ASIP-based reconfigurable architectures for power-efficient and real-time image/video processing

To meet both flexibility and performance requirements, particularly when implementing high-end real-time image/video processing algorithms, the paper proposes to combine the application specific instruction-set processor (ASIP) paradigm with the reconfigurable hardware one. As case studies, the design of partially reconfigurable ASIP (r-ASIP) architectures is presented for two classes of algorithms with widespread diffusion in image/video processing: motion estimation and retinex filtering. Design optimizations are addressed at both algorithmic and architectural levels. Special processor concepts used to trade-off performance versus flexibility and to enable new features of post-fabrication configurability are shown. Silicon implementation results are compared to known ASIC, DSP or reconfigurable designs; the proposed r-ASIPs stand for their better performance–flexibility figures in the respective algorithmic class.

Atomic force microscopy of protein films and crystals

A customized atomic force microscopy (AFM) instrument optimized for imaging protein crystals in solution is described. The device was tested on crystals and Langmuir-Blodgett (LB) films of two proteins with quite different molecular weights. This approach enables the periodicity and morphology of crystals to be studied in their mother liquid, thereby preserving the native periodic protein crystal structure, which is typically destroyed by drying. Moreover, the instrument appears to distinguish protein crystals from salt crystals, which under the optical microscope are frequently quite similar, the difference between them often being revealed only during x-ray analysis. AFM estimates of the packing, order, and morphology of the given single proteins appear quite similar in the LB thin film and in the crystals, which means that routine crystal measurements can be performed at high resolution. The AFM consists of a custom-built measuring head and a homemade flexible SPM controller which can drive the head for contact, noncontact and spectroscopy modes, thus providing the user with a high degree of customization for crystal measurement.     

Development of an auxiliary system for the execution of vascular catheter interventions with a reduced radiological risk; system description and first experimental results

Vascular catheterization is a common procedure in clinical medicine. It is normally performed by a specialist using an X-ray fluoroscopic guide and contrast-media. In the present paper, an image-guided navigation system which indicates a path providing guidance to the desired target inside the vascular tree is described with the aim of reducing the exposure of personnel and patients to X-rays during the catheterization procedure. A 3D model of the patient vascular tree, reconstructed with data collected by an angiography before starting the intervention, is used as a guide map instead of fluoroscopic scans. An accurate spatial correspondence between the body of the patient and the 3D reconstructed vascular model is established and, by means of a position indicator installed over the catheter tip, the real-time position/orientation of the tip is indicated correctly. This paper describes the system and the operational procedures necessary to use the proposed method efficiently during a catheter intervention. Preliminary experimental results on a phantom are also reported.     

Real-time scheduling for energy harvesting sensor nodes

Energy harvesting has recently emerged as a feasible option to increase the operating time of sensor networks. If each node of the network, however, is powered by a fluctuating energy source, common power management solutions have to be reconceived. This holds in particular if real-time responsiveness of a given application has to be guaranteed. Task scheduling at the single nodes should account for the properties of the energy source, capacity of the energy storage as well as deadlines of the single tasks. We show that conventional scheduling algorithms (like e.g. EDF) are not suitable for this scenario. Based on this motivation, we have constructed optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Further we present an admittance test that decides for arbitrary task sets, whether they can be scheduled without deadline violations. To this end, we introduce the concept of energy variability characterization curves (EVCC) which nicely captures the dynamics of various energy sources. Simulation results show that our algorithms allow significant reductions of the battery size compared to Earliest Deadline First scheduling.

Generalization in the XCSF classifier system: analysis, improvement, and extension

We analyze generalization in XCSF and introduce three improvements. We begin by showing that the types of generalizations evolved by XCSF can be influenced by the input range. To explain these results we present a theoretical analysis of the convergence of classifier weights in XCSF which highlights a broader issue. In XCSF, because of the mathematical properties of the Widrow-Hoff update, the convergence of classifier weights in a given subspace can be slow when the spread of the eigenvalues of the autocorrelation matrix associated with each classifier is large. As a major consequence, the system's accuracy pressure may act before classifier weights are adequately updated, so that XCSF may evolve piecewise constant approximations, instead of the intended, and more efficient, piecewise linear ones. We propose three different ways to update classifier weights in XCSF so as to increase the generalization capabilities of XCSF: one based on a condition-based normalization of the inputs, one based on linear least squares, and one based on the recursive version of linear least squares. Through a series of experiments we show that while all three approaches significantly improve XCSF, least squares approaches appear to be best performing and most robust. Finally we show how XCSF can be extended to include polynomial approximations.     

Simulating Emergent Properties of Coordination in Maude: the Collective Sort Case

Recent coordination languages and models are moving towards the application of techniques coming from the research context of complex systems: adaptivity and self-organization are exploited in order to tackle the openness, dynamism and unpredictability of today's distributed systems. In this area, systems are to be described using stochastic models, and simulation is a valuable tool both for analysis and design. Accordingly, in this work we focused on modelling and simulating emergent properties of coordination techniques.We first develop a framework acting as a general-purpose engine for simulating stochastic transition systems, built as a library for the Maude term rewriting system. We then evaluate this tool to a coordination problem called collective sort, where autonomous agents move tuples across different tuple spaces according to local criteria, and resulting in the emergence of the complete clustering property.     

A low-power wireless video sensor node for distributed object detection

In this paper we propose MicrelEye, a wireless video node for cooperative distributed video processing applications that involve image classification. The node is equipped with a low-cost VGA CMOS image sensor, a reconfigurable processing engine (FPGA, Microcontroller, SRAM) and a Bluetooth 100-m transceiver. It has a size of few cubic centimeters and its typical power consumption is approximately ten times less than that of typical commercial DSP-based solutions. As regards classification, a highly optimized hardware-oriented support vector machine-like (SVM-like) algorithm called ERSVM is proposed and implemented. We describe our hardware and software architecture, its performance and power characteristics. The case study considered in this paper is people detection. The obtained results suggest that the present technology allows for the design of simple intelligent video nodes capable of performing classification tasks locally.

Algorithmic and architectural design for real-time and power-efficient Retinex image/video processing

This paper presents novel algorithmic and architectural solutions for real-time and power-efficient enhancement of images and video sequences. A programmable class of Retinex-like filters, based on the separation of the illumination and reflectance components, is proposed. The dynamic range of the input image is controlled by applying a suitable non-linear function to the illumination, while the details are enhanced by processing the reflectance. An innovative spatially recursive rational filter is used to estimate the illumination. Moreover, to improve the visual quality results of two-branch Retinex operators when applied to videos, a novel three-branch technique is proposed which exploits both spatial and temporal filtering. Real-time implementation is obtained by designing an Application Specific Instruction-set Processor (ASIP). Optimizations are addressed at algorithmic and architectural levels. The former involves arithmetic accuracy definition and linearization of non-linear operators; the latter includes customized instruction set, dedicated memory structure, adapted pipeline, bypasses, custom address generator, and special looping structures. The ASIP is synthesized in standard-cells CMOS technology and its performances are compared to known Digital signal processor (DSP) implementations of real-time Retinex filters. As a result of the comparison, the proposed algorithmic/architectural design outperforms state-of-art Retinex-like operators achieving the best trade-off between power consumption, flexibility, and visual quality.