
Comprehensive exploration of advanced process control techniques, covering linearization, modeling, stability analysis, stochastic processes, and control design methods for complex industrial systems.

Comprehensive exploration of embedded systems design, covering processors, FPGAs, signal processing, Arduino, power management, real-time OS, modeling techniques, and hardware synthesis.

Rapid overview of optimal control concepts, covering controllability, observability, LQR, and Kalman filter. Applies main approaches to simple Matlab examples for practical understanding.

The course is about basic electronic circuits, both analog and digital. In the analog part, diode circuits, BJT amplifiers, Op-Amp circuits will be covered. In the digital part, combinatorial and sequential circuits will be covered.

Explore recursive filters, including average, moving average, and low-pass filters, with practical MATLAB examples. Learn the foundations of Kalman filtering for enhanced estimation and data analysis skills.

Comprehensive exploration of communication engineering principles, covering analog and digital modulation techniques, noise analysis, and practical applications in various transmission systems.

Learn ARM-based embedded system design, from architecture to practical applications. Covers microcontrollers, sensors, actuators, and IoT, with hands-on experiments using STM32F401 and Arduino boards.

Comprehensive exploration of digital signal processing concepts, including systems, transforms, filters, and design techniques. Covers theory and practical applications with problem-solving sessions.

Comprehensive exploration of Z-Transform concepts, including inverse methods, properties, and practical applications, with additional insights on CSIR NET exam preparation.

Explora técnicas avanzadas de aprendizaje automático, sistemas dinámicos y control con ejemplos prácticos en Matlab y Python, basados en el libro "Data-Driven Science and Engineering" de Brunton y Kutz.

Digital systems represent and manipulate digital signals. Such signals represent only finite number of discreet values. A signal can be discreet by nature whereas, a continuous signal can be discretized for digital processing and then converted back.

Explore Kalman Filter implementation on inverted pendulum using Matlab, covering theory and practical application in dynamical systems and control engineering.

Learn to use GNU Radio Companion for signal processing, including FFT display, data types, GUI layout, and audio input, preparing you for SDR projects.

Explore psychophysics, visual perception, haptics, and challenges in VR/AR. Gain insights into human sensory systems and cutting-edge interfaces for immersive experiences.

Comprehensive guide to calculating, simulating, measuring, and filtering noise in operational amplifier and ADC circuits, covering theoretical concepts and practical applications with real-world examples.

Explore Digital Signal Processing with MATLAB: from impulse responses to Fourier transforms, filter designs, and spectral analysis. Gain practical skills for real-world DSP applications.

Explore advanced wireless communication techniques including MIMO, beamforming, and channel modeling. Gain insights into spatial diversity, multiplexing, and hybrid approaches for improved signal processing and data transmission.

Explore sensor systems for predicting, preventing, and treating cardiovascular disease. Learn about left ventricular assist devices, cardiac imaging techniques, and innovative approaches to monitor heart function.

Explore cutting-edge stroke detection technology and its potential to revolutionize patient care. Learn about ZeitMedical's innovative approach to early stroke intervention and monitoring.

Explore video compression techniques, including motion estimation and compensation, in this Stanford lecture on advanced data compression methods for efficient multimedia storage and transmission.

Explore conditional entropy and advanced data compression techniques beyond IID distributions in this Stanford lecture, enhancing understanding of complex information theory concepts.