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

Differential tolerance of native and invasive tree seedlings from arid African deserts to drought and shade

Efforts to understand why some species become successful invaders and why some habitats are more at risk from invasive species is an important research focus in invasion ecology. With current global climate change, evaluation of the effects of shade and drought on cohabiting native and invasive species from extreme ecosystems is especially important. Acacia tortilis subsp. raddiana is a tree taxon native to arid African deserts. Prosopis glandulosa, native to the southwestern United States and Mexico, is invading African arid and semiarid regions that are habitat for A. t. subsp. raddiana. The aim of this study was to evaluate and compare the tolerance and responses of the seedlings of these two tree species to shade, water stress and their interactions. We measured and recorded growth rates and morphological, biochemical and physiological plant traits under two radiation and two water treatments in greenhouse conditions. Radiation intensity was a stronger driver of the performance of both species than water availability. Beyond the independent effects of shade and drought, the interactions of these factors yielded synergistic effects on seedlings of both tree species, affecting key plant traits. The seedlings of A. t. subsp. raddiana were able to implement important shifts in key functional traits in response to altering abiotic stress conditions, behaving as a stress-tolerant species that is well-adapted to the habitat it occupies in hot arid African deserts. In contrast, the fast-growing seedlings of P. glandulosa were stress-avoiding. The alien P. glandulosa seedlings were highly sensitive to water and shade stress. Moreover, they were particularly sensitive to drought in shade conditions. However, although alien P. glandulosa seedlings were exposed to high stress levels, they were able to avoid permanent damage to their photosynthetic apparatus by mechanisms such as increasing energy dissipation by heat emission and by adjusting the relative allocation of resources to above- and below-ground structures. Our results are useful for conservation planning and restoration of invaded hyperarid ecosystems

Correlator implementation for orthogonal CSS used in an ultrasonic LPS

This paper presents a new architecture for the correlation of orthogonal complementary sets of sequences (OCSS) and their performance in an ultrasonic local positioning system (U-LPS). OCSS are sets of sequences whose addition of correlation functions has ideal properties, that makes interference-free code-division multiple access (CDMA) possible. They can be used to encode the signals emitted by a CDMA based U-LPS, enhancing the performance of such systems in terms of immunity against noise, multipath propagation, and near-far effect. Also, the orthogonality of the codes offers an operation resistance to multiaccess interference, which endows the U-LPS with the capability of simultaneous emission from different beacons. On the other hand, the detection of OCSS codes can be performed by means of efficient algorithms. This paper presents an optimization of previous proposals allowing the simultaneous correlation of OCSS by using fewer operations and memory elements. Furthermore, the hardware implementation of the proposed optimization is also addressed, and an U-LPS based on this proposal is presented.Fil: Peréz Rubio, M. Carmen. Universidad de Alcalá; EspañaFil: Sanz Serrano, Rebeca. Universidad de Alcalá; EspañaFil: Ureña Ureña, Jesús. Universidad de Alcalá; EspañaFil: Hernández Alonso, Álvaro. Universidad de Alcalá; EspañaFil: de Marziani, Carlos Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Álvarez Franco, Fernando J.. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ingeniería - Sede Comodoro; Argentin

Trans-oligomerization of duplicated aminoacyl-tRNA synthetases maintains genetic code fidelity under stress

Aminoacyl-tRNA synthetases (aaRSs) play a key role in deciphering the genetic message by producing charged tRNAs and are equipped with proofreading mechanisms to ensure correct pairing of tRNAs with their cognate amino acid. Duplicated aaRSs are very frequent in Nature, with 25,913 cases observed in 26,837 genomes. The oligomeric nature of many aaRSs raises the question of how the functioning and oligomerization of duplicated enzymes is organized. We characterized this issue in a model prokaryotic organism that expresses two different threonyl-tRNA synthetases, responsible for Thr-tRNAThr synthesis: one accurate and constitutively expressed (T1) and another (T2) with impaired proofreading activity that also generates mischarged Ser-tRNAThr. Low zinc promotes dissociation of dimeric T1 into monomers deprived of aminoacylation activity and simultaneous induction of T2, which is active for aminoacylation under low zinc. T2 either forms homodimers or heterodimerizes with T1 subunits that provide essential proofreading activity in trans. These findings evidence that in organisms with duplicated genes, cells can orchestrate the assemblage of aaRSs oligomers that meet the necessities of the cell in each situation. We propose that controlled oligomerization of duplicated aaRSs is an adaptive mechanism that can potentially be expanded to the plethora of organisms with duplicated oligomeric aaRSs.Ministerio de Economía y Competitividad BFU2010–19544, BFU2013–44686-

Tracking Control Based on Recurrent Neural Networks for Nonlinear Systems with Multiple Inputs and Unknown Deadzone

