scholarly journals Special Issue on “Graph Algorithms and Applications”

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 150
Author(s):  
Serafino Cicerone ◽  
Gabriele Di Stefano
Keyword(s):  
Computational Complexity ◽  
Computer Network ◽  
Real Life ◽  
Data Communication ◽  
Image Data ◽  
Biological Data ◽  
Special Issue ◽  
Graph Models ◽  
Research Fields ◽  
Data Communication Network

The mixture of data in real life exhibits structure or connection property in nature. Typical data include biological data, communication network data, image data, etc. Graphs provide a natural way to represent and analyze these types of data and their relationships. For instance, more recently, graphs have found new applications in solving problems for emerging research fields such as social network analysis, design of robust computer network topologies, frequency allocation in wireless networks, and bioinformatics. Unfortunately, the related algorithms usually suffer from high computational complexity, since some of these problems are NP-hard. Therefore, in recent years, many graph models and optimization algorithms have been proposed to achieve a better balance between efficacy and efficiency. The aim of this Special Issue is to provide an opportunity for researchers and engineers from both academia and the industry to publish their latest and original results on graph models, algorithms, and applications to problems in the real world, with a focus on optimization and computational complexity.

scholarly journals Investigating the Potential of Network Optimization for a Constrained Object Detection Problem

2021 ◽  
Vol 7 (4) ◽  
pp. 64
Author(s):  
Tanguy Ophoff ◽  
Cédric Gullentops ◽  
Kristof Van Beeck ◽  
Toon Goedemé
Keyword(s):  
Computational Complexity ◽  
Object Detection ◽  
Network Optimization ◽  
Real Life ◽  
Optimization Techniques ◽  
Training Data ◽  
Single Shot ◽  
Standard Object ◽  
Number Of Classes

Object detection models are usually trained and evaluated on highly complicated, challenging academic datasets, which results in deep networks requiring lots of computations. However, a lot of operational use-cases consist of more constrained situations: they have a limited number of classes to be detected, less intra-class variance, less lighting and background variance, constrained or even fixed camera viewpoints, etc. In these cases, we hypothesize that smaller networks could be used without deteriorating the accuracy. However, there are multiple reasons why this does not happen in practice. Firstly, overparameterized networks tend to learn better, and secondly, transfer learning is usually used to reduce the necessary amount of training data. In this paper, we investigate how much we can reduce the computational complexity of a standard object detection network in such constrained object detection problems. As a case study, we focus on a well-known single-shot object detector, YoloV2, and combine three different techniques to reduce the computational complexity of the model without reducing its accuracy on our target dataset. To investigate the influence of the problem complexity, we compare two datasets: a prototypical academic (Pascal VOC) and a real-life operational (LWIR person detection) dataset. The three optimization steps we exploited are: swapping all the convolutions for depth-wise separable convolutions, perform pruning and use weight quantization. The results of our case study indeed substantiate our hypothesis that the more constrained a problem is, the more the network can be optimized. On the constrained operational dataset, combining these optimization techniques allowed us to reduce the computational complexity with a factor of 349, as compared to only a factor 9.8 on the academic dataset. When running a benchmark on an Nvidia Jetson AGX Xavier, our fastest model runs more than 15 times faster than the original YoloV2 model, whilst increasing the accuracy by 5% Average Precision (AP).

Security Threats and Risks of Intelligent Building Systems

2013 ◽  
pp. 1-16
Author(s):  
David Brooks
Keyword(s):  
Open Data ◽  
Data Communication ◽  
Mitigation Strategies ◽  
Security Threats ◽  
Security Risk ◽  
Protection Measures ◽  
Intelligent Buildings ◽  
Data Communication Network ◽  
Security Risk Management ◽  
Physical Access

Intelligent Buildings (IB) are facility-wide systems that connect, control, and monitor the plant and equipment of a facility. The aim of IB is to ensure a facility is more efficient, productive, and safe, at a reduced cost. A typical IB integrates diverse subsystems into a common and open data communication network, using both software and hardware; however, IBs suffer from diverse generic vulnerabilities. Identified vulnerabilities may include limited awareness of security threats and system vulnerabilities, physical access to parts of the system, compromise of various networks, insertion of foreign devices, lack of physical security, and reliance on utility power. IB risks are contextual and aligned with the threat exposure of the facility. Nevertheless, there are generic mitigation strategies that can be put in place to protect IB systems. Strategies include threat-driven security risk management, an understanding of system criticality, greater integration of departments, network isolation, layered protection measures, and increased security awareness.

