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您的检索式：作者名="Akram Alomainy"
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| 1 | Experimental Characteriza-tion of UWB on-body Radio Channel in Indoor EnvironmentConsidering Different Antennas 显示文摘 | Andrea Sani Akram Alomainy | 2010 | IEEE Transactions onAntennas and Propagation2010，58，4： | 1 |
| 2 | AMC Integrated Multilayer Wearable Antenna for Multiband WBAN Applications显示文摘In this paper,a compact,efficient and easy to fabricate wearable antenna integrated with Artificial Magnetic Conductor(AMC)is presented.Addition of slots and bevels/cuts in the rectangular monopole patch antenna yield a wide bandwidth along with band notches.The proposed antenna is backed with an AMC metasurface that changes the bidirectional radiation pattern to a unidirectional,thus,considerably reducing the Specific Absorption Ratio(SAR).The demonstrated antenna has a good coverage radiating away from the body and presents reduced radiation towards the body with a front-to-back ratio of 13 dB and maximum gain of 3.54 dB.The proposed design operates over a wide frequency band of 2.9 to 12 GHz(exceeding the designated 3.1−10.6 GHz Ultra-Wideband(UWB)band).The band notches were created using slots on the radiating patch in the sub-bands from 5.50 to 5.67 GHz and 7.16 to 7.74 GHz.The overall dimensions of the structure are 33×33×6.75 mm3.The antenna’s radiation performance increased considerably with the addition of the AMC layer.The SAR values for the antenna are reduced by 85.3%when the AMC is used and are 0.083 W/kg which is well below the FCC SAR limits.The simple design,miniaturized profile,low SAR and wide operating bands with multiple band notches make the presented antenna an appealing choice for several UWB wearable body area network(WBAN)applications. | Iqra Aitbar Nosherwan Shoaib Akram Alomainy Abdul Quddious Symeon Nikolaou Muhammad Ali Imran Qammer H.Abbasi | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 3 | Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves显示文摘Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality. | Adnan Zahid Kia Dashtipour Hasan T.Abbas Ismail Ben Mabrouk Muath Al-Hasan Aifeng Ren Muhammad A.Imran Akram Alomainy Qammer H.Abbasi | 2022 | Defence Technology(防务技术)2022，18，8： | 0 |
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