Boosting Secondary-User Performance

2012 ◽  
pp. 289-300
Author(s):  
Terry N. Guo
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Critical Issue ◽  
Physical Layer ◽  
Machine Learning Algorithms ◽  
User Performance ◽  
Computing Power ◽  
Security Enhancement ◽  
Channel Characteristic ◽  
Unique Signature

This chapter addresses a few challenges and issues in developing Cognitive Radio Networks (CRNs), and provides unique solutions to enhance security at physical layer and to boost CRN computing power in a distributed manner. In this age of vast connectivity, network security becomes more and more prominent. In addition to the security means added at the upper layers, security can be further enhanced at physical layer. In particular, location based wideband channel characteristic as a unique signature can be utilized for security enhancement. Such a scheme is proposed and examined in different configurations. Lack of computing power is another critical issue, as CRN is expected to have more and more features. Instead of increasing onboard computing power, off-board computing resources can be connected to boost overall computing power. With increased computing power, the CRNs would be able to undertake computationally heavy tasks such as executing machine-learning algorithms and performing radio intrusion detection.

Big Data-Based Spectrum Sensing for Cognitive Radio Networks Using Artificial Intelligence

2020 ◽  
pp. 146-159 ◽  
Author(s):  
Suriya Murugan ◽  
Sumithra M. G.
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Spectrum Utilization ◽  
Candidate Solution ◽  
Learning Techniques ◽  
Efficient Data

Cognitive radio has emerged as a promising candidate solution to improve spectrum utilization in next generation wireless networks. Spectrum sensing is one of the main challenges encountered by cognitive radio and the application of big data is a powerful way to solve various problems. However, for the increasingly tense spectrum resources, the prediction of cognitive radio based on big data is an inevitable trend. The signal data from various sources is analyzed using the big data cognitive radio framework and efficient data analytics can be performed using different types of machine learning techniques. This chapter analyses the process of spectrum sensing in cognitive radio, the challenges to process spectrum data and need for dynamic machine learning algorithms in decision making process.

scholarly journals Invited perspectives: How machine learning will change flood risk and impact assessment

2020 ◽  
Vol 20 (4) ◽  
pp. 1149-1161
Author(s):  
Dennis Wagenaar ◽  
Alex Curran ◽  
Mariano Balbi ◽  
Alok Bhardwaj ◽  
Robert Soden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flood Risk ◽  
Learning Algorithms ◽  
Model Development ◽  
Remote Sensing Data ◽  
Machine Learning Algorithms ◽  
Process Models ◽  
Mitigation Measures ◽  
Response Preparation ◽  
Computing Power

Abstract. Increasing amounts of data, together with more computing power and better machine learning algorithms to analyse the data, are causing changes in almost every aspect of our lives. This trend is expected to continue as more data keep becoming available, computing power keeps improving and machine learning algorithms keep improving as well. Flood risk and impact assessments are also being influenced by this trend, particularly in areas such as the development of mitigation measures, emergency response preparation and flood recovery planning. Machine learning methods have the potential to improve accuracy as well as reduce calculating time and model development cost. It is expected that in the future more applications will become feasible and many process models and traditional observation methods will be replaced by machine learning. Examples of this include the use of machine learning on remote sensing data to estimate exposure and on social media data to improve flood response. Some improvements may require new data collection efforts, such as for the modelling of flood damages or defence failures. In other components, machine learning may not always be suitable or should be applied complementary to process models, for example in hydrodynamic applications. Overall, machine learning is likely to drastically improve future flood risk and impact assessments, but issues such as applicability, bias and ethics must be considered carefully to avoid misuse. This paper presents some of the current developments on the application of machine learning in this field and highlights some key needs and challenges.

scholarly journals Disturbance Simulation in the Packaging Process of Confectionary Using Virtual Commissioning

Machines ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 19
Author(s):  
Johanna Wolf ◽  
Sebastian Carsch ◽  
Clemens Troll ◽  
Jens-Peter Majschak
Keyword(s):  
Machine Learning ◽  
Test Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Visual Effects ◽  
Computing Power ◽  
Virtual Commissioning ◽  
Sensor Signals ◽  
Assistance Systems ◽  
Sufficient Test

Operator assistance systems can help to reduce disturbance-related machine downtime in food production and packaging processes, especially when combined with machine learning algorithms. These assistance systems analyze the available sensor signals of the process control over time to help operators identify the causes of disturbances. Training such systems requires sufficient test data, which often are hardly available. Thus, this paper presents a study to investigate how test data for teaching machine learning algorithms can be generated by numerical simulation. The potential of using virtual commissioning (VC) software for simulating disturbances of discrete processes is examined, considering the example of a friction and collision-afflicted sub-process from an intermitting wrapping machine for confectionary. In this study the software industrialPhysics (iP) is analyzed regarding accuracy of static and dynamic friction and restitution. The values are verified by setting up virtual substitute tests and comparing the results with analytically determined values. Subsequently, prerecorded disturbances are classified, and seven selected elements are simulated in VC software, recording visual effects and switching the characteristics of sensors. The verification shows that VC software is generally adequate for the assigned task. Restrictions occur regarding the computing power required of the built-in physics engine and the resulting reduction of the machine to be simulated.

