An AER handshake-less modular infrastructure PCB with x8 2.5Gbps LVDS serial links

Nowadays spike-based brain processing emulation is taking off. Several EU and others worldwide projects are demonstrating this, like SpiNNaker, BrainScaleS, FACETS, or NeuroGrid. The larger the brain process emulation on silicon is, the higher the communication performance of the hosting platforms has to be. Many times the bottleneck of these system implementations is not on the performance inside a chip or a board, but in the communication between boards. This paper describes a novel modular Address-Event-Representation (AER) FPGA-based (Spartan6) infrastructure PCB (the AER-Node board) with 2.5Gbps LVDS high speed serial links over SATA cables that offers a peak performance of 32-bit 62.5Meps (Mega events per second) on board-to-board communications. The board allows back compatibility with parallel AER devices supporting up to x2 28-bit parallel data with asynchronous handshake. These boards also allow modular expansion functionality through several daughter boards. The paper is focused on describing in detail the LVDS serial interface and presenting its performance.Ministerio de Ciencia e Innovación TEC2009-10639-C04-02/01Ministerio de Economía y Competitividad TEC2012-37868-C04-02/01Junta de Andalucía TIC-6091Ministerio de Economía y Competitividad PRI-PIMCHI-2011-076

Spike-based VITE control with Dynamic Vision Sensor applied to an Arm Robot.

Spike-based motor control is very important in the field of robotics and also for the neuromorphic engineering community to bridge the gap between sensing / processing devices and motor control without losing the spike philosophy that enhances speed response and reduces power consumption. This paper shows an accurate neuro-inspired spike-based system composed of a DVS retina, a visual processing system that detects and tracks objects, and a SVITE motor control, where everything follows the spike-based philosophy. The control system is a spike version of the neuroinspired open loop VITE control algorithm implemented in a couple of FPGA boards: the first one runs the algorithm and the second one drives the motors with spikes. The robotic platform is a low cost arm with four degrees of freedom.Ministerio de Ciencia e Innovación TEC2009-10639-C04-02/01Ministerio de Economía y Competitividad TEC2012-37868-C04-02/0

FireFly Mosaic: A Vision-Enabled Wireless Sensor Networking System

Abstract — With the advent of CMOS cameras, it is now possible to make compact, cheap and low-power image sensors capable of on-board image processing. These embedded vision sensors provide a rich new sensing modality enabling new classes of wireless sensor networking applications. In order to build these applications, system designers need to overcome challanges associated with limited bandwith, limited power, group coordination and fusing of multiple camera views with various other sensory inputs. Real-time properties must be upheld if multiple vision sensors are to process data, com-municate with each other and make a group decision before the measured environmental feature changes. In this paper, we present FireFly Mosaic, a wireless sensor network image processing framework with operating system, networking and image processing primitives that assist in the development of distributed vision-sensing tasks. Each FireFly Mosaic wireless camera consists of a FireFly [1] node coupled with a CMUcam3 [2] embedded vision processor. The FireFly nodes run the Nano-RK [3] real-time operating system and communicate using the RT-Link [4] collision-free TDMA link protocol. Using FireFly Mosaic, we demonstrate an assisted living application capable of fusing multiple cameras with overlapping views to discover and monitor daily activities in a home. Using this application, we show how an integrated platform with support for time synchronization, a collision-free TDMA link layer, an underlying RTOS and an interface to an embedded vision sensor provides a stable framework for distributed real-time vision processing. To the best of our knowledge, this is the first wireless sensor networking system to integrate multiple coordinating cameras performing local processing. I

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

The JPL telerobot operator control station. Part 1: Hardware

The Operator Control Station of the Jet Propulsion Laboratory (JPL)/NASA Telerobot Demonstrator System provides the man-machine interface between the operator and the system. It provides all the hardware and software for accepting human input for the direct and indirect (supervised) manipulation of the robot arms and tools for task execution. Hardware and software are also provided for the display and feedback of information and control data for the operator's consumption and interaction with the task being executed. The hardware design, system architecture, and its integration and interface with the rest of the Telerobot Demonstrator System are discussed

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions