Dr. John J. Salerno Profile

Dr. John J. Salerno

Principal Engineer at Griffiss Institute

SPIE Involvement:

Author

Publications (19)

Proceedings Article | 15 June 2023 Presentation + Paper

Integrating complex valued hyperdimensional computing with modular artificial neural networks

Nathan McDonald, Lisa Loomis, Richard Davis, John Salerno, Ashay Stephen, Clare Thiem

Proceedings Volume 12542, 125420K (2023) https://doi.org/10.1117/12.2664585

KEYWORDS: Artificial neural networks, Machine learning, Neurons, Sensors, Associative arrays

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Traditional approaches using Deep Neural Networks for classification, while unquestionably successful, struggle with more general intelligence tasks such as “on the fly” learning as demonstrated by biological systems. Organisms possess myriad sensory organs for interacting with their environment. By the time these diverse sensory signals reach the brain; however, they are all converted into a spiking information representation, over which the brain itself operates. In a similar manner, myriad machine learning (ML) algorithms today compute on equally diverse data modalities; but without a consistent information representation for their respective outputs, these algorithms are frequently used independently of each other. Consequently, there is growing interest in information representations to unify these algorithms, with the larger goal of designing ML modules that may be arbitrarily arranged to solve larger-scale ML problems, analogous to digital circuit design today. One promising information representation is that of a “symbol” expressed as a high-dimensional vector, thousands of elements long. Hyperdimensional computing (HDC) is an algebra for the creation, manipulation, and measurement of correlations among “symbols” expressed as hypervectors. Towards this goal, an external plexiform layer (EPL) network, echo state network (ESN), and modern Hopfield network were adapted to implement the mathematical operations of complex phasor based HDC. Further, since symbol error correction is an important consideration for computing with networks of ML modules, a task agnostic minimum query similarity for complete symbol error correction was measured as a function of hypervector length. Based on these results, problem-independent similarities have been established within which HDC equations should be designed. Lastly, these ANNs were tested against several tasks representative of online and “plug & play” ML among expeditionary robots. For all criteria considered, the modern Hopfield network was the most capable ANN evaluated for use with complex phasor based HDC, providing 100% symbol recovery in a single time step for nearly all parameter settings.

Proceedings Article | 23 May 2013 Paper

Summary of human social, cultural, behavioral (HSCB) modeling for information fusion panel discussion

Erik Blasch, John Salerno, Ivan Kadar, Shanchieh Yang, Laurie Fenstermacher, Mica Endsley, Lynne Grewe

Proceedings Volume 8745, 87451I (2013) https://doi.org/10.1117/12.2016305

KEYWORDS: Data modeling, Information fusion, Cognitive modeling, Systems modeling, Data fusion, Social networks, Sensors, Tin, Process modeling, Computer simulations

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Proceedings Article | 21 May 2011 Paper

The national operational environment model (NOEM)

John Salerno, Brian Romano, Warren Geiler

Proceedings Volume 8060, 80600J (2011) https://doi.org/10.1117/12.888858

KEYWORDS: Systems modeling, Analytical research, Data modeling, Defense and security, Autoregressive models, Information security, Neodymium, Statistical modeling, Heart, Visual process modeling

