Dynamic Core Flexion Strength is Important for Using Arm-Swing to Improve Countermovement Jump Height

Background: Specificity of core strength training to sports events is crucial for performance improvement. The purpose of this study was to examine the specific relationship between core strength and countermovement jump (CMJ) performance. Methods: Twenty active college students (7 male, 13 female) participated in the project. CMJ heights with (HAS) and without arm-swing (HNAS) were estimated from vertical ground reaction force data collected using a force platform. Twelve dynamic and static core strength measurements of flexion and extension were tested using a dynamometer. The shared variance between CMJ height and core strength measurements was estimated using the square of Pearson correlation coefficients (R2). Linear regression analyses were conducted to determine which independent variables in core strength measurements were major predictors of CMJ height. Results: Significant correlations (p \u3c 0.05) were observed between all 12 core strength measurements and CMJ height with/without arm-swing. Normalized (normalized with individuals’ body mass) peak torque during dynamic flexion at 180°per second (NPDF180) and normalized peak torque during static flexion at 120° (NPSF120) shared 72.0% variance with HAS, and NPSF120 shared 57.0% variance with HNAS. Conclusion: Dynamic core flexion strength is vital for using arm-swing to improve CMJ height. The structure of kinematic and kinetic core training could be considered to improve CMJ performance for coaches as well as professional and recreational athletes

Primary Channel Gain Estimation for Spectrum Sharing in Cognitive Radio Networks

In cognitive radio networks, the channel gain between primary transceivers, namely, primary channel gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary channel gain is estimated in the primary system and thus unavailable at the CT. To deal with this issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signals, a ML estimator is first developed. After demonstrating the high computational complexity of the ML estimator, a median based (MB) estimator with proved low complexity is then proposed. Furthermore, the estimation accuracy of the MB estimation is theoretically characterized. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Numerical results show that the estimation errors of the ML estimator and the MB estimator can be as small as $0.6$ dB and $0.7$ dB, respectively.Comment: Submitted to IEEE Transactions on Communication