A survey of autonomic networking architectures: towards a Unified Management Framework

Academic and industrial research initiatives have sought to make fully autonomic networks a reality. Some of these initiatives pursued a holistic approach, while others focused on setting up functionalities for specific networking domains. These efforts did not succeed in being extensively deployed, because the goals of network operators were not satisfactorily met. These goals include unification of management operations, enablement of end‐to‐end management and enhancement of the overall system performance in a trusted way, while reducing management cost. In this paper, we analyse a set of existing autonomic management architectures and frameworks with respect to a selected set of criteria. We then identify missing parts and challenges and propose a framework to unify the most promising attributes towards a novel approach of realization of autonomic networking management. We call this proposal Unified Management Framework (UMF). Copyright © 2013 John Wiley & Sons, Ltd.     

Increasing the Efficiency of Rake Receivers for Ultra-Wideband Applications

In diversity rich environments, such as in Ultra-Wideband (UWB) applications, the a priori determination of the number of strong diversity branches is difficult, because of the considerably large number of diversity paths, which are characterized by a variety of power delay profiles (PDPs). Several Rake implementations have been proposed in the past, in order to reduce the number of the estimated and combined paths. To this aim, we introduce two adaptive Rake receivers, which combine a subset of the resolvable paths considering simultaneously the quality of both the total combining output signal-to-noise ratio (SNR) and the individual SNR of each path, reducing the number of combined paths, while keeping the desirable performance. These schemes achieve better adaptation to channel conditions compared to other known receivers, without further increasing the complexity. Their performance is evaluated in different practical UWB channels, whose models are based on extensive propagation measurements. The proposed receivers compromise between the power consumption, complexity and performance gain for the additional paths, resulting in important savings in power and computational resources.     

Self-Organizing Maps for advanced learning in cognitive radio systems

Modern everyday life keeps making wireless communications more and more popular. The wireless communications landscape is highly varying and its success depends on the efficient provision of a physically limited natural source namely, radio spectrum. Cognitive radio systems (CRSs) have been proposed as a very promising technology for addressing this situation by facilitating more flexible and intelligent spectrum management. However, the processes of a CRS are often proved to be rather arduous and time consuming. Accordingly, a learning mechanism, capable of building knowledge to the system can speed up the whole cognition process. Framed within this statement, this paper introduces and evaluates a mechanism which is based on the well-known unsupervised learning technique, called Self-Organizing Maps (SOMs), and is used for assisting a CRS to predict the raw data rate that can be obtained, when it senses specific input data from its environment. Results show that the proposed method can provide predictions which are correct up to a percentage of 78.9% while exhibiting performance comparable to other supervised neural network-based learning schemes.     

Mobile guides: Taxonomy of architectures,context awareness,technologies and applications

Portable devices are increasingly employed in a wide range of mobile guidance applications. Typical examples are guides in urban areas, museum guides, and exhibition space aids. The demand is for the delivery of context-specific services, wherein the context is typically identified by a combination of data related to location, time, user profile, device profile, network conditions and usage scenario. A context-aware mobile guide is intended to provide guidance services adjusted to the context of the received request. The adjustment may refer to tailoring the user interface to the perceived context, as well as delivering the right type of information to the right person at the right time and the right location. It may also refer to intermediary adaptation, as in the case of mobile multimedia transmission. This paper offers a taxonomy of mobile guides considering multiple criteria. The taxonomy considers several aspects of the mobile applications space, including context awareness, client architectures, mobile user interfaces, as well as offered functionalities, highlighting functional, architectural, technological, and implementation issues. Existing implementations are classified accordingly and a discussion of research issues and emerging trends is offered.     

