Abstract
Performance and power efficiency in edge and embedded systems can benefit from specialized hardware. To avoid the effort of manual hardware design, we explore the generation of accelerator circuits from binary instruction traces for several Instruction Set Architectures.

Abstract

Abstract
Typical analogue-to-digital conversion (ADC) architectures, at Nyquist rate, tend to occupy a big portion of the integrated circuit die area and to consume more power than desired. Recently, with the rise of Interet-of-Things (IoT), there is a high demand for architectures that can have both reduced area and power consumption. Time encoding machines (TEM) might be a promising alternative. These types of encoders result in very simple and low-power analogue circuits, shifting most of its complexity to the decoding stage, typically stationed in a place with access to more resources. This paper focuses on a particular TEM, the integrate-and-fire neuron (IFN). The IFN modulation is based on a simplified first-order model of neural operation and it encodes the signal in a very power efficient manner. In the end, a novel hardware architecture for the reconstruction of the IFN encoded signal based on a spiking model will be presented. The method is demonstrated and implemented on FPGA, reaching an ENOB as high as 8.23.

Abstract
Harmonic representation models are widely used, notably in speech coding and synthesis. In this paper, we describe two fully parametric harmonic representation and signal reconstruction alternatives that rely on a shift-invariant harmonic phase model and that implement accurate frame-based synthesis in the frequency-domain, and accurate pitch pulse-based synthesis in the time-domain. We use natural spoken and sung voice signals in order to assess the objective and subjective quality of both alternatives when parameters are exact, and when they are replaced by compact and shift-invariant harmonic phase and magnitude approximation models. We highlight the flexibility of these models and present results indicating that not only does the compact shift-invariant phase model cause a smaller impact than that caused by harmonic magnitude modeling, but it also compares favorably to results presented in the literature.

Abstract
In this paper, we present a computationally efficient and fully parametric harmonic speech model that is suitable for real-time flexible frame-based analysis and synthesis implementation in the frequency domain. We carry out a performance comparison between this vocoder and similar ones, such as WORLD and HPMD. Then, a deliberate manipulation of the speaker's fundamental frequency micro-variations is performed in order to understand in which way it conveys prosodic and idiosyncratic information. We conclude our discussion by evaluating the impact of these manipulations through the realization of perceptual tests. © 2020 IEEE.

Abstract
The Blue Economy has been growing in sectors such as offshore renewable energy, aquaculture, marine biotechnology, and deep sea mining. However, suitable wireless and mobile communications are lacking offshore. On the one hand, there is no coverage from terrestrial networks; on the other hand, satellite communications are still narrowband and expensive. Recently, the use of multi-hop airborne communications has been proposed to extend the coverage from terrestrial networks offshore but the communications range of these solutions is highly dependent on the height of the communications nodes. In this paper, we study the RF signal propagation in the maritime environment when the height of the receiver is changed and propose a position control approach for airborne multi-hop networks that maximizes the network capacity by taking full advantage of the signal reflections on the sea surface. The results obtained show that the proposed approach can provide lower propagation losses and higher network throughputs than random or fixed height approaches.