MSAFIS: an evolving fuzzy inference system

In this paper, the problem of learning in big data is considered. To solve this problem, a new algorithm is proposed as the combination of two important evolving and stable intelligent algorithms: the sequential adaptive fuzzy inference system (SAFIS), and stable gradient descent algorithm (SGD). The modified sequential adaptive fuzzy inference system (MSAFIS) is the SAFIS with the difference that the SGD is used instead of the Kalman filter for the updating of parameters. The SGD improves the Kalman filter, because it first obtains a better learning in big data. The effectiveness of the introduced method is verified by two experiments

Motion Detection by Microcontroller for Panning Cameras

Motion detection is the first essential process in the extraction of information regarding moving objects. The approaches based on background difference are the most used with fixed cameras to perform motion detection, because of the high quality of the achieved segmentation. However, real time requirements and high costs prevent most of the algorithms proposed in literature to exploit the background difference with panning cameras in real world applications. This paper presents a new algorithm to detect moving objects within a scene acquired by panning cameras. The algorithm for motion detection is implemented on a Raspberry Pi microcontroller, which enables the design and implementation of a low-cost monitoring system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras

Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full image results in multiple image blocks or windows to analyze, which could make the time performance of the system very poor when implemented on low cost devices. This paper presents a system which attempts to detect abnormal moving objects within an area covered by a PTZ camera while it is panning. The decision about the block of the image to analyze is based on a mixture distribution composed of two components: a uniform probability distribution, which represents a blind random selection, and a mixture of Gaussian probability distributions. Gaussian distributions represent windows in the image where anomalous objects were detected previously and contribute to generate the next window to analyze close to those windows of interest. The system is implemented on a Raspberry Pi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Connecting distinct realms along multiple dimensions: A meta-ecosystem resilience perspective

Resilience research is central to confront the sustainability challenges to ecosystems and human societies in a rapidly changing world. Given that social-ecological problems span the entire Earth system, there is a critical need for resilience models that account for the connectivity across intricately linked ecosystems (i.e., freshwater, marine, terrestrial, atmosphere). We present a resilience perspective of meta-ecosystems that are connected through the flow of biota, matter and energy within and across aquatic and terrestrial realms, and the atmosphere. We demonstrate ecological resilience sensu Holling using aquatic-terrestrial linkages and riparian ecosystems more generally. A discussion of applications in riparian ecology and meta-ecosystem research (e.g., resilience quantification, panarchy, meta-ecosystem boundary delineations, spatial regime migration, including early warning indications) concludes the paper. Understanding meta-ecosystem resilience may have potential to support decision making for natural resource management (scenario planning, risk and vulnerability assessments)

An HST/NICMOS view of the prototypical giant HII region NGC604 in M33

We present the first high-spatial resolution near-infrared (NIR) imaging of NGC 604, obtained with the NICMOS camera aboard the Hubble Space Telescope (HST). These NICMOS broadband images reveal new NIR point sources, clusters, and diffuse structures. We found an excellent spatial correlation between the 8.4 GHz radio continuum and the 2.2mu-m nebular emission. Moreover, massive young stellar object candidates appear aligned with these radio peaks, reinforcing the idea that those areas are star-forming regions. Three different scaled OB associations are recognized in the NICMOS images. The brightest NIR sources in our images have properties that suggest that they are red supergiant stars, of which one of them was previously known. This preliminary analysis of the NICMOS images shows the complexity of the stellar content of the NGC 604 nebula.Comment: Paper presented in the Workshop "Young massive star clusters: initial conditions and environments" (Granada, Spain - Sept 2007). Astrophysics & Space Science in press, 7 pages, 4 figure

gem-selective cross-dimerization and cross-trimerization of alkynes with silylacetylenes promoted by a Rhodium-Pyridine-N-heterocyclic carbene catalyst

The gem-selective cross-dimerization and -trimerization of silylacetylenes with alkynes through C[BOND]H activation using a rhodium(I)–pyridine–N-heterocyclic carbene catalyst have been developed. This reaction is applied to various aliphatic or aromatic terminal alkynes, internal alkynes, and gem-1,3-disubsituted enynes to afford the corresponding enynes and dienynes with high regio- and stereoselectivities and in good isolated yields (up to 91 %).Financial support from the Spanish Ministerio de Economía y Competitividad (MEC/FEDER) of Spain Project (CTQ2010-15221), the Diputación General de Aragón (E07), the KFUPMUNIZAR agreement, and CONSOLIDER INGENIO-2010, under the Project MULTICAT (CSD2009-00050) are gratefully acknowledged. L. R.-P. thanks CONACyT (Mexico, 186898 and 204033) for a postdoctoral fellowship.Peer Reviewe

What drives consumers to use P2P payment systems? An analytical approach based on the stimulus– organism–response (S-O-R) model

Purpose – Traditional payment systems based on cash and bank cards are being replaced by new innovative formats. This research analyzes the success factors in the adoption by customers of Bizum, a peer-to-peer (P2P) mobile payment system widely used in Spain. This study proposes a theoretical framework based on the Stimulus–Organism–Response (S-O-R) model and includes the analysis of the moderating effect of perceived risk and the mediating effect of perceived trust. Design/methodology/approach – To achieve the proposed objectives, an online questionnaire was administered to 701 Spanish smartphone users, potential users of the proposed P2P payment systems. Findings – The results show that perceived usefulness is the most important predictor of intention to use. Additionally, a medium predictive relevance performance of the proposed model is found. Originality/value – This research contributes to a more holistic understanding of the adoption of P2P payment systems and provides new business opportunities that companies can exploit through the use of this technology.FEDER (B-SEJ-209-UGR18