I. Courses Taught at BITS Pilani, Hyderabad Campus

(1) Signals and Systems (BE 2nd Year ECE/EEE/INSTR, 1 time). Lecture notes and Lab manual are available at Notes1 

Course details: Introduction to signals, Classification of Signals & Signal operations, Orthogonal Signal set & Fourier series, Aperiodic Signal Representation, Fourier Transforms & its properties, Classification of Systems, Linear convolution (LC) & LC using Fourier Transform, Laplace transform & its properties, Solution of LTI continuous time systems using Laplace transforms, Sampling & reconstruction, Discrete-time signals  & Signal operations, Discrete Time Fourier Transform & its properties, Z-transforms & its properties,  Z-transforms converting difference equations into algebraic equations, DFT & its Properties, Discrete time systems, Discrete time convolution (graphical procedure), Fast Fourier Transform, DIT FFT & DIF FFT algorithms, DFT using FFT & Inverse DFT, Discrete-time convolution using FFT.

(2) Neural Network and Fuzzy Logic (BE 3rd Year ECE/EIE/EEE/CSE, 5 times). Lecture notes and Lab manual are available at Notes2 

Course details: Introduction to machine learning, Supervised, unsupervised and semi-supervised learning, Classification and regression problems, Linear regression, gradient descent (Batch gradient descent and stochastic gradient descent), Logistic regression, multiclass extension of logistic regression (One Vs One and One Vs All Multiclass coding schemes), Performance Measures for Classifiers (binary class and multiclass), What is Neural Network?, Human Brain and Biological Neuron, Model of an Artificial Neuron, Activation functions, Neural Network Architectures, Single Layer Perceptron, Linear Separability, XOR Problem, Perceptron Learning rules, Multilayer Perceptron, Back-propagation Algorithm and parameters selection and tuning, Radial-Basis Function Networks, various kernel functions used in RBFN, Autoencoder, Sparse autoencoder, Denoising autoencoder, Deep neural network based on stacking of autoencoders, Extreme learning machines, Kernel Extreme learning machine, Convolutional neural network, Convolutional Layer, Pooling Layer, and Fully-Connected Layer, Crisp Sets and Crisp relations, Fuzzy sets and Fuzzy relations, Crisp Logic and Fuzzy Logic Membership function, Fuzzification, Fuzzy Inference, Defuzzification Methods, Applications of Fuzzy Logic, Neuro-Fuzzy System, Takagi-Sugeno’s Approach (ANFIS), Fuzzy Backpropagation Networks, Advantages and Applications of hybrid Neuro-Fuzzy Systems.

(3) Introduction to Artificial Neural Network (ME 1st year Communication/ Embedded system, 2 times).

course details: Introduction to machine learning, Supervised, unsupervised and semi-supervised learning, Classification and regression problems, Linear regression, gradient descent (Batch gradient descent and stochastic gradient descent), Logistic regression, multiclass extension of logistic regression (One Vs One and One Vs All Multiclass coding schemes), Performance Measures for Classifiers (binary class and multiclass), What is Neural Network?, Human Brain and Biological Neuron, Model of an Artificial Neuron, Activation functions, Neural Network Architectures, Single Layer Perceptron, Linear Separability, XOR Problem, Perceptron Learning rules, Multilayer Perceptron, Back-propagation Algorithm and parameters selection and tuning, Radial-Basis Function Networks, various kernel functions used in RBFN, Autoencoder, Sparse autoencoder, Denoising autoencoder, Deep neural network based on stacking of autoencoders, Extreme learning machines, Kernel Extreme learning machine, Convolutional neural network, Convolutional Layer, Pooling Layer, and Fully-Connected Layer , BPCNN algorithm.

(4) Digital Signal Processing (BE, ECE/EIE/EEE students,  2 times). Lecture notes and Lab manual are available at Notes2

Course details: Introduction to DSP, CTFT, DTFT, Phase and group delay, Basics of Z- transform and its use for analysis of LTI systems, Numeric representation used in DSP, Architectural details of a typical DSP processor, DFT, FFT, DITFFT and DIFFT algorithms, Analog filter design, Butterworth, Chebyshev, Elliptic filter design, Design of HP, BP and BS filters, Sampling of lowpass & bandpass signals, Different LTI systems as frequency selective device, IIR filter design: IIT, BLT, Linear phase FIR filters, FIR Filter Design, Realization of IIR filters, Realization of FIR filters, Finite Word-Length Effects in FIR and IIR filters, multi-rate signal processing, Decimators & Interpolators, Wiener Filter, Introduction and Concepts of Adaptive filtering, Wavelet transform concepts and brief applications.

(5) Advance Digital Signal Processing (ME Communication system, 3 times). Lecture notes and Lab manual are available at Notes3 

Course details: z-transform, DTFT principles, matrix algebra, complex gradients, Random variables and random processes and basic probability theory for statistical signal analysis, Special types of random processes, signal modeling and approximation methods (Pade, Prony), Stochastic Models , AR, MA and ARMA, Levinson-Durbin Recursion Algorithm and Lattice Filter Structure, Cholesky Decomposition, Introduction to filtering, Optimal FIR filtering: Wiener filter, Kalman filter, Non parametric spectrum estimation, Minimum variance spectrum estimation, Parametric spectrum estimation, Frequency estimation: Pisarenko, MUSIC, Steepest descent algorithm and convergence analysis LMS, NLMS, Adaptive filters,  Least Square methods and The RLS algorithm, Acoustic Echo Cancellation.

