scholarly journals Street Sign Recognition Using Histogram of Oriented Gradients and Artificial Neural Networks

2019 ◽  
Vol 5 (4) ◽  
pp. 44 ◽  
Author(s):  
Kh Islam ◽  
Sudanthi Wijewickrema ◽  
Ram Raj ◽  
Stephen O’Leary
Keyword(s):  
Global Positioning System ◽  
Autonomous Vehicle ◽  
Region Of Interest ◽  
Machine Learning Techniques ◽  
Street Sign ◽  
Sign Recognition ◽  
Learning Techniques ◽  
Global Positioning ◽  
High Level ◽  
Autonomous Vehicle Navigation

Street sign identification is an important problem in applications such as autonomous vehicle navigation and aids for individuals with vision impairments. It can be especially useful in instances where navigation techniques such as global positioning system (GPS) are not available. In this paper, we present a method of detection and interpretation of Malaysian street signs using image processing and machine learning techniques. First, we eliminate the background from an image to segment the region of interest (i.e., the street sign). Then, we extract the text from the segmented image and classify it. Finally, we present the identified text to the user as a voice notification. We also show through experimental results that the system performs well in real-time with a high level of accuracy. To this end, we use a database of Malaysian street sign images captured through an on-board camera.

scholarly journals PALM VEIN AUTHENTICATION TECHNOLOGY

2020 ◽  
Vol 6 (12) ◽  
pp. 6-10
Author(s):  
Jagruti Jain ◽  
Chitra Desai ◽  
Mrunali Chavan
Keyword(s):  
Region Of Interest ◽  
The Body ◽  
Machine Learning Techniques ◽  
Vein Pattern ◽  
Learning Techniques ◽  
Benchmark Database ◽  
Vein Recognition ◽  
Palm Vein ◽  
High Level ◽  
Very High

Palm vein authentication has high level of accuracy because it is located inside the body and does not change over the life and cannot be stolen. These papers present an analysis of palm vein pattern recognition algorithms, techniques, methodologies and system. It discusses the technical aspects of recent approaches for the following processes; detection of region of interest (ROI), segment of palm vein pattern, features extraction, and matching. The results show that, there is no benchmark database exists for palm vein recognition. For all processes, there are many machine learning techniques with very high accuracy.

Intersession reliability of GPS-based and accelerometer-based physical variables in small-sided games with and without the offside rule

2021 ◽  
pp. 175433712098764
Author(s):  
Igor Junio de Oliveira Custódio ◽  
Gibson Moreira Praça ◽  
Leandro Vinhas de Paula ◽  
Sarah da Glória Teles Bredt ◽  
Fabio Yuzo Nakamura ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Global Positioning System ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Physical Demands ◽  
Intraclass Correlation Coefficients ◽  
Total Distance ◽  
Global Positioning ◽  
High Level

This study aimed to analyze the intersession reliability of global positioning system (GPS-based) distances and accelerometer-based (acceleration) variables in small-sided soccer games (SSG) with and without the offside rule, as well as compare variables between the tasks. Twenty-four high-level U-17 soccer athletes played 3 versus 3 (plus goalkeepers) SSG in two formats (with and without the offside rule). SSG were performed on eight consecutive weeks (4 weeks for each group), twice a week. The physical demands were recorded using a GPS with an embedded triaxial accelerometer. GPS-based variables (total distance, average speed, and distances covered at different speeds) and accelerometer-based variables (Player Load™, root mean square of the acceleration recorded in each movement axis, and the root mean square of resultant acceleration) were calculated. Results showed that the inclusion of the offside rule reduced the total distance covered (large effect) and the distances covered at moderate speed zones (7–12.9 km/h – moderate effect; 13–17.9 km/h – large effect). In both SSG formats, GPS-based variables presented good to excellent reliability (intraclass correlation coefficients – ICC > 0.62) and accelerometer-based variables presented excellent reliability (ICC values > 0.89). Based on the results of this study, the offside rule decreases the physical demand of 3 versus 3 SSG and the physical demands required in these SSG present high intersession reliability.

scholarly journals Running Performance of High-Level Soccer Player Positions Induces Significant Muscle Damage and Fatigue Up to 24 h Postgame

2021 ◽  
Vol 12 ◽  
Author(s):  
Lucas Albuquerque Freire ◽  
Michele Andrade de Brito ◽  
Natã Sant’anna Esteves ◽  
Márcio Tannure ◽  
Maamer Slimani ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Professional Soccer ◽  
Global Positioning ◽  
Recovery Strategies ◽  
Soccer Teams ◽  
Lower Load ◽  
Response And Recovery ◽  
High Level ◽  
The Impact ◽  
Player Positions

This study aimed to determine the impact of a soccer game on the creatine kinase (Ck) response and recovery and the specific Global Positioning System (GPS)-accelerometry-derived performance analysis during matches and comparing playing positions. A sample composed of 118 observations of 24 professional soccer teams of the Brazil League Serie A was recruited and classified according to playing positions, i.e., Left/Right Defenders (D = 30, age: 25.2 ± 5.8 years, height: 187 ± 5.5 cm, weight: 80 ± 5.8 kg), Offensive Midfielders (OM = 44, age: 25.1 ± 0.2 years, height: 177 ± 0.3 cm, weight: 73 ± 1.2 kg), Forwards (F = 9, age: 25.1 ± 0.2 years, height: 176.9 ± 4.3 cm, weight: 74.5 ± 2.1 kg), Left/Right Wingers (M = 23, age: 24.5 ± 0.5 years, height: 175 ± 1.1 cm, weight: 74 ± 4.4 kg), and Strikers (S = 12, age: 28 ± 0.2 years, height: 184 ± 1.0 cm, weight: 80 ± 1.4 kg). Blood Ck concentration was measured pre-, immediately post-, and 24 h postgame, and the GPS-accelerometry parameters were assessed during games. Findings demonstrated that Ck concentrations were higher at all postgame moments when compared with pregame, with incomplete recovery markers being identified up to 24 h after the game (range: 402–835 U/L). Moreover, Midfielders (108.6 ± 5.6 m/min) and Forwards (109.1 ± 8.3 m/min) had a higher relative distance vs. other positions (100.9 ± 10.1 m/min). Strikers [8.2 (8.1, 9.05) load/min] and Defenders [8.45 (8, 8.8) load/min] demonstrated lower load/min than Wingers [9.5 (9.2, 9.8) load/min], Midfielders [10.6 (9.9, 11.67) load/min], and Forwards [11 (10.65, 11, 15) load/min]. These results could be used to adopt specific training programs and recovery strategies after match according to the playing positions.

scholarly journals Adaptive Model for Credit Card Fraud Detection

2020 ◽  
Vol 14 (03) ◽  
pp. 54
Author(s):  
Imane Sadgali ◽  
Naoual Sael ◽  
Faouzia Benabbou
Keyword(s):  
Credit Card ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Adaptive Model ◽  
Credit Card Fraud ◽  
New Methods ◽  
Learning Techniques ◽  
Multi Level ◽  
Set Up ◽  
High Level

<p>While the flow of banking transactions is increasing, the risk of credit card fraud is becoming greater particularly with the technological revolution that we know, fraudulent are improve and always find new methods to deal with the preventive measures that financial systems set up. Several studies have proposed predictive models for credit card fraud detection based on different machine learning techniques. In this paper, we present an adaptive approach to credit card fraud detection that exploits the performance of the techniques that have given high level of accuracy and consider the type of transaction and the client's profile. Our proposition is a multi-level framework, which encompasses the banking security aspect, the customer profile and the profile of the transaction itself.</p>

Variations of the Precipitation Water Vapor by Using GPS Data Triggered Raining in the Summer of Thailand

2015 ◽  
Vol 804 ◽  
pp. 279-282
Author(s):  
Nithiwatthn Choosakul
Keyword(s):  
Water Vapor ◽  
Global Positioning System ◽  
Gps Data ◽  
Positioning System ◽  
Global Positioning ◽  
Precipitation Water ◽  
High Level

The variation of water vapor can be detected from the Global Positioning System (GPS) data. The GPS signal was delayed when propagated through the wet atmosphere. The delayed signal can be retrieved into Precipitation Water Vapor (PWV) data. The GPS data of CUSV station from 2009 to 2012 were used in this research. The results showed that the PWV varied during the summer of Thailand. The PWV were slightly increased from 20 mm at the beginning of the season to 40 mm at the end of season. The increased PWV data were shown as linear line. A slope of the linear line may relate with the amount of the cumulative rain in the season. The steeper line might relate to the great number of raining in the end of the season, otherwise, the fairly gradual line might relate to the raining at any time in the season. The high level of PWV up to around 33 mm could induce the rain in the summer of Thailand.