Comparison of block matching and differential methods for motion analysis of the carotid artery wall from ultrasound images

Motion of the carotid artery wall is important for the quantification of arterial elasticity and contractility and can be estimated with a number of techniques. In this paper, a framework for quantitative evaluation of motion analysis techniques from B-mode ultrasound images is introduced. Six synthetic sequences were produced using 1) a real image corrupted by Gaussian and speckle noise of 25 and 15 dB, and 2) the ultrasound simulation package Field II. In both cases, a mathematical model was used, which simulated the motion of the arterial wall layers and the surrounding tissue, in the radial and longitudinal directions. The performance of four techniques, namely optical flow (OF (HS)), weighted least-squares optical flow (OF (LK(WLS))), block matching (BM), and affine block motion model (ABMM), was investigated in the context of this framework. The average warping indices were lowest for OF (LK(WLS)) (1.75 pixels), slightly higher for ABMM (2.01 pixels), and highest for BM (6.57 pixels) and OF (HS) (11.57 pixels). Due to its superior performance, OF (LK(WLS)) was used to quantify motion of selected regions of the arterial wall in real ultrasound image sequences of the carotid artery. Preliminary results indicate that OF (LK(WLS)) is promising, because it efficiently quantified radial, longitudinal, and shear strains in healthy adults and diseased subjects.     

Elucidating Carfilzomib’s Induced Cardiotoxicity in an In Vivo Model of Aging: Prophylactic Potential of Metformin

Background: Carfilzomib is a first-line proteasome inhibitor indicated for relapsed/refractory multiple myeloma (MM), with its clinical use being hampered by cardiotoxic phenomena. We have previously established a translational model of carfilzomib cardiotoxicity in young adult mice, in which metformin emerged as a prophylactic therapy. Considering that MM is an elderly disease and that age is an independent risk factor for cardiotoxicity, herein, we sought to validate carfilzomib’s cardiotoxicity in an in vivo model of aging. Methods: Aged mice underwent the translational two- and four-dose protocols without and with metformin. Mice underwent echocardiography and were subsequently sacrificed for molecular analyses in the blood and cardiac tissue. Results: Carfilzomib decreased proteasomal activity both in PBMCs and myocardium in both protocols. Carfilzomib induced mild cardiotoxicity after two doses and more pronounced cardiomyopathy in the four-dose protocol, while metformin maintained cardiac function. Carfilzomib led to an increased Bip expression and decreased AMPKα phosphorylation, while metformin coadministration partially decreased Bip expression and induced AMPKα phosphorylation, leading to enhanced myocardial LC3B-dependent autophagy. Conclusion: Carfilzomib induced cardiotoxicity in aged mice, an effect significantly reversed by metformin. The latter possesses translational importance as it further supports the clinical use of metformin as a potent prophylactic therapy.     

Misidentification of genome assemblies in public databases: The case of Naumovozyma dairenensis and proposal of a protocol to correct misidentifications

Online sequence databases such as NCBI GenBank serve as a tremendously useful platform for researchers to share and reuse published data. However, submission systems lack control for errors such as organism misidentification, which once entered in the database can be propagated and mislead downstream analyses. Here we present an illustrating case of misidentification of Candida albicans from a clinical sample as Naumovozyma dairenensis based on whole‐genome shotgun data. Analyses of phylogenetic markers, read mapping and single nucleotide polymorphisms served to correct the identification. We propose that the routine use of such analyses could help to detect misidentifications arising from unsupervised analyses and correct them before they enter the databases. Finally, we discuss broader implications of such misidentifications and the difficulty of correcting them once they are in the records.     

Network Load Predictions Based on Big Data and the Utilization of Self-Organizing Maps

The pervasiveness of computers in everyday life has already increased and keeps increasing the available digital data both in volume and variety/disparity. This large and dynamic availability of digital data is referred to as Big Data and is very promising in bringing forward new insights and knowledge. For obtaining these insights, the proper combination and processing of the data is required. However, the dynamicity and the increasing size of data start making their handling impossible for analysts and raise many concerns on the manner in which data will be processed from now on. Towards this direction, this paper proposes a tool that processes and combines disparate data in order to create insights regarding a future network load. In particular, the tool (based on the unsupervised machine learning technique of Self-Organizing Maps) builds knowledge on the network load that is encountered with respect to the date of interest, the location, the weather, and the features of the day (e.g., weekend, bank holiday, etc.). The obtained results reveal that the tool is capable of learning the traffic pattern and thus predicting the network load that will be encountered in the near or distant future given information for the above presented parameters with small deviations (up to 0.000553 in terms of Mean Square Error). Moreover, the tool maintains only the most representative data instances and thus reduces the data storage requirements with no loss of information.