(6) Digital Image Processing (BE, ECE/CSE/EEE/EIE, 2 times). Lecture notes and Lab manual are available at Notes4 

Course details: Introduction and digital image fundamentals, Some basic gray level transformations, Histogram processing, Spatial filtering, 2D Fourier Transform, Image smoothing and sharpening using Frequency domain filters, Noise Models, Inverse filtering, Information Theory, Huffman coding, Basic Compression Methods, JPEG compression, Erosion, dilation, Opening closing, Hit-or-miss transformation, some basic morphological algorithms, Point, line and edge detection, thresholding, Principal components analysis (PCA), transfer learning based feature extraction, VGG16, VGG19, ResNet50, InceptionV3, XCeptionNet etc., U-net based image segmentation, applications as Optic disc segmentation in fundus or retinal image, blood vessel segmentation in fundus image, lungs segmentation in X-ray images etc., Convolutional neural network, recurrent neural network, self-attention and vision transformers for image classification.

(7) Machine Learning for Electronics Engineers (BE, ECE/EEE/EIE, 1 time)

Course details: Introduction to machine learning, Supervised, unsupervised and semi-supervised learning, and Reinforcement learning, Classification and regression problems, Linear regression, gradient descent, bias-variance and regularizations, Logistic regression, multiclass extension of logistic regression (One Vs One and One Vs All Multiclass coding schemes), Selection of training and test instances for classifiers: hold-out validation, 5-fold and 10-fold cross-validation, leave-one-out cross-validation, Performance measures for binary and multiclass classifiers, regression model performance metrics, Human Brain and Biological Neuron, Model of an Artificial Neuron, Activation functions, Single Layer Perceptron, and multiclass perceptron, Multilayer Perceptron, Back-propagation Algorithm and parameters selection and tuning, Autoencoder, Sparse autoencoder, Denoising autoencoder, Deep autoencoders, Deep neural network using stacked autoencoder, Convolutional neural network, Convolutional Layer, Pooling Layer, Fully-Connected Layer, batch normalization, drop-out layers, Convolutional autoencoder, Different types of convolutions and Transfer learning: VGG, ResNet, InceptionNet, EfficientNet, DenseNet, XceptionNet, Sequence models, Recurrent neural network, back propagation through time, LSTM, GRU models, Fixed point representation, integer representation, Quantization and pruning, deployment of deep learning models on Android devices.

(8) Communication System (BE, ECE/EEE/EIE, 1 time)

Course details: History of electronic communications, blocks of a typical communication system, Electronic Communication Channels, twisted pair, cable, wave guide, wireless channels, need for modulation, concept of a carrier, analog and digital communication concepts, Different Amplitude Modulation Techniques: DSB-SC, SSB-SC, VSB, AM with carrier: BW requirements of above modulation schemes. Circuits for Generation and demodulation. Noise performance of different AM systems. Frequency Division Multiplexing, Super Heterodyne Receivers, Practical Circuits, Angle Modulation, FM Transmitters and Receivers, Interference and Bandwidth Considerations, Comparison of AM and FM, FM Generation and Demodulation, Noise Performance of Different Angle Modulation Systems. Sampling theorem, aliasing, quantization and encoding, PAM, TDM, PPM, PWM, Quantization, PCM, Delta Modulation, Recap of Random variables & processes, statistical averages, Power spectral density, Gaussian process, Noise, Nature of noise, Sources of Noise, white noise, KTB, Noise Figure and Noise temperature, calculations, Signal-to-Noise ratio. Line codes, NRZ etc, Inter Symbol Interference (ISI), eye diagram, Nyquist Criterion for Distortion less transmission, pulse shaping, equalization, Band-Pass Transmission Model, Binary PSK, FSK and QAM, M-Array Data Transmission Systems, Noise performance of PSK & FSK Systems, AWGN Channel, Different Receivers – ML and MAP, Matched Filter, Likelihood Ratio and Detection Regions, Error performance of M-PAM, M-PSK, M-QAM, M-ary Orthogonal Signaling, Union Bound on Error Probability, Spectrum and Power Efficiency of Different Modulation Schemes, Goals of Communication system designer, Error probability plane, Nyquist bandwidth, Shannon-Hartley capacity theorem, Modulation & coding trade-offs, Designing digital communication systems, Modulation & coding for Bandwidth limited channels, Wireless Communications: A brief overview of fading – definition, types, and impact on performance, introduction to OFDM, Basic definition of entropy, mutual information, capacity, importance of source coding, and channel coding.

II. Labs Handled at BITS Pilani, Hyderabad Campus

1. Signals and system using MATLAB (4 times).

2. Neural Network Lab using MATLAB and Python (2 times).

3. DSP Lab using MATLAB and DSK TMS320C6748 kit (3 times)

4. Advance DSP lab (3 times).

5. Machine learning Lab (1 time)

6. Communication system Lab (1 time)

III. Courses Taught at Siksha 'O' Anusandhan Deemed to be University

(1) Introduction to Information Theory (EET3061, BTech 7th Sem, ECE)

(2) Introduction to MATLAB (MTH 3006, BTech 4th Sem, EEE and CSE)

IV. Teaching Assistant in Courses at IIT Guwahati

(1) Biomedical Signal Processing (EE 626, Course Instructor: Prof. Samarendra Dandapat, Branches: BTech 7th Sem, MTech 1st sem, PhD) 

(2) Linear Algebra and Optimization (EE 504, Course Instructor: Prof. Samarendra Dandapat, Branches: MTech 1st sem, PhD)

(3) Probability and random Processes (EE 506, Course Instructor: Prof. Samarendra Dandapat, Branches: MTech 1st sem, PhD)