many countries in the world pay attention to. Many applications of localization use RFID technology. In the present date, there are many papers of localization reviewed. However, indoor localization system for real applications is still limited. This research proposes indoor localization system using fingerprint technique to find the estimated location of the target. This technique is compared its signal (or ID information) pattern with a previously recorded database of known signal (or ID information)-location data. The Dot Product is applied as the pattern matching method. The maximum and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> maximum values of the dot product are considered. The fingerprint locations providing the maximum and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> maximum values of the dot product are averaged. This average location is referred to as the location of the target. The average error distance is approximately 75 cm. This result shows a good estimation results since the error of the target estimation are not more than the gap between two fingerprint locations (120 cm). From the result, we can conclude that our proposed method can be applied in the real situation and application.

A radio map is a collection of fingerprints and collected location information. To build more accurate Wi-Fi positioning system (WPS), a more precise radio map should be prepared in advance. Despite GPS signals are usually unreachable in most indoor venues, GPS signals are commonly used as reference locations to tag the collected fingerprints. As a result, WPS has not been able to achieve high performance in accuracy indoors, even if the place is rich in Wi-Fi signals. This paper introduces a novel method to construct a radio map without any use of GPS signals. Users' home or office address is used instead to tag the locations of collected Wi-Fi signals. Since the method utilizes Wi-Fi signals collected indoors, a more precise radio map construction is possible. For evaluation, we tested the method at four deliberately selected residential and downtown areas in Daejeon and Seoul, Korea. Once the data collection rate rises above 50%, the average error distance was within 10m or less. This indicates that address-based crowdsourcing radio map construction can be an effective means to construct global-scale but precise radio maps at low cost.

SummaryAs wireless communications and microelectronic technology rapidly develop, diverse applications and services based on smart handheld devices have drawn the attention of researchers. The popularity of Indoor Location Based services and applications has also gradually increased. Therefore, how to improve indoor positioning accuracy becomes a very important issue. Although indoor positioning has been performed using various techniques in recent years, the computational complexity of ensuring positioning accuracy and positioning is an unsolved problem. Current indoor positioning systems typically use only the receiver or the transmitter to obtain the reference point data, and only the K‐Nearest Neighbors (KNN) or Trilateration algorithm is used to perform positioning. Therefore, positioning accuracy is limited by the use of reference point data from a single source and by the positioning algorithm used. The Novel Fingerprinting Mechanisms (NFM) indoor positioning system proposed in this study, however, uses both the receiver and transmitter to obtain positioning data and employs six positioning mechanisms to improve the current positioning accuracy. The experimental results show that the average error distance is 1.18 m in the NFM indoor positioning system. That is the system outperforms both KNN and Trilateration systems, which have average error distances of 1.35 m and 2.23 m, respectively. This study proves that the positioning accuracy is actually improved. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Background: Slackline-training has been shown to improve mainly task-specific balancing skills. Non-task specific effects were assessed for tandem stance and preferred one-leg stance on stable and perturbed force platforms with open eyes. It is unclear whether transfer effects exist for other balancing conditions and which component of the balancing ability is affected. Also, it is not known whether slackline-training can improve non-visual-dependent spatial orientation abilities, a function mainly supported by the hippocampus.Objective: To assess the effect of one-month of slackline-training on different components of balancing ability and its transfer effects on non-visual-dependent spatial orientation abilities.Materials and Methods: Fifty subjects aged 18–30 were randomly assigned to the training group (T) (n = 25, 23.2 ± 2.5 years; 12 females) and the control group (C) (n = 25, 24.4 ± 2.8 years; 11 females). Professional instructors taught the intervention group to slackline over four consecutive weeks with three 60-min-trainings in each week. Data acquisition was performed (within 2 days) by blinded investigators at the baseline and after the training. Main outcomes Improvement in the score of a 30-item clinical balance test (CBT) developed at our institute (max. score = 90 points) and in the average error distance (in centimeters) in an orientation test (OT), a triangle completion task with walking and wheelchair conditions for 60°, 90°, and 120°.Results: Training group performed significantly better on the closed-eyes conditions of the CBT (1.6 points, 95% CI: 0.6 to 2.6 points vs. 0.1 points, 95% CI: –1 to 1.1 points; p = 0.011, = 0.128) and in the wheelchair (vestibular) condition of the OT (21 cm, 95% CI: 8–34 cm vs. 1 cm, 95% CI: –14–16 cm; p = 0.049, = 0.013).Conclusion: Our results indicate that one month of intensive slackline training is a novel approach for enhancing clinically relevant balancing abilities in conditions with closed eyes as well as for improving the vestibular-dependent spatial orientation capability; both of the benefits are likely caused by positive influence of slackline-training on the vestibular system function.

