Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/203

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Cognitive Radio Networks for Smart Grid Communications Potential Applications, Protocols, and Research Challenges
(CRC PRESS-TAYLOR & FRANCIS GROUP6000 BROKEN SOUND PARKWAY NW, STE 300, BOCA RATON, FL 33487-2742 USA, 2016) Kogias, Dimitris; Tuna, Gurkan; Gungor, Vehbi Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Gungor, Vehbi Cagri
Cognitive Radio Networks for Smart Grid Communications Potential Applications, Protocols, and Research Challenges
Communications Technologies for Smart Grid Applications: A Review of Advances and Challenges
(IGI Global, 2022) Tuna, Gurkan; Daş, Resul; Gungor, Vehbi Cagri; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Gungor, Vehbi Cagri
Smart grid is a modern power grid infrastructure for improved efficiency, reliability, and safety, with smooth integration of renewable and alternative energy sources, through automated control and modern communications technologies. The smart grid offers several advantages over traditional power grids such as reduced operational costs and opening new markets to utility providers, direct communication with customer premises through advanced metering infrastructure, self-healing in case of power drops or outage, providing security against several types of attacks, and preserving power quality by increasing link quality. Typically, a heterogeneous set of networking technologies is found in the smart grid. In this chapter, smart grid communications technologies along with their advantages and disadvantages are explained. Moreover, research challenges and open research issues are provided.
Energy harvesting and battery technologies for powering wireless sensor networks
(ELSEVIER, 2016) Tuna, Gürkan; Güngör, Vehbi Çağrı; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Güngör, Vehbi Çağrı
Due to the advances in wireless sensor networks (WSNs), factory and plant process automation systems are being reinvented. WSN-based industrial applications often cost much less than wired networks in both the short and long terms; automation engineers are empowering existing solutions with the new capabilities of WSNs. On the other hand, since industrial wireless sensor networks (IWSNs) consist of thousands of nodes, the problem of powering the nodes is critical. Power to the nodes is usually provided through primary batteries and this necessitates replacement when the batteries are depleted. However, the replacement may not be cost-effective or even feasible in most industrial applications.Though advancements in integrated circuit technologies help in saving more energy by leading to lower energy consumption levels, they do not eliminate the use of battery power. In this regard, energy harvesting technologies play a key role in extending the battery lifetime of the nodes. Wireless sensor nodes within industrial plants can operate from energy harvested from available energy sources such as heat, mechanical motion or vibration, indoor lighting, electromagnetic fields, and air flow. In this chapter, a review of existing energy storage technologies and various energy-harvesting techniques is given. The chapter then discusses open research issues in these topics.
Integrating Gene Ontology Based Grouping and Ranking into the Machine Learning Algorithm for Gene Expression Data Analysis
(SPRINGER INTERNATIONAL PUBLISHING AGGEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND, 2021) Yousef, Malik; Sayici, Ahmet; Bakir-Gungor, Burcu; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Sayici, Ahmet; Bakir-Gungor, Burcu
Recent advances in the high throughput technologies resulted in the production of large gene expression data sets for several phenotypes. Via comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc., one could identify biomarkers. As opposed to traditional gene selection approaches, integrative gene selection approaches incorporate domain knowledge from external biological resources during gene selection, which improves interpretability and predictive performance. In this respect, Gene Ontology provides cellular component, molecular function and biological process terms for the products of each gene. In this study, we present Gene Ontology based feature selection approach for gene expression data analysis. In our approach, we used the ontology information as grouping (term) information and embedded this information into a machine learning algorithm for selecting the most significant groups (terms) of ontology. Those groups are used to build the machine learning model in order to perform the classification task. The output of the tool is a significant ontology group for the task of 2-class classification applied on the gene expression data. This knowledge allows the researcher to perform more advanced gene expression analyses. We tested our approach on 8 different gene expression datasets. In our experiments, we observed that the tool successfully found the significant Ontology terms that would be used as a classification model. We believe that our tool will help the geneticists to identify affected genes in transcriptomic data and this information could enable the design of platforms to assist diagnosis, to assess patients' prognoses, and to create patient treatment plans.
Realizing the wireless technology in internet of things (IoT)
(Springer Singapore, 2018) Kogias, Dimitrios G.; Michailidis, Emmanouel T.; Tuna, Gurkan; Gungor, Vehbi Cagri; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Gungor, Vehbi Cagri
The evolution of the Internet of Things (IoT) has been highly based on the advances on wireless communications and sensing capabilities of smart devices, along with a, still increasing, number of applications that are being developed which manage to cover various small and more important aspects of every people's life. This chapter aims at presenting the wireless technologies and protocols that are used for the IoT communications, along with the main architectures and middleware that have been proposed to serve and enhance the IoT capabilities and increase its efficiency. Finally, since the generated data that are spread in an IoT ecosystem might include sensitive information (e.g., personal medical data by sensors), we will also discuss the security and privacy hazards that are introduced from the advances in the development and application of an IoT environment.

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