scholarly journals Wireless Network Indoor Positioning Method Using Nonmetric Multidimensional Scaling and RSSI in the Internet of Things Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Shuxia Wang
Keyword(s):  
Multidimensional Scaling ◽  
Target Location ◽  
Indoor Localization ◽  
Dimensional Space ◽  
Nonmetric Multidimensional Scaling ◽  
Localization Algorithm ◽  
Dissimilarity Matrix ◽  
Internet Of Things Environment ◽  
Positioning Method ◽  
Low Dimensional

Aiming at the problem that the indoor target location algorithm based on received signal strength (RSSI) in the IoT environment is susceptible to interference and large fluctuations, an indoor localization algorithm combining RSSI and nonmetric multidimensional scaling (NMDS) is proposed (RSSI- NMDS). First, Gaussian filtering is performed on the received plurality of sets of RSSI signals to eliminate abnormal fluctuations of the RSSI. Then, based on the RSSI data, the dissimilarity matrix is constructed, and the relative coordinates of the nodes in the low-dimensional space are obtained by NMDS solution. Finally, according to the actual coordinates of the reference node, the coordinate transformation is performed by the planar four-parameter model, and the position of the node in the actual coordinate system is obtained. The simulation results show that the proposed method has strong anti-RSSI perturbation and high positioning accuracy.

Online Long-Term Object Tracking Based on Compressed Haar-Like Features via Sparse Representation

2014 ◽  
Vol 989-994 ◽  
pp. 1610-1614
Author(s):  
Ming Zhao ◽  
Lu Ping Wang ◽  
Lu Ping Zhang
Keyword(s):  
Sparse Representation ◽  
Target Location ◽  
Model Updating ◽  
Dimensional Space ◽  
Reconstruction Error ◽  
Illumination Changes ◽  
Spatio Temporal ◽  
Low Dimensional ◽  
Scale Variation

Online long-term tracking is a challenging problem as data streams change over time. In this paper, sparse representation has been applied to visual tracking by finding the most correct sample with minimal reconstruction error using compressed Haar-like features. However, most sparse representation tracking algorithm introduce l1 regularization into the PCA reconstruction using samples directly, which leads to complexity computation and can not adapt to occlusion, rotation and change in size. Our model updating not only uses the samples from the training set, but also generates the warped versions (include scale variation, rotation, occlusion and illumination changes) for the previous tracking result. Also, we do not use the samples in models for sparse representation directly, but the Haar-like features instead which are compressed in a very low-dimensional space. In addition, we use a robust and fast algorithm which exploits the spatio-temporal context for predicting the target location in the next frame. This step will lead to the reduction of the searching range by the detector. We demonstrate the proposed method is able to track objects well under pose and scale variation, rotation, occlusion and illumination with great real-time performance on challenging image sequences.

scholarly journals Research on 3D Fingerprint Positioning Based on MIMO Base Station

2021 ◽  
Author(s):  
Chang Xia ◽  
Yijie Ren ◽  
Xiaojun Wang ◽  
Weiguang Sun ◽  
Fei Tang ◽  
...  
Keyword(s):  
Reference Point ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Base Station ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
High Base ◽  
Effective Use ◽  
Three Dimensional Space

The aim of this article is to solve the problem that the accuracy of traditional positioning algorithm decreases in complex environment and to provide some ideas for the few researches of fingerprint localization algorithm in three-dimensional space. This paper builds a system model in a three-dimensional space, provides three reference point distribution methods, and discusses the positioning performance under these distribution methods. After that, based on the high base station deployment density, multi-point fusion positioning method is used to locate the target, which further improves the positioning accuracy and makes more effective use of reference point resources. Finally, a backward-assisted positioning method is proposed, which uses the position information of the positioned points to assist the positioning of the current point. Research shows that this method can improve the positioning accuracy and has good versatility. (Foundation items: Social Development Projects of Jiangsu Science and Technology Department (No.BE2018704).)

scholarly journals Fast Multidimensional Scaling Through Sampling, Springs and Interpolation

