scholarly journals A Geometric Approach to Noisy EDM Resolution in FTM Measurements

Computers ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 33
Author(s):  
Jerome Henry ◽  
Nicolas Montavont ◽  
Yann Busnel ◽  
Romaric Ludinard ◽  
Ivan Hrasko
Keyword(s):  
Euclidean Distance ◽  
Geometric Approach ◽  
Least Square ◽  
Location Problems ◽  
Second Step ◽  
Floor Plan ◽  
Sensor Location ◽  
Indoor Location ◽  
Ranging Error ◽  
Asymmetrically Distributed

Metric Multidimensional Scaling is commonly used to solve multi-sensor location problems in 2D or 3D spaces. In this paper, we show that such technique provides poor results in the case of indoor location problems based on 802.11 Fine Timing Measurements, because the number of anchors is small and the ranging error asymmetrically distributed. We then propose a two-step iterative approach based on geometric resolution of angle inaccuracies. The first step reduces the effect of poor ranging exchanges. The second step reconstructs the anchor positions, starting from the distances of highest likely-accuracy. We show that this geometric approach provides better location accuracy results than other Euclidean Distance Metric techniques based on Least Square Error logic. We also show that the proposed technique, with the input of one or more known location, can allow a set of fixed sensors to auto-determine their position on a floor plan.

Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model

2021 ◽  
pp. 1-20
Author(s):  
Chaojie Liu ◽  
Jie Lu ◽  
Wenjing Fu ◽  
Zhuoyi Zhou
Keyword(s):  
Real Estate ◽  
Geographically Weighted Regression ◽  
Euclidean Distance ◽  
Evaluation Model ◽  
Housing Prices ◽  
Information Criterion ◽  
Access Point ◽  
Least Square ◽  
Weighted Regression ◽  
Spatial Error

How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.

scholarly journals RSS Based Wi-Fi Positioning Method Using Recursive Least Square (RLS) Algorithm

2018 ◽  
Vol 7 (2.24) ◽  
pp. 492
Author(s):  
Sreevardhan Cheerla ◽  
D Venkata Ratnam
Keyword(s):  
Least Square ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Positioning Systems ◽  
Indoor Location ◽  
Positioning Method ◽  
Accurate Position ◽  
Software And Hardware ◽  
Newton Raphson ◽  
Raphson Method

Due to rapid increase in demand for services which depends upon exact location of devices leads to the development of numerous Wi-Fi positioning systems. It is very difficult to find the accurate position of a device in indoor environment due to substantial development of structures. There are many algorithms to determine the indoor location but they require expensive software and hardware. Hence receiving signals strength (RSS) based algorithms are implemented to find the self-positioning. In this paper Newton-Raphson, Gauss-Newton and Steepest descent algorithms are implemented to find the accurate location of Wi-Fi receiver in Koneru Lakshmaiah (K L) University, Guntur, Andhra Pradesh, India. From the results it is evident that Newton -Raphson method is better in providing accurate position estimations. 

scholarly journals A Method to Incorporate Floor Plan Constraints into Indoor Location Tracking: A Voronoi Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
John D. Hobby ◽  
Marzieh Dashti
Keyword(s):  
Indoor Localization ◽  
Research Effort ◽  
Location Tracking ◽  
Floor Plan ◽  
Location Based Service ◽  
Indoor Location ◽  
Filtering Algorithm ◽  
Localization Algorithms ◽  
Specific Localization ◽  
Floor Plans

Indoor localization has attracted a lot of research effort in recent years due to the explosion of indoor location-based service (LBS) applications. Incorporating map constraints into localization algorithms reduces the uncertainty of walking trajectories and enhances location accuracy. Suitable maps for computer-aided localization algorithms are not readily available, and hence most researchers working on localization solutions manually create maps for their specific localization scenarios. This paper presents a method of generating indoor maps suitable for localization algorithms from CAD floor plans. Our solution is scalable for mass-market LBS deployment. We also propose an adapted map-filtering algorithm that utilizes map information extracted from CAD floor plans. We evaluate the performance of our solution via real-world Wi-Fi RF measurements.

Multiple subtraction using statistically estimated inverse wavelets

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. WB247-WB254 ◽  
Author(s):  
Yike Liu ◽  
Degang Jin ◽  
Xu Chang ◽  
Peng Li ◽  
Hongchuan Sun ◽  
...  
Keyword(s):  
Real Data ◽  
Least Square ◽  
Second Step ◽  
Multiple Events ◽  
Amplitude Distortion ◽  
Computational Costs ◽  
Multiple Elimination

Surface-related multiple elimination (SRME) typically consists of two steps: The first step is prediction and the second step is subtraction. In subtraction, it is important to effectively attenuate multiple events and preserve primary events. When multiples cross with or overlap on primaries, least-square subtraction usually cannot subtract multiples effectively and may also damage the primaries. When multiples overlap with primaries, least-square subtraction cannot always subtract multiples accurately and often damages the primaries. To remedy this problem, we propose to statistically estimate the inverse source wavelet, correct for errors in the estimate of the inverse wavelet, and then use the corrected inverse wavelets for multiple subtraction. Synthetic tests and real data examples show that the proposed method can effectively attenuate multiples, while they also preserve the continuity of reflection events and successfully avoid amplitude distortion. The proposed method is characterized by low computational costs and ease of implementation.

scholarly journals NEURO-FUZZY SYSTEM FOR DETECTING PD PATIENTS BASED ON EUCLID DISTANCE, FFT, AND PCA

2020 ◽  
Vol 20 (09) ◽  
pp. 2040017
Author(s):  
SEOK-WOO JANG ◽  
SANG-HONG LEE
Keyword(s):  
Pressure Sensor ◽  
Euclidean Distance ◽  
Performance Enhancement ◽  
Principal Component ◽  
Sensor Data ◽  
Second Step ◽  
Neuro Fuzzy ◽  
Euclidean Distances ◽  
Left And Right ◽  
The Individual

This study proposes a method to distinguish between healthy people and Parkinson’s disease patients using sole pressure sensor data, neural network with weighted fuzzy membership (NEWFM), and preprocessing techniques. The preprocessing techniques include fast Fourier transform (FFT), Euclidean distance, and principal component analysis (PCA), to remove noise in the data for performance enhancement. To make the features usable as inputs for NEWFM, the Euclidean distances between the left and right sole pressure sensor data were used at the first step. In the second step, the frequency scales of the Euclidean distances extracted in the first step were divided into individual scales by the FFT using the Hamming method. In the final step, 1–15 dimensions were extracted as the features of NEWFM from the individual scales by the FFT extracted in the second step by the PCA. An accuracy of 75.90% was acquired from the eight dimensions as the inputs of NEWFM.

scholarly journals Quality Improvement for Exemplar-based Image Inpainting using a Modified Searching Mechanism

2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Mariwan Wahid Ahmed ◽  
Alan Anwer Abdulla
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Euclidean Distance ◽  
Image Inpainting ◽  
Input Image ◽  
Second Step ◽  
Research Areas ◽  
Potential Applications ◽  
Priority Function ◽  
Popular Images

Digital image processing has a significant impact in different research areas including medical image processing, biometrics, image inpainting, object detection, information hiding, and image compression. Image inpainting is a science of reconstructing damaged parts of digital images and filling-in regions in which information are missing which has many potential applications such as repairing scratched images, removing unwanted objects, filling missing area, and repairing old images. In this paper, an image inpainting algorithm is developed based on exemplar, which is one of the most important and popular images inpainting technique, to fill-in missing area that caused either by removing unwanted objects, by image compression, by scratching image, or by image transformation through internet. In general, image inpainting consists of two main steps: The first one is the priority function. In this step, the algorithm decides to select which patch has the highest priority to be filled at the first. The second step is the searching mechanism to find the most similar patch to the selected highest priority patch to be inpainted. This paper concerns the second step and an improved searching mechanism is proposed to select the most similar patch. The proposed approach entails three steps: (1) Euclidean distance is used to find the similarity between the highest priority patches which need to be inpainted with each patch of the input image, (2) the position/location distance between those two patches is calculated, and (3) the resulted value from the first step is summed with the resulted value obtained from the second step. These steps are repeated until the last patch from the input image is checked. Finally, the smallest distance value obtained in step 3 is selected as the most similar patch. Experimental results demonstrated that the proposed approach gained a higher quality in terms of both objectives and subjective compared to other existing algorithms.

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