Compressive spectral imaging (CSI) systems capture the 3D spatiospectral data by measuring the 2D compressed focal plane array (FPA) coded projection with the help of reconstruction algorithms exploiting the sparsity of signals. However, the contradiction between the multi-dimension of the scenes and the limited dimension of the sensors has limited improvement of recovery performance. In order to solve the problem, a novel CSI system based on a coded aperture snapshot spectral imager, RGB-CASSI, is proposed, which has two branches, one for CASSI, another for RGB images. In addition, considering that conventional reconstruction algorithms lead to oversmoothing, a RGB-guided low-rank (RGBLR) method for compressive hyperspectral image reconstruction based on compressed sensing and coded aperture spectral imaging system is presented, in which the available additional RGB information is used to guide the reconstruction and a low-rank regularization for compressive sensing and a non-convex surrogate of the rank is also used instead of nuclear norm for seeking a preferable solution. Experiments show that the proposed algorithm performs better in both PSNR and subjective effects compared with other state-of-art methods.

MapReduce still suffers from a problem known as skew, where load is unevenly distributed among tasks. Existing solutions follow a similar pattern that estimates the load of each task and then rebalances the load among tasks. However, these solutions often incur heavy overhead due to the load estimation and rebalancing. In this paper, we present DynamicAdjust, a dynamic resource adjustment technique for mitigating skew in MapReduce. Instead of rebalancing the load among tasks, DynamicAdjust adjusts resources dynamically for the tasks that need more computation, thereby accelerating these tasks. Through experiments using real MapReduce workloads on a 21-node Hadoop cluster, we show that DynamicAdjust can effectively mitigate the skew and speed up the job completion time by up to 37.27% compared to the native Hadoop YARN.

In this letter, we propose an Adaptive Modulation and Coding (AMC) scheme with relay protocols, such as Amplify-and-Forward (AF), Decode-and-Forward (DF) and De-Modulate-and-Forward (DMF). We perform simulations based on 3GPP Long Term Evolution-Advanced (LTE-A) parameters to compare the performance of an adaptive Modulation and Coding Scheme (MCS) using relay protocols of AF, DF, and DMF with non-adaptive MCS, with the same relay protocols. We analyze the performance of the proposed scheme and observe how the proposed AMC scheme with DMF performs at various Signal to Noise Ratio (SNR) regions. The simulation results have shown that the performance of the proposed AMC scheme with relay protocols of DMF is much better at lower and a higher SNR regions and also provides higher average throughput.

Cooperative relaying, while effective in mitigating fading effects, might reduce the overall network throughput since its overhead such as additional time slot and frequency band can be significant. In order to overcome this problem, this paper proposes a superposition coding based cooperative relay scheme to provide reliable transmission with little or no overhead. This scheme exploits the superimposed messages for users in the network to achieve the simultaneous transmission of two or more independent data streams. This scheme reduces the number of transmission phases to the same as that of conventional cooperative relay schemes. The symbol error performance of the proposed scheme is analyzed and simulated.

A Hyper-Universal Switch Box (HUSB) [1]-[3] can yield a feasible (detailed) routing solution for any given routing requirement of multi-pin nets or multi-point connections of surrounding terminals. This flexible routing structure obviously possesses multiple potential applications for re-configurable systems such as FPGAs and communication switching networks [4],[5]. Based on the same decomposition theory developed in the design scheme of such powerful switching structure, a simple routing algorithm can also be developed. The router is exact in terms of its assured capability in finding a routing solution, and it is efficient due to the divide and conquer nature and simple mapping scheme for pre-analyzed routing patterns saved in data base.

In recent years, techniques such as multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) have been developed and combined in MIMO-OFDM systems to provide higher data rates. In addition, the system can be optimized by setting modulation and coding adaptively according to the channel conditions. The overall system performance depends on how accurately the system obtains the channel state information (CSI) and feeds it back to the transmitter. In this paper, we propose a signal-to-noise-ratio (SNR) estimation algorithm in which the preamble is known by both sides of the transceiver. Through simulations of several channel environments, we prove that our proposed algorithm is more accurate than traditional algorithms.

Sparse arrays can usually achieve larger array apertures than uniform linear arrays (ULA) with the same number of physical antennas. However, the conventional direction-of-arrival (DOA) estimation algorithms for sparse arrays usually require the spatial smoothing operation to recover the matrix rank which inevitably involves heavy computational complexity and leads to a reduction in the degrees-of-freedom (DOFs). In this paper, a low-complex DOA estimation algorithm by exploiting the discrete Fourier transform (DFT) is proposed. Firstly, the spatial spectrum of the virtual array constructed from the sparse array is established by exploiting the DFT operation. The initial DOA estimation can obtain directly by searching the peaks in the DFT spectrum. However, since the number of array antennas is finite, there exists spectrum power leakage which will cause the performance degradation. To further improve the angle resolution, an iterative process is developed to suppress the spectrum power leakage. Thus, the proposed algorithm does not require the spatial smoothing operation and the computational complexity is reduced effectively. In addition, due to the extention of DOF with the application of the sparse arrays, the proposed algorithm can resolve the underdetermined DOA estimation problems. The superiority of the proposed algorithm is demonstrated by simulation results.

A new hybrid method for characterizing the irregular power/ground plane pair is developed in this paper by combining the conventional eigen-mode expansion method with the new-presented inverted composition method and a simple model order reduction. By the approach, the eigen-mode expansion method can be extended to the characteristics research of the power/ground plane pair with holes. In this gridless method, ports and decoupling capacitors can be arbitrarily placed on the plane pair. The numerical example demonstrates its good validity.

We present the design of a secure identifier-based inter-domain routing, SIR, for the identifier/locator split network. On the one hand, SIR is a distributed path-vector protocol inheriting the flexibility of BGP. On the other hand, SIR separates ASes into several groups called trust groups, which assure the trust relationships among ASes by enforceable control and provides strict isolation properties to localize attacks and failures. Security analysis shows that SIR can provide control plane security that can avoid routing attacks including some smart attacks which S-BGP/soBGP can be deceived. Meanwhile, emulation experiments based on the current Internet topology with 47,000 ASes from the CAIDA database are presented, in which we compare the number of influenced ASes under attacks of subverting routing policy between SIR and S-BGP/BGP. The results show that, the number of influenced ASes decreases substantially by deploying SIR.