9,411 research outputs found
Seebeck Coefficients in Nanoscale Junctions: Effects of Electron-vibration Scattering and Local Heating
We report first-principles calculations of inelastic Seebeck coefficients in
an aluminum monatomic junction. We compare the elastic and inelastic Seebeck
coefficients with and without local heating. In the low temperature regime, the
signature of normal modes in the profiles of the inelastic Seebeck effects is
salient. The inelastic Seebeck effects are enhanced by the normal modes, and
further magnified by local heating. In the high temperature regime, the
inelastic Seebeck effects are weakly suppressed due to the quasi-ballistic
transport.Comment: 3 Figure
Energy-Efficient Non-Orthogonal Transmission under Reliability and Finite Blocklength Constraints
This paper investigates an energy-efficient non-orthogonal transmission
design problem for two downlink receivers that have strict reliability and
finite blocklength (latency) constraints. The Shannon capacity formula widely
used in traditional designs needs the assumption of infinite blocklength and
thus is no longer appropriate. We adopt the newly finite blocklength coding
capacity formula for explicitly specifying the trade-off between reliability
and code blocklength. However, conventional successive interference
cancellation (SIC) may become infeasible due to heterogeneous blocklengths. We
thus consider several scenarios with different channel conditions and
with/without SIC. By carefully examining the problem structure, we present in
closed-form the optimal power and code blocklength for energy-efficient
transmissions. Simulation results provide interesting insights into conditions
for which non-orthogonal transmission is more energy efficient than the
orthogonal transmission such as TDMA.Comment: accepted by IEEE GlobeCom workshop on URLLC, 201
A prediction approach for multichannel EEG signals modeling using local wavelet SVM
Accurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal. © 2006 IEEE.published_or_final_versio
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