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CYBR 707 - Research Methods, Design, and Analysis - Salina Campus

This guide provides reference and citation resources for students enrolled in CYBR 707 at K-State Salina.

Library Research Guide

Staying Informed

Staying Informed! As you pursue your Master's Degree, it is essential that you stay informed on the latest research and developments in your field.  Remember that you are not a mere consumer of information, but rather a contributor of knowledge in your field of study.  In order to remain an active participant in the conversation, you must remain engaged in the latest news and research.

This page provides the latest articles from some of the top publications in Aerospace Systems and Materials, Machine Learning and Autonomous Systems, Cybernetics and Cyber Defense Systems as well as Electronic Engineering and Systems Management.  All articles are distributed through RSS.  This page will update as new articles are added to the respective journals.

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Cyber Defense Articles from ACM

Here you'll find some of the latest articles from ACM on Cyber Defense concerns, procedures, events, and the latest research systems and design.

ACM Transactions on Autonomous and Adaptive Systems

TAAS aims to publish papers that provably advance the state of the art, or that provide new insights and knowledge into specific issues related to autonomous and adaptive systems. Here you will find some of the latest and best articles on Machine Learning and Autonomous Systems research.

IEEE Transactions On Aerospace and Electronic Systems

IEEE Transactions on Aerospace and Electronic Systems focuses on the organization, design, development, integration, and operation of complex systems for space, air, ocean, or ground environment.  These systems include, but are not limited to, navigation, avionics, spacecraft, aerospace power, radar, sonar, telemetry, defense, transportation, automated testing, and command and control.

  • Table of ContentsThis link opens in a new windowApr 11, 2025
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  • IEEE Aerospace and Electronic Systems Society Publication InformationThis link opens in a new windowApr 11, 2025
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  • FDA Jamming Against Airborne Phased-MIMO Radar-Part I: Matched Filtering and Spatial FilteringThis link opens in a new windowFeb 24, 2025
    Phased multiple-input–multiple-output (phased-MIMO) radar has received increasing attention for enjoying the advantages of waveform diversity and range-dependency from frequency diverse array MIMO (FDA-MIMO) radar without sacrificing coherent processing gain through partitioning transmit subarray. By using a frequency offset larger than the signal bandwidth, phased-MIMO radar can be divided into the phased array (PA) radar and orthogonal FDA-MIMO radar instead of coherent FDA radar. This two-part series proposes a framework of electronic countermeasures (ECM) inspired by frequency diverse array (FDA) radar, called FDA jamming, evaluating its effectiveness for countering airborne phased-MIMO radar. This part introduces the principles and categories of FDA jammers and proposes the FDA jamming signal model based on two cases of phased-MIMO radar. Moreover, the effects of FDA jamming on matched filtering and spatial filtering of PA and FDA-MIMO radar are analyzed. Numerical results verify the theoretical analysis and validate the effectiveness of the proposed FDA jamming in countering phased-MIMO radar.
  • Target Motion Analysis With Passive Measurements and Partial Prior InformationThis link opens in a new windowDec 9, 2024
    This work demonstrates a simple but effective method by which prior information on the target range can be included in the likelihood function (i.e., in a non-Bayesian framework) for bearings-only as well as Doppler-bearings target motion analysis (tracking with passive measurements). The prior information is treated as a pseudomeasurement on the initial target range, i.e., the target range relative to the observer during the first time instance of the tracking period. The pseudomeasurement may be modeled using a Gaussian distribution or a Gaussian mixture model distribution. An estimation technique is derived as an extension to the well-known maximum likelihood estimator. The performance bounds naturally follow as an extension to the Cramer–Rao lower bound. The use of a range pseudomeasurement adds additional design parameters to the estimation process. Practical methodology and illustrative examples are provided for parameter set design. The statistical efficiency of the estimator is confirmed using Monte-Carlo trials on several experimental configurations. The simulated scenarios include bearings-only measurements as well as Doppler-bearings measurements.
  • Robust Bayesian Acoustic DOA Estimation With Passive Synthetic Aperture ArraysThis link opens in a new windowDec 3, 2024
    Traditional synthetic aperture direction-of-arrival (DOA) estimation methods are sensitive to the spatial and temporal incoherence introduced by the towed array shape deformation and phase unstability. This motivates us to propose a Bayesian acoustic DOA estimator, which is less sensitive to fluctuations in source phase and perturbations in array manifold in this article. The proposed technique extends the physical aperture in beamspace by leveraging the Fourier coefficients of the collected data computed at a given frequency for a successive time interval. A parameterized stochastic model for nonideal signal conditions is developed, and an interpretation of how the signal decorrelation is accomplished within a Bayesian framework is presented. Based on the probabilistic model, an iterative algorithm is developed by maximizing the marginal likelihood. Since this learning procedure is computationally intractable, we derive a variational expectation–maximization algorithm, which approximates the posterior probability distributions for the computation of the expectations over the latent variables. In addition, a 1-D search in the reconstruction result is designed to refine the coarse DOA estimates. Multisource simulations are used to illustrate the robustness of our learning algorithm to various data perturbations.
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IEEE Transactions on Cybernetics

EEE Transactions on Cybernetics includes computational approaches to the field of Cybernetics. This journal publishes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.

