scholarly journals Scan Matching by Cross-Correlation and Differential Evolution

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 856
Author(s):  
Jaromir Konecny ◽  
Pavel Kromer ◽  
Michal Prauzek ◽  
Petr Musilek
Keyword(s):  
Differential Evolution ◽  
Cross Correlation ◽  
Indoor Localization ◽  
Differential Evolution Algorithm ◽  
Location Based Services ◽  
Scan Matching ◽  
Laser Range ◽  
Good Ability ◽  
Wide Range ◽  
High Level

Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.

scholarly journals Image-Based Indoor Localization Using Smartphone Camera

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shuang Li ◽  
Baoguo Yu ◽  
Yi Jin ◽  
Lu Huang ◽  
Heng Zhang ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Multipath Propagation ◽  
Reference Points ◽  
Location Based Services ◽  
Depth Images ◽  
Research Areas ◽  
Novel Approach ◽  
Public Data ◽  
Wide Range ◽  
Increasing Demand

With the increasing demand for location-based services such as railway stations, airports, and shopping malls, indoor positioning technology has become one of the most attractive research areas. Due to the effects of multipath propagation, wireless-based indoor localization methods such as WiFi, bluetooth, and pseudolite have difficulty achieving high precision position. In this work, we present an image-based localization approach which can get the position just by taking a picture of the surrounding environment. This paper proposes a novel approach which classifies different scenes based on deep belief networks and solves the camera position with several spatial reference points extracted from depth images by the perspective- n -point algorithm. To evaluate the performance, experiments are conducted on public data and real scenes; the result demonstrates that our approach can achieve submeter positioning accuracy. Compared with other methods, image-based indoor localization methods do not require infrastructure and have a wide range of applications that include self-driving, robot navigation, and augmented reality.

scholarly journals RETHINKING INDOOR LOCALIZATION SOLUTIONS TOWARDS THE FUTURE OF MOBILE LOCATION-BASED SERVICES

2017 ◽  
Vol IV-4/W4 ◽  
pp. 235-247 ◽  
Author(s):  
C. Guney
Keyword(s):  
Indoor Localization ◽  
Smart Cities ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Outdoor Environment ◽  
Indoor Environments ◽  
Navigation Systems ◽  
Positioning Systems ◽  
Indoor Location ◽  
High Level

Satellite navigation systems with GNSS-enabled devices, such as smartphones, car navigation systems, have changed the way users travel in outdoor environment. GNSS is generally not well suited for indoor location and navigation because of two reasons: First, GNSS does not provide a high level of accuracy although indoor applications need higher accuracies. Secondly, poor coverage of satellite signals for indoor environments decreases its accuracy. So rather than using GNSS satellites within closed environments, existing indoor navigation solutions rely heavily on installed sensor networks. There is a high demand for accurate positioning in wireless networks in GNSS-denied environments. However, current wireless indoor positioning systems cannot satisfy the challenging needs of indoor location-aware applications. Nevertheless, access to a user’s location indoors is increasingly important in the development of context-aware applications that increases business efficiency. In this study, how can the current wireless location sensing systems be tailored and integrated for specific applications, like smart cities/grids/buildings/cars and IoT applications, in GNSS-deprived areas.

scholarly journals An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

2020 ◽  
Vol 45 (2) ◽  
pp. 97-124
Author(s):  
Pikul Puphasuk ◽  
Jeerayut Wetweerapong
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Systems Of Nonlinear Equations ◽  
Wide Range ◽  
Evolution Algorithm

AbstractDesigning an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at e ciency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and di culties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions.

scholarly journals Differential Evolution Algorithm with Self-Adaptive Population Resizing Mechanism

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Xu Wang ◽  
Shuguang Zhao
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Control Parameters ◽  
De Algorithm ◽  
Wide Range ◽  
Evolution Algorithm ◽  
Mutation Strategies ◽  
Nonparametric Statistical ◽  
Nonparametric Statistical Procedures ◽  
Self Adaptive

A differential evolution (DE) algorithm with self-adaptive population resizing mechanism, SapsDE, is proposed to enhance the performance of DE by dynamically choosing one of two mutation strategies and tuning control parameters in a self-adaptive manner. More specifically, more appropriate mutation strategies along with its parameter settings can be determined adaptively according to the previous status at different stages of the evolution process. To verify the performance of SapsDE, 17 benchmark functions with a wide range of dimensions, and diverse complexities are used. Nonparametric statistical procedures were performed for multiple comparisons between the proposed algorithm and five well-known DE variants from the literature. Simulation results show that SapsDE is effective and efficient. It also exhibits much more superiorresultsthan the other five algorithms employed in the comparison in most of the cases.

The way of value of Correlation of genomic DNA microsatellite loci and live weight of Chukchi reindeer

2020 ◽  
pp. 27-32
Author(s):  
V. Dodokhov ◽  
N. Pavlova ◽  
T. Rumyantseva ◽  
L. Kalashnikova
Keyword(s):  
Microsatellite Markers ◽  
Agricultural Production ◽  
Live Weight ◽  
Biodiversity Loss ◽  
Tribal Community ◽  
High Germination ◽  
Wide Range ◽  
The Mean ◽  
High Level ◽  
Dna Microsatellite

The article presents the genetic characteristic of the Chukchi reindeer breed. The object of the study was of the Chukchi reindeer. In recent years, the number of reindeer of the Chukchi breed has declined sharply. Reduced reindeer numbers could lead to biodiversity loss. The Chukchi breed of deer has good meat qualities, has high germination viability and is adapted in adverse tundra conditions of Yakutia. Herding of the Chukchi breed of deer in Yakutia are engaged only in the Nizhnekolymsky district. There are four generic communities and the largest of which is the agricultural production cooperative of nomadic tribal community «Turvaurgin», which was chosen to assess the genetic processes of breed using microsatellite markers: Rt6, BMS1788, Rt 30, Rt1, Rt9, FCB193, Rt7, BMS745, C 143, Rt24, OheQ, C217, C32, NVHRT16, T40, C276. It was found that microsatellite markers have a wide range of alleles and generally have a high informative value for identifying of genetic differences between animals and groups of animal. The number of identified alleles is one of the indicators of the genetic diversity of the population. The total number of detected alleles was 127. The Chukchi breed of deer is characterized by a high level of heterozygosity, and the random crossing system prevails over inbreeding in the population. On average, there were 7.9 alleles (Na) per locus, and the mean number of effective alleles (Ne) was 4.1. The index of fixation averaged 0.001. The polymorphism index (PIC) ranged from 0.217 to 0.946, with an average of 0.695.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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