Properties of Bipolar Fuzzy Hypergraphs

In this article, we apply the concept of bipolar fuzzy sets to hypergraphs and investigate some properties of bipolar fuzzy hypergraphs. We introduce the notion of $A-$ tempered bipolar fuzzy hypergraphs and present some of their properties. We also present application examples of bipolar fuzzy hypergraphs

Non-additive interval-valued F-transform

International audienceThis article proposes a new interval-valued fuzzy transform. Its construction is based on a possibilistic interpretation of the partition on which the fuzzy transform is built. The main advantage of this approach is that it provides specific interval valued functions whose interpretation is straightforward. This interpretation relates to a traditional sampling/reconstruction framework where little is known about the sampling and/or reconstructing kernels. Numerous properties of the proposed approach are proved that could be useful for function analysis and comparison. In the experimental section, we illustrate some properties of the proposed transform while highlighting interesting features of the obtained framework

Low-power SNN-based audio source localisation using a Hilbert Transform spike encoding scheme

Sound source localisation is used in many consumer electronics devices, to help isolate audio from individual speakers and to reject noise. Localization is frequently accomplished by "beamforming" algorithms, which combine microphone audio streams to improve received signal power from particular incident source directions. Beamforming algorithms generally use knowledge of the frequency components of the audio source, along with the known microphone array geometry, to analytically phase-shift microphone streams before combining them. A dense set of band-pass filters is often used to obtain known-frequency "narrowband" components from wide-band audio streams. These approaches achieve high accuracy, but state of the art narrowband beamforming algorithms are computationally demanding, and are therefore difficult to integrate into low-power IoT devices. We demonstrate a novel method for sound source localisation in arbitrary microphone arrays, designed for efficient implementation in ultra-low-power spiking neural networks (SNNs). We use a novel short-time Hilbert transform (STHT) to remove the need for demanding band-pass filtering of audio, and introduce a new accompanying method for audio encoding with spiking events. Our beamforming and localisation approach achieves state-of-the-art accuracy for SNN methods, and comparable with traditional non-SNN super-resolution approaches. We deploy our method to low-power SNN audio inference hardware, and achieve much lower power consumption compared with super-resolution methods. We demonstrate that signal processing approaches can be co-designed with spiking neural network implementations to achieve high levels of power efficiency. Our new Hilbert-transform-based method for beamforming promises to also improve the efficiency of traditional DSP-based signal processing

An algorithm design environment for signal processing

Also issued as Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.Includes bibliographical references (p. 253-256).Supported in part by the Defense Advanced Research Projects Agency and monitored by the Office of Naval Research. N00014-89-J-1489 Supported in part by the National Science Foundation. MIP 87-14969 Supported in part by Sanders Associates, Incorporated.Michele Mae Covell

Bayesian modelling for determining material properties

Sound wave propagation in materials has been used extensively for nondestructive testing of materials, studying the internal structure of Earth or for oil/gas/mineral exploration. The propagation characteristics have been exploited and studied using signal processing tools for analyzing the material properties or for examining the sub-surface features. The signal processing tools can be innovated in accordance with the characteristics of the sound wave propagation in the media for improvement of the signal to noise ratio. A synthetic model of one dimensional chain of spherical particles is used to generate space time responses when impulse moves along the chain, it gives information about the longitudinal wave propagation (P-wave). The space time responses obtained from the synthetic model are used by the Bayesian inference technique to obtain the properties of the media (particle size/mass distribution of the chain). Finally, the importance of Bayesian inference technique as a signal processing tool is discussed upon

AN IMPROVED METHOD FOR THE IDENTIFICATION AND INVERSION OF MULTI-MODE RAYLEIGH SURFACE WAVE DISPERSION COLLECTED FROM NON-UNIFORM ARRAYS UTILIZING A MOVING SOURCE APPROACH

An improved method using a moving source approach is utilized in the analysis of Rayleigh surface waves for the accurate identification of higher mode propagation used in inversion. Two non invasive surface wave methods, Multi- station Analysis of Surface Waves (MASW) and Refraction Microtremor (ReMi) were used for the construction of composite dispersion curves representing the relationship of Rayleigh phase velocity (VR) with frequency. Multiple tests were executed with source offsets increasing with each successive test in order to account for near field effects and higher mode attenuation levels. The resulting dispersions were combined to form a composite dispersion which effectively maps all participating modes of propagation. The inversion was executed using a genetic algorithm (GA) which takes advantage of the Rayleigh forward problem. The results show good ability to identify intermediate high and low velocity layers and agree well with downhole results

Study of star-forming galaxies in SDSS up to redshift 0.4 II. Evolution from the fundamental parameters: mass, metallicity & SFR

To understand the formation and evolution of galaxies, it is important to have a full comprehension of the role played by the metallicity, star formation rate (SFR), morphology, and color. The interplay of these parameters at different redshifts will substantially affect the evolution of galaxies and, as a consequence, the evolution of them will provide important clues and constraints on the galaxy evolution models. In this work we focus on the evolution of the SFR, metallicity of the gas, and morphology of galaxies at low redshift in search of signs of evolution. We use the S2N2 diagnostic diagram as a tool to classify star--forming, composite, and AGN galaxies. We analyzed the evolution of the three principal BPT diagrams, estimating the SFR and specific SFR (SSFR) for our samples of galaxies, studying the luminosity and mass-metallicity relations, and analyzing the morphology of our sample of galaxies through the g-r color, concentration index, and SSFR. We found that the S2N2 is a reliable diagram to classify star--forming, composite, and AGNs galaxies. We demonstrate that the three principal BPT diagrams show an evolution toward higher values of [OIII]5007/Hb due to a metallicity decrement. We found an evolution in the mass-metallicity relation of ~ 0.2 dex for the redshift range 0.3 < z < 0.4 compared to our local one. From the analysis of the evolution of the SFR and SSFR as a function of the stellar mass and metallicity, we discovered a group of galaxies with higher SFR and SSFR at all redshift samples, whose morphology is consistent with those of late-type galaxies. Finally, the comparison of our local (0.04<z<0.1) with our higher redshift sample (0.3<z<0.4), show that the metallicity, the SFR and morphology, evolve toward lower values of metallicity, higher SFRs, and late--type morphologies for the redshift range 0.3<z<0.4Comment: 16 pages, 15 figures. Accepted for publication in A&

Workshop on Fuzzy Control Systems and Space Station Applications

The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented