An Event-Driven Multi-Kernel Convolution Processor Module for Event-Driven Vision Sensors

Event-Driven vision sensing is a new way of sensing visual reality in a frame-free manner. This is, the vision sensor (camera) is not capturing a sequence of still frames, as in conventional video and computer vision systems. In Event-Driven sensors each pixel autonomously and asynchronously decides when to send its address out. This way, the sensor output is a continuous stream of address events representing reality dynamically continuously and without constraining to frames. In this paper we present an Event-Driven Convolution Module for computing 2D convolutions on such event streams. The Convolution Module has been designed to assemble many of them for building modular and hierarchical Convolutional Neural Networks for robust shape and pose invariant object recognition. The Convolution Module has multi-kernel capability. This is, it will select the convolution kernel depending on the origin of the event. A proof-of-concept test prototype has been fabricated in a 0.35 m CMOS process and extensive experimental results are provided. The Convolution Processor has also been combined with an Event-Driven Dynamic Vision Sensor (DVS) for high-speed recognition examples. The chip can discriminate propellers rotating at 2 k revolutions per second, detect symbols on a 52 card deck when browsing all cards in 410 ms, or detect and follow the center of a phosphor oscilloscope trace rotating at 5 KHz.Unión Europea 216777 (NABAB)Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

Microbial induced corrosion by ferric–reducing bacteria isolated from an oil separation tank

Se requiere identificar a las poblaciones microbianas que participan en la Corrosión Inducida por Microorganismos, con la finalidad de implementar estrategias de monitoreo eficiente y de control. Las poblaciones de microorganismos anaerobios presentes en la industria petrolera, particularmente en la producción de gas y petróleo, así como en las líneas de transporte y en los tanques de almacenamiento, han sido estudiadas muy pobremente y los estudios presentes se han enfocado principalmente en bacterias sulfatorreductoras de los géneros Desulfovibrio y Desulfobacter. Sin embargo, las bacterias fermentativas también tienen gran relevancia en la corrosión de metales, como se describió en 1997, por el grupo de Magot y colaboradores, quienes caracterizaron una bacteria no sulfidogénica pero con capacidad de producir corrosión. En este estudio se aisló de un tanque de separación, una bacteria anaerobia, fermentativa y reductora de fierro, perteneciente al género Sedimentibacter, con capacidad de producir corrosión en el acero al carbón SAE1018.It has required the characterization and identification of the microbial populations responsible for Microbial Induced Corrosion (MIC), and their interactions with distinctive microorganisms allocated on metallic surfaces, in order to implement efficient monitoring and control strategies. Microbial anaerobic communities present at oil and gas producing, transporting and storage facilities have been poorly characterized and studies had mainly focused on Desulfovibrio and Desulfobacter genus. However, fermentative bacteria have important participation on corrosion metals as described by Magot et al. (1997), which characterization of non-SRB sulfidogenic bacteria was able to produce corrosion. In this study, it was isolated of an oil-water tank separation, an anaerobic bacterium, fermentative and ferric-reducing, belong to Sedimentibacter genus with corrosion capability on Carbon Steel SAE1018

A FPGA Spike-Based Robot Controlled with Neuro-inspired VITE

This paper presents a spike-based control system applied to a fixed robotic platform. Our aim is to take a step forward to a future complete spikes processing architecture, from vision to direct motor actuation. This paper covers the processing and actuation layer over an anthropomorphic robot. In this way, the processing layer uses the neuro-inspired VITE algorithm, for reaching a target, based on PFM taking advantage of spike system information: its frequency. Thus, all the blocks of the system are based on spikes. Each layer is implemented within a FPGA board and spikes communication is codified under the AER protocol. The results show an accurate behavior of the robotic platform with 6-bit resolution for a 130º range per joint, and an automatic speed control of the algorithm. Up to 96 motor controllers could be integrated in the same FPGA, allowing the positioning and object grasping by more complex anthropomorphic robots.Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Ministerio de Economía y Competitividad TEC2012-37868-C04-0

Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm

The backpropagation (BP) algorithm is a gradient-based algorithm used for training a feedforward neural network (FNN). Despite the fact that BP is still used today when FNNs are trained, it has some disadvantages, including the following: (i) it fails when non-differentiable functions are addressed, (ii) it can become trapped in local minima, and (iii) it has slow convergence. In order to solve some of these problems, metaheuristic algorithms have been used to train FNN. Although they have good exploration skills, they are not as good as gradient-based algorithms at exploitation tasks. The main contribution of this article lies in its application of novel memetic approaches based on the Gravitational Search Algorithm (GSA) and Chaotic Gravitational Search Algorithm (CGSA) algorithms, called respectively Memetic Gravitational Search Algorithm (MGSA) and Memetic Chaotic Gravitational Search Algorithm (MCGSA), to train FNNs in three classical benchmark problems: the XOR problem, the approximation of a continuous function, and classification tasks. The results show that both approaches constitute suitable alternatives for training FNNs, even improving on the performance of other state-of-the-art metaheuristic algorithms such as ParticleSwarm Optimization (PSO), the Genetic Algorithm (GA), the Adaptive Differential Evolution algorithm with Repaired crossover rate (Rcr-JADE), and the Covariance matrix learning and Bimodal distribution parameter setting Differential Evolution (COBIDE) algorithm. Swarm optimization, the genetic algorithm, the adaptive differential evolution algorithm with repaired crossover rate, and the covariance matrix learning and bimodal distribution parameter setting differential evolution algorithm.El algoritmo de retropropagación (BP) es un algoritmo basado en gradientes que se utiliza para entrenar una red neuronal feedforward (FNN). A pesar de que BP todavía se usa hoy en día cuando se entrenan las FNN, tiene algunas desventajas, incluidas las siguientes: (i) falla cuando se abordan funciones no diferenciables, (ii) puede quedar atrapada en mínimos locales y (iii) ) tiene convergencia lenta. Para resolver algunos de estos problemas, se han utilizado algoritmos metaheurísticos para entrenar FNN. Aunque tienen buenas habilidades de exploración, no son tan buenos como los algoritmos basados ​​en gradientes en las tareas de explotación. La principal contribución de este artículo radica en la aplicación de nuevos enfoques meméticos basados ​​en los algoritmos Gravitational Search Algorithm (GSA) y Chaotic Gravitational Search Algorithm (CGSA), llamados respectivamente Algoritmo de búsqueda gravitacional memético (MGSA) y Algoritmo de búsqueda gravitacional caótico memético (MCGSA), para entrenar FNN en tres problemas de referencia clásicos: el problema XOR, la aproximación de una función continua y tareas de clasificación. Los resultados muestran que ambos enfoques constituyen alternativas adecuadas para el entrenamiento de FNN, incluso mejorando el rendimiento de otros algoritmos metaheurísticos de última generación como ParticleSwarm Optimization (PSO), el Algoritmo Genético (GA), el algoritmo de Evolución Diferencial Adaptativa con Tasa de cruce reparada (Rcr-JADE) y el algoritmo de evolución diferencial (COBIDE) de configuración de parámetros de distribución bimodal y aprendizaje de matriz de covarianza. Optimización de enjambre, el algoritmo genético, el algoritmo de evolución diferencial adaptativo con tasa de cruce reparada

A Memetic Chaotic Gravitational Search Algorithm for unconstrained global optimization problems

Metaheuristic optimization algorithms address two main tasks in the process of problem solving: i) exploration (also called diversification) and ii) exploitation (also called intensification). Guaranteeing a trade-off between these operations is critical to good performance. However, although many methods have been proposed by which metaheuristics can achieve a balance between the exploration and exploitation stages, they are still worse than exact algorithms at exploitation tasks, where gradient-based mechanisms outperform metaheuristics when a local minimum is approximated. In this paper, a quasi-Newton method is introduced into a Chaotic Gravitational Search Algorithm as an exploitation method, with the purpose of improving the exploitation capabilities of this recent and promising population-based metaheuristic. The proposed approach, referred to as a Memetic Chaotic Gravitational Search Algorithm, is used to solve forty-five benchmark problems, both synthetic and real-world, to validate the method. The numerical results show that the adding of quasi-Newton search directions to the original (Chaotic) Gravitational Search Algorithm substantially improves its performance. Also, a comparison with the state-of-the-art algorithms: Particle Swarm Optimization, Genetic Algorithm, Rcr-JADE, COBIDE and RLMPSO, shows that the proposed approach is promising for certain real-world problems.Los algoritmos de optimización metaheurística abordan dos tareas principales en el proceso de resolución de problemas: i) exploración (también llamada diversificación ) y ii) explotación (también llamada intensificación ). Garantizar una compensación entre estas operaciones es fundamental para un buen desempeño. Sin embargo, aunque se han propuesto muchos métodos mediante los cuales las metaheurísticas pueden lograr un equilibrio entre las etapas de exploración y explotación , siguen siendo peores que los algoritmos exactos en las tareas de explotación, donde los mecanismos basados ​​en gradientes superan a las metaheurísticas cuando se aproxima a un mínimo local. En este artículo, se introduce un método cuasi-Newton en un sistema caóticoAlgoritmo de Búsqueda Gravitacional como método de explotación, con el propósito de mejorar las capacidades de explotación de esta reciente y prometedora metaheurística basada en población. El enfoque propuesto, denominado algoritmo de búsqueda gravitacional caótica memética, se utiliza para resolver cuarenta y cinco problemas de referencia, tanto sintéticos como del mundo real, para validar el método. Los resultados numéricos muestran que la adición de direcciones de búsqueda cuasi-Newton al algoritmo de búsqueda gravitacional original (caótico) mejora sustancialmente su rendimiento. Además, una comparación con los algoritmos de última generación: Particle Swarm Optimization, Genetic Algorithm, Rcr-JADE, COBIDE y RLMPSO, muestra que el enfoque propuesto es prometedor para ciertos problemas del mundo real

