Opin vísindi
Opin vísindi er varðveislusafn vísindaefnis og doktorsritgerða í opnum aðgangi á vegum íslenskra háskóla og Landsbókasafns Íslands - Háskólabókasafns.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Nýlega bætt við
Design and performance analysis of a compact planar mimo antenna for iot applications
(2021-12-01) Thiruvenkadam, Saminathan; Parthasarathy, Eswaran; Palaniswamy, Sandeep Kumar; Kumar, Sachin; Wang, Lulu; Department of Engineering
This article presents a quad-band multiple-input-multiple-output (MIMO) antenna for the Internet of Things (IoT) applications. The proposed antenna consists of four quar-ter-wavelength asymmetrical meandered radiators, microstrip feed lines, and modified ground planes. The antenna elements are arranged in a chiral pattern to improve isolation between them, with two radiators and two ground planes placed on the front side of the substrate and the other two on the back side. The MIMO antenna has an operating bandwidth (S11 ≤ −10 dB) of 1.76–1.84 GHz, 2.37–2.56 GHz, 3.23–3.68 GHz, and 5.34–5.84 GHz, covering GSM, WLAN, WiMAX, and 5G frequency bands. The isolation between the radiating elements is greater than 18 dB in the operating bands. The peak gain of the antenna is 3.6 dBi, and the envelope correlation coefficient (ECC) is less than 0.04. Furthermore, the proposed antenna is validated for IoT-based smart home (SH) applications. The prototype MIMO antenna is integrated with a commercially available ZigBee device, and the measured values are found to be consistent with the expected results. The proposed MIMO antenna could be a good candidate for IoT systems/modules due to its low profile, compact size, lightweight, and easy integration with wireless communication devices.
Design and Equivalent Circuit Model Extraction of a Fractal Slot-Loaded 3–40 GHz Super Wideband Antenna
(2024-11) Alamro, Wasan; Seet, Boon Chong; Wang, Lulu; Parthiban, Prabakar; Department of Engineering
In this paper, we present the design and equivalent circuit model (ECM) of a fractal slot-loaded super wideband (SWB) antenna for compact and high-performance applications operating in the 3–40 GHz range. The proposed antenna features a compact dimension of 40 × 35 × 1.57 mm³, a measured bandwidth ratio of 13:1, a peak gain of 9.7 dBi, an average radiation efficiency of 94%, and a low cross-polarization level across the entire bandwidth. The presented ECM is derived using transmission line theory and incorporates the individual behavior of each constituting element of the antenna. A dual sequential optimization approach is employed to determine the optimal element values. The ECM results show good agreement with both simulated and measured results in terms of the magnitude of reflection coefficient | (Formula presented.) | and both real and imaginary impedances with low mean absolute percentage errors of 4.9%, 7.5%, and 7.7%, respectively, demonstrating the model’s ability to accurately predict the antenna’s performance.
Design and Characterization of a Compact Four-Element Microstrip Array Antenna for WiFi-5/6 Routers
(2023) Paul, Liton Chandra; Ankan, Sarker Saleh Ahmed; Rani, Tithi; Jim, Md Tanvir Rahman; Karaaslan, Muharrem; Shezan, Sk A.; Wang, Lulu; Department of Engineering
The WiFi-5 band was the most popular WiFi band until the Federal Communications Commission (FCC) announced a new spectrum of 6 GHz WiFi (5.925-7.125 GHz) for unlicensed users. Our proposed work is about to cover both the 5 GHz and 6 GHz WiFi bands. These two bands have a great impact in the wireless communication field. A low-loss Rogers RT 5880 material is used as the substrate layer, which helps us to make the antenna compact (23×40×0.79 mm3) keeping a good performance profile over the latest high-speed WiFi-5/6 band. The proposed antenna covers a huge bandwidth (simulated BW: 2.85 GHz ranging from 4.50 to 7.35 GHz and measured BW: 2.83 GHz ranging from 4.50 to 7.33 GHz), which can be used for the latest WiFi-5 and WiFi-6 routers. The antenna also has omnidirectional properties. Besides that, the gain and directivity of the antenna are quite good, and the measured results buttress the simulated results. The presented different detail parametric studies indicate the antenna's optimization level, which is excellent. The minimum values of reflection coefficient and voltage standing wave ratio make it a compatible candidate for the implementation of high-speed WiFi-5/6 routers.
Demographic characteristics, gambling engagement, mental health, and associations with harmful gambling risk among UK Armed Forces serving personnel
(2025-12-01) Jones, Matthew; Champion, H.; Dighton, G.; Larcombe, J.; Fossey, M.; Dymond, S.; Department of Psychology
Introduction Harmful gambling negatively impacts individuals, families and communities. Growing international evidence indicates that the Armed Forces (AF) community may be at a comparatively higher risk of experiencing harm from gambling than the general population. The current study sought to identify general predictors of harmful gambling and gambling engagement among UK AF serving personnel (AFSP). Methods We conducted a cross-sectional, exploratory survey to identify associations between demographic factors, mental health, gambling engagement and gambling type in a sample (N=608) of AFSP. Results Most of the sample reported past-year gambling, with 23% having experienced harm. Male gender, younger age and lower educational attainment all predicted harmful gambling, as did mental health variables of prior generalised anxiety and post-traumatic stress symptomatology. Strategy-based gambling and online sports betting were also predictive of experiencing harm from gambling. Conclusions The risk of harm from gambling is associated with demographic, mental health and gambling engagement variables among AFSP. Better understanding of these predictors is important for the development of individualised treatment approaches for harmful gambling.
Deep Reinforcement Adversarial Learning against Botnet Evasion Attacks
(2020-12) Apruzzese, Giovanni; Andreolini, Mauro; Marchetti, Mirco; Venturi, Andrea; Colajanni, Michele; Department of Computer Science
As cybersecurity detectors increasingly rely on machine learning mechanisms, attacks to these defenses escalate as well. Supervised classifiers are prone to adversarial evasion, and existing countermeasures suffer from many limitations. Most solutions degrade performance in the absence of adversarial perturbations; they are unable to face novel attack variants; they are applicable only to specific machine learning algorithms. We propose the first framework that can protect botnet detectors from adversarial attacks through deep reinforcement learning mechanisms. It automatically generates realistic attack samples that can evade detection, and it uses these samples to produce an augmented training set for producing hardened detectors. In such a way, we obtain more resilient detectors that can work even against unforeseen evasion attacks with the great merit of not penalizing their performance in the absence of specific attacks. We validate our proposal through an extensive experimental campaign that considers multiple machine learning algorithms and public datasets. The results highlight the improvements of the proposed solution over the state-of-the-art. Our method paves the way to novel and more robust cybersecurity detectors based on machine learning applied to network traffic analytics.
Flokkar í Opnum vísindum
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- University of Iceland
- University of Akureyri
- Bifröst University
- Hólar University College
- IRIS
- Agricultural University of Iceland
- National and University Library of Iceland
- Iceland University of the Arts