Posts by Collection

portfolio

Ninja Luxe Cafe Project

Led the development of advanced barista assist technology, milk froth control algorithms, and temperature control systems for the Ninja Luxe Cafe series.

Nespresso & Coffee Projects

Conceptualized/Designed/Implemented various signal processing/sensing/software tools and controllers for Heated/Beverage products.

Thirsti CO2 Flavored Water Soda Machine Project

Conceptualized, Engineered, and Executed a range of signal processing, harware selection, sensory, and software mechanisms, as well as control systems, for Ninja Thirsti Project. Three patents Under Review - Determining an amount of carbon dioxide in a gas source in a carbonation system (US20240246041A1) and - Beverage carbonation systems (WO2024155439A2, WO2024155439A3) and - Detecting liquid temperature for a beverage carbonation system (US20250001371A1).

Real Time Data Collection Apparatus/Application

Conceptualized and Designed Python application for R&D team to collect real-time data from software by communicating with unit’s MCU.


Also a profiler is designed and developed for R&D and testing team to visualize and save real time sensor and scale data.

publications

Designing Adaptive Intensive Interventions Using Methods from Engineering

Published in Journal of Consulting and Clinical Psychology, 2014

Adaptive intensive interventions are introduced and new methods from the field of control engineering for use in their design are illustrated.

Recommended citation: Lagoa, C. M., Bekiroglu, K., Lanza, S. T., & Murphy, S. A. (2014). Designing adaptive intensive interventions using methods from engineering. Journal of Consulting and Clinical Psychology, 82(5), 868–878. https://doi.org/10.1037/a0037736 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4176810/

Computer Vision Based Control of an Autonomous Blimp

Published in Turkish Journal of Electrical Engineering & Computer Sciences, 2016

The objective of this study is twofold: to approximate a model of a blimp, and to use this model to develop a setup to track a target with the blimp that is outfitted with a wireless camera and radio-controlled propellers.

Recommended citation: Bekiroglu, K., Sznaier, M., Lagoa, C., & Shafai, B. (2016). Computer vision-based control of an autonomous blimp. Turkish Journal of Electrical Engineering and Computer Sciences, 24(5), 4015-4026. https://journals.tubitak.gov.tr/cgi/viewcontent.cgi?article=2548&context=elektrik

Designing Adaptive Intensive Interventions Using Methods from Engineering

Published in IEEE Transactions on Control Systems Technology, 2017

A robust control approach is used to address the problem of adaptive behavioral treatment design.

Recommended citation: Bekiroglu, K., Lagoa, C., Murphy, S. A., & Lanza, S. T. (2016). Control engineering methods for the design of robust behavioral treatments. IEEE Transactions on Control Systems Technology, 25(3), 979-990. https://ieeexplore.ieee.org/ielaam/87/7876877/7501575-aam.pdf

A Randomized Algorithm for Parsimonious Model Identification

Published in IEEE Transactions on Automatic Control, 2017

Algorithms for parsimonious linear time invariant system identification aimed at identifying low-complexity models which i) incorporate aprioriknowledge on the system (e.g., stability), ii) allow for data with missing/nonuniform measurements, and iii) are able to use data obtained from several runs of the system with different unknown initial conditions.

Recommended citation: Yılmaz, B., Bekiroglu, K., Lagoa, C., & Sznaier, M. (2017). A randomized algorithm for parsimonious model identification. IEEE Transactions on Automatic Control, 63(2), 532-539. https://ieeexplore.ieee.org/document/7970196

Validation of model order assumption and noise reduction method for the impact resonance testing of asphalt concrete

Published in Journal of Nondestructive Evaluation volume, 2017

The second order equation of motion assumption in the modeling of the impact resonance test response was evaluated for asphalt concrete testing.

Recommended citation: Boz, I., Bekiroglu, K., Solaimanian, M., Tavassoti-Kheiry, P., & Lagoa, C. (2017). Validation of model order assumption and noise reduction method for the impact resonance testing of asphalt concrete. Journal of Nondestructive Evaluation, 36(3), 1-13. https://link.springer.com/article/10.1007/s10921-017-0436-2

Evaluating the effect of smoking cessation treatment on a complex dynamical system

Published in Drug and Alcohol Dependence, 2017

The second order equation of motion assumption in the modeling of the impact resonance test response was evaluated for asphalt concrete testing.

Recommended citation: Bekiroglu, K., Russell, M. A., Lagoa, C. M., Lanza, S. T., & Piper, M. E. (2017). Evaluating the effect of smoking cessation treatment on a complex dynamical system. Drug and alcohol dependence, 180, 215-222. https://link.springer.com/article/10.1007/s10921-017-0436-2

An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings

Published in Applied Energy, 2019

This paper presents the Internet of Things (IoT) prototype which implements a smart and scalable control approach called the Smart-Token Based Scheduling Algorithm (Smart-TBSA) to minimize energy in commercial building HVAC systems.