A VLC (Visible Light Communication) system using fluorescent lights has been developed for indoor guidance of the visually impaired. While it is relatively straightforward to provide generalized location information for a blind user, precise location information is much more difficult to determine. We propose that the effective data reception range and the receiver's precise location can be calculated using measured sensor angles. A series of experiments have been performed in a practical platform with 22 fluorescent lights, 39 measuring points (MP). The average distance error could reach as low as 10 cm. This development will provide greater accuracy and therefore less stress for blind users.

Computed tomography angiography (CTA) allows for not only diagnosis of coronary artery disease (CAD) with high spatial resolution but also monitoring the remodeling of vessel walls in the progression of CAD. Alignment of coronary arteries in CTA images acquired at different times (with a 3-7 years interval) is required to visualize and analyze the geometric and structural changes quantitatively. Previous work in image registration primarily focused on large anatomical structures and leads to suboptimal results when applying to registration of coronary arteries. In this paper, we develop a novel method to directly align the straightened coronary arteries in the cylindrical coordinate system guided by the extracted centerlines. By using a Hidden Markov Model (HMM), image intensity information from CTA and geometric information of extracted coronary arteries are combined to align coronary arteries. After registration, the pathological features in two straightened coronary arteries can be directly visualized side by side by synchronizing the corresponding cross-sectional slices and circumferential rotation angles. By evaluating with manually labeled landmarks, the average distance error is 1.6 mm.

A new methodology combining data mining technology with statistical methods is proposed for the prediction of tropical cyclones’ characteristic factors which contain latitude, longitude, the lowest center pressure, and wind speed. In the proposed method, the best track datasets in the years 1949~2012 are used for prediction. Using the method, effective criterions are formed to judge whether tropical cyclones land on Hainan Island or not. The highest probability of accurate judgment can reach above 79%. With regard to TCs which are judged to land on Hainan Island, related prediction equations are established to effectively predict their characteristic factors. Results show that the average distance error is improved compared with the National Meteorological Centre of China.

ObjectivesThe purpose of this study was to assess the feasibility of 3D intraoral scanning for documentation of palatal soft tissue by evaluating the accuracy of shape, color, and curvature.Materials and methodsIntraoral scans of ten participants’ upper dentition and palate were acquired with the TRIOS® 3D intraoral scanner by two observers. Conventional impressions were taken and digitized as a gold standard. The resulting surface models were aligned using an Iterative Closest Point approach. The absolute distance measurements between the intraoral models and the digitized impression were used to quantify the trueness and precision of intraoral scanning. The mean color of the palatal soft tissue was extracted in HSV (hue, saturation, value) format to establish the color precision. Finally, the mean curvature of the surface models was calculated and used for surface irregularity.ResultsMean average distance error between the conventional impression models and the intraoral models was 0.02 ± 0.07 mm (p = 0.30). Mean interobserver color difference was − 0.08 ± 1.49° (p = 0.864), 0.28 ± 0.78% (p = 0.286), and 0.30 ± 1.14% (p = 0.426) for respectively hue, saturation, and value. The interobserver differences for overall and maximum surface irregularity were 0.01 ± 0.03 and 0.00 ± 0.05 mm.ConclusionsThis study supports the hypothesis that the intraoral scan can perform a 3D documentation of palatal soft tissue in terms of shape, color, and curvature.Clinical relevanceAn intraoral scanner can be an objective tool, adjunctive to the clinical examination of the palatal tissue.

In wireless sensor networks, DV-Hop localization algorithm which uses average hop distance to represent the actual distance is a commonly used range-free localization technology. But this algorithm has great error and node energy consumption in practical applications. Evolutionary algorithm has a branch which is differential evolution algorithm. DE algorithm has been widely used in a large number of fields, as a result of DE algorithm has simple structure and can combine with other methods easily. To solve the disadvantages of DV-Hop algorithm, an advanced DV-Hop localization algorithm on basic of differential evolution in this paper has been proposed. To reduce the hop distance error, the improved algorithm advances weighted process in the second step of DV-hop algorithm by leading the average hop distance error correction value. The differential evolution algorithm is used to optimize the positioning result of the unknown node in the third step of DV-hop algorithm. From the advanced localization algorithm’s simulation, the positioning accuracy has improved.

Because of the DV-Hop algorithm has a big error in the estimation of the average hop distance, this paper proposed a weighted hop distance correction localization algorithm. The improved algorithm is carried out by introducing the average hop distance error correction value of the weighting processing, thereby reducing the hop distance error, and avoid the accumulation of errors in the subsequent computation process. The simulation results show that the improved DV-Hop algorithm reduces localization error effectively and has good stability without additional devices; therefore, it is a practical localization solution for WSN.