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The National Operational Environment Model (NOEM) is a strategic analysis/assessment tool that provides insight into the complex state space (as a system) that is today's modern operational environment. The NOEM supports baseline forecasts by generating plausible futures based on the current state. It supports what-if analysis by forecasting ramifications of potential "Blue" actions on the environment. The NOEM also supports sensitivity analysis by identifying possible pressure (leverage) points in support of the Commander that resolves forecasted instabilities, and by ranking sensitivities in a list for each leverage point and response. The NOEM can be used to assist Decision Makers, Analysts and Researchers with understanding the inter-workings of a region or nation state, the consequences of implementing specific policies, and the ability to plug in new operational environment theories/models as they mature. The NOEM is built upon an open-source, license-free set of capabilities, and aims to provide support for pluggable modules that make up a given model. The NOEM currently has an extensive number of modules (e.g. economic, security & social well-being pieces such as critical infrastructure) completed along with a number of tools to exercise them. The focus this year is on modeling the social and behavioral aspects of a populace within their environment, primarily the formation of various interest groups, their beliefs, their requirements, their grievances, their affinities, and the likelihood of a wide range of their actions, depending on their perceived level of security and happiness. As such, several research efforts are currently underway to model human behavior from a group perspective, in the pursuit of eventual integration and balance of populace needs/demands within their respective operational environment and the capacity to meet those demands. In this paper we will provide an overview of the NOEM, the need for and a description of its main components. We will also provide a detailed discussion of the model and sample use cases.

Proceedings Article | 6 May 2008 Paper

Results from levels 2/3 fusion implementations: issues, challenges, retrospectives, and perspectives for the future an annotated perspective

Ivan Kadar, Eloi Bosse, John Salerno, Dale Lambert, Subrata Das, Enrique Ruspini, Bradley Rhodes, Joachim Biermann

Proceedings Volume 6968, 696812 (2008) https://doi.org/10.1117/12.782740

KEYWORDS: Data modeling, Systems modeling, Information fusion, Data fusion, Sensors, Data processing, Cognitive modeling, Analytical research, Process modeling, Mathematical modeling

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Proceedings Article | 15 May 2007 Paper

Issues and challenges in resource management and its interaction with levels 2/3 fusion with applications to real-world problems: an annotated perspective

Erik Blasch, Ivan Kadar, Kenneth Hintz, Joachim Biermann, Chee-Yee Chong, John Salerno, Subrata Das

Proceedings Volume 6567, 656712 (2007) https://doi.org/10.1117/12.719613

KEYWORDS: Sensors, Information fusion, Weapons, Data fusion, Data processing, Data acquisition, Target detection, Performance modeling, Inspection, Optimization (mathematics)

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Proceedings Article | 17 May 2006 Paper

Issues and challenges of knowledge representation and reasoning methods in situation assessment (Level 2 Fusion)

Erik Blasch, Ivan Kadar, John Salerno, Mieczyslaw Kokar, Subrata Das, Gerald Powell, Daniel Corkill, Enrique Ruspini

Proceedings Volume 6235, 623510 (2006) https://doi.org/10.1117/12.669779

KEYWORDS: Data modeling, Process modeling, Systems modeling, Information fusion, Sensors, Knowledge discovery, Error analysis, Data fusion, Cognitive modeling, Process control

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Proceedings Article | 18 April 2006 Paper

Detecting people of interest from internet data sources

Raymond Cardillo, John Salerno

Proceedings Volume 6241, 62410H (2006) https://doi.org/10.1117/12.665718

KEYWORDS: Internet, Visualization, Visual analytics, Analytical research, Social network analysis, Algorithm development, Data mining, Optical character recognition, Detection and tracking algorithms, Standards development

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Proceedings Article | 18 April 2006 Paper

Realizing situation awareness within a cyber environment

George Tadda, John Salerno, Douglas Boulware, Michael Hinman, Samuel Gorton

Proceedings Volume 6242, 624204 (2006) https://doi.org/10.1117/12.665763

KEYWORDS: Data fusion, Data modeling, Systems modeling, Analytical research, Sensors, Computing systems, System identification, Cognitive modeling, Information fusion, Detection and tracking algorithms

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Proceedings Article | 25 May 2005 Paper

Situation assessment for aggregated vehicle merging at an unknown location

Kanupriya Salaria, Suman Das, Michael Hinman, John Salerno, Li Bai

Proceedings Volume 5809, (2005) https://doi.org/10.1117/12.606013

KEYWORDS: Signal processing, Detection and tracking algorithms, Data processing, Data modeling, Algorithm development, Systems modeling, Cognitive modeling, Databases, Data fusion, Computer simulations

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