FPGA-based smart camera mote for pervasive wireless network

International audienceSmart camera networks raise challenging issues in many fields of research, including vision processing, communication protocols, distributed algorithms or power management. The ever increasing resolution of image sensors entails huge amounts of data, far exceeding the bandwidth of current networks and thus forcing smart camera nodes to process raw data into useful information. Consequently, on-board processing has become a key issue for the expansion of such networked systems. In this context, FPGA-based platforms, supporting massive, fine grain data parallelism, offer large opportunities. Besides, the concept of a middleware, providing services for networking, data transfer, dynamic loading or hardware abstraction, has emerged as a means of harnessing the hardware and software complexity of smart camera nodes. In this paper, we prospect the development of a new kind of smart cameras, wherein FPGAs provide high performance processing and general purpose processors support middleware services. In this approach, FPGA devices can be reconfigured at run-time through the network both from explicit user request and transparent middleware decision. An embedded real-time operating system is in charge of the communication layer, and thus can autonomously decide to use a part of the FPGA as an available processing resource. The classical programmability issue, a significant obstacle when dealing with FPGAs, is addressed by resorting to a domain specific high-level programming language (CAPH) for describing operations to be implemented on FPGAs

Distributed FPGA-based smart camera architecture for computer vision applications

International audienceSmart camera networks (SCN) raise challenging issues in many fields of research, including vision processing, communication protocols, distributed algorithms or power management. Furthermore, application logic in SCN is not centralized but spread among network nodes meaning that each node must have to process images to extract significant features, and aggregate data to understand the surrounding environment. In this context, smart camera have first embedded general purpose processor (GPP) for image processing. Since image resolution increases, GPPs have reached their limit to maintain real-time processing constraint. More recently, FPGA-based platforms have been studied for their massive parallelism capabilities. This paper present our new FPGA-based smart camera platform supporting cooperation between nodes and run-time updatable image processing. The architecture is based on a full reconfigurable pipeline driven by a softcore

DreamCAM: A FPGA-based platform for smart camera networks

International audience—The main challenges in smart camera networks come from the limited capacity of network communications. Indeed, wireless protocols such as the IEEE 802.15.4 protocol target low data rate, low power consumption and low cost wireless networking in order to fit the requirements of sensor networks. Since nodes more and more often integrate image sensors, network bandwidth has become a strong limiting factor in application deployment. This means that data must be processed at the node level before being sent on the network. In this context, FPGA-based platforms, supporting massive data parallelism, offer large opportunities for on-board processing. We present in this paper our FPGA-based smart camera platform, called DreamCam, which is able to autonomously exchange processed information on an Ethernet network

Parallel Image Gradient Extraction Core For FPGA-based Smart Cameras

International audienceOne of the biggest efforts in designing pervasive Smart Camera Networks (SCNs) is the implementation of complex and computationally intensive computer vision algorithms on resource constrained embedded devices. For low-level processing FPGA devices are excellent candidates because they support massive and fine grain data parallelism with high data throughput. However, if FPGAs offers a way to meet the stringent constraints of real-time execution, their exploitation often require significant algorithmic reformulations. In this paper, we propose a reformulation of a kernel-based gradient computation module specially suited to FPGA implementations. This resulting algorithm operates on-the-fly, without the need of video buffers and delivers a constant throughput. It has been tested and used as the first stage of an application performing extraction of Histograms of Oriented Gradients (HOG). Evaluation shows that its performance and low memory requirement perfectly matches low cost and memory constrained embedded devices

Area-energy aware dataflow optimisation of visual tracking systems

This paper presents an orderly dataflow-optimisation approach suitable for area-energy aware computer vision applications on FPGAs. Vision systems are increasingly being deployed in power constrained scenarios, where the dataflow model of computation has become popular for describing complex algorithms. Dataflow model allows processing datapaths comprised of several independent and well defined computations. However, compilers are often unsuccessful in identifying domain-specific optimisation opportunities resulting in wasted resources and power consumption. We present a methodology for the optimisation of dataflow networks, according to patterns often found in computer vision systems, focusing on identifying optimisations which are not discovered automatically by an optimising compiler. Code transformation using profiling and refactoring provides opportunities to optimise the design, targeting FPGA implementations and focusing on area and power abatement. Our refactoring methodology, applying transformations to a complex algorithm for visual tracking resulted in significant reduction in power consumption and resource usage

Decreased Cerebrospinal Fluid Flow Is Associated With Cognitive Deficit in Elderly Patients

Background: Disruptions in cerebrospinal fluid (CSF) flow during aging could compromise protein clearance from the brain and contribute to the etiology of Alzheimer’s Disease (AD).Objective: To determine whether CSF flow is associated with cognitive deficit in elderly patients (>70 years).Methods: We studied 92 patients admitted to our geriatric unit for non-acute reasons using phase-contrast magnetic resonance imaging (PC-MRI) to calculate their ventricular and spinal CSF flow, and assessed their global cognitive status, memory, executive functions, and praxis. Multivariable regressions with backward selection (criterion p < 0.15) were performed to determine associations between cognitive tests and ventricular and spinal CSF flow, adjusting for depression, anxiety, and cardiovascular risk factors.Results: The cohort comprised 71 women (77%) and 21 (33%) men, aged 84.1 ± 5.2 years (range, 73–96). Net ventricular CSF flow was 52 ± 40 μL/cc (range, 0–210), and net spinal CSF flow was 500 ± 295 μL/cc (range, 0–1420). Ventricular CSF flow was associated with the number of BEC96 figures recognized (β = 0.18, CI, 0.02–0.33; p = 0.025). Spinal CSF flow was associated with the WAIS Digit Span Backward test (β = 0.06, CI, 0.01–0.12; p = 0.034), and categoric verbal fluency (β = 0.53, CI, 0.07–0.98; p = 0.024) and semantic verbal fluency (β = 0.55, CI, 0.07–1.02; p = 0.024).Conclusion: Patients with lower CSF flow had significantly worse memory, visuo-constructive capacities, and verbal fluency. Alterations in CSF flow could contribute to some of the cognitive deficit observed in patients with AD. Diagnosis and treatment of CSF flow alterations in geriatric patients with neurocognitive disorders could contribute to the prevention of their cognitive decline

Pluto's lower atmosphere and pressure evolution from ground-based stellar occultations, 1988-2016

Context. The tenuous nitrogen (N2) atmosphere on Pluto undergoes strong seasonal effects due to high obliquity and orbital eccentricity, and has recently (July 2015) been observed by the New Horizons spacecraft. Aims. The main goals of this study are (i) to construct a well calibrated record of the seasonal evolution of surface pressure on Pluto and (ii) to constrain the structure of the lower atmosphere using a central flash observed in 2015. Methods. Eleven stellar occultations by Pluto observed between 2002 and 2016 are used to retrieve atmospheric profiles (density, pressure, temperature) between altitude levels of ~5 and ~380 km (i.e. pressures from ~ 10 μbar to 10 nbar). Results. (i) Pressure has suffered a monotonic increase from 1988 to 2016, that is compared to a seasonal volatile transport model, from which tight constraints on a combination of albedo and emissivity of N2 ice are derived. (ii) A central flash observed on 2015 June 29 is consistent with New Horizons REX profiles, provided that (a) large diurnal temperature variations (not expected by current models) occur over Sputnik Planitia; and/or (b) hazes with tangential optical depth of ~0.3 are present at 4–7 km altitude levels; and/or (c) the nominal REX density values are overestimated by an implausibly large factor of ~20%; and/or (d) higher terrains block part of the flash in the Charon facing hemisphere