Vladimir Kulyukin

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Associate Professor

Utah State University

United States

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About

Vladimir Kulyukin is an Associate Professor at Utah State University in the United States. His research focuses primarily on discrete time series forecasting related to honey bee colonies, as well as several aspects of computability and artificial neural networks. He has published extensively in areas such as non-invasive monitoring of managed honey bee colonies and the axiomatization of signifiable computation.

Recent Grants

Grant: Close

BeePi: Honeybees Meet AI: Stage 3

Open Date: 2021-01-01

Close Date:

Grant: Close

BeePi: Honeybees Meet AI: Stage 2

Open Date: 2019-09-01

Close Date: 2021-09-01

Grant: Close

Mobile Instructional Particle Image Velocimetry (mI-PIV): Hands-on Flow Visualization and Experimentation for Improved Student Interest and Technical Capacity in Navy-relevant Engineering Careers

Open Date: 2018-01-01

Close Date: 2022-01-01

Grant: Close

Predictive Data Integration and Analysis of Heterogeneous Data Streams

Open Date: 2018-01-01

Close Date: 2018-05-01

Grant: Close

BeePi: A Multisensor, Electronic Beehive Monitor

Open Date: 2017-09-01

Close Date: 2019-09-01

Articles (16)

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2021

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2021

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2021

Collaborators (2)

Angela Minichiello

Associate Professor

Utah State University

UNITED STATES

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Jack Elliott

Assistant Professor

Minnesota State University Mankato

UNITED STATES

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Vladimir Kulyukin

Research Interests

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About

Recent Grants

Articles (16)

Discrete Time Series Forecasting in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part II: Are Hive Weight and In-Hive Temperature Seasonal and Colony-Specific?

On Programmatic Aspects of the Universality, Parameter, and Recursion Theorems of Classical Computability

On Signifiable Computability: Part II: An Axiomatization of Signifiable Computation and Debugger Theorems

On Signifiable Computability: Part I: Signification of Real Numbers, Sequences, and Types

Discrete Time Series Forecasting of Hive Weight, In-Hive Temperature, and Hive Entrance Traffic in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part I

Accuracy vs. Energy: An Assessment of Bee Object Inference in Videos from On-Hive Video Loggers with YOLOv3, YOLOv4-Tiny, and YOLOv7-Tiny

On Correspondences between Feedforward Artificial Neural Networks on Finite Memory Automata and Classes of Primitive Recursive Functions

Ambient Electromagnetic Radiation as a Predictor of Honey Bee (Apis mellifera) Traffic in Linear and Non-Linear Regression: Numerical Stability, Physical Time and Energy Efficiency

Ambient Electromagnetic Radiation as a Predictor of Honey Bee (<i>Apis mellifera</i>) Traffic in Linear and Non-Linear Regression: Numerical Stability, Physical Time and Energy Efficiency

On the Computability of Primitive Recursive Functions by Feedforward Artificial Neural Networks

Integration of Scales and Cameras in Nondisruptive Electronic Beehive Monitoring: On the Within-Day Relationship of Hive Weight and Traffic in Honeybee (Apis mellifera) Colonies in Langstroth Hives in Tucson, Arizona, USA

On Primitive Recursive Characteristics of Chess

DFT Study of Microsolvated [NO<sub>3</sub>·(H<sub>2</sub>O)<i><sub>n</sub></i>]<sup>−</sup> (<i>n</i> = 1–12) Clusters and Molecular Dynamics Simulation of Nitrate Solution

On Image Classification in Video Analysis of Omnidirectional Apis Mellifera Traffic: Random Reinforced Forests vs. Shallow Convolutional Networks

Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring

Developing a mobile application‐based particle image velocimetry tool for enhanced teaching and learning in fluid mechanics: A design‐based research approach

Collaborators (2)

Angela Minichiello

Jack Elliott

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