2003 ◽  
Vol 2 (1) ◽  
pp. 68-77 ◽  
Author(s):  
Alistair Morrison ◽  
Greg Ross ◽  
Matthew Chalmers
Keyword(s):  
Multidimensional Scaling ◽  
Dimensional Space ◽  
Hybrid Approach ◽  
Real Data ◽  
Visual Exploration ◽  
Data Sets ◽  
Proximity Data ◽  
Stochastic Sampling ◽  
Similarity Relations ◽  
Low Dimensional

The term ‘proximity data’ refers to data sets within which it is possible to assess the similarity of pairs of objects. Multidimensional scaling (MDS) is applied to such data and attempts to map high-dimensional objects onto low-dimensional space through the preservation of these similarity relations. Standard MDS techniques have in the past suffered from high computational complexity and, as such, could not feasibly be applied to data sets over a few thousand objects in size. Through a novel hybrid approach based upon stochastic sampling, interpolation and spring models, we have designed an algorithm running in O( N√N). Using Chalmers’ 1996 O( N2) spring model as a benchmark for the evaluation of our technique, we compare layout quality and run times using sets of synthetic and real data. Our algorithm executes significantly faster than Chalmers’ 1996 algorithm, while producing superior layouts. In reducing complexity and run time, we allow the visualisation of data sets of previously infeasible size. Our results indicate that our method is a solid foundation for interactive and visual exploration of data.

Methods for Binary Multidimensional Scaling

2002 ◽  
Vol 14 (5) ◽  
pp. 1195-1232 ◽  
Author(s):  
Douglas L. T. Rohde
Keyword(s):  
Neural Networks ◽  
Multidimensional Scaling ◽  
Dimensional Space ◽  
Discrete Space ◽  
High Dimensional ◽  
Continuous Space ◽  
Low Dimensional ◽  
Set Of Points ◽  
Lower Dimensional Space ◽  
Lower Dimensional

Multidimensional scaling (MDS) is the process of transforming a set of points in a high-dimensional space to a lower-dimensional one while preserving the relative distances between pairs of points. Although effective methods have been developed for solving a variety of MDS problems, they mainly depend on the vectors in the lower-dimensional space having real-valued components. For some applications, the training of neural networks in particular, it is preferable or necessary to obtain vectors in a discrete, binary space. Unfortunately, MDS into a low-dimensional discrete space appears to be a significantly harder problem than MDS into a continuous space. This article introduces and analyzes several methods for performing approximately optimized binary MDS.

scholarly journals Metric Multidimensional Scaling for Large Single-Cell Data Sets using Neural Networks

2021 ◽  
Author(s):  
Stefan Canzar ◽  
Van Hoan Do ◽  
Slobodan Jelic ◽  
Soeren Laue ◽  
Domagoj Matijevic ◽  
...  
Keyword(s):  
Multidimensional Scaling ◽  
Single Cell ◽  
State Of The Art ◽  
Dimensional Space ◽  
Linear Mapping ◽  
Alternative Methods ◽  
Dimensional Euclidean Space ◽  
Data Sets ◽  
Metric Multidimensional Scaling ◽  
Low Dimensional

Metric multidimensional scaling is one of the classical methods for embedding data into low-dimensional Euclidean space. It creates the low-dimensional embedding by approximately preserving the pairwise distances between the input points. However, current state-of-the-art approaches only scale to a few thousand data points. For larger data sets such as those occurring in single-cell RNA sequencing experiments, the running time becomes prohibitively large and thus alternative methods such as PCA are widely used instead. Here, we propose a neural network based approach for solving the metric multidimensional scaling problem that is orders of magnitude faster than previous state-of-the-art approaches, and hence scales to data sets with up to a few million cells. At the same time, it provides a non-linear mapping between high- and low-dimensional space that can place previously unseen cells in the same embedding.

scholarly journals ON MULTIDIMENSIONAL SCALING WITH EUCLIDEAN AND CITY BLOCK METRICS

2006 ◽  
Vol 12 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Antanas Žilinskas ◽  
Julius Žilinskas
Keyword(s):  
Multidimensional Scaling ◽  
Search Algorithm ◽  
Dimensional Space ◽  
Optimization Method ◽  
Data Sets ◽  
Proximity Data ◽  
Information Elicitation ◽  
Evolutionary Metaheuristic ◽  
Unknown Structure ◽  
Low Dimensional

