5G MEC-enabled vehicle discovery service for streaming-based CAM applications

Cooperative perception represents an important technology to fulfil the higher automation levels of connected and automated mobility (CAM). In cooperative perception, the sensor data, either raw or processed, is shared among neighbour vehicles with the objective of enhancing or complementing the perception obtained by on-board sensors. The vehicle that requests this external perception data needs to have this data quickly. However, it first needs to discover the network address of the neighbour vehicle that wants to connect to. Specially in a dense urban area or in a congested radio channel, an inefficient method for neighbour vehicle discovery could prevent a timely start of the cooperative perception session. This paper describes a novel 5G multi-access edge computing (MEC) solution that that boosts the selection of interesting neighbour vehicles according to a geographical region of interest (ROI) after applying pertinent adjustments considering vehicles’ dynamics and network communication latencies. In contrast to broadcast-based methods, in the proposed method the vehicles are only sending their periodical position data to a MEC service, which centralises the vehicle discovery requests. The objective of this Vehicle Discovery Service (VDS) is to support the startup of Web Real-Time Communications (WebRTC)-based Extended Sensors CAM applications. The proposed VDS has been validated using a public vehicular traffic dataset evaluating geo-position accuracy. The WebRTC-based streaming pipeline has been validated testing its feasibility for a See-Through video streaming application.

     

Shrimp processing residue as an alternative ingredient for new product development

Shrimp residues were dried (65 °C), grounded, and posteriorly used as an ingredient to the production of ‘spiced shrimp flour’ and ‘shrimp flavoured biscuits’. Both products were packed in modified atmosphere (100% N₂) and stored for 180 days (25 °C) for shelf life evaluation. The centesimal composition, physicochemical, microbiological and sensory analyses were carried out in triplicate. The microbiological analysis (residue and shrimp flour) was within the limits of the legislation, confirming the hygienic–sanitary care during processing. The protein content was the most outstanding (40.13% for the spiced shrimp flour and 20.52% for the shrimp flavoured biscuits). The microbiological evaluation for the ‘spiced shrimp flour’ and ‘shrimp flavoured biscuits’ was below the legal limit, and both products were accepted by sensory analysis. The shelf life evaluation demonstrated stability for 6 months. Thus, we concluded that the Litopenaeus vannamei shrimp residue is a promising ingredient in the food industry.     

Mapping low-Reynolds-number microcavity flows using microfluidic screening devices

Low-Reynolds-number flows in cavities, characterized by separating and recirculating flows are increasingly used in microfluidic applications such as mixing and sorting of fluids, cells, or particles. However, there is still a lack of guidelines available for selecting the appropriate or optimized microcavity configuration according to the specific task at hand. In an effort to provide accurate design guidelines, we investigate quantitatively low-Reynolds-number cavity flow phenomena using a microfluidic screening platform featuring rectangular channels lined with cylindrical cavities. Using particle image velocimetry (PIV), supported by computational fluid dynamics (CFD) simulations, we map the entire spectrum of flows that exist in microcavities over a wide range of low-Reynolds numbers (Re = 0.1, 1, and 10) and dimensionless geometric parameters. Comprehensive phase diagrams of the corresponding microcavity flow regimes are summarized, capturing the gradual transition from attached flow to a single vortex and crossing through two- and three-vortex recirculating systems featuring saddle-points. Finally, we provide design insights into maximizing the rotational frequencies of recirculating single-vortex microcavity systems. Overall, our results provide a complete and quantitative framework for selecting cavities in microfluidic-based microcentrifuges and vortex mixers.     

