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Turing computability deals with the question of what is theoretically computable on a digital computer, and hence is relevant whenever digital hardware is used. In this paper we study different possibilities to define computable bandlimited signals and systems. We consider a definition that uses finite Shannon sampling series as approximating functions and another that employs computable continuous functions together with an effectively computable time concentration. We discuss the advantages and drawbacks of both definitions and analyze the connections and differences.

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We focus on the Multi-Rate Sampling (MRS) Compressed Sensing (CS) scheme, in which several Analog-to-Digital Converters (ADC) sample in parallel at different sub-Nyquist rates. In this paper, we postulate that a good signal recovery requires the measurement matrix rank (MMR) to be as high as possible. We also present an upper-bound for the MMR. Choosing pairwise coprime sampling rates allows to reach this upper-bound.

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28 Views

The estimation of the frequencies of multiple complex sinusoids in the presence of noise is required in many applications such as sonar, speech processing, communications, and power systems. This problem can be reformulated as a separable nonlinear least squares problem (SNLLS). In this paper, such formulation is derived and a variable projection (VP) optimization is proposed for solving the SNLLS problem and estimate the frequency parameters. We also apply a lethargy type theorem for quantifying the difficulty of the optimization.

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30 Views

Capacity requirements of the fixed access network keep increasing towards multi-gigabit connections. For Hybrid Fiber Coaxial (HFC) networks, aggregated rates around 30 Gbit/s can be achieved by increasing the DOCSIS spectrum to 3GHz, assuming a spectral efficiency around 10 bit/s/Hz. Replacement of spectrum limiting components such as passive taps in the HFC network is an efficient way to achieve these data rates, compared with the cost of fiber to the home (FTTH).

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20 Views

Wireless distributed computing presents new opportunities to execute intelligent tasks on mobile devices for low-latency applications, by wirelessly aggregating the computation and storage resources among mobile devices. However, for low-latency applications, the key bottleneck lies in the exchange of intermediate results among mobile devices for data shuffling. To improve communication efficiency therein, we establish a novel interference alignment condition by exploiting the locally computed intermediate values as side information.

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17 Views

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