Signals & Control Systems (SCS)

Information

Scopes

Digital Signal Processing (DSP): Developing algorithms and techniques for processing signals in digital form, such as filtering, noise reduction, data compression, and feature extraction. This can apply to audio, video, images, and sensor data.
Time-Frequency and Spectral Analysis: Researching methods for analyzing signals in both the time and frequency domains, such as wavelets, Fourier transforms, and spectral estimation.
Multidimensional Signal Processing: Extending signal processing techniques to multidimensional data, such as image and video processing, where signals have more than one dimension (e.g., 2D for images or 3D for video).
Adaptive Signal Processing: Designing systems that can adjust themselves based on input signals, for applications such as noise cancellation, echo suppression, or speech enhancement.
Speech and Audio Processing: Focusing on techniques for processing speech and audio signals, including speech recognition, speaker identification, sound localization, and enhancement.
Image and Video Processing: Applying signal processing techniques to improve or analyze visual data, such as image enhancement, object detection, compression, and feature extraction in video streams.
Biomedical Signal Processing: Researching techniques for processing physiological signals like ECG (electrocardiograms), EEG (electroencephalograms), and EMG (electromyograms), often aimed at medical diagnosis or health monitoring.

Classical Control Theory: Researching traditional methods of control system design, such as Proportional-Integral-Derivative (PID) controllers, and their application to stabilize dynamic systems.
Modern Control Theory: Focusing on more advanced methods of control, such as state-space representation, optimal control, and robust control, which are applied to systems with more complex dynamics and uncertainties.
Nonlinear Control: Investigating the control of systems with nonlinear dynamics, which are more difficult to model and control than linear systems. This may include techniques like sliding mode control, backstepping, or Lyapunov methods.
Adaptive Control: Designing control systems that can adapt to changing parameters or environments. This is especially useful in systems where precise modeling is difficult or in systems subject to disturbances.
Stochastic Control: Dealing with systems affected by randomness or uncertainty. Research in this area focuses on how to optimize the performance of systems in the presence of noise and disturbances.
Distributed Control: Exploring systems where control is shared between multiple controllers, often in networked or multi-agent systems, such as in industrial automation or robotics.
Optimal and Model Predictive Control (MPC): Developing advanced control techniques that optimize system performance based on predictive models. MPC is especially useful in systems that need to operate under constraints, like robotics or automotive systems.

Parameter Estimation: Developing methods for estimating the parameters of a system's model based on observed data. This is useful for system design, fault detection, or predictive maintenance.
System Modeling: Creating models of dynamic systems (e.g., mechanical, electrical, biological) from real-world data, often using machine learning techniques to improve model accuracy and prediction capabilities.

Robot Motion Planning and Control: Researching algorithms that allow robots to plan and control their movements in dynamic environments, including path planning, obstacle avoidance, and trajectory tracking.
Sensor Fusion and Localization: Developing techniques for integrating data from multiple sensors (e.g., cameras, LiDAR, IMUs) to estimate a robot’s position and environment. This is critical for autonomous navigation and SLAM (Simultaneous Localization and Mapping).
Multi-Robot Systems: Investigating control strategies and algorithms for coordinating multiple robots working together, often focusing on cooperative behavior, communication, and task allocation.
Human-Robot Interaction: Researching ways to enable robots to safely and efficiently interact with humans in collaborative environments, such as in healthcare, manufacturing, or service robots.

Wireless Communication: Applying signal processing techniques to wireless communication systems, such as error correction, modulation, channel estimation, and interference mitigation in technologies like 5G/6G, Wi-Fi, and IoT.
MIMO Systems: Researching multiple-input multiple-output (MIMO) systems, which use multiple antennas at both the transmitter and receiver to improve communication reliability and data rates.
Cognitive Radio and Spectrum Sensing: Investigating techniques for dynamically allocating radio spectrum resources, allowing for more efficient use of the frequency spectrum.
Network Coding and Information Theory: Exploring advanced techniques in data transmission and network efficiency, such as coding strategies that improve data rates and network throughput in communication systems.

Kalman Filtering: Developing algorithms for estimating the state of a system from noisy or incomplete measurements, widely used in navigation, control systems, and signal processing.
Particle Filtering: A more general method for nonlinear and non-Gaussian estimation, used in robotics, tracking systems, and communications.
Bayesian Inference: Applying probabilistic models for estimating unknown parameters or states in systems subject to uncertainty, useful in fields like radar, tracking, and sensor networks.

Machine Learning in Signal Processing: Investigating how machine learning algorithms can improve traditional signal processing tasks, such as classification, clustering, feature selection, or anomaly detection.
Reinforcement Learning for Control: Using reinforcement learning (RL) to develop control systems that learn optimal behaviors through interaction with the environment, useful for robotics, autonomous vehicles, and energy management.
Deep Learning for Systems Identification: Exploring how deep learning models can be used for system identification, fault detection, and system modeling, particularly in complex and nonlinear systems.

Industrial Automation: Developing control systems for industrial processes, robotics, and production lines, improving efficiency, safety, and automation in industries like manufacturing, chemical processing, and logistics.
Automotive Systems: Researching control systems in autonomous and electric vehicles, including trajectory planning, adaptive cruise control, and vehicle-to-vehicle (V2V) communication.
Aerospace and Defense: Developing control systems for aircraft, satellites, and missile guidance, often under challenging conditions such as high dynamics or real-time constraints.
Medical Systems: Applying signal processing and control theory to medical technologies, including imaging systems, biosignal monitoring, and the development of assistive technologies for rehabilitation or prosthetics.

Convex Optimization: Researching optimization techniques for solving control problems, especially where the problem can be expressed in a convex framework, such as in robust control or optimal power flow in electrical systems.
Control of Large-Scale Systems: Investigating how to control complex, large-scale systems, such as power grids, transportation networks, or communication systems, which require decentralized or hierarchical control strategies.

Activities

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