Mining for Creativity: Determining the Creativity of Ideas Through Data Mining Techniques

2017 ◽  
Author(s):  
Christine A. Toh ◽  
Elizabeth M. Starkey ◽  
Conrad S. Tucker ◽  
Scarlett R. Miller
Keyword(s):  
Machine Learning ◽  
Design Research ◽  
Machine Learning Techniques ◽  
Numeric Model ◽  
Design Creativity ◽  
Large Sets ◽  
Learning Techniques ◽  
Ideation Methods ◽  
High Level ◽  
Design Ideas

The emergence of ideation methods that generate large volumes of early-phase ideas has led to a need for reliable and efficient metrics for measuring the creativity of these ideas. However, existing methods of human judgment-based creativity assessments, as well as numeric model-based creativity assessment approaches suffer from low reliability and prohibitive computational burdens on human raters due to the high level of human input needed to calculate creativity scores. In addition, there is a need for an efficient method of computing the creativity of large sets of design ideas typically generated during the design process. This paper focuses on developing and empirically testing a machine learning approach for computing design creativity of large sets of design ideas to increase the efficiency and reliability of creativity evaluation methods in design research. The results of this study show that machine learning techniques can predict creativity of ideas with relatively high accuracy and sensitivity. These findings show that machine learning has the potential to be used for rating the creativity of ideas generated based on their descriptions.

scholarly journals ORB-SLAM accelerated on heterogeneous parallel architectures

2021 ◽  
Vol 229 ◽  
pp. 01055
Author(s):  
Ayoub Mamri ◽  
Mohamed Abouzahir ◽  
Mustapha Ramzi ◽  
Rachid Latif
Keyword(s):  
Autonomous Vehicle ◽  
Computational Time ◽  
Heterogeneous Architecture ◽  
Ongoing Research ◽  
Efficient System ◽  
Software Optimization ◽  
Slam Algorithm ◽  
High Level ◽  
Autonomous Vehicle Navigation ◽  
Robotic Application

SLAM algorithm permits the robot to cartography the desired environment while positioning it in space. It is a more efficient system and more accredited by autonomous vehicle navigation and robotic application in the ongoing research. Except it did not adopt any complete end-to-end hardware implementation yet. Our work aims to a hardware/software optimization of an expensive computational time functional block of monocular ORB-SLAM2. Through this, we attempt to implement the proposed optimization in FPGA-based heterogeneous embedded architecture that shows attractive results. Toward this, we adopt a comparative study with other heterogeneous architecture including powerful embedded GPGPU (NVIDIA Tegra TX1) and high-end GPU (NVIDIA GeForce 920MX). The implementation is achieved using high-level synthesis-based OpenCL for FPGA and CUDA for NVIDIA targeted boards.

scholarly journals Convolutional Neural Network Computation for Autonomous Vehicle

2020 ◽  
pp. 368-372
Author(s):  
Aires Da Conceicao ◽  
Sheshang D. Degadwala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Autonomous Vehicle ◽  
Human Interaction ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Robust Feature Extraction

Self-driving vehicle is a vehicle that can drive by itself it means without human interaction. This system shows how the computer can learn and the over the art of driving using machine learning techniques. This technique includes line lane tracker, robust feature extraction and convolutional neural network.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2021 ◽  
pp. 358-386
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

scholarly journals Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3113 ◽  
Author(s):  
Miguel Ángel Antón ◽  
Joaquín Ordieres-Meré ◽  
Unai Saralegui ◽  
Shengjing Sun
Keyword(s):  
Ambient Intelligence ◽  
Real Life ◽  
Machine Learning Techniques ◽  
Transfer Of Knowledge ◽  
Noise Levels ◽  
Non Invasive ◽  
Learning Techniques ◽  
Potential Benefits ◽  
High Level

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.

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