We analyze a power adaptation method to maximize the achievable rate under the finite block length regime, for MIMO block fading channel with channel state information available at both the transmitter and receiver side. We find a convex approximation to the lower bound of the achievable rate, and it leads to a simple power and rate adaptation method. We show that the method achieves near optimal channel rate under the finite block length regime. Compared to the classical waterfilling method, the proposed method can further improve achievable rate especially for short block lengths.

Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in classical single kernel support vector machine optimization scheme, wherein the sequential minimal optimization (SMO) training algorithm is adopted, for brain image analysis. However, to satisfy the optimization conditions required in the single kernel case, it is unreasonably assumed that the spatial regularization parameter is equal to the anatomical one. In this letter, this approach is improved by combining SMO algorithm with multiple kernel learning to avoid that assumption and optimally estimate two parameters. The improvement is comparably demonstrated by experimental results on classification of Alzheimer patients and elderly controls.

We propose a new framework named ROICF based on reinforcement learning orienting reliable compilation optimization sequence generation. On the foundation of the LLVM standard compilation optimization passes, we can obtain specific effective phase ordering for different programs to improve program reliability.

A parallel multiplexing scheduling (PMS) scheme is proposed for distributed antenna systems (DAS), which greatly improves average system throughput due to multi-user diversity and multi-user multiplexing. However, PMS has poor fairness because of the use of the "best channel selection" criteria in the scheduler. Thus we present a parallel proportional fair scheduling (PPFS) scheme, which combines PMS with proportional fair scheduling (PFS) to achieve a tradeoff between average throughput and fairness. In PPFS, the "relative signal to noise ratio (SNR)" is employed as a metric to select the user instead of the "relative throughput" in the original PFS. And only partial channel state information (CSI) is fed back to the base station (BS) in PPFS. Moreover, there are multiple users selected to transmit simultaneously at each slot in PPFS, while only one user occupies all channel resources at each slot in PFS. Consequently, PPFS improves fairness performance of PMS greatly with a relatively small loss of average throughput compared to PFS.

Recently, a high dimensional classification framework has been proposed to introduce spatial structure information in classical single kernel support vector machine optimization scheme for brain image analysis. However, during the construction of spatial kernel in this framework, a huge adjacency matrix is adopted to determine the adjacency relation between each pair of voxels and thus it leads to very high computational complexity in the spatial kernel calculation. The method is improved in this manuscript by a new construction of tensorial kernel wherein a 3-order tensor is adopted to preserve the adjacency relation so that calculation of the above huge matrix is avoided, and hence the computational complexity is significantly reduced. The improvement is verified by experimental results on classification of Alzheimer patients and cognitively normal controls.

Considering that existing clutter cancellation methods process information either in the time domain or in the spatial domain, this paper proposes a new clutter cancellation method that utilizes joint multi-domain information for passive radar. Assuming that there is a receiving array at the surveillance channel, firstly we propose a multi-domain information clutter cancellation model by constructing a time domain weighted matrix and a spatial weighted vector. Secondly the weighted matrix and vector can be updated adaptively utilizing the constant modulus constraint. Finally the weighted matrix is derived from the principle of optimal filtering and the recursion formula of weighted vector is obtained utilizing the Gauss-Newton method. Making use of the information in both time and spatial domain, the proposed method attenuates the noise and residual clutter whose directions are different from that of the target echo. Simulation results prove that the proposed method has higher clutter attenuation (CA) compared with the traditional methods in the low signal to noise ratio condition, and it also improves the detection performance of weak targets.