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        Reconfi gurable Quad-Band Antenna for Wireless Communication

        Koduri Sreelakshmi,Gottapu Sasibhushana Rao 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.5

        This letter presents a thorough procedural investigation of a highly compact reconfi gurable quad-band monopole antenna providing multiband operation at 1.85 GHz (GSM 1900 MHz), 2.42 GHz Bluetooth/IMTE, 3.4 GHz (Wi-MAX) and 5.3 GHz wireless LAN (WLAN) for personal wireless applications (PWA). The investigated antenna structure consists of a ‘5′- shaped stub that facilitates operation for GSM applications and a semicircular arc for Bluetooth/IMT-E applications, while an inverted L-shaped stub facilitates dual-band operation for Wi-MAX and wireless LAN applications. The presented antenna is fed using an asymmetrical coplanar strip-line (ACS) feed. The triple-band frequency reconfi guration operation, over the Bluetooth, Wi-MAX, and WLAN frequency bands, is achieved by embedding two PIN diode RF switches. The investigated antenna is developed on a highly economical FR-4 glass epoxy substrate that is 1 mm thick with a compact size of 24 × 10 mm 2 . The presented antenna has measured impedance bandwidths of 1.7–1.91 GHz (BW = 11.63%), 2.35–2.55 GHz (BW = 10.31%), 3.3–3.7 GHz (BW = 11.428%) and 5.10–5.6 GHz (BW = 9.345%) providing frequency reconfi gurable quad-band operation at the aforementioned frequency bands. The antenna off ers simulated gains of 1.8, 3.45, 4.91 and 0.86 dBi; measured gains of 2.3, 2.7, 2.82 and 2.6 dBi and radiation effi ciencies of 83%, 61%, 82% and 86% at 1.85 GHz, 2.42 GHz, 3.4 GHz and 5.3 GHz, respectively. The investigated antenna exhibits nearly symmetrical radiation patterns and stable acceptable gains across all application bands, thus making the antenna suitable for wireless communication.

      • KCI등재

        SVD-based IWFA for next generation wireless MIMO communication

        Vinodh Kumar Minchula,G. Sasibhushana Rao 한국통신학회 2018 ICT Express Vol.4 No.3

        Point-to-point MIMO systems without channel state information at the transmitter (No CSIT) and with perfect channel state information at the transmitter (Perfect CSIT) were considered for the application of a novel singular-value-decomposition-based (SVD-based) iterative water-filling algorithm (IWFA). The optimal transmit power was computed for Rayleigh fading-based eigenchannels using an estimated iterative water-filling power allocation algorithm to maximize ergodic capacity. The results obtained with the proposed method showed significant improvements in ergodic capacity compared with the average power allocation method.

      • KCI등재

        Segmentation of Multi‑Modal MRI Brain Tumor Sub‑Regions Using Deep Learning

        B. Srinivas,Gottapu Sasibhushana Rao 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.4

        In medical imaging, extraction of brain tumor region in the magnetic resonance image (MRI) is not sufcient, but fnding the tumor extension is necessary to plan best treatment to improve the survival rate as it depends on tumor’s size, location, and patient’s age. Manually extracting the brain tumor sub-regions from MRI volume is tedious, time consuming and the inherently complex brain tumor images requires a profcient radiologist. Thus, a reliable multi-modal deep learning models are proposed for automatic segmentation to extract the sub-regions like enhancing tumor (ET), tumor core (TC), and whole tumor (WT). These models are constructed on the basis of U-net and VGG16 architectures. The whole tumor is obtained by segmenting T2-weighted images and cross-check the edema’s extension in T2 fuid attenuated inversion recovery (FLAIR). ET and TC are both extracted by evaluating the hyper-intensities in T1-weighted contrast enhanced images. The proposed method has produced better results in terms of dice similarity index, Jaccard similarity index, accuracy, specifcity, and sensitivity for segmented sub regions. The experimental results on BraTS 2018 database shows the proposed DL model outperforms with average dice coefcients of 0.91521, 0.92811, 0.96702, and Jaccard coefcients of 0.84715, 0.88357, 0.93741 for ET, TC, and WT respectively

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