FUZZY LOGIC-BASED TARGET CLASSIFICATION USING KINEMATIC DATA

The need for fast and accurate decisions in activities related to air surveillance and control has led to the development of systems to reduce operators' workload. In this context, a solution for aerial target classification is presented, using kinematic data (speed and acceleration) as input parameters. These can be estimated by a system composed of primary surveillance radar and a tracking algorithm. The implemented classifier is based on a fuzzy inference system that is capable of giving in real-time the membership degrees of a target to three defined classes, which is useful in the absence of data from secondary/IFF (Identification Friend or Foe) radars or imaging sensors.     

Tools and strategies for debugging distributed stream processing applications

Distributed data stream processing applications are often characterized by data flow graphs consisting of a large number of built‐in and user‐defined operators connected via streams. These flow graphs are typically deployed on a large set of nodes. The data processing is carried out on‐the‐fly, as tuples arrive at possibly very high rates, with minimum latency. It is well known that developing and debugging distributed, multi‐threaded, and asynchronous applications, such as stream processing applications, can be challenging. Thus, without domain‐specific debugging support, developers struggle when debugging distributed applications. In this paper, we describe tools and language support to support debugging distributed stream processing applications. Our key insight is to view debugging of stream processing applications from four different, but related, perspectives. First, debugging the semantics of the application involves verifying the operator‐level composition and inspecting the flows at the logical level. Second, debugging the user‐defined operators involves traditional source‐code debugging, but strongly tied to the stream‐level interactions. Third, debugging the deployment details of the application require understanding the runtime physical layout and configuration of the application. Fourth, debugging the performance of the application requires inspecting various performance metrics (such as communication rates, CPU utilization, etc.) associated with streams, operators, and nodes in the system. In light of this characterization, we developed several tools such as a debugger‐aware compiler and an associated stream debugger, composition and deployment visualizers, and performance visualizers, as well as language support, such as configuration knobs for logging and tracing, deployment configurations such as operator‐to‐process and process‐to‐node mappings, monitoring directives to inspect streams, and special sink adapters to intercept and dump streaming data to files and sockets, to name a few. We describe these tools in the context of Spade —a language for creating distributed stream processing applications, and System S —a distributed stream processing middleware under development at the IBM Watson Research Center. Published in 2009 by John Wiley & Sons, Ltd.     

Multi‐criteria IoT resource discovery: a comparative analysis

The growth of real‐world objects with embedded and globally networked sensors allows to consolidate the Internet of things paradigm and increase the number of applications in the domains of ubiquitous and context‐aware computing. The merging between cloud computing and Internet of things named cloud of things will be the key to handle thousands of sensors and their data. One of the main challenges in the cloud of things is context‐aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi‐criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi‐objective decision methods and their quality of selection comparing them with the Pareto‐optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy. Copyright © 2016 John Wiley & Sons, Ltd.     

A finite element model for the analysis of 3D axisymmetric laminated shells with piezoelectric sensors and actuators

This paper presents the development of a semi-analytical axisymmetric shell finite element model with piezoelectric layers using the 3D linear elasticity theory. The piezoelectric effect of the material could be used as sensors and/or actuators in way to control shell deformation. In the present 3D axisymmetric model, the equations of motion are expressed by expanding the displacement field using Fourier series in the circumferential direction. Thus, the 3D elasticity equations of motion are reduced to 2D equations involving circumferential harmonics. In the finite element formulation the dependent variables, electric potential and loading are expanded in truncated Fourier series. Special emphasis is given to the coupling between symmetric and anti-symmetric terms for laminated materials with piezoelectric rings. Numerical results obtained with the present model are found to be in good agreement with other finite element solutions.     

Joint evaluation of recovery and performance of a COTS DBMS in the presence of operator faults

A major cause of failures in large database management systems (DBMS) is operator/administrator faults. Although most of the complex DBMS available today have comprehensive recovery mechanisms, the effectiveness of these mechanisms is difficult to characterize. On the other hand, the tuning of a large database is very complex and database administrators tend to concentrate on performance tuning and disregard the recovery mechanisms. Above all, database administrators seldom have feedback on how good a given configuration is concerning recovery. This paper proposes an experimental approach to characterize both the performance and the recoverability of DBMS. Our approach is presented through a concrete example of benchmarking the performance and recovery of an Oracle DBMS running the standard TPC-C benchmark, extended to include two new elements: a faultload based on operator faults and measures related to recoverability. A classification of operator/administrator faults in DBMS is proposed. A set of tools have been designed and built to reproduce operator faults in an Oracle 8i DBMS, using exactly the same means used in the field by the real database administrator. This experimental approach is generic (i.e., can be applied to any DBMS) and is fully automatic. The paper ends with the discussion of the results and the proposal of guidelines to help database administrators in finding the balance between performance and recovery tuning.     

Non-destructive Investigation of Paintings on Canvas by Continuous Wave Terahertz Imaging and Flash Thermography

Terahertz (THz) imaging is increasingly used in the cultural heritage field. In particular, continuous wave (CW) and low frequency THz is attracting more attention. The first application of the THz technique inherent to the cultural heritage field dates back 10 years ago. Since 2006, tangible improvements have been conducted in the refinement of the technique, with the aim to produce clear maps useful for any art restorer. In this paper, a CW THz (0.1 THz) imaging system was used to inspect paintings on canvas both in reflection and in transmission modes. In particular, two paintings were analyzed: in the first one, similar materials and painting execution of the original artwork were used, while in the second one, the canvas layer is slightly different. Flash thermography was used herein together with the THz method in order to observe the differences in results for the textile support materials. A possible application of this method for the detection of artwork forgery requires some parameterization and analysis of various materials or thickness influence which will be addressed in a future study. In this work, advanced image processing techniques including principal component thermography (PCT) and partial least squares thermography (PLST) were used to process the infrared data. Finally, a comparison of CW THz and thermographic results was conducted.     

Large scale additive manufacturing of eco-composites

The evolution of additive manufacturing processes is enabling the production of parts with improved dimensional accuracy, mechanical, physical and chemical properties [1]. New materials also contribute to this trend, and in this scope, eco-composites, materials with environmental and ecological advantages, which include natural polymers, have been acquiring increased relevance [2]. The purpose of this study is to develop composite material parts manufactured from recycled thermoplastics and natural fibres, in this case, wood residues. Additive manufacturing (fused deposition modelling) will be accomplished using a robot combined with extrusion unit. The objective is to access the influence of the main manufacturing parameters, such as temperature, distance between layers or deposition speed, on the final part characteristics, especially dimensional accuracy. Reverse engineering and several material analysis techniques will be employed to achieve this goal.     

Human single-chain Fv antibodies to MUC1 core peptide selected from phage display libraries recognize unique epitopes and predominantly bind adenocarcinoma

The tumor-associated antigen MUC1 is overexpressed and underglycosylated in human adenocarcinomas of diverse origins, such as breast, ovary, and colon. We isolated and describe five human single-chain (sc) Fv antibodies specific for the MUC1 variable number of tandem repeats region isolated by in vitro selection from a large naive phage antibody library containing over 6 x 10(9) different scFv antibodies. A synthetic biotinylated 100-mer peptide corresponding to five tandem repeats of the MUC1 peptide core was used for selection. Two of the antibodies were highly specific for MUC1 as judged by ELISA and flow cytometry. In immunohistochemistry, antibody clone 10A stained MUC1 in the cytoplasm and membrane of adenocarcinoma cells of breast and ovary, whereas in normal epithelium, only cytoplasmic or no staining was observed. With antibody clone 10B, staining was less pronounced and was not always membrane associated in adenocarcinoma. Determination of the fine specificity of 10A and 10B using a novel "indirect epitope fingerprinting" ELISA showed that both antibodies recognize unique epitopes that have not been described for hybridoma-derived anti-mucin antibodies of mouse origin. The selected human antibodies, like many of the murine MUC1 antibodies, recognize epitopes on the protein core of MUC1 that are abundantly present in the underglycosylated form of cell surface mucin on adenocarcinoma. The best human scFv, clone 10A, appears to distinguish normal cells from adenocarcinoma cells, which makes it an attractive candidate for use in antibody-based tumor targeting.     

Input parallel–output parallel connected modular nonisolated DC-DC converters with current self-sharing capability operating in discontinuous conduction mode

This paper covers the self-sharing analysis of dc-dc nonisolated converters with input parallel–output parallel (IPOP) configuration and operating in discontinuous conduction mode (DCM). The main contribution of the proposed system is its capability of providing self-sharing of the currents on both sides of each individual converter, without average current sharing control, even in the face of parametric variations. This self-balance only occurs for DCM. When the addressed converters operate in continuous conduction mode (CCM), the self-sharing does not occur naturally under parametric differences among them, requiring the use of additional control loops. The use of self-sharing converters in nonisolated converters simplifies the control system, it makes the modular solution being attractive for many applications, and it increases the power range that the DCM converters may be applied. This paper brings the theoretical study of self-sharing of the current mechanism to six basic nonisolated converters operating in DCM. The self-sharing is verified by experimental results, which are obtained from three modules of dc-dc SEPIC converters. Both converters were designed to operate with 200-V input voltage, 125-V output voltage, 1500-W rated power (500 W each module), and switching frequency at 30 kHz.     

Dynamic intermittent Q‐learning–based model‐free suboptimal co‐design of ‐stabilization

This paper proposes an intermittent model‐free learning algorithm for linear time‐invariant systems, where the control policy and transmission decisions are co‐designed simultaneously while also being subjected to worst‐case disturbances. The control policy is designed by introducing an internal dynamical system to further reduce the transmission rate and provide bandwidth flexibility in cyber‐physical systems. Moreover, a Q‐learning algorithm with two actors and a single critic structure is developed to learn the optimal parameters of a Q‐function. It is shown by using an impulsive system approach that the closed‐loop system has an asymptotically stable equilibrium and that no Zeno behavior occurs. Furthermore, a qualitative performance analysis of the model‐free dynamic intermittent framework is given and shows the degree of suboptimality concerning the optimal continuous updated controller. Finally, a numerical simulation of an unknown system is carried out to highlight the efficacy of the proposed framework.