Design of compact ultra wideband antenna for microwave medical imaging application

Othman, Ahmad Azam; Shaari, Nur Ishmah Sabirah; Shairi, N. A.; Zakaria, Zahriladha

doi:10.11591/ijeecs.v15.i3.pp1197-1202

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…In this system, to determine intrusion, the intrusion detection system runs independently on the individual node. All decisions made about a particular activity depend solely on information collected at its own node, as there is no collaboration between nodes in the network [16]. Therefore, there is no transfer of information.…”

Section: The Standalone Architecturementioning

confidence: 99%

A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

Laqtib

Yassini

Hasnaoui

2020

IJECE

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Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network Inception-CNN, Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning methods in MANET.

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Section: The Standalone Architecturementioning

confidence: 99%

A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

Laqtib

Yassini

Hasnaoui

2020

IJECE

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“…The comparison graphs of different mobility models are obtained in Figures 3, 4 and 5. It shows the impact of mobility models on OLSR based [27]on three performance metrics: Packet Delivery Ratio (PDR), Throughput and End-to-End delay. with varying number of nodes connected [28][29][30][31].…”

Section: Scenario 1: Varying the Number Of Nodesmentioning

confidence: 99%

Link-state QoS routing protocol under various mobility models

Laqtib¹,

Yassini²,

Hasnaoui

2019

IJEECS

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<p>Mobile Ad Hoc Network (MANET) consists of a group of mobile or wireless nodes that are placed randomly and dynamically that causes the continual change between nodes. A mobility model attempts to mimic the movement of real mobile nodes that change the speed and direction with time. The mobility model that accurately represents the characteristics of the mobile nodes in an ad hoc network is the key to examine whether a given protocol. The aim of this paper is to compare the performance of four different mobility models (i.e. Random Waypoint, Random Direction, Random walk, and Steady-State Random Waypoint) in MANET. These models were configured with Optimized Link State Routing (OLSR) protocol under three QoS (Quality of Service) <a title="Learn more about Metrics" href="https://www.sciencedirect.com/topics/engineering/metrics">metrics</a> such as the Packet Delivery Ratio (PDR), Throughput, End-to-End delay. The simulation results show the effectiveness of Steady-State Random Waypoint Mobility Models and encourage further investigations to extend it in order to guarantee other QoS requirements.</p>

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“…Where εreff can be found and given by the equation below so represented of the dielectric substrate [11][12][13][14][15].…”

Section: Size and Position Of The Slotsmentioning

confidence: 99%

Calculate the optimum slot area of elliptical microstrip antenna for mobile application

Thaher

Jassim

2019

IJEECS

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<p>Designing and analysis the antenna parameter, for the calculation optimum the slot area for a new broadband elliptical antenna for mobile application, to enhancement impedance bandwidth. Two equations have been obtained, representing the first relationship between the area of the slot and the area of the patch, and the second represents the area of the slot to the wavelength of the band frequency. The antenna was implemented and gets simulation result using CST software and manufacturing on an FR-4 material with =4.3, tan (δ) =0.002 and the feeding line has a characteristic impedance of 50Ω. The proposed antenna is operating in the bandwidth 24GHz and the gain (6.8) dBi.</p>

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Design of compact ultra wideband antenna for microwave medical imaging application

Cited by 12 publications

References 10 publications

A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

Link-state QoS routing protocol under various mobility models

Calculate the optimum slot area of elliptical microstrip antenna for mobile application

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