An AER Spike-Processing Filter Simulator and Automatic VHDL Generator Based on Cellular Automata

Spike-based systems are neuro-inspired circuits implementations traditionally used for sensory systems or sensor signal processing. Address-Event- Representation (AER) is a neuromorphic communication protocol for transferring asynchronous events between VLSI spike-based chips. These neuro-inspired implementations allow developing complex, multilayer, multichip neuromorphic systems and have been used to design sensor chips, such as retinas and cochlea, processing chips, e.g. filters, and learning chips. Furthermore, Cellular Automata (CA) is a bio-inspired processing model for problem solving. This approach divides the processing synchronous cells which change their states at the same time in order to get the solution. This paper presents a software simulator able to gather several spike-based elements into the same workspace in order to test a CA architecture based on AER before a hardware implementation. Furthermore this simulator produces VHDL for testing the AER-CA into the FPGA of the USBAER AER-tool.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

A modular hybrid simulation framework for complex manufacturing system design

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated

Smart Video Systems in Police Cars

poster abstractThe use of video cameras in police cars has been found to have significant value and the number of such installed systems has been increasing. In addition to recording the events in routine traffic stops for later use in legal settings, in-car video cameras can be used to analyze in real-time or near real-time to detect critical events and notify police headquarters for help. This poster presents methods for detecting critical events in such police car videos. The specific critical events are person running out of a stopped car and officer falling down while approaching a stopped car. In the above situations, the aim is to alert the control center immediately for backup forces, especially in the last example when the officer is incapacitated. In order to implement real-time video processing so that a quick response can be generated without employing complex, slow, and brittle video processing algorithms, we use the reduced spatiotemporal representation (1D projection profile) and Hidden Markov Model to detect these events. The methods are tested on many video shots under various environmental and illumination conditions

EEG-informed fMRI reveals spatiotemporal characteristics of perceptual decision making

Single-unit and multiunit recordings in primates have already established that decision making involves at least two general stages of neural processing: representation of evidence from early sensory areas and accumulation of evidence to a decision threshold from decision-related regions. However, the relay of information from early sensory to decision areas, such that the accumulation process is instigated, is not well understood. Using a cued paradigm and single-trial analysis of electroencephalography (EEG), we previously reported on temporally specific components related to perceptual decision making. Here, we use information derived from our previous EEG recordings to inform the analysis of fMRI data collected for the same behavioral task to ascertain the cortical origins of each of these EEG components. We demonstrate that a cascade of events associated with perceptual decision making takes place in a highly distributed neural network. Of particular importance is an activation in the lateral occipital complex implicating perceptual persistence as a mechanism by which object decision making in the human brain is instigated

Query Analyzer and Manager for Complex Event Processing as a Service

Complex Event Processing (CEP) is a set of tools and techniques that can be used to obtain insights from high-volume, high-velocity continuous streams of events. CEP-based systems have been adopted in many situations that require prompt establishment of system diagnostics and execution of reaction plans, such as in monitoring of complex systems. This article describes the Query Analyzer and Manager (QAM) module, a first effort toward the development of a CEP as a Service (CEPaaS) system. This module is responsible for analyzing user-defined CEP queries and for managing their execution in distributed cloud-based environments. Using a language-agnostic internal query representation, QAM has a modular design that enables its adoption by virtually any CEP system

Simulador de sistemas AER basados en eventos

XXIII Simposium Nacional de la Unión Científica Internacional de Radio (URSI 2008). Madrid, 22-24 Septiembre 2008.Address-Event-Representation (AER) is a communications protocol for transferring (visual) information between chips, originally developed for bio-inspired vision and audition systems. Such systems may consist of a complicated multi-layer hierarchical structure with many chips that transmit events among them in real time, while performing some complex processing (for example, convolutions, competitions, etc). This sensing and processing technology is capable of very high speed throughput, because it does not rely on sensing and processing sequences of frames, and because it allows for complex hierarchically structured cortical-like layers for sophisticated processing. In this paper we present an effective tool that simulates the behaviour of such kind of structures. AER stream sources are fed to the software simulation tool and AER streams at all nodes of the network are computed. The tool has been developed in MATLAB and is event driven. It has been conceived as an open tool, so that any user can add extra functional blocks easily, or provide more elaborate or more simplified descriptions of already available blocks.Ministerio de Ciencia y Tecnología 2006-11730-C03-01 (Samanta2)Unión europea EU IST-2001-34124 (Caviar)Junta de Andalucía P06-TIC-0141

Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

A Hilbert Space Geometric Representation of Shared Awareness and Joint Decision Making

Two people in the same situation may ascribe very different meanings to their experiences. They will form different awareness, reacting differently to shared information. Various factors can give rise to this behavior. These factors include, but are not limited to, prior knowledge, training, biases, cultural factors, social factors, team vs. individual context, time, resources, and technology. At the individual level, the differences in attaining separate actions by accessing shared information may not be considered as an anomaly from the perspective of rational decision-making. But for group behavior, reacting differently to the shared information can give rise to conflicts and deviations from an expected behavior, and are categorized as an anomaly or irrational behavior. The lack of proper recognition of the reasons for differences can even impede the shared action towards attaining a common objective. The manifestation of differences becomes noticeable in complex situations. The shared awareness approaches that originate from available situational awareness models fail to recognize the reasons of an unexpected decision in these situations. One reason for this is that in complex situations, incompatible events can become dominant. Human information processing is sensitive to the compatibility of the events. This, and various other human psychological characteristics, require models to be developed that include comprehensive formalisms for both compatible and incompatible events in complex situations. Quantum probability provides a geometrical probabilistic formalism to study the decision and the dynamic cognitive systems in complex situations. The event representation in Hilbert space provides the necessary foundation to represent an individual\u27s knowledge of a situation. Hilbert space allows representing awareness as a superposition of indefinite states. These states form a complete N-dimensional Hilbert space. Within the space generated, events are represented as a subspace. By using these characteristics of Hilbert space and quantum geometrical probabilities, this study introduces a representation of self and other-than-self in a situation. An area of awareness with the possibility of projection onto the same event allows representing shared awareness geometrically. This formalism provides a coherent explanation of shared awareness for both compatible and incompatible events. Also, by using the superposition principles, the dissertation introduces spooky action at a distance concept in studying shared awareness

AER Filtering Using GLIDER: VHDL Cellular Automata Description

Cellular Automata (CA) is a bio-inspired processing model for problem solving, initially proposed by Von Neumann. This approach modularizes the processing by dividing the solution into synchronous cells that change their states at the same time in order to get the solution. The communication between them is crucial to achieve the correct solution. On the other hand, the Address-Event-Representation (AER) is a neuromorphic communication protocol for transferring asynchronous events between VLSI chips. These neuro-inspired implementations have been used to design sensor chips (retina, cochleas), processing chips (convolutions, filters) and learning chips, which makes it possible to develop complex, multilayer, multichip neuromorphic systems. This paper presents the fusion of both bio-inspired solutions for implementing a vision filter based on 3x3 convolutions. The GLIDER software tool for developing CA has been used to implement the filter in VHDL and synthesize it into the Spartan II 200 of the USB-AER.Junta de Andalucía P06-TIC-0141

Stepping back to look ahead: Neuter encapsulation and referent extension in counter-argumentative and causal relations in Spanish

In discourse comprehension, if all goes well, people tend to create a rich and coherent mental representation of the events described in the text. To do so, referential and relational coherence must be established in order to construct a connected discourse. The objective of this follow-up eye-tracking study (N = 72) is to explore the existence of an interaction effect between two factors: (a) the extension of the referent (short and long antecedent), and (b) the semantic relation (counter-argumentative a pesar de, and causal por), when processing the neuter pronoun ello in texts written in Spanish. No previous study has systematically compared the on-line processing of texts in which different extensions of the encapsulated anaphoric antecedent by the neuter pronoun ello ('this' or 'it' in English) are presented in diverse marked semantic relations (causal and counter-argumentative). Based on three eye-tracking measures, we found distinctive patterns of reading behavior when anaphoric neuter reference and semantic relations must be processed conjointly in order to construct a coherent mental representation. The main findings show that reading longer and more complex antecedents encapsulated by the neutral pronouns ello exerts more cognitive effort in late processing (Look Back measure), particularly when simultaneously and in the same discourse construction there is an explicitly marked counter-argumentative semantic relation. Implications for theories of referential and relational coherence are discussed