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We propose a stability preserved Arnoldi algorithm for matrix exponential in the time domain simulation of large-scale power delivery networks (PDNs), which are formulated as semi-explicit differential-algebraic equations (DAEs). The matrix exponential and vector products (MEVPs) compose the solution of DAEs in multistep integration methods and can be efficiently approximated with the rational Krylov subspace. To produce stable simulation results for the ill-conditioned system from semi-explicit DAEs, the revised Arnoldi algorithm introduces a new structured orthogonalization process to construct the Krylov subspace. We demonstrate the performance of the new algorithm with theoretical proof and experiments. In the computation of MEVPs, we utilize the exponential related φ functions to improve the numerical accuracy. We further explore the optimal ratio to confine the spectrum in the rational Krylov subspace. Finally, the transient framework is tested on a group of system-level PDNs, showing that matrix exponential-based algorithms could achieve high efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) is becoming a hot topic in recent years for its advantages such as low-power consumption, low memory requirement, and low sampling frequency. However, high-dimensional nonlinear signals will inevitably introduce notable complexity and low efficiency in signal recovery. Generalized turbo signal recovery (G-Turbo-SR) is a cutting-edge method, which efficiently reduces complexity with a partial discrete Fourier transform (DFT) sensing matrix. However, in practical applications, G-Turbo-SR still suffers from high complexity for probability computations and matrix multiplications. This article optimizes the algorithm of G-Turbo-SR in scheduling to reduce the matrix multiplications by half. High-precision numerical approximation method is proposed to replace the complex integral calculation, which efficiently reduces the hardware cost with acceptable performance degradation. Based on the data-flow graph (DFG) analysis, detailed hardware architecture is proposed with module designs. Proper quantization scheme is selected according to the mean square error (MSE) performance. Pipelining, folding, and variable precision quantization (VPQ) scheme are employed for higher hardware efficiency. FPGA implementation on Xilinx 7k325tffg900-2 shows a higher throughput and hardware efficiency compared to existing recovery methods for CS. [ABSTRACT FROM AUTHOR]

With abundant aggregate network bandwidth, continuous data streams are commonly used in scientific and commercial applications. Correspondingly, there is an increasing demand of authenticating these data streams. Existing strategies explore data stream authentication by using message authentication codes (MACs) on a certain number of data packets (a data block) to generate a message digest, then either embedding the digest into the original data, or sending the digest out-of-band to the receiver. Embedding approaches inevitably change the original data, which is not acceptable under some circumstances (e.g., when sensitive information is included in the data). Sending the digest out-of-band incurs additional communication overhead, which consumes more critical resources (e.g., power in wireless devices for receiving information) besides network bandwidth. In this paper, we propose a novel strategy, DaTA, which effectively authenticates data streams by selectively adjusting some interpacket delay. This authentication scheme requires no change to the original data and no additional communication overhead. Modeling-based analysis and experiments conducted on an implemented prototype system in an LAN and over the Internet show that our proposed scheme is efficient and practical. [ABSTRACT FROM AUTHOR]