Experimental sciences collect large amounts of data. Different techniques are available for information elicitation from data. Frequently statistical analysis should be combined with the experience and intuition of researchers. Human heuristic abilities are developed and oriented to patterns in space of dimensionality up to 3. Multidimensional scaling (MDS) addresses the problem how objects represented by proximity data can be represented by points in low dimensional space. MDS methods are implemented as the optimization of a stress function measuring fit of the proximity data by the distances between the respective points. Since the optimization problem is multimodal, a global optimization method should be used. In the present paper a combination of an evolutionary metaheuristic algorithm with a local search algorithm is used. The experimental results show the influence of metrics defining distances in the considered spaces on the results of multidimensional scaling. Data sets with known and unknown structure and different dimensionality (up to 512 variables) have been visualized.

The use of multidimensional scaling in analysing multi-attribute genotype response across environments

1982 ◽  
Vol 33 (3) ◽  
pp. 473 ◽  
Author(s):  
KE Basford
Keyword(s):  
Multidimensional Scaling ◽  
Dimensional Space ◽  
Separate Analysis ◽  
Response Patterns ◽  
Data Set ◽  
Underlying Space ◽  
Attribute Analysis ◽  
Low Dimensional ◽  
Different Response ◽  
Genotype Response

Three-way Multidimensional Scaling is presented as a single analysis of genotype x environment x attribute data. The concept and underlying model of this technique are discussed, and its usefulness is investigated by applying the analysis to a well-known soybean data set. The measured attributes were considered to represent the line response in each environment. It was assumed the lines could be considered in an underlying space of r dimensions. The environments were able to perceive different response patterns in that different importances could be placed on these underlying dimensions. By applying this technique a spatial representation of the lines in a low-dimensional space was obtained. Thus a single multi-attribute analysis is achieved rather than attempting to combine separate analyses for each attribute. The resulting line response pattern was examined in relation to previous reports on this data set. The inclusion of all six attributes revealed information not previously obtained by separate analysis of two of these attributes. If general inference about genotype response is to be made rather than inference on one particular attribute, then the use of a technique such as this is recommended.

scholarly journals A Generative-Discriminative Framework that Integrates Imaging, Genetic, and Diagnosis into Coupled Low Dimensional Space

NeuroImage ◽  
2021 ◽  
pp. 118200
Author(s):  
Sayan Ghosal ◽  
Qiang Chen ◽  
Giulio Pergola ◽  
Aaron L. Goldman ◽  
William Ulrich ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Low Dimensional

scholarly journals Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4454 ◽  
Author(s):  
Marek Piorecky ◽  
Vlastimil Koudelka ◽  
Jan Strobl ◽  
Martin Brunovsky ◽  
Vladimir Krajca
Keyword(s):  
Data Structure ◽  
Spatial Clustering ◽  
Dimensional Space ◽  
Head Movements ◽  
Nonlinear Dimensionality Reduction ◽  
Space Resolution ◽  
Additional Information ◽  
Nonlinear Dimension ◽  
The Time Domain ◽  
Low Dimensional

Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time resolution (hdEEG) and space resolution (fMRI). However, EEG measurements in the scanner contain an electromagnetic field that is induced in leads as a result of gradient switching slight head movements and vibrations, and it is corrupted by changes in the measured potential because of the Hall phenomenon. The aim of this study is to design and test a methodology for inspecting hidden EEG structures with respect to artifacts. We propose a top-down strategy to obtain additional information that is not visible in a single recording. The time-domain independent component analysis algorithm was employed to obtain independent components and spatial weights. A nonlinear dimension reduction technique t-distributed stochastic neighbor embedding was used to create low-dimensional space, which was then partitioned using the density-based spatial clustering of applications with noise (DBSCAN). The relationships between the found data structure and the used criteria were investigated. As a result, we were able to extract information from the data structure regarding electrooculographic, electrocardiographic, electromyographic and gradient artifacts. This new methodology could facilitate the identification of artifacts and their residues from simultaneous EEG in fMRI.

Sign in / Sign up

Export Citation Format

Share Document