Complex dynamics of neuro-physiological signals

Cardiac function is regulated by a network of control mechanisms comprising of the central autonomic network, sympathetic and parasympathetic branches of the autonomic nervous system (ANS), central and peripheral oscillators, humoral factors, and the sinus node (Malliani, Pagani, Lombardi, & Cerutti, 1991; Task Force of the European Society of Cardiology, 1996). The interplay of these control mechanisms constitute a complex system (Kantelhardt, 2008) with non-linearly interacting components (Billman, 2013), whose global behavior cannot be explained as the sum of its parts (Kantelhardt, 2008). This notion is often ignored in traditional analyses (e.g. averages and heart rate variability). The aim of this project is to develop non-linear methods to analyze the complex dynamics of physiological signal.

  • Characterizing and clustering of scaling dynamics in physiological signals
  • Analyzing nonlinear dynamics of physiological processes underlying signals through Recurrent Neural Networks (RNN) modeling


  • H. Saghir, T. Chau, A. Kushki, “Clustering of time-evolving scaling dynamics in a complex signal “, under review.

  • H. Saghir, T. Chau, A. Kushki, “Atypical autonomic nervous system complexity accompanies social cognition tasks in ASD”, under review.

  • H. Saghir, T. Chau, A. Kushki, “Moving deterended fluctuation analysis for inspecting time evolution of scale invariant structures in biomedical signals”, IUPESM World Congress on Medical Physics and Biomedical Engineering 2015, Toronto, Ontario, Canada.

  • H. Saghir, T. Chau, A. Kushki, “Fractal Analysis of Autonomic Nervous System Function in ASD”, The International Meeting for Autism Research (IMFAR) 2015, Salt Lake City, Utah, USA.

Past projects:

Planning and Control of Legged locomotion


© 2013 ADVR, IIT (

Quadrupedal locomotion holds the promise of achieving similar levels of mobility and agility as seen in some animals such as cats, dogs and goats. The successful development of such robots will open a wide range of applications; from search and rescue, to inspection and providing assistance in different situations. HyQ is a fully torque-controlled hydraulically actuated Quadruped robot, developed in the Department of Advanced Robotics at the IIT. HyQ is designed to move over rough terrain and perform highly dynamic tasks such as jumping and running with different gaits (up to 3-4m/s). To achieve the required high joint speeds and torques, a combination of hydraulic cylinders and electric motors are actuating the robots 12 active joints. This 70kg robot is actuated by hydraulic actuators and is able to perform highly dynamic motions such as running and jumping, as well as careful navigation over rough terrain. It is a fully torque controlled quadruped robot equipped with inertial measurement units, laser range finders and stereo cameras. In this project planning and control of legged locomotion for the HyQ robot is taken care of.

**Emotional Learning and Control **

Recently, emotions have been widely used in systems that may face uncertainty or unknown situations. Various theories have been put forward for explaining emotions and their roles in mind. The model of emotional masks is a newly developed AI paradigm based on Minsky’s model of emotional mind in his recent book –The emotion machine‖. This model takes a resource management approach toward modeling the mind and views different processes of mind as resources that need to be managed.

This work leads to the development of an intelligent controller (SCELIC) that is based on ideas from emotional masks, Self-Organizing Controllers and network-based fuzzy systems. On the way to the development of SCELIC, two more controllers are also introduced; the masker controller and the SOANFIC. The masker masks a present rule-base so that it can reduce the computations needed for the system, and SOANFIC is a further development of the Self-Organizing controllers that eliminates the need for knowledge of an incremental model and time lags in the process under control. SCELIC is a model free controller which benefits from several learning tasks. Multiple learning and the inherent adaptive structure make it a powerful adaptive and self-learning tool that performs the tasks of system identification and control in parallel. Emotions are produced in a system through a learning process, which learns to control the attention of the controller based on a performance measure. SCELIC is especially useful when we need a satisfactory response from a system in a short time and with the least computation costs.