scholarly journals A Handy Open-Source Application Based on Computer Vision and Machine Learning Algorithms to Count and Classify Microplastics

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2104
Author(s):  
Carmine Massarelli ◽  
Claudia Campanale ◽  
Vito Felice Uricchio
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Learning Algorithm ◽  
Unsupervised Classification ◽  
Critical Issue ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Environmental Matrices ◽  
Traditional Research ◽  
Nearest Neighbours

Microplastics have recently been discovered as remarkable contaminants of all environmental matrices. Their quantification and characterisation require lengthy and laborious analytical procedures that make this aspect of microplastics research a critical issue. In light of this, in this work, we developed a Computer Vision and Machine-Learning-based system able to count and classify microplastics quickly and automatically in four morphology and size categories, avoiding manual steps. Firstly, an early machine learning algorithm was created to count and classify microplastics. Secondly, a supervised (k-nearest neighbours) and an unsupervised classification were developed to determine microplastic quantities and properties and discover hidden information. The machine learning algorithm showed promising results regarding the counting process and classification in sizes; it needs further improvements in visual class classification. Similarly, the supervised classification demonstrated satisfactory results with accuracy always greater than 0.9. On the other hand, the unsupervised classification discovered the probable underestimation of some microplastic shape categories due to the sampling methodology used, resulting in a useful tool for bringing out non-detectable information by traditional research approaches adopted in microplastic studies. In conclusion, the proposed application offers a reliable automated approach for microplastic quantification based on counts of particles captured in a picture, size distribution, and morphology, with considerable prospects in method standardisation.

scholarly journals Intelligent Cognitive Radio Ad-Hoc Network: Planning, Learning and Dynamic Configuration

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 254
Author(s):  
Kwang-Eog Lee ◽  
Joon Goo Park ◽  
Sang-Jo Yoo
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Network Planning ◽  
Machine Learning Algorithms ◽  
System Structure ◽  
System Model ◽  
Dynamic Configuration ◽  
Network Operation

Cognitive radio (CR) is an adaptive radio technology that can automatically detect available channels in a wireless spectrum and change transmission parameters to improve the radio operating behavior. A CR ad-hoc network (CRAHN) should be able to coexist with primary user (PU) systems and other CR secondary systems without causing harmful interference to licensed PUs as well as dynamically configure autonomous and decentralized networks. Therefore, an intelligent system structure is required for efficient spectrum use. In this paper, we present a learning-based distributed autonomous CRAHN network system model for network planning, learning, and dynamic configuration. Based on the system model, we propose machine learning-based optimization algorithms for spectrum sensing, cluster-based ad-hoc network configuration, and context-aware signal classification. Using the sensing engine and the cognitive engine, the surrounding spectrum usage and the neighbor network operation status can be analyzed. The proposed policy engine can create network operation policies for the dynamically changing surrounding wireless environment, detect policy conflicts, and infer the optimal policy for the current situation. The decision engine finally determines and configures the optimal CRAHN configuration parameters through cooperation with a learning engine, in which we implement the proposed machine-learning algorithms. The simulation results show that the proposed machine-learning CRAHN algorithms can construct CR cluster networks that have a long network lifetime and high spectrum utility. Additionally, with high signal context recognition performance, we can ensure coexistence with neighboring systems.

scholarly journals Invited perspectives: How machine learning will change flood risk and impact assessment

2019 ◽  
Author(s):  
Dennis Wagenaar ◽  
Alex Curran ◽  
Mariano Balbi ◽  
Alok Bhardwaj ◽  
Robert Soden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Impact Assessment ◽  
Flood Risk ◽  
Learning Algorithms ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Process Models ◽  
Mitigation Measures ◽  
Response Preparation ◽  
Computing Power

Abstract. Increasing amounts of data, together with more computing power and better machine learning algorithms to analyse the data are causing changes in almost every aspect of our lives. This trend is expected to continue as more data becomes available, computing power increases and machine learning algorithms improve. Flood risk and impact assessments are also being influenced by this trend, particularly in areas such as the development of mitigation measures, emergency response preparation, and flood recovery planning. Machine learning methods have the potential to improve accuracy as well as reduce calculating time and model development cost. It is expected that in the future more applications become feasible and many process models and traditional observation methods will be replaced by machine learning. Examples of this include the use of machine learning on remote sensing data to estimate exposure or on social media data to improve flood response. Some improvements may require new data collection efforts, such as for the modelling of flood damages or defence failures. In other fields, machine learning may not be suitable or should be applied complementary to process models, for example in hydrodynamic applications. Overall, machine learning is likely to drastically improve future flood risk and impact assessments, but issues such as applicability, bias and ethics must be considered carefully. This paper presents some of the current developments on the application of machine learning for flood risk and impact assessment, and highlights some key needs and challenges.

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