  • Table of ContentsThis link opens in a new windowMay 16, 2025
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  • IEEE SYSTEMS, MAN, AND CYBERNETICS SOCIETYThis link opens in a new windowMay 16, 2025
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  • Quality Control in Extrusion-Based Additive Manufacturing: A Review of Machine Learning ApproachesThis link opens in a new windowApr 18, 2025
    Additive manufacturing (AM) revolutionizes product creation with its unique layer-by-layer construction method but faces obstacles in widespread industrial use due to quality assurance and defect challenges. Integrating machine learning (ML) into AM quality control (QC) systems presents a viable solution, utilizing ML’s ability to autonomously detect patterns and extract important data, reducing the reliance on manual intervention. This study conducts an in-depth literature review to scrutinize the role of ML in augmenting QC mechanisms within extrusion-based AM processes. Our primary objective is to pinpoint ML models that excel in monitoring manufacturing activities and facilitating instantaneous defect corrections via parameter adjustments. Our analysis highlights the efficacy of convolutional neural networks (CNNs) models in defect detection, leveraging camera-based systems for an in-depth examination of printed parts. For 1-D data processing, support vector machines (SVMs) and long short-term memory (LSTM) networks have shown significant application and effectiveness. Furthermore, the study classifies various sensors and defects that can effectively benefit from ML-driven QC approaches. Our findings accentuate the essential role of ML, especially CNNs, in detecting and rectifying production flaws and also detail the synergy between different sensor technologies in creating a comprehensive monitoring framework. By integrating ML with a multisensor approach and employing real-time corrective strategies, such as dynamic parameter adjustments and the use of advanced control systems, this research underscores ML’s transformative potential in elevating AM QC. Thus, our contribution lays the groundwork for harnessing ML technologies to ensure superior quality parts production in AM, paving the way for its broader industrial adoption.
  • On Consensus Control of Uncertain Multiagent Systems Based on Two Types of Interval ObserversThis link opens in a new windowApr 18, 2025
    In this article, we investigate the multiagent robust consensus problem under model uncertainties, where the uncertain matrices and initial values are bounded by prior intervals. Based on the positive system theory, the related upper and lower dynamic systems are constructed to guarantee that the state value remains within a specified range. Subsequently, in accordance with the Lyapunov stability principle, the observation and consensus errors converge to zero, that is, the real states are reconstructed and consensus is achieved. Both local and neighborhood protocols, which are utilized to realize robust consensus, are presented. Notably, the proposed methods increase the design freedom and eliminate the Metzler constraint on the error matrix by introducing two novel parametric matrices. Without loss of generality, the topology in this article is assumed to contain a directed spanning tree, which can be directly degenerated to the undirected graph. Finally, numerical simulations validating the theoretical results are described.
  • Markov Switching Topology-Based Reliable Control Design for Delayed Discrete-Time System: An Ellipsoidal Attracting ApproachThis link opens in a new windowApr 2, 2025
    This article presents reachable set synthesis for a discrete-time Markov jump system (DTMJS) with mode-dependent time-varying delays, subjected to uncertain transition probabilities and actuator faults, based on the ellipsoidal attracting approach. The focus is mainly to reflect more realistic control behaviors for the proposed DTMJS, in which the class of partially asynchronous reliable control (PARC) scheme is designed for the first time under the Markov switching topology. In this regard, the state-feedback and mode-dependent time-varying delayed state-feedback controllers are coupled by employing the Bernoulli variable. Under this framework, the hidden Markov model is formulated, revealing the asynchronism among switching topology, controller, actuators and proposed system in different operational modes. By constructing a double mode-dependent stochastic Lyapunov-Krasovskii functional, the sufficient conditions are derived in terms of linear matrix inequalities, which not only ascertain the stochastic stability of the resultant Markov jump system but also ensure that all reachable states remain within compact ellipsoidal boundaries. Finally, numerical simulations are provided to verify the effectiveness and merits of the presented method.
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