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Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency. This can be achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs) to perform inference at very low energy consumption. Spiking networks are characterized by their ability to process information efficiently, in a sparse cascade of binary events in time called spikes.

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The energy efficiency of modern MPSoCs is enhanced by complex hardware features such as Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM). This paper introduces a new method, based on convex problem solving, that determines the most energy efficient operating point in terms of frequency and number of active cores in an MPSoC. The solution can challenge the popular approaches based on never-idle (or As-Slow-As-Possible (ASAP)) and race-to-idle (or As-Fast-As-Possible (AFAP)) principles.

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Full-duplex systems require very strong self-interference cancellation in order to operate correctly and a significant part of the self-interference signal is due to non-linear effects created by various transceiver impairments. As such, linear cancellation alone is usually not sufficient and sophisticated non-linear cancellation algorithms have been proposed in the literature. In this work, we investigate the use of a neural network as an alternative to the traditional non-linear cancellation method that is based on polynomial basis functions.

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Millimeter wave frequencies paired up with MIMO antennas
employing beamforming are seen as critical enablers of next gen-
eration networks. However, selecting the most beneficial beamform-
ing weights in a codebook-enabled downlink transmitter is a lengthy
task, as the existing methods rely on some form of channel mea-
surement. In fact, if the used codebook is too large, the traditional
methods might fail to select an appropriate entry within the channel
coherence time.
In this paper, a new method to assist the beam selection is pro-

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Mobile cameras have come a long way since their evolution and have replaced digital still cameras. However, their lowlight photography performance needs significant improvement. Dual camera systems consisting of a monochrome sensor and a Bayer sensor offer us a way to improve the low-light photography. The existing dual camera systems use post-processing methods after Image Signal Processor (ISP) for image fusion which are computationally intensive and use two ISPs. We propose a novel architecture in which the image fusion can be done in Bayer domain prior to the ISP.

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