Accelerated hardware video object segmentation: From foreground detection to connected components labelling

This is the preprint version of the Article - Copyright @ 2010 ElsevierThis paper demonstrates the use of a single-chip FPGA for the segmentation of moving objects in a video sequence. The system maintains highly accurate background models, and integrates the detection of foreground pixels with the labelling of objects using a connected components algorithm. The background models are based on 24-bit RGB values and 8-bit gray scale intensity values. A multimodal background differencing algorithm is presented, using a single FPGA chip and four blocks of RAM. The real-time connected component labelling algorithm, also designed for FPGA implementation, run-length encodes the output of the background subtraction, and performs connected component analysis on this representation. The run-length encoding, together with other parts of the algorithm, is performed in parallel; sequential operations are minimized as the number of run-lengths are typically less than the number of pixels. The two algorithms are pipelined together for maximum efficiency

Design and management of image processing pipelines within CPS: Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints

PickCells: A Physically Reconfigurable Cell-composed Touchscreen

Touchscreens are the predominant medium for interactions with digital services; however, their current fixed form factor narrows the scope for rich physical interactions by limiting interaction possibilities to a single, planar surface. In this paper we introduce the concept of PickCells, a fully reconfigurable device concept composed of cells, that breaks the mould of rigid screens and explores a modular system that affords rich sets of tangible interactions and novel acrossdevice relationships. Through a series of co-design activities – involving HCI experts and potential end-users of such systems – we synthesised a design space aimed at inspiring future research, giving researchers and designers a framework in which to explore modular screen interactions. The design space we propose unifies existing works on modular touch surfaces under a general framework and broadens horizons by opening up unexplored spaces providing new interaction possibilities. In this paper, we present the PickCells concept, a design space of modular touch surfaces, and propose a toolkit for quick scenario prototyping

A framework for flexible and reconfigurable vision inspection systems

Reconfiguration activities remain a significant challenge for automated Vision Inspection Systems (VIS), which are characterized by hardware rigidity and time-consuming software programming tasks. This work contributes to overcoming the current gap in VIS reconfigurability by proposing a novel framework based on the design of Flexible Vision Inspection Systems (FVIS), enabling a Reconfiguration Support System (RSS). FVIS is achieved using reprogrammable hardware components that allow for easy setup based on software commands. The RSS facilitates offline software programming by extracting parameters from real images, Computer-Aided Design (CAD) data, and rendered images using Automatic Feature Recognition (AFR). The RSS offers a user-friendly interface that guides non-expert users through the reconfiguration process for new part types, eliminating the need for low-level coding. The proposed framework has been practically validated during a 4-year collaboration with a global leading automotive half shaft manufacturer. A fully automated FVIS and the related RSS have been designed following the proposed framework and are currently implemented in 7 plants of GKN global automotive supplier, checking 60 defect types on thousands of parts per day, covering more than 200 individual part types and 12 part families

Building Blocks for Adaptive Modular Sensing Systems

This thesis contributes towards the development of systems and strategies by which sensor and actuator components can be combined to produce flexible and robust sensor systems for a given application. A set of intelligent modular blocks (building blocks) have been created from which composite sensors (made up of multiple sensor and actuator components) can be rapidly reconfigured for the construction of Adaptive Modular Sensing Systems. The composite systems are expected to prove useful in several application domains including industrial control, inspection systems, mobile robotics, monitoring and data acquisition. The intelligent building blocks, referred to as transducer interface modules, contain embedded knowledge about their capabilities and how they can interact with other modules. These modules encapsulate a general purpose modular hardware architecture that provides an interface between the sensors, the actuators, and the communication medium. The geometry of each transducer interface module is a cube. A connector mechanism implemented on each face of the module enables physical connection of the modules. Each module provides a core functionality and can be connected to other modules to form more capable composite sensors. Once the modules are combined, the capabilities (e.g., range, resolution, sample rate, etc.) and functionality (e.g., temperature measurement) of the composite sensor is determined and communicated to other sensors in the enviornment. For maximum flexibility, a distributed software architecture is executed on the blocks to enable automatic acquisition of configuration-specific algorithms. This logical algorithm imparts a collective identity to the composite group, and processes data based on the capabilities and functionalities of the transducers present in the system. A knowledge representation scheme allows each module in the composite group to store and communicate its functionality and capabilities to other connected modules in the system

A Multi-Site NFV Testbed for Experimentation With SUAV-Based 5G Vertical Services

[EN] With the advent of 5G technologies, vertical markets have been placed at the forefront, as fundamental drivers and adopters of technical developments and new business models. Small Unmanned Aerial Vehicles (SUAVs) are gaining traction in multiple vertical sectors, as key assets to generate, process, and distribute relevant information for the provision of value-added services. However, the enormous potential of SUAVs to support a exible, rapid, and cost-effective deployment of vertical applications is still to be exploited. In this paper, we leverage our prior work on Network Functions Virtualization (NFV) and SUAVs to design and build a multi-site experimentation testbed based on open-source technologies. The goal of this testbed is to explore synergies among NFV, SUAVs, and vertical services, following a practical approach primarily governed by experimentation. To verify our testbed design, we realized a reference use case where a number of SUAVs, cloud infrastructures, and communication protocols are used to provide a multi-site vertical service. Our experimentation results suggest the potential of NFV and SUAVs to exibly support vertical services. The lessons learned have served to identify missing elements in our NFV platform, as well as challenging aspects for potential improvement. These include the development of speci c mechanisms to limit processing load and delays of service deployment operations.This work was supported in part by the European Commission under the European Union's Horizon 2020 program (5GRANGE Project, grant agreement number 777137), and in part by the 5GCity Project funded by the Spanish Ministry of Economy and Competitiveness under Grant TEC2016-76795-C6-1R, Grant TEC2016-76795-C6-3R, and Grant TEC2016-76795-C6-5R

LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft