Abstract
This work presents the design and analysis of a biological signal processing accelerator, including an interface controller and memory subsystem for a low-power CGRA. The controller design supports several operation modes, which can perform several applications when paired with the CGRA reconfiguration capabilities. Physical synthesis shows that the controller introduces only a 6 percent area and power overhead compared to the CGRA core, while allowing independent processing of inner loops at high frequencies and the exploitation of pipelining and parallelism. In-depth power analysis based on layout information was performed, including an evaluation of the use of power gating techniques. A practical case study (ECG signal processing) was also evaluated. © 2018 IEEE.

Abstract
Source routing (SR) minimum cost forwarding (MCF) – SRMCF – is a reactive, energy-efficient routing protocol proposed to improve the existent MCF methods utilized in heterogeneous wireless sensor networks (WSN). This paper presents an analytical analysis with experimental support that demonstrates the effectiveness of the proposed protocol. SRMCF stems from SR concepts and MCF methods exploited in ad hoc WSNs, where all unicast communications (between sensor nodes and the base station, or vice versa) use minimum cost paths. The protocol utilized in the present work was updated and now also handles link and node failures. Theoretical analysis and simulations show that the final protocol exhibits better throughput and energy consumption than MCF. Memory requirements for the routing table in the base station are also analyzed. Experimental results in a real scenario were obtained for implementations of both protocols, MCF and SRMCF, deployed in a small network of TelosB motes. Results show that SRMCF presents a 33% higher throughput and 24% less energy consumption than MCF. Extensive © 2019 River Publishers

Abstract

Abstract
In this work we present a reconfigurable and scalable custom processor array for solving optimization problems using cellular genetic algorithms (cGAs), based on a regular fabric of processing nodes and local memories. Cellular genetic algorithms are a variant of the well-known genetic algorithm that can conveniently exploit the coarse-grain parallelism afforded by this architecture. To ease the design of the proposed computing engine for solving different optimization problems, a high-level synthesis design flow is proposed, where the problem-dependent operations of the algorithm are specified in C++ and synthesized to custom hardware. A spectrum allocation problem was used as a case study and successfully implemented in a Virtex-6 FPGA device, showing relevant figures for the computing acceleration.

Abstract
Objectives: To describe the results of the acoustic analysis of a database of 90 voice samples with distinct dysphonia levels, using four different - commercial and open source - software programs. Study design: Exploratory, transversal. Methods: The samples were analyzed by four different types of software programs that perform acoustical evaluation - one open source software (Praat) and three commercial ones (Multi Dimensional Voice Program - MDVP by Kay Elemetrics; VoiceStudio by Seegnal; and Dr. Speech by Tiger Electronics) - for comparison among the most commonly used acoustic measures (frequency, perturbation and noise measures). Results: There is a moderate to strong,correlation, positive and statistically significant among the software programs. The mean FO is not statistically different among the used applications. The other acoustic measures revealed statistically significant differences. Conclusion: Even though it is easier to access software programs and there are numerous proposals for acoustic measures, not all of them are statistically representative nor have numeric semblance among the different applications.

Abstract
Magnitude-oriented approaches dominate the voice analysis front-ends of most current technologies addressing e.g. speaker identification, speech coding/compression, voice reconstruction and re-synthesis. A popular technique is all-pole vocal tract modeling. The phase response of all-pole models is known to be non-linear and highly dependent on the magnitude frequency response. In this paper, we use a shift-invariant phase-related feature that is estimated from signal harmonics in order to study the impact of all-pole models on the phase structure of voiced sounds. We relate that impact to the phase structure that is found in natural voiced sounds to conclude on the physiological validity of the group delay of all-pole vocal tract modeling. Our findings emphasize that harmonic phase models are idiosyncratic, and this is important in speaker identification, and in fostering the quality and naturalness of synthetic and reconstructed speech. © 2018 KASHYAP.