Bob Griffin

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Proceedings Publications

• Chan, S., Krunz, M. M., & Griffin, R. L. (2021, December). Adaptive Time-Frequency Synthesis for Waveform Discernment in Wireless Communications. In 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 0988-0996.
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  The discernment of waveforms for the purpose of identifying the underlying wireless technologies and validating if observed transmissions are legitimate or not remains a challenge within the communications sector and beyond. Conventional techniques struggle to robustly process Signals under Test (SuTs) in real-time. A particular difficulty relates to the selection of an appropriate window size for the processed data when pertinent contextual information on SuTs is not known a priori. The disadvantage of applying a predetermined fixed window size is that of length and shape (i.e., coarse resolution). In contrast, an adaptive window size offers more optimally tuned resolution. Towards this end, we propose a novel approach that uses an Adaptive Resolution Transform (ART) to either maintain a constant (prespecified) resolution, via a Variable Window Size and Shape (VWSS), or adjust the resolution (again using the VWSS technique) to match latency requirements. Central to this approach is the utilization of Continuous Wavelet Transforms (CWTs), which do not substantively suffer from those energy leakage issues found in more commonly used transforms such as Discrete Wavelet Transforms (DWT). A robust numerical implementation of CWTs is presented via a particular class of Convolutional Neural Networks (CNNs) called Robust Convex Relaxation (RCR)-based Convolutional Long Short-Term Memory Deep Neural Networks (a.k.a., CLSTMDNNs or CLNNs). By employing small convolutional filters, this class leverages deeper cascade learning, which nicely emulates CWTs. In addition to its use for convex relaxation adversarial training, the RCR framework also improves the bound tightening for the successive convolutional layers (which contain the cascading of ever smaller “CWT-like” convolutional filters). In this paper, we explore this particular architecture for its discernment capability among the SuT time series being compared. To operationalize this architectural paradigm, non-conventional Nonnegative Matrix Factorization (NMF) and Multiresolution Matrix Factorization (MMF) is used in conjunction to facilitate the capture of the structure and content of the involved matrices so as to achieve higher resolution and enhanced discernment accuracy. The desired WT (a.k.a., Corresponding WT or CORWT) resulting from the MMF is implemented as a translation-invariant CWT PyWavelet to better illuminate the intricate structural characteristics of the SuT and facilitate the analysis/discernment of their constituent Waveforms of Interest (WoIs). A precomputed hash and lookup table is utilized to facilitate WoI classification and discernment in quasi-real-time.
• Chan, S., Krunz, M. M., & Griffin, R. L. (2021, September). AI-based Robust Convex Relaxations for Supporting Diverse QoS in Next-Generation Wireless Systems. In 2021 IEEE 41st International Conference on Distributed Computing Systems Workshops (ICDCSW), 41-48.
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  Supporting diverse Quality of Service (QoS) requirements in 5G and beyond wireless systems often involves solving a succession of convex optimization problems, with varied approaches to optimally resolve each problem. Even when the input set is specifically designed/architected to segue to a convex paradigm, the resultant output set may still turn out to be nonconvex, thereby necessitating a transformation to a convex optimization problem via certain relaxation techniques. This transformation in itself may spawn yet other nonconvex optimization problems, highlighting the need/opportunity to utilize a Robust Convex Relaxation (RCR) framework. In this paper, we explore a particular class of Convolutional Neural Networks (CNNs), namely Deep Convolutional Generative Adversarial Network (DCGANs), to solve not only the QoS-related convex optimization problems but also to leverage the same RCR mechanism for tuning its own hyperparameters. This approach gives rise to various technical challenges. For example, Particle Swarm Optimization (PSO) is often used for hyperparameter reduction/tuning. When implemented on a DCGAN, PSO requires converting continuous/discontinuous hyperparameters to discrete values, which may result in premature stagnation of particles at local optima. The involved implementation mechanics, such as increasing the inertial weighting, may spawn yet other convex optimization problems. We introduce a RCR framework that capitalizes upon the feed-forward structure of the “You Only Look Once” (YOLO)- based DCGAN. Specifically, we use a squeezed Deep Convolutional-YOLO-Generative Adversarial Network (DC-YOLO-GAN), hereinafter referred to as a Modified Squeezed YOLO v3 Implementation (MSY3I), combined with convex relaxation adversarial training to improve the bound tightening for each successive neural network layer and to better facilitate the global optimization via a specific numerical stability implementation within MSY3I.
• Chan, S., & Griffin, R. L. (2019, September). Annealed Cyber Resiliency: Cyber Discernment for the Launch Providers of Space Systems. In The Fourth International Conference on Cyber-Technologies and Cyber-Systems (CYBER 2019), 55-61.
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  Out-of-the-box and outside-the-wire thinking is required to identify sophisticated synthetic aberrations, which would bypass prototypical cyber defense systems. The various tools and techniques are somewhat important within the ecosystem, but an assessment methodology that embodies diligence, persistence, and learning over time can be even more vital than the various tools and techniques. This paper posits that the depth and breadth of any cyber investigation foray can well be achieved by employing an approach that is termed Cyber Discernment. In Cyber Discernment, a methodological robust decision engineering framework, Karassian Netchain Analysis (KNA), among others, is utilized to understand Negative Influence Dominating Sets (NIDS) or areas of instability and Positive Influence Dominating Sets (PIDS) or islands of stability. By ascertaining PIDS and understanding how best to mitigate NIDS, a form of annealed cyber resiliency, enhanced cyber security, and latent cyber stability can be achieved, thereby mitigating against unintended consequences, undesired elements of instability, and “perfect storm” crises lurking within the system.