Towards the Real-Life EEG Applications

2019 ◽  
pp. 305-317
Author(s):  
Guangyi Ai
Keyword(s):  
Data Analysis ◽  
Real Life ◽  
The Real ◽  
Life Condition ◽  
Research Fields ◽  
Eeg Data ◽  
Artifact Suppression ◽  
Electroencephalogram Eeg ◽  
Real Life Condition ◽  
Brain Monitoring

Electroencephalogram (EEG) is one of the most popular approaches for brain monitoring in many research fields. While the detailed working flows for in-lab neuroscience-targeted EEG experiments conditions have been well established, carrying out EEG experiments under a real-life condition can be quite confusing because of various practical limitations. This chapter gives a brief overview of the practical issues and techniques that help real-life EEG experiments come into being, and the well-known artifact problems for EEG. As a guideline for performing a successful EEG data analysis with the low-electrode-density limitation of portable EEG devices, recently proposed techniques for artifact suppression or removal are briefly surveyed as well.

Digital Image Steganography

2017 ◽  
pp. 324-346 ◽  
Author(s):  
Chitra A. Dhawale ◽  
Naveen D. Jambhekar
Keyword(s):  
Communication Network ◽  
Performance Analysis ◽  
Information Security ◽  
Digital Image ◽  
Data Communication ◽  
Image Steganography ◽  
Digital Data ◽  
Confidential Information ◽  
Unauthorized Access ◽  
Data Communication Network

Digital data transmitted over the insecure communication can be prone to attacks. Intruders try various attacks to unauthorized access of the confidential information. The Steganography is such as security system that provide the protection to the images, text and other type of data digitally transferred through the data communication network. This chapter elaborates the basics of Digital Image Steganographic techniques from ancient era to digital edge, types of images used for the steganography, payload used for the steganography, various attacks and different algorithms that can provide the information security. The performance analysis of the various Digital Image Steganographic algorithms are discussed. The current applications and their necessities are discussed in this chapter.

scholarly journals Permanent Magnet Synchronous Machines

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2830 ◽  
Author(s):  
Sandra Eriksson
Keyword(s):  
Permanent Magnet ◽  
Subject Area ◽  
Synchronous Machines ◽  
Permanent Magnet Machines ◽  
Special Issue ◽  
Permanent Magnet Synchronous Machines ◽  
Hydro Power ◽  
Brushless Dc Motors ◽  
Research Fields ◽  
Wide Range

Interest in permanent magnet synchronous machines (PMSMs) is continuously increasing worldwide, especially with the increased use of renewable energy and electrification of transports. This special issue contains the successful invited submissions of fifteen papers to a Special Issue of Energies on the subject area of “Permanent Magnet Synchronous Machines”. The focus is on permanent magnet synchronous machines and the electrical systems they are connected to. The presented work represents a wide range of areas. Studies of control systems, both for permanent magnet synchronous machines and for brushless DC motors, are presented and experimentally verified. Design studies of generators for wind power, wave power and hydro power are presented. Finite element method simulations and analytical design methods are used. The presented studies represent several of the different research fields on permanent magnet machines and electric drives.

scholarly journals The Embeddedness of Social Sciences and Economics in Research on Resources

Resources ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 15
Author(s):  
Juan Uribe-Toril ◽  
José Luis Ruiz-Real ◽  
Jaime de Pablo Valenciano
Keyword(s):  
Social Sciences ◽  
State Of The Art ◽  
Local Development ◽  
The State ◽  
Special Issue ◽  
Continuous Review ◽  
The Past ◽  
Research Fields ◽  
Wide Range ◽  
Knowledge Area

Sustainability, local development, and ecology are keywords that cover a wide range of research fields in both experimental and social sciences. The transversal nature of this knowledge area creates synergies but also divergences, making a continuous review of the existing literature necessary in order to facilitate research. There has been an increasing number of articles that have analyzed trends in the literature and the state-of-the-art in many subjects. In this Special Issue of Resources, the most prestigious researchers analyzed the past and future of Social Sciences in Resources from an economic, social, and environmental perspective.

scholarly journals Privacy-Preserving Broker-ABE Scheme for Multiple Cloud-Assisted Cyber Physical Systems

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5463 ◽  
Author(s):  
Po-Wen Chi ◽  
Ming-Hung Wang
Keyword(s):  
Data Privacy ◽  
Service Providers ◽  
Real Life ◽  
Data Communication ◽  
Physical Space ◽  
Cloud Service ◽  
Cyber Physical Systems ◽  
Privacy Preserving ◽  
Sensor Data ◽  
Physical Systems

Cloud-assisted cyber–physical systems (CCPSs) integrate the physical space with cloud computing. To do so, sensors on the field collect real-life data and forward it to clouds for further data analysis and decision-making. Since multiple services may be accessed at the same time, sensor data should be forwarded to different cloud service providers (CSPs). In this scenario, attribute-based encryption (ABE) is an appropriate technique for securing data communication between sensors and clouds. Each cloud has its own attributes and a broker can determine which cloud is authorized to access data by the requirements set at the time of encryption. In this paper, we propose a privacy-preserving broker-ABE scheme for multiple CCPSs (MCCPS). The ABE separates the policy embedding job from the ABE task. To ease the computational burden of the sensors, this scheme leaves the policy embedding task to the broker, which is generally more powerful than the sensors. Moreover, the proposed scheme provides a way for CSPs to protect data privacy from outside coercion.

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