Recommended citation: Png, E., Srinivasan, S., Bekiroglu, K., Chaoyang, J., Su, R., & Poolla, K. (2019). An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings. Applied Energy, 239, 408-424. https://www.sciencedirect.com/science/article/abs/pii/S0306261919302582?dgcid=coauthor#!

Sensor Placement Algorithm With Range Constraints for Precision Agriculture

Published in IEEE Aerospace and Electronic Systems Magazine, 2019

Optimal sensor placement has emerged as key. Here we examine it and propose Sensor Placement Algorithm with Range Constraints (SPARC) as a potential solution.

Recommended citation: Visalini, K., Subathra, B., Srinivasan, S., Palmieri, G., Bekiroglu, K., & Thiyaku, S. (2019). Sensor placement algorithm with range constraints for precision agriculture. IEEE Aerospace and Electronic Systems Magazine, 34(6), 4-15. https://ieeexplore.ieee.org/document/8786990/authors#authors

Predictability of the physical shipping market by freight derivatives

Published in IEEE Transactions on Engineering Management, 2019

This article investigates the predictability of dry bulk shipments’ physical shipping costs while testing the predictive significance of derivative products. Accordingly, a comprehensive grid search procedure is needed to simulate combinations of model structures subject to a cross-validation process.

Recommended citation: Duru, O., Gulay, E., & Bekiroglu, K. (2021). Predictability of the physical shipping market by freight derivatives. IEEE Transactions on Engineering Management. https://ieeexplore.ieee.org/abstract/document/9321714

Continuous-time model identification: application on a behavioural (miLife) study

Published in International Journal of Control , 2019

The developed algorithm provides an effective way to leverage these ‘non-standard’ datasets to identify continuous time dynamical models that are compatible with a-priori information available on the process.

Recommended citation: Bekiroglu, K., Russell, M. A., Lagoa, C., Su, R., Sznaier, M., Lanza, S. T., & Odgers, C. L. (2021). Continuous-time model identification: application on a behavioural (miLife) study. International Journal of Control, 94(9), 2318-2329. https://www.tandfonline.com/doi/abs/10.1080/00207179.2019.1706101

Recursive approximation of complex behaviours with IoT-data imperfections

Published in IEEE/CAA Journal of Automatica Sinica, 2020

This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect (noisy and incomplete) measurements in the internet of things (IoT) based distributed decision-making problems.

Recommended citation: Bekiroglu, K., Srinivasan, S., Png, E., Su, R., & Lagoa, C. (2020). Recursive approximation of complex behaviours with IoT-data imperfections. IEEE/CAA Journal of Automatica Sinica, 7(3), 656-667. https://ieeexplore.ieee.org/abstract/document/9080611

Real-time extremum seeking controller for brushless DC hub motors in electric vehicles

Published in IET Electric Power Applications, 2020

This study presents an extremum seeking‐proportional–integral and derivative (ES‐PID) controller design for brushless direct current motors and its implementation in electric vehicles.

Recommended citation: Ramaraj, R., Dharmaraj, G., Srinivasan, S., Balasubramanian, S., Periyasamy, M., & Bekiroglu, K. (2020). Real‐time extremum seeking controller for brushless DC hub motors in electric vehicles. IET Electric Power Applications, 14(12), 2438-2449. https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-epa.2020.0117

Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes

Published in Renewable Energy, 2020

This paper presents an eXplainable Fault Detection Systems (XFDS) for incipient faults in PV panels.

Recommended citation: Sairam, S., Seshadhri, S., Marafioti, G., Srinivasan, S., Mathisen, G., & Bekiroglu, K. (2022). Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes. Renewable Energy, 185, 1425-1440. https://www.sciencedirect.com/science/article/abs/pii/S0960148121015226

Predictability of the physical shipping market by freight derivatives

Published in Knowledge-Based Systems, 2021

This paper proposes a dynamic ensemble algorithm to combine forecasting results from multiple methodologies subject to their local (recent) predictive performance.

Recommended citation: Bekiroglu, K., Gulay, E., & Duru, O. (2022). A multi-method forecasting algorithm: Linear unbiased estimation of combine forecast. Knowledge-Based Systems, 239, 107990. https://www.sciencedirect.com/science/article/abs/pii/S0950705121011059?dgcid=author

Multimedia Data-Based Artificial Pancreas for Type 2 Diabetes

Published in IEEE MultiMedia, 2022

In this article, we investigate the role of multimedia data to enable the advanced control techniques that could personalize AP in elderly type 2 diabetes patients.

Recommended citation: S. Keshary, P. T, K. Bekiroglu, S. Seshadhri and S. Srinivasan (2022). Multimedia Data-Based Artificial Pancreas for Type 2 Diabetes. IEEE MultiMedia, vol. 29, no. 1, pp. 18-27, 1 Jan.-March 2022. https://ieeexplore.ieee.org/abstract/document/9721600

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

Published in Journal of Clinical Medicine., 2023

This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients.