M.Sc. Thesis Project, Hamidreza Saghir. Supervisor: Prof. Saeed Bagheri Shouraki


  • H. R. Saghir and S. B. Shouraki, “Self-Organizing Adaptive Network-Based Fuzzy Intelligent Controller (SOANFIC),” International Journal of Machine Learning and Computing vol. 2, no. 6, pp. 864-868, 2012. [pdf]

  • Saghir, H. R., Shouraki, S. B., Self Constructing Emotional Learning based Intelligent Controller (SCELIC). Proceedings of the International Multi Conferences of Engineers and Computer Scientists 2011 (IMECS 2011), Vol I, pp 148-153. [pdf]

  • Saghir, H. R., Shouraki, S. B., Emotional Control of a comprehensive nonlinear ball and beam system using SCELIC. Proceedings of the 2011 IEEE International Conference on Computer Control and Automation (ICCCA 2011), pp 244-248. [pdf]

  • Saghir, H. R., Shouraki, S. B., Intelligent Control of a comprehensive nonlinear ball and beam system using SOANFIC. Proceedings of the 2011 IEEE International Conference on Computer Control and Automation (ICCCA 2011), pp 249-253. [pdf]

  • Saghir, H. R., Shouraki, S. B., Fuzzy Control of an Inverted Pendulum based on Emotional Masking and Attention Control, Proceedings of the 2011 IEEE International Conference on Computer Control and Automation (ICCCA 2011), pp 254-258. [pdf]


This work introduces a novel approach for measuring low pressures based on MEMS technology. In this technique the mechanism of squeeze film damping is used. A voltage is applied to a fixed-fixed MEMS beam and its step response is obtained; for each pressure there is a different response. Then the settling time is measured and we can relate each settling time with a defined pressure. Here, first we use some equations to relate pressure with the squeeze film damping effect; after that we use a micro beam model and relate its parameters with pressure. Then we use numerical analysis and simulation to show the procedure of pressure measuring. All simulation results are shown and discussed.

People involved: Ghafari A., Ghanbari A., Kamanzadeh S., Abbasian K., Saghir, H. R. Supervisor: Dr. Ghanbari


  • Ghafari A., Ghanbari A., Kamanzadeh S., Abbasian K., Saghir, H. R., Developing a New Pressure Measurement Mechanism Based on Squeeze Film Damping Effect, The 2nd International Conference on Control Instrumentation and Automation (ICCIA2011). [pdf]

Parallel Robotics

We performed the inverse dynamic and kinematic analysis of 3-RPS and 3-UPU parallel manipulators as well as writing the required MATLAB codes to simulate and control these systems. The manipulators had 3 DOF and consisted of two triangular platforms connected to each other using three legs with prismatic joints. All MATLAB codes for the kinematics and dynamics analyses were developed by me and were tested on two different maneuvers(x direction and z direction translation). The forces of actuated joints were calculated using the Natural Orthogonal Component (NOC) method, and they were found by considering both leg inertias and neglecting them. The time history of the length, velocity, acceleration, condition number and the forces were reported.

People involved: Hamidreza Saghir Supervisor: Dr. Abbas Fattah

**Serial Robotics **

The second project was simulation and kinematic performance analysis of a PUMA robot. In this project the optimization of the condition number of Jacobian matrix was surveyed. After calculating transformation matrices, the two parts of Jacobian were derived using the components of End effector’s transformation matrix. Optimizations were done by the MATLAB software in two methods: constrained and unconstrained function.

People involved: Hamidreza Saghir Supervisor: Dr. Abbas Fattah

Dynamics and Control

modeling and Control of several projects. In Modeling, Control and Simulation of a Quad-rotor UAV system, a symmetric quad-rotor platform was modeled and controlled using PID and LQR techniques. The code for this project was developed in MATLAB. This work was followed by two other projects on intelligent control techniques namely Dynamic Simulation and control of a cart pole system using Reinforcement Learning and Fuzzy Control, Analysis and simulation of an Inverted pendulum system. These projects were mainly performed and presented as course research projects.

People involved: Hamidreza Saghir Supervisor: Dr. Amir Ali Khayyat