Proactive defragmentation in elastic optical networks under dynamic load conditions

The main weakness of elastic optical networks (EON), under dynamic traffic conditions, stems from spectrum fragmentation. A lot of research efforts have been dedicated during recent years to spectrum defragmentation. In this work, a thorough study about proactive defragmentation is carried out. Effects of the different defragmentation parameters on the EON performance are analyzed, and appropriate values of the defragmentation period, which guarantee suitable network performance while keeping the network control complexity at reasonable values, are obtained by means of extensive simulations. Benefit obtained by applying different defragmentation strategies, in terms of increase in the supported load at a given bandwidth blocking probability, is also reported. Different traffic conditions and network topologies are simulated to assess the validity of the obtained results.     

Fault Tolerance Mechanisms for FPGA-Based Regular Expression Matching

Traditional fault-tolerance techniques relying on spatial and temporal redundancy typically imply high power, delay, and area overheads. Cost-effective solutions often depend on system’s design and hardware platform at hand. Particularly for Field-Programmable Gate Arrays (FPGAs), soft errors on the configuration memory are a significant dependability threat. In this work, we present an extended and comprehensive fault tolerance mechanism especially suited for dealing with configuration faults on FPGA-based systems that must deal multiple failure modes. Each failure mode may present different criticality and probability of occurrence, and these properties are measured and exploited to provide low-cost solutions when compared to standard approaches such as triple modular redundancy. The exploited properties are typically found in critical monitoring systems that may trigger security- or safety-critical alarms and warnings in general. In such systems, failing to trigger an alarm when necessary is frequently regarded as more critical than providing an occasional false alarm. For instance, Regular Expression Matching (REM), a compute-intensive mechanism heavily used to perform Deep Packet Inspection in critical network applications, presents such properties, and it can be greatly accelerated by FPGAs to meet performance constraints in high-throughput networks. Therefore, we use FPGA-based REM engines as a case study to demonstrate the effectiveness of the proposed techniques. Additionally, a mutually-aware placement and scrubbing mechanism is introduced to reduce the repair time, improving the system reliability and availability. Experimental results show that the failure rate and the repair time can be reduced by 95 and 90% respectively while avoiding the costs of triplication.     

Modeling of program, erase and retention characteristics of charge-trap gate all around memories

This paper describes the effect of geometry in charge-trap (CT) memory devices. We first theoretically analyze the impact of the curvature radius on the behavior of the gate current in Gate-All-Around devices, and then describe the change to make to planar model in order to fit the cylindrical devices characteristics. This model is used to simulate Nanocrystal and SONOS program, erase and retention behaviors. The dynamics enhancement during program/erase due to the bending of the active region in such cylindrical devices is explained. The scaling perspectives conclude this paper.     

A unified approach to superresolution and multichannel blind deconvolution.

This paper presents a new approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene. We do not assume any prior information about the shape of degradation blurs. The proposed approach consists of building a regularized energy function and minimizing it with respect to the original image and blurs, where regularization is carried out in both the image and blur domains. The image regularization based on variational principles maintains stable performance under severe noise corruption. The blur regularization guarantees consistency of the solution by exploiting differences among the acquired low-resolution images. Several experiments on synthetic and real data illustrate the robustness and utilization of the proposed technique in real applications.     

A simple model for SAR azimuth speckle, focusing, and interferometric decorrelation

The phenomenon of speckle in synthetic aperture radar (SAR) images is well known as a characteristic grainy appearance of radar images. Speckle is frequently a significant obstacle to visual interpretations of radar data or target identification. In addition, it is usually the dominant noise source in SAR interferometry, since it is responsible for image decorrelation that degrades interferometric fringes, places severe constraints on orbits, and limits the accuracy of height measurements. This communication deals with the geometric sources of speckle. This conventional picture is extended to the case of vertically separated scatterers, and the formulation that results is applied to the structurally similar topics of azimuth focusing, interferometric decorrelation from defocusing, and atmospheric phase delays.     

Paraxial approximation effect on a dielectric interface analysis

We are presenting a fast new method of analysing a dielectric interface which is assumed to be cylindrically symmetrical. It is based on a multimode Gaussian beam development. Then two different cases are considered and we show the kind of results we can expect from this process. Finally, we explain in detail the relationships between the paraxial approximation and further justified simplifications which lead to an important calculation time decrease.     

Consistency,availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peer-to-peer networking

Annals of Telecommunications - Blockchain is a disruptive technology that relies on the distributed nature of the peer-to-peer network while performing an agreement, or consensus, a mechanism to...     

On the relationship between the GLRT and UMPI tests for the detection of signals with unknown parameters

The generalized likelihood ratio test (GLRT) is widely used in signal processing applications such as image processing, wireless communications, medical imaging, classification, and signal detection. However, the GLRT does not have many known properties, other than that it is invariant, uniformly most powerful invariant (UMPI) for problems that fit the linear model, and asymptotically (N/spl rarr//spl infin/) UMPI in general. Since it is invariant, it belongs to the class of tests for which the UMPI test is optimal. In this paper, we consider a general class of detection problems in which unknown signal parameters imply a problem invariance that can be described analytically by orthogonal subgroups. This invariance is natural for problems with unknown signal parameters and, for example, include those of the matched subspace detectors of Scharf and Friedlander. We derive the GLRT and UMPI detectors for this general signal class for the case of Gaussian noise. An expression is found that relates the two test statistics showing the UMPI statistic to be the sum of two terms, one of which is the GLRT. Using this, we find that the GLRT and UMPI tests are asymptotically equivalent as signal-to-noise ratio (SNR) approaches infinity (or as probability of false alarm approaches zero). These results are illustrated by extending an example given by Nicolls and de Jager to show the analytic relationship between the GLRT and UMPI tests. The results indicate that the performance between the tests becomes close at signal-to-noise ratios (SNRs) associated with operating points of the receiver operating curve that are typically of interest in signal detection applications.     

Customizing MRI-Compatible Multifunctional Neural Interfaces through Fiber Drawing

Fiber drawing enables scalable fabrication of multifunctional flexible fibers that integrate electrical, optical, and microfluidic modalities to record and modulate neural activity. Constraints on thermomechanical properties of materials, however, have prevented integrated drawing of metal electrodes with low-loss polymer waveguides for concurrent electrical recording and optical neuromodulation. Here, two fabrication approaches are introduced: 1) an iterative thermal drawing with a soft, low melting temperature (T_m) metal indium, and 2) a metal convergence drawing with traditionally non-drawable high T_m metal tungsten. Both approaches deliver multifunctional flexible neural interfaces with low-impedance metallic electrodes and low-loss waveguides, capable of recording optically-evoked and spontaneous neural activity in mice over several weeks. These fibers are coupled with a light-weight mechanical microdrive (1 g) that enables depth-specific interrogation of neural circuits in mice following chronic implantation. Finally, the compatibility of these fibers with magnetic resonance imaging is demonstrated and they are applied to visualize the delivery of chemical payloads through the integrated channels in real time. Together, these advances expand the domains of application of the fiber-based neural probes in neuroscience and neuroengineering.     

Ultrafast Doppler imaging of blood flow dynamics in the myocardium

Imaging intramyocardial vascular flows in real-time could strongly help to achieve better diagnostic of cardiovascular diseases. To date, no standard imaging modality allows describing accurately myocardial blood flow dynamics with good spatial and temporal resolution. We recently introduced a novel ultrasonic Doppler imaging technique based on compounded plane waves transmissions at ultrafast frame rate. The high sensitivity of this ultrafast Doppler technique permits to image the intramyocardial blood flow and its dynamics. A dedicated demodulation-filtering process is implemented to compensate for the large tissue velocity of the myocardium during the cardiac cycle. A signed power Doppler processing provides the discrimination between arterial and venous flows. Experiments were performed in vivo in a large animal open chest model ( N = 5 sheep) using a conventional ultrasonic probe placed at the surface of the heart. Results show the capability of the technique to image intramyocardial vascular flows in normal physiological conditions with good spatial (200 μm) and temporal resolution (10 ms). Flow dynamics over the cardiac cycle were investigated and the imaging method demonstrated a phase opposition of flow waveforms between arterial and venous flows. Finally, ultrafast Doppler combined with tissue motion compensation was found able to reveal vascular flow disruption in ischemic regions during occlusion of the main diagonal coronary artery.