Recommended citation: Thamotharan, P.; Srinivasan, S.; Kesavadev, J.; Krishnan, G.; Mohan, V.; Seshadhri, S.; Bekiroglu, K.; Toffanin, C. Human Digital Twin for Personalized Elderly Type 2 Diabetes Management. J. Clin. Med. 2023, 12, 2094. https://www.mdpi.com/2077-0383/12/6/2094

Trustworthy AI-Based Personalized Insulin Recommender for Elderly People Who Have Type-2 Diabetest

Published in IEEE Computer., 2024

We propose TRAINER, a TRustworthy Artificial Intelligence-based iNsulin recommendER for elderly individuals with type 2 diabetes, ensuring reliability and trust in insulin dosage recommendations. TRAINER exemplifies this trustworthiness and addresses such concerns by offering reliable insulin recommendations supported by clinical evidence.

Recommended citation: Padmapritha, T., Korkut Bekiroglu, Subathra Seshadhri, and Seshadhri Srinivasan. "Trustworthy AI-Based Personalized Insulin Recommender for Elderly People Who Have Type-2 Diabetes." Computer 57, no. 3 (2024): 35-45. https://ieeexplore.ieee.org/abstract/document/10461645

Predictive performance of denoising algorithms in S&P 500 and Bitcoin returns

Published in Expert Systems with Applications., 2025

We propose TRAINER, a TRustworthy Artificial Intelligence-based iNsulin recommendER for elderly individuals with type 2 diabetes, ensuring reliability and trust in insulin dosage recommendations. TRAINER exemplifies this trustworthiness and addresses such concerns by offering reliable insulin recommendations supported by clinical evidence.

Recommended citation: Emrah Gulay, Omer Burak Akgun, Korkut Bekiroglu, Okan Duru. "Predictive performance of denoising algorithms in S&P 500 and Bitcoin returns." Expert Systems with Applications 260 (2025): 125400. https://www.sciencedirect.com/science/article/abs/pii/S095741742402267X

talks

teaching

ETC/CET 101 Fundamentals of Electrical & Computer Engineering Lab

Undergraduate course, SUNY Polytechnic Institute, Electrical/Computer Engineering Technology, 2021

Taught - (S21): Introduction to programming with MATLAB and Python. Provides to basic properties of the MATLAB/Python, a powerful programming language and development tool for different disciplines, and programming concepts for engineers for various engineering application.

ETC/CET 102 Electric Circuits Lab

Undergraduate course, SUNY Polytechnic Institute, Electrical/Computer Engineering Technology, 2021

Taught - (F19, F20): Introduction to theory, analysis and design of electric circuits. Units and definitions. Ohm’s Law and Kirchhoff’s Laws. Analysis of resistive circuits. Circuit analysis using superposition, nodal and mesh methods, Norton Thevenin theorems, and current and voltage divider rules. Transient and sinusoidal steady state response of circuits containing resistors, capacitors, and inductors.

ETC 275 Intro to Programming for Eng.

Undergraduate course, SUNY Polytechnic Institute, Electrical/Mechanical Engineering Technology, 2021

Taught - (S20, F20, F21): Introduction to programming with MATLAB. Provides to basic properties of the MATLAB, a powerful programming language and development tool for different disciplines, and programming concepts for engineers for various engineering application. Students will be doing sample MATLAB engineering problems in real time.

ETC 331 Control Systems/Lab

Undergraduate course, SUNY Polytechnic Institute, Electrical Engineering Technology, 2021

Taught - (F18, S19, F19, F20, S21, F21): Basic control systems studied using Laplace transforms. Principles of electro-mechanical control systems (electrical and mechanical), measuring means, components and their characteristics, and controller characteristics. Analysis of a control system by the frequency/phase responses and stability criteria.

ETC/MTC 357 Mechatronic Design/Lab

Undergraduate course, SUNY Polytechnic Institute, Electrical/Mechanical Engineering Technology, 2021

Taught - (F19, S20): This course is an introduction to designing mechatronic systems, which require integration of the mechanical and electrical engineering disciplines within a unified framework. There are significant project-based design experiences. Topics covered in the course include: fundamental of electronics, 2D/3D drawing, sensor technology, actuators, principles of mechatronics, low-level interfacing of software with hardware, DC stepper motors and control.

ETC/CET 433 Automatic Control Systems/Lab

Undergraduate course, SUNY Polytechnic Institute, Electrical/Computer Engineering Technology, 2021

Taught - (S19, S21): In this course, the students will develop an understanding of the basic principles of automatic control theory, with emphasis on time and frequency domain methods. The principle of the elements of control systems will be implemented in Python and/or MATLAB. The coding and representing the automatic control algorithms will be applied to case-studies (Temperature Control in this course) for various engineering problems. Data fitting and differential equation representation of systems from several engineering disciplines will be studied. The students will study the principle elements of control systems such as: Sensors, Controls (P, PI and PID), and Actuators and test them in lab environment.

Graduate Student Advising

Advising, SUNY Polytechnic Institute, Computer Science, 2021

Gnana Nishitha Chowdary Aluri (Master student at Computer Science, F19, S20, F20)