Software-defined networking (SDN) is an emerging paradigm in enterprise networks because of its flexible and cost-effective nature. By decoupling control and data plane, SDN can provide various defense solutions for securing futuristic networks. However, the architectural design and characteristics of SDN attract several severe attacks. Distributed Denial of Service (DDoS) is considered as a major destructive cyber attack that makes the services of controller unavailable for its legitimate users. In this research paper, an intrusion detection framework is proposed to detect DDoS attacks against SDN. The proposed framework relies on voting based ensemble model for the attack detection. Ensemble model is a combination of multiple machine learning classifiers for prediction of final results. In this research paper, we propose and analyze three ensemble models named as Voting-CMN, Voting-RKM, and Voting-CKM particularly to benchmarking datasets like UNSW-NB15, CICIDS2017, and NSL-KDD, respectively. For validation of the proposed models, a cross validation technique is used with the prediction algorithms. The effectiveness of proposed models is evaluated in terms of prominent metrics (accuracy, precision, recall, and F measure). Experimental results indicate that the proposed models achieve better performance in terms of accuracy as compared to other existing models.

New reliability model for prediction of data retention in Flash memories, that taking into account cell type, workin temerature, endurance, tunnel width all togther. The model is accurate, simple and easy to use.

This paper deals with microwave subsurface imaging obtained by a migration-like inversion scheme, for a 2D monostatic scalar configuration and a two-layered background medium. The focus is on the determination of a data sampling strategy which allows to reduce the number of required measurements and at the same time keep the same performance in the reconstructions. To this end, the measurement points are determined in order to approximate the point-spread function corresponding to the ideal continuous case (i.e., the case in which the data space is not sampled at all). Basically, thanks to suitable variable transformations the point-spread functions is recast as a Fourier-like operator and this provides insight to devise the sampling scheme. It is shown that resulting measurement spatial positions are non-uniformly arranged across the measurement domain and their number can be much lower than the one provided by some literature standard sampling criteria. The study also contains a comparison with the free space case so as to highlight the role played by the half-space that schematized the subsurface scattering scenario on the number and the locations of the measurement points. Numerical examples are also included to check the theoretical arguments.

A broadband dual-polarized single-layer 1-bit unit cell is proposed for achieving the independently controllable dual-beam reflectarray antenna. The unit cell independently provides two-state phase compensation for two orthogonally linearly-polarized waves. The 180-degree reflective phase difference between the two states is achieved by tuning the magnetic resonance of State 0 and the electrical resonance of State 1. With its two resonances close to each other, the unit cell has a reflective phase difference of 180±20 degrees between two states over a broad bandwidth of 27.2-51.1 GHz. The cross-polarization levels of below -30 dB ensure the high isolation between two polarizations. Using the proposed dual-polarized unit cells, a 1-bit dual-beam reflectarray antenna is designed and excited by a dual-polarized horn to show the ability of independently controlling two orthogonally linearly-polarized waves. At 33 GHz, the beams direct to -15 degrees and 20 degrees for the feeding of horizontally and vertically polarized port, respectively. The 1.5-dB gain bandwidth is greater than 20% for both polarizations. The proposed dual-polarized reflectarray antenna with independently controllable 1-bit dual beams provides an alternative design for the multiuser multiple-input multiple-output applications.

The circuit architecture of a compact and broadband RF phase shifter is presented along with simulation verification.

Additive manufacturing is one of the most promising emerging technologies for building prosthetic sockets. However, there is no reliable way to estimate the factor of safety and the lifetime of 3D printed prosthetic sockets. Here, we explore 360° 3D digital image correlation (DIC) and discover how this new tool can increase our understanding of prosthetic structural failures. We establish that this new technology can dramatically improve the prosthetic design cycle by identifying local strain concentrations and by highlighting limitations in current simulated models. Overall, 360° 3D DIC technology empowers prosthetic engineers to characterize the performance of new materials and create innovative designs that are both safe and affordable.