Metal Mobility in Embryonic-to-Proto-Ni-Laterite Profiles from Non-Tropical Climates

We evaluated the mobility of a wide suite of economic metals (Ni, Co, REE, Sc, PGE) in Ni-laterites with different maturities, developed in the unconventional humid/hyper-humid Mediterranean climate. An embryonic Ni-laterite was identified at Los Reales in southern Spain, where a saprolite profile of ~1.5mthick was formed at the expense of peridotites of the subcontinental lithospheric mantle. In contrast, a more mature laterite was reported from Camán in south-central Chile, where the thicker (~7 m) weathering profile contains well-developed lower and upper oxide horizons. This comparative study reveals that both embryonic and mature laterites can form outside the typical (sub)-tropical climate conditions expected for lateritic soils, while demonstrating a similar chemical evolution in terms of major (MgO, Fe2O3, and Al2O3), minor (Ni, Mn, Co, Ti, Cr), and trace (REE, Y, Sc, PGE, Au) element concentrations. We show that, even in the earliest stages of laterization, the metal remobilization from primary minerals can already result in uneconomic concentration values.MECRAS Project A-RNM-356-UGR20 “Proyectos de I+D+i en el marco del Programa Operativo FEDER Andalucía 2014-2020” of the Consejería de Economía, Conocimiento, Empresa

Towards AER VITE: building spike gate signal

Neuromorphic engineers aim to mimic the precise and efficient mechanisms of the nervous system to process information using spikes from sensors to actuators. There are many available works that sense and process information in a spike-based way. But there are still several gaps in the actuation and motor control field in a spike-based way. Spike-based Proportional-Integrative-Derivative controllers (PID) are present in the literature. On the other hand, neuro-inspired control models as VITE (Vector Integration To End point) and FLETE (Factorization of muscle Length and muscle Tension) are also present in the literature. This paper presents another step toward the spike implementation of those neuro-inspired models. We present a spike-based ramp multiplier. VITE algorithm generates the way to achieve a final position targeted by a mobile robotic arm. The block presented is used as a gate for the way involved and it also puts the incoming movement on speed with a variable slope profile. Only spikes for information representation were used and the process is in real time. The software simulation based on Simulink and Xilinx System Generator shows the accurate adjust to the traditional processing for short time periods and the hardware tests confirm and extend the previous simulated results for any time. We have implemented the spikes generator, the ramp multiplier and the low pass filter into the Virtex-5 FPGA and connected this with an USB-AER (Address Event Representation) board to monitor the spikes.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

Fully Digital AER Convolution Chip for Vision Processing

We present a neuromorphic fully digital convolution microchip for Address Event Representation (AER) spike-based processing systems. This microchip computes 2-D convolutions with a programmable kernel in real time. It operates on a pixel array of size 32 x 32, and the kernel is programmable and can be of arbitrary shape and size up to 32 x 32 pixels. The chip receives and generates data in AER format, which is asynchronous and digital. The paper describes the architecture of the chip, the test setup, and experimental results obtained from a fabricated prototype.European Union IST-2001-34124 (CAVIAR)Comisión Interministerial de Ciencia y Tecnología TIC-2003-08164-C03-01Ministerio de Educación y Ciencia TEC2006-11730-C03-01Junta de Andalucía P06-TIC-0141

Obtención de un número difuso utilizable en aplicaciones a partir de datos ordenados

Los datos imprecisos son tratados mediante números difusos en aplicaciones reales. El problema principal radica en la obtención de la función de pertenencia del número difuso a partir de un conjunto de datos. En este trabajo se trata el problema de la recapitulación de datos como número difuso, más concretamente, se presenta un método para la obtención de un número difuso trapezoidal a partir de un conjunto de datos ordenado. El método tiene un orden de complejidad O(n2)

Software Generation of Address-Event-Representation for Interchip Images Communications

Address-Event-Representation (AER) is a communications protocol for transferring images between chips, originally developed for bio-inspired image processing systems. Such systems may consist of a complicated hierarchical structure with many chips that transmit images among them in real time, while performing some processing (for example, convolutions). In developing AER based systems it is very convenient to have available some kind of means of generating AER streams from on-computer stored images. In this paper we present a method for generating AER streams in real time from images stored in a computer’s memory. The method exploits the concept of linear feedback shift register random number generators. This method has been tested by software and compared to other possible algorithms for generating AER streams. It has been found that the proposed method yields a minimum error with respect to the ideal situation. A hardware platform that exploits this technique is currently under development