Bootstrap-based bandwidth choice for log-periodogram regression

Abstract.  The choice of the bandwidth in the local log-periodogram regression is of crucial importance for estimation of the memory parameter of a long memory time series. Different choices may give rise to completely different estimates, which may lead to contradictory conclusions, for example about the stationarity of the series. We propose here a data-driven bandwidth selection strategy that is based on minimizing a bootstrap approximation of the mean-squared error (MSE). Its behaviour is compared with other existing techniques for optimal bandwidth selection in a MSE sense, revealing its better performance in a wider class of models. The empirical applicability of the proposed strategy is shown with two examples: the widely analysed in a long memory context Nile river annual minimum levels and the input gas rate series of Box and Jenkins.     

Semiparametric Inference in Seasonal and Cyclical Long Memory Processes

Several semiparametric estimates of the memory parameter in standard long memory time series are now available. They consider only local behaviour of the spectrum near zero frequency, about which the spectrum is symmetric. However long-range dependence can appear as a spectral pole at any Nyqvist frequency (reflecting seasonal or cyclical long-memory), where the spectrum need display no such symmetry. We introduce Seasonal/Cyclical Asymmetric Long Memory (SCALM) processes that allow differing rates of increase on either side of such a pole. To estimate the two consequent memory parameters we extend two semiparametric methods that were proposed for the standard case of a spectrum diverging at the origin, namely the log-periodogram and Gaussian or Whittle methods. We also provide three tests of symmetry. Monte Carlo analysis of finite sample behaviour and an empirical application to UK inflation data are included. Our models and methods allow also for the possibility of negative dependence, described by a possibly asymmetric spectral zero.     

Probing the thermal decomposition process of layered double hydroxides through in situ <Superscript>57</Superscript>Fe Mössbauer and in situ X-ray diffraction experiments

The thermal decomposition properties of Mg–Fe hydrotalcites were studied through in situ ⁵⁷Fe Mössbauer spectroscopy and in situ X-ray diffraction. Abrupt changes in the quadrupolar splitting measured in the Mössbauer spectra revealed a phase transition from the starting lamellar structure to a new crystalline arrangement. By analyzing the Mössbauer parameters we show that the material is highly disordered in the 300–400 °C temperature range. This hypothesis is confirmed by the X-ray results whose diffractograms indicated the collapse of the lamellar structure and the formation of a solid solution.     

Capture device identification from digital images using Kullback-Leibler divergence

Multimedia Tools and Applications - It is proposed a forensic method for the capture device identification from digital images, which requires two elements: i) a digital image subject to...     

Modelling the vibration response of a gas turbine using machine learning

This work deals with modelling the vibration response of a gas turbine obtained during the start-up process until reaching the nominal speed for power generation. Analysing the vibrations of a complex systems like a gas turbine is useful for the diagnostic of faults or damages in the internal mechanical components of the different stages that integrate a turbine. This work focuses on the study of the shaft vibrations of the bearing radial type mounted between the shaft and the bearing compressor associated with the speed of the turbine. This relationship is studied using experimental data collected from a particular gas turbine model. In particular, we propose a methodology to synthesize a computational model following a supervised learning approach implemented through different machine learning techniques, including a multi-layers perceptron network, support vector machine (SVM), random forest (RF) and genetic programming (GP) with local search. Results show that SVM, RF and GP perform very well in this task, producing accurate predictive models. Moreover, there are some interesting trade-offs between the methods, regarding generalization error, overfitting and model interpretability that are relevant for future applications and research.     

Fuzzy Structure of Complex Belief Systems: Fuzzy Relations and Fuzzy Belief Sets

In previous work, Nescolarde-Selva and Usó-Doménech (2014a Nescolarde-Selva, J. and J. L. Usó-Doménech. “Semiotic Vision of Ideologies.” Found Sci 19 (2014a): 263–282.[Crossref], [Web of Science ®] , [Google Scholar], b) discussed the theory that complex belief systems have a topological structure. In this article it is suggested that this structure is also fuzzy. We introduce the concepts of fuzzy sets in the context of beliefs (substantive and derived), and between derived beliefs themselves. Also introduced are the concepts of fuzzy covering and fuzzy invariance and the relationships between them.