This paper deals with the problem of trajectory tracking for a broad class of uncertain nonlinear systems with multiple inputs each one subject to an unknown symmetric deadzone. On the basis of a model of the deadzone as a combination of a linear term and a disturbance-like term, a continuous-time recurrent neural network is directly employed in order to identify the uncertain dynamics. By using a Lyapunov analysis, the exponential convergence of the identification error to a bounded zone is demonstrated. Subsequently, by a proper control law, the state of the neural network is compelled to follow a bounded reference trajectory. This control law is designed in such a way that the singularity problem is conveniently avoided and the exponential convergence to a bounded zone of the difference between the state of the neural identifier and the reference trajectory can be proven. Thus, the exponential convergence of the tracking error to a bounded zone and the boundedness of all closed-loop signals can be guaranteed. One of the main advantages of the proposed strategy is that the controller can work satisfactorily without any specific knowledge of an upper bound for the unmodeled dynamics and/or the disturbance term

SaS-BCI: A New Strategy to Predict Image Memorability and use Mental Imagery as a Brain-Based Biometric Authentication

Security authentication is one of the most important levels of information security. Nowadays, human biometric techniques are the most secure methods for authentication purposes that cover the problems of older types of authentication like passwords and pins. There are many advantages of recent biometrics in terms of security; however, they still have some disadvantages. Progresses in technology made some specific devices, which make it possible to copy and make a fake human biometric because they are all visible and touchable. According to this matter, there is a need for a new biometric to cover the issues of other types. Brainwave is human data, which uses them as a new type of security authentication that has engaged many researchers. There are some research and experiments, which are investigating and testing EEG signals to find the uniqueness of human brainwave. Some researchers achieved high accuracy rates in this area by applying different signal acquisition techniques, feature extraction and classifications using Brain–Computer Interface (BCI). One of the important parts of any BCI processes is the way that brainwaves could be acquired and recorded. A new Signal Acquisition Strategy is presented in this paper for the process of authorization and authentication of brain signals specifically. This is to predict image memorability from the user’s brain to use mental imagery as a visualization pattern for security authentication. Therefore, users can authenticate themselves with visualizing a specific picture in their minds. In conclusion, we can see that brainwaves can be different according to the mental tasks, which it would make it harder using them for authentication process. There are many signal acquisition strategies and signal processing for brain-based authentication that by using the right methods, a higher level of accuracy rate could be achieved which is suitable for using brain signal as another biometric security authentication

A novel algorithm for the modelling of complex processes

In this investigation, a new algorithm is developed for the updating of a neural network. It is concentrated in a fuzzy transition between the recursive least square and extended Kalman filter algorithms with the purpose to get a bounded gain such that a satisfactory modeling could be maintained. The advised algorithm has the advantage compared with the mentioned methods that it eludes the excessive increasing or decreasing of its gain. The gain of the recommended algorithm is uniformly stable and its convergence is found. The new algorithm is employed for the modeling of two synthetic examples

A combined micro-Raman, X-ray absorption and magnetic study to follow the glycerol-assisted growth of epsilon-iron oxide sol-gel coatings

[EN] Epsilon iron oxide (ε-FeO) coatings on Si(100) substrates are obtained by an easy one-pot sol-gel recipe assisted by glycerol in an acid medium. Glycerol, given its small dimensions, enables the formation of ε-FeO nanoparticles with a size of a few nanometers and the highest purity is reached in coatings after a densification treatment at 960 °C. The structural and compositional evolution up to 1200 °C is studied by confocal Raman microscopy and X-ray absorption spectroscopy techniques, correlating the existing magnetic properties. We report a novel characterization method, which allows monitoring the evolution of the precursor micelles as well as the intermediate and final phases formed. Furthermore, the inherent industrial technology transfer of the sol-gel process is also demonstrated with the ε-FeO polymorph, impelling its application in the coatings form.This work has been supported by the Ministerio de Ciencia e Innovación (MCINN, Spain) through the projects PIE: 2021-60-E-030, PIE: 2010-6-OE-013, PID2019-104717RB-I00 (2020–2022), MAT2017-86540-C4-1-R, RTI2018-095856-B-C21 (2019–2021), RTI2018-097895-B-C43 and RTI2018-095303-A-C52. The authors are grateful to The ESRF (France), MCINN and Consejo Superior de Investigaciones Científicas (CSIC, Spain) for the provision of synchrotron radiation facilities and to the BM25-SpLine Staff for their valuable help. A.S.and A.M.-N acknowledge financial support from Comunidad de Madrid (Spain) for an “Atracción de Talento Investigador” Contract 2017-t2/IND5395 and 2018-T1/IND-10360, respectivel