We propose networked exponential families for non-parametric machine learning from massive network-structured datasets (“big data over networks”). High-dimensional data points are interpreted as the realizations of a random process distributed according to some exponential family. Networked exponential families allow to jointly leverage the information contained in high-dimensional data points and their network structure. For data points representing individuals, we obtain perfectly personalized models which enable high-precision medicine or more general recommendation systems.We learn the parameters of networked exponential families, using the network Lasso which implicitly pools (or clusters) the data points according to the intrinsic network structure and a local likelihood function. Our main theoretical result characterizes how the accuracy of network Lasso depends on the network structure and the information geometry of the node-wise exponential families. The network Lasso can be implemented as highly scalable message-passing over the data network. Such message passing is appealing for federated machine learning relying on edge computing. The proposed method is also privacy preserving in the sense that no raw data but only parameter (estimates) are shared among different nodes.

Abstract—Recent trends in the field of prosthetic technology are shifting towards all-digital approaches. These digital technologies have the potential to augment the current models of clinical healthcare and reach more amputees than currently possible. Unfortunately, most of the equipment needed to design and build digitally shaped prostheses is expensive or requires a high level of expertise. Objective: This paper developed several methods to enable the digital fabrication of prostheses using only low cost tools that do not require extensive specialized training. Methods: The authors built a cell phone application in Android Studio that enables automated collection of image data which can be rendered into accurate 3D-models using photogrammetry on a remote server. They then developed a new protocol to shape these residual limb models into functional prosthetic sockets using the freely available software Autodesk Meshmixer. Results: Finally, the authors ran a case study where they rectified a prosthetic socket for an amputee and compared this model to a socket built by a clinician. Conclusion: This case study showed that these new methods can successfully create accurate and functional prosthetic sockets. Significance: By leveraging ubiquitous low cost smartphone technology, the authors believe that amputees in underserved areas, who currently face large barriers both in cost and physical access to proper care, could soon be able to obtain affordable prosthetic limbs.

Abstract Radio frequency interference (RFI) is occurring in both satellite and terrestrial communication systems. In order to mitigate RFI efficiently, it has to be detected robustly. Toward this end, through the computation of an eigenvalue-based test statistic, an eigenvalue-based blind RFI detector is proposed for single-input multiple-output systems that may suffer from RFI. For medium to large interference-tonoise ratio (INR) regimes and under sample starved settings, Monte-Carlo simulations corroborate that the proposed blind detector manifests a comparable detection performance with a generalized likelihood ratio test (GLRT) detector fed with the knowledge of the signal of interest (SOI) channel, and a matched subspace detector fed with the knowledge of the SOI and RFI channels. Such performance signifies the applicability of the proposed RFI detector for real-time applications.

Fruit and vegetable classification using Convolutional Neural Networks (CNNs) has become a popular application in the agricultural industry, however, to the best of our knowledge no previously recorded study has designed and evaluated such an application on a mobile platform. In this paper, we propose a power-efficient CNN model, FruitVegCNN, to perform classification of fruits and vegetables in a mobile multi-processor system-on-a-chip (MPSoC). We also evaluated the efficacy of FruitVegCNN compared to popular state-of-the-art CNN models in real mobile plat- forms (Huawei P20 Lite and Samsung Galaxy Note 9) and experimental results show the efficacy and power efficiency of our proposed CNN architecture.

Conformal antennas lack of general analysis methods of their radiation properties. For a circumference source, we examine the role of the angular width of the observation domain both in far and near zone in determining the set of radiated fields. By an inverse problem approach, the evaluation of the number of independent pieces of information, i.e. the number of degrees of freedom (NDF), and the analysis of the reconstructions of point-like sources allow to introduce optimal array configurations. The results are relevant to the radiation pattern synthesis problem and to array diagnostics applications.

Empirical channel modeling is necessary for the deployment of the fifth-generation (5G) millimeter-wave (mmWave) cellular system in actual environments. In this paper, cluster-based analyses of mmWave channel characteristics in two typical dense urban environments are performed. First, radio propagation measurement campaigns are conducted at two primary 5G bands of 28 GHz and 39 GHz in a central business district and a dense residential area. The custom-designed channel sounder supports high-efficiency directional scanning sounding, which helps to collect sufficient data for statistical channel modeling. Next, using an improved autoclustering algorithm, multipath clusters and their scattering sources are identified. Mapping results show that multiple reflections from exterior walls and diffraction over building corners or rooftops enhance the coverage for non-line-of-sight (NLoS) links and the influences of these propagation mechanisms are intuitively embodied as changes in the topologies of deployment environments. Finally, an appropriate measure for cluster-level channel characteristics is provided including cluster number, Ricean K-factor, root mean squared (RMS) delay spread, RMS angular spread, and their correlations. Comparisons of these parameters across two mmWave bands are also given. The measurement and modeling results shed light on a fully understanding of mmWave channels in dense urban environments across multiple bands.

This manuscript has been accepted for publication on IEEE Transactions on Antennas and Propagation. Abstract: The paper adopts an inverse problem approach to examine the role of some 2D geometries in the source reconstruction from far zone data. It aims at evaluating the number of independent pieces of information, i.e. the number of degrees of freedom (NDF), of the source and pointing out the set of far zone fields corresponding to stable solutions of the inverse problem. Some of the results are relevant to the synthesis problem of conformal antennas, since a general comparison of different source geometries in providing radiation pattern specifications is proposed.

Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this brief, we study how computer vision based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi- processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC.

This manuscript has been accepted for publication on IEEE Transactions on Antennas and Propagation. Abstract: The paper adopts an inverse problem approach to investigate the far zone radiation of a collection of 2D linear sources. By the evaluation of the number of independent pieces of information, i.e. the number of degrees of freedom (NDF), and the analysis of the reconstruction of a focusing beam, the role of the geometry can be examined in determining the set of possible radiation patterns. The results are relevant to the radiation pattern synthesis problem, since they allow to define the optimal source geometry for the hemispherical coverage with identical radiation patterns.

This paper presents and illustrates the application of an elastic-plastic Generalised Beam Theory (GBT) formulation, based on J2-flow plasticity theory, that makes it possible to perform physically and geometrically non-linear (post-buckling) analyses of prismatic thin-walled members (i) with arbitrary cross-section shapes, (ii) exhibiting any type of deformation pattern (global, local, distortional, warping, shear), (iii) made from non-linear materials with isotropic strain-hardening and (iv) containing initial imperfections, namely residual stresses and/or geometric imperfections, having generic distributions. After providing a brief overview of the main GBT assumptions, kinematical relations and equilibrium equations, the development of a novel non-linear beam finite element (FE) is addressed in some detail. Moreover, its application is illustrated through the presentation and discussion of numerical results concerning the post-buckling behaviour of a fixed-ended I-section steel column exhibiting local initial geometrical imperfections, namely (i) non-linear equilibrium paths, (ii) displacement profiles, (iii) stress diagrams/distributions and (iv) deformed configurations. For validation purposes, the GBT results are also compared with values yielded by Abaqus rigorous shell FE analyses.

This paper proposes a novel probabilistic framework for the design of probabilistic message passing mechanism for complex and large dynamical systems that are operating and governing under a decentralized way. The proposed framework considers the evaluation of probabilistic messages that can be passed between mutually interacting quasi-independent subsystems that will not be restricted by the assumption of homogeneity or conformability of the subsystems components. The proposed message passing scheme is based on the evaluation of the marginal density functions of the states that need to be passed from one subsystem to another. An additional contribution is the development of stochastic controllability analysis of the controlled subsystems that constitute a complex system. To facilitate the understanding and the analytical analysis of the proposed message passing mechanism and the controllability analysis, theoretical developments are demonstrated on linear stochastic Gaussian systems.

An efficient multilayer machine learning-assisted optimization (ML-MLAO)-based robust design method is proposed for antenna and array applications. Machine learning methods are introduced into multiple layers of the robust design process, including worst-case analysis (WCA), maximum input tolerance hypervolume (MITH) searching, and robust optimization, considerably accelerating the whole robust design process. First, based on a surrogate model mapping between the design parameters and performance, WCA is performed using a genetic algorithm to ensure reliability. MITH searching is then carried out using a double-layer MLAO (DL-MLAO) framework to find the MITH of the given design point. Next, based on the training set obtained using DL-MLAO, correlations between the design parameters and the MITH are learned. The robust design is carried out using surrogate models for both the performance and the MITH, and these models are updated online following the ML-MLAO scheme. Furthermore, two examples, including an array synthesis problem and an antenna design problem, are used to verify the proposed ML-MLAO method. Finally, the numerical results and computation time are discussed to demonstrate the effectiveness of the proposed method.