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Dynamic Spectrum Sharing (DSS) is an enabler for a seamless transition from 4G Long TermEvolution (LTE) to 5G New Radio (NR) by utilizing existing LTE bands without static spectrum re-farming. In this paper, we propose a cross-band DSS scheme that utilizes the Multimedia BroadcastMulticast Service over a Single Frequency Network (MBSFN) feature of an LTE network and theMulticast Broadcast Service (MBS) feature of an NR network.
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Dynamic Spectrum Sharing (DSS) is an enabler for a seamless transition from 4G Long TermEvolution (LTE) to 5G New Radio (NR) by utilizing existing LTE bands without static spectrum re-farming. In this paper, we propose a cross-band DSS scheme that utilizes the Multimedia BroadcastMulticast Service over a Single Frequency Network (MBSFN) feature of an LTE network and theMulticast Broadcast Service (MBS) feature of an NR network.
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These data is state estimation accuracy of the proposed algorithm When the adjust factor is 1
These data includes the position estimation accuracy and velocity estimation accuracy of the algorithm.
The data are explained as follows:
rmse_ckf_1,rmse_ukf_1,rmse_vakf_1,rmse_vakfpr_1,rmse_okf_1 are the position accuracy of the CKF, UKF, the proposed IW_VACKF, VACKF_PR and CKF-TNCM, respectively.
rmse_ckf_2,rmse_ukf_2,rmse_vakf_2,rmse_vakfpr_2,rmse_okf_2 are the velocity accuracy of the CKF, UKF, the proposed IW_VACKF, VACKF_PR and CKF-TNCM, respectively.
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With the growth of Internet of Things (IoT) applications, the need for accurate indoor positioning systems (IPS) has become urgent. While GPS has limitations in indoor scenarios, Visible Light Positioning (VLP) presents promising results. This paper addresses the challenge of estimating the receiver's height in three-dimensional (3D) positioning scenarios, a crucial problem in VLP. We propose a novel 3D VLP algorithm, adopting a multiple estimation strategy for height estimation to minimize random errors.
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This study uses a dataset from 5 subjects, including 4 with Parkinson's Disease (PD) and 1 with Essential Tremor (ET). The data includes Inertial Measurement Unit (IMU) and surface electromyography (sEMG) signals. The dataset supports conclusions in the article "An OpenSim-based closed-loop biomechanical wrist model for pathological tremor simulation."
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Human biomechanics is still an active topic of research that requires more technological advancements and data collection of various human body movements. There is a need for methodologies to identify daily activities in various scenarios, such as one while carrying a school bag. Deakin university has developed an Internet of Things (IoT) enabled smart school bag consisting of motion analysis sensors that would recognize the activities performed while carrying the school bag.
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Progress toward the use of S-band SoOp in sea surface remote sensing was demonstrated in a 2012-2013 experiment based at the Harvest Oil Platform located at 34.469° N and 120.682° W, roughly 11 km from Point Conception, Santa Barbara, CA. Satellite transmissions from the XM-radio service were observed, using one channel each from the “Rhythm” (located above 85°W) and the “Blues” (115°W) satellites. Each downlink channel had a bandwidth of 1.886 MHz with a symbol rate of 1.64 Msps in Quadrature Phase Shift Key (QPSK) modulation.
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We present a sEMG signal database corresponding to the Indian population named “ElectroMyography Analysis of Human Activities - DataBase -2 (EMAHA-DB2).” This data set consists of two different weight training activities which involve isotonic and isometric contractions. Weight training activities are effective for improving muscle strength, overall health, and regaining limb functionality for people undergoing rehabilitation post stroke-related episodes. The EMG signals acquired during weight training can be used for muscle recruitment analysis.
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Over 34,000 frames from 60 commercial-off-the-shelf ZigBee devices were collected in various scenarios including indoor/outdoor and line-of-sight/non-line-of-sight (LOS/NLOS). The ZigBee devices are hybrid, with 36 equipped with power amplifiers and the other 24 not. The ZigBee device uses the CC2530 chip, while the power amplifier is the RFX2401C chip. The signal frames in each scenario are placed in a separate folder, where all device numbers are fixed. Each frame reaches its maximum length, which includes 266 symbols.
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This dataset is associated with the manuscript entitled "Data-efficient Human Walking Speed Intent Inference". The data represent the measurements taken from 15 able-bodied human subjects as the made speed changes while walking on a treadmill. Each subject is associated with a .mat file that contains 8 variables. Four variables are associated with the training dataset while four are associated with